Bowdoin Science Journal

Biology

Cause of Sea Star Wasting Disease Epidemic Linked to Common Bacteria

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Fig. 1. Photo of a sunflower sea star (Pycnopodia helianthoides) in a kelp forest. (Mazza, Marco. The Independent, June 21, 2024.)

Since its emergence in 2013, sea star wasting disease (SSWD) has quickly spread along the west coast of North America, infecting dozens of sea star species from Mexico to Alaska and upending marine ecosystems. A variety of causes of SSWD have been proposed over the past decade, but no clear cause has been isolated for what is now considered one of the largest marine epidemics. Sunflower sea stars, or Pycnopodia helianthoides, are considered one of the most vulnerable species to SSWD, with billions dying from SSWD since its emergence. Although sunflower sea stars once inhabited the entirety of the west coast of North America, they are now considered functionally extinct in much of their southern range. Over 87% of the population has been lost in the remaining northern areas, earning the species a classification of critically endangered. The large-scale decline of sunflower sea stars due to SSWD has had a cascading effect on ecosystems, in which sea urchin populations have experienced uninhibited growth in the absence of predation. This ecological imbalance has led to the mass destruction of kelp forests and the creation of “urchin barrens” (locations where a previous kelp forest was destroyed by sea urchin overgrazing), demonstrating the profound impact SSWD has on kelp ecosystems and the species that rely on them.

After a series of exposure experiments and genetic sequencing tests of sunflower sea stars infected with SSWD, scientists identified the common bacterium Vibrio pectenicida as a causative agent (a pathogen that directly leads to disease, but may occur under the influence of other environmental or physical conditions) for SSWD. These findings may have lasting impacts on attempts to stem the spread and population losses caused by SSWD, including future efforts to recover the population of sunflower sea stars. 

Over the course of three years (2021-2024), scientists conducted a total of seven exposure experiments on sunflower sea stars. Using tissue extracts, coelomic fluid injections (an essential fluid similar to blood for sea stars that circulates immune system cells), and tank water from diseased sunflower sea stars, exposed sea stars were infected with SSWD. Healthy sunflower sea stars were collected in Washington state or raised at Friday Harbor Laboratories, and were first isolated in a 2-week quarantine period to ensure that collected stars did not develop SSWD after potential exposure in the wild. All exposure methods led to transmission of SSWD, with 92% (46/50) of exposed individuals displaying symptoms of SSWD. The disease stages were progressively categorized as “arm twisting,” “arm autonomy,” and “mortality.” Stars exposed to SSWD often died between 6 days to 2 weeks post exposure, usually within a week after showing the first symptoms of the disease. 

While using diseased coelomic fluid and tissue sample injections to infect healthy sea stars, scientists also utilized control samples, in which tissues or coelomic fluid from a diseased star were first treated with heat or filtered before injection into a healthy star. All 54 individuals injected with treated samples survived, with limited indications of SSWD. Most sea stars injected with untreated tissue (24 out of 26) or coelomic fluid (16 out of 18) samples from diseased stars contracted SSWD. The dramatic decrease in disease spread after heat treatment indicated that the causative agent (pathogen) of SSWD was likely cellular.

Fig. 2. Diagram of exposure experiment process using treated and untreated Vibrio pectenicida bacteria and diseased tissues. (Prentice et al., 2025)
Fig. 2. Diagram of exposure experimental process using treated and untreated Vibrio pectenicida bacteria and diseased tissues. (Prentice et al., 2025)

After identifying that the cause of SSWD was likely cellular, scientists genetically sequenced diseased sea star coelomic fluid and tissues from both in-lab sea stars and sea stars at field outbreak sites. Coelomic fluid from healthy stars and stars exposed to SSWD was also collected to contrast the microbes present in sea stars at all disease stages. After RNA and DNA analysis (particularly using 16S ribosomal RNA gene amplicon datasets), the most significant microbial difference between healthy and diseased groups was identified to be the bacterium V. pectenicida (r^2 ≥ 0.90), which was found in abundance in samples from stars with SSWD and was absent in samples from healthy stars. This difference in microbial presence allowed scientists to pinpoint V. pectenicida as a likely causative agent of SSWD. Small bacterial loads of V. pectenicida were found in healthy stars, leading scientists to propose that sea stars can remain healthy with low concentrations of V. pectenicida in ideal environmental conditions. This may indicate that outbreaks occur when environmental conditions (such as increasing temperatures) compromise the star’s immune system and allow the bacterium to flourish.

After genetic sequencing identified V. pectenicida as a candidate for the causative agent of SSWD, scientists conducted a series of exposure experiments using pure V. pectenicida cultures isolated from infected stars. When injected into healthy sea stars, V. pectenicida bacterium strains FHCF-3 and FHCF-5 cultures resulted in SSWD. Healthy sea stars were then injected with high (10^5 colony forming units) and low (10^3 c.f.u.) amounts of V. pectenicida strain FHCF-3 and heat-treated controls. 13 out of 14 stars injected with living bacteria all contracted SSWD and died, while all stars injected with heat treated (dead) bacteria survived. The disease progressed faster in stars injected with a higher concentration of V. pectenicida strain FHCF-3, with mortality occurring 6-11 days post exposure. Meanwhile, the group exposed to a lower concentration of live bacteria progressed through the disease more slowly, with mortality occurring 11-16 days post exposure.

Fig. 3. Chart of disease progression in sunflower sea stars using different methods of exposure to SSWD. Visual representations of disease symptoms are displayed below. (Prentice et al., 2025)
Fig. 3. Chart of disease progression in sunflower sea stars using different methods of exposure to SSWD. Visual representations of disease symptoms are displayed below. (Prentice et al., 2025)

After identifying V. pectenicida as a strong possible cause of SSWD, gene sampling was also conducted at field sites across British Columbia in May and October 2023. Although no individuals sampled at the five sites exhibited signs of SSWD or had V. pectenicida in May, V. pectenicida was identified in two outbreak populations in October. Vibrio pectenicida was found in 16% of healthy stars from visually unaffected sites, 74% of visually normal stars in outbreak sites, and 86% of diseased stars in outbreak sites. The analysis of a genetic database from southeast Alaska in 2016 during an SSWD outbreak also found V. pectenicida in both diseased and normal stars in outbreak sites but not healthy sites, suggesting that V. pectenicida also played a role in past outbreaks of SSWD. Scientists hypothesized that instances of Vibrio pectenicida in apparently disease-free stars may be due to exposure to other diseased stars in the wild. 

The discovery of V. pectenicida as a contributing cause of SSWD has strong implications for future research and conservation efforts for struggling sea star populations. V. pectenicida has been found globally (ranging from Australia to Asia to Europe to the US) from 2009-2019 in a variety of marine hosts, particularly in shellfish and bivalve aquaculture. Future research can focus on the mechanism of V. pectenicida as a pathogen, further distinguishing where the bacterium can be found, and modes of transmission both between sea stars and from prey shellfish populations. Scientists proposed that warming oceans due to climate change may make stars more vulnerable to outbreaks of V. pectenicida and other pathogens that thrive in warmer environments, which would support an observed trend between SSWD and warming water temperatures. Since sea stars respond to unfavorable environmental conditions (such as warming water) with similar symptoms to SSWD, it has been difficult to classify SSWD outbreaks. The discovery of V. pectenicida as a causative agent allows researchers to identify V. pectenicida as an indicator of SSWD in sampling, supporting the expansion of sampling across different environments and sea star species. This is essential for continuing to understand SSWD and crafting a response to protect struggling sea star populations and affected ecosystems. 

 

References:

Mazza, Marco. “How Sunflower Stars Can Save California’s Vanishing Kelp Forests.” The Independent, Santa Barbara Independent, 21 June 2024, https://www.independent.com/2024/06/21/how-sunflower-stars-can-save-californias-vanishing-kelp-forests/ 

Prentice, M.B., Crandall, G.A., Chan, A.M. et al. “Vibrio pectenicida strain FHCF-3 is a causative agent of sea star wasting disease.” Nat Ecol Evol 9, 1739–1751 (2025). https://doi.org/10.1038/s41559-025-02797-2

Lobsters and Telomerase: How to Reach Longevity

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Introduction

How does Aging work? This has been a question that has stumped scientists since the dawn of civilization. What are the causes and implications of Aging? Is it preventable or reversible? In “Longevity of Lobsters is Linked to Ubiquitous Telomerase Expression” by Klapper, Kühne, and others, they sought to investigate telomerase activity in animals that grow indeterminately. Lobsters (Homarus americanus) were the main subject of their study. Through their investigation of Telomerase activity, they found that, unlike most animals, lobsters continue to grow throughout their entire lives and that the onset of senescence (referring to cellular deterioration or aging) is slowed. The researchers discovered that telomerase was present throughout the lobster’s body, including many of its organs and tissues. Furthermore, they concluded that this high amount of telomerase activity throughout the body is what allows them to live such long lives; the life expectancy of Lobsters (Homarus americanus) is over 100 years (Geggel). This was a unique discovery because most animals, like Humans and other mammals, have telomerase found in limited areas of high cell turnover, such as gametes, to reduce the risk of unregulated division, also known as cancer (Robinson). This will be further elaborated upon later in this paper. So why should we care? How do these researchers’ findings of telomerase slowing aging help us? Well, the cool implications of telomerase research would be understanding how aging and cancer work. Could studying lobsters help scientists find safer ways to use telomerase to slow aging or aid tissue regeneration in humans? Or could we understand cancer well enough to eradicate it from humans?

 

Background: What is Telomerase, and how does it affect aging?

Before we dive into any of these big words, we need to explain some terms being discussed in this paper. Aging has been linked to chromosomal damage during cell division. Every time your body needs to grow new cells, your cells must divide. Each division causes small amounts of DNA damage. However, telomeres act like protective caps that shield the DNA inside your chromosomes every time your cells divide (Schumacher). Instead of damaging the chromosomes, the telomeres simply become shorter. After many years of cell division, telomeres can shorten, increasing the risk of chromosomal damage. Telomerase is an enzyme that helps regenerate telomeres by fixing the areas that were shortened. Through this process, certain species, like the Lobster, can avoid chromosomal damage and age much more slowly in comparison. 

Earlier, I referenced how finding high amounts of Telomerase activity over a lobster’s entire body is atypical compared to other species. The reason for this is that telomerase is often linked to hyperproliferative cells, a term used to refer to cells that grow abnormally fast. This is helpful for animals that grow continuously, like lobsters and axolotls (Springhetti), but can be extremely harmful to other organisms because hyperproliferative cells can be cancerous. In humans, for example, we have small amounts of telomerase outside our gametes and stem cells because our bodies avoid our cells becoming cancerous. In Robinson’s “Telomerase in cancer: Function, regulation, and clinical implications,” the author explains, “Cancer cells often up-regulate (produce more of) telomerase to sustain indefinite division.” This same attribute that makes cancerous cells so dangerous, the ability to sustain indefinite division, is what makes organisms like lobsters and axolotls so good at regenerating.

So why is this important? When we understand how telomerase maintains chromosome integrity, we gain insight into both the aging process and the mechanisms that allow cancer to thrive.

 

Method & Results

How did these researchers determine that lobsters have telomerase activity throughout their bodies? They did this through a series of experiments. First, they extracted tissue from a lobster to analyze it. Then they used a TRAP (Telomeric Repeat Amplification Protocol) assay to identify where telomerase is active. The way that TRAP works is that TS primers (tiny pieces of DNA) are created to bind to the ends of telomerase. There is a test on humans where telomerase adds TTAGGG, but lobster telomerase adds TAGG (so they changed the test to match lobster telomeres). So if lobster telomerase is active, it will do something like: TS primer → TAGG TAGG TAGG TAGG… If the lobster has no telomerase, nothing gets added; in this sense, the primer acts kind of like a tag to help researchers find where and how concentrated telomerase is. You can’t see these tiny repeats directly, so they run PCR (a method to make millions of copies of a specific piece of DNA) to amplify and analyze the tags. 

The results of this test indicated that in all the tissues extracted from the lobsters, there was telomerase present. Certain organs, such as the hepatopancreas (a digestive organ) and the heart, exhibited really high levels of telomerase expression, although telomerase was found in high concentrations throughout their entire bodies (Figure 1). The continuous presence of telomerase throughout its body was found to slow down the aging process and increase regeneration in lobsters. This raised the question of how lobster telomerase compares to human and other known telomerases, and so the researchers performed a test, heat and RNase (which destroys RNA) inactivated telomerase, confirming it functions like other known telomerases.

 

Figure 1: These are the results from the TRAP Assay. High telomerase activity was seen throughout the body, but there were exceptionally high amounts of telomerase present in areas like the Hepatopancreas (A) and the Heart (B). The location of the peak along the x-axis illustrates the size of the sequence (TAGG), with the smallest sequence on the left. The peak amplitude shows the concentration of the primer binding to the telomerase found in the area, thus showing the concentration of the telomerase in that area. Overall, the figure shows that there are large concentrations of telomerase in the smaller telomerase products, as there were high amounts of primer binding to these areas. 

Broader Implications and Reflections

Understanding how lobsters maintain high amounts of telomerase activity throughout their bodies opens several routes of understanding telomerase in other organisms. Studying the regulation of telomerase in long-lived, indeterminately growing species may help scientists understand how to implement and understand continuous regeneration without the negative sides of uncontrolled cell division. Robinson, in their paper “Telomerase in cancer: Function, regulation, and clinical implications. Cancers,” said “Cancer cells often up-regulate telomerase (or activate the alternative lengthening of telomeres, ALT) to sustain indefinite division.” Researching these regulatory mechanisms can help us understand how to safely activate telomerase in human tissues for therapeutic purposes, such as improving wound healing, organ repair, or treatments for degenerative diseases. 

Furthermore, comparing the telomerase in lobsters to humans may reveal some differences that explain why one supports regeneration while the other is closely linked to cancer. This could help us design a way of inhibiting the negative cancer-related telomerase or activating the regeneration-related telomerase to help combat aging. By investing more into understanding how telomerase works within different species, we could combat some of humanity’s oldest ailments and ultimately contribute to new biomedical technologies that enhance human health span.

 

Citations:

Klapper, Wolfram, et al. “Longevity of Lobsters Is Linked to Ubiquitous Telomerase Expression.” FEBS Letters, vol. 439, no. 1–2, 1998, pp. 143–146. Elsevier

https://www.sciencedirect.com/science/article/pii/S001457939801357X

Laura Geggel, “Do Lobsters Live Forever?” 2016

https://www.livescience.com/55392-do-lobsters-live-forever.html

Schumacher, B., Pothof, J., Vijg, J., & Hoeijmakers, J. H. J. (2021). The central role of DNA damage in the ageing process. Nature, 592(7856), 695-703

https://pmc.ncbi.nlm.nih.gov/articles/PMC9844150/

Cong, Y. S., Wright, W. E., & Shay, J. W. (2002). Human telomerase and its regulation. Microbiology and Molecular Biology Reviews, 66(3), 407-425.

https://pmc.ncbi.nlm.nih.gov/articles/PMC120798/

Robinson, N. J. (2022). Telomerase in cancer: Function, regulation, and clinical implications. Cancers, 14(3), 808.

https://pmc.ncbi.nlm.nih.gov/articles/PMC8834434/

Springhetti, S., Bucan, V., Liebsch, C., Lazaridis, A., Vogt, P. M., & Strauß, S. (2022). An identification and characterization of the axolotl (Ambystoma mexicanum, Amex) telomerase reverse transcriptase (Amex TERT). Genes, 13(2), 373.

https://pmc.ncbi.nlm.nih.gov/articles/PMC8924892/#:~:text=In%20this%20study%2C%20we%20focused,already%20transformed%20cells%20%5B28%5D.

 

Systemic Antibiotics for Acne: Implications for the Gut Microbiome

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Are prescription antibiotics for acne worth it? Understanding the effects of long-term antibiotic usage on the gut microbiome offers new answers to this question.

Home to a diverse population of over 100 trillion microorganisms, the gut microbiome boasts significant influence over skin health through a complex, bidirectional relationship known as the gut-skin axis. When the gut microbiome is in equilibrium, or balance, skin health is enhanced through the promotion of skin barrier function and a decrease in inflammation. However, when the gut microbiome is in dysbiosis, or imbalance, skin disorders like acne vulgaris are more likely to occur (Munteanu et al., 2025).

Acne vulgaris is a chronic illness classified by inflammatory lesions, or areas with abnormal or damaged tissue. The most common treatment for moderate-to-severe acne involves prescription tetracycline-class antibiotics, such as minocycline or doxycycline. These antibiotics act broadly against both Gram-positive and Gram-negative bacteria, which differ in their membrane and cell wall compositions. This broad-spectrum activity helps address acne by killing any bacteria that may be contributing to causative inflammation. However, this also results in disruptions to the microbial composition of the gut microbiome. To shed more light on this complicated relationship, Moura and colleagues sought to elucidate the impacts of long-term antibiotic usage on the gut microbiome in relation to the treatment of acne.

The experiment conducted by Moura et al. used in vitro gut models, consisting of live components outside of living organisms. Each of the three models was treated with a different tetracycline-class antibiotic before a recovery period to analyze the long-term consequences of each antibiotic treatment. The in vitro “Gut Models” comprised a triple-stage system with three vessels representing the proximal, medial, and distal colons. Within each model, in vivo physiological conditions – conditions that occur within biological organisms – including pH, oxygen content, temperature, and nutrient availability, were monitored. Reference Figure #1 for the experimental setup and timeline for each model.

Bacteria were introduced into all three vessels of the three models using fecal slurry (homogenized fecal matter) from five healthy adults who had not taken antibiotics in the past three months. Microbial populations had two weeks to reach a “steady state” before once daily treatment for three weeks with either 17 milligrams of sarecycline, 19.3 milligrams of minocycline, or 22 milligrams of doxycycline in models S, M, and D, respectively. Dosages were prepared based on reported antibiotic colonic values in humans.

Following antibiotic treatment, microbial populations in each model were monitored for three weeks in a microbiota recovery phase. This allowed the gut microbiome time to repopulate, if possible. Bacterial colonies were sampled daily from the models and subject to an enumeration protocol. In this procedure, they were grown on agar plates with different added components to distinguish microbial kinetics during antibiotic treatment and following withdrawal.

 

Diagram depicting the experimental gut model setup.
Figure 1: Experimental Gut Model (Moura et al., 2022)

Following the conclusion of the microbial recovery phase, results pertaining to treatments of sarecycline, minocycline, and doxycycline, respectively, were achieved.

Single daily dosages of sarecycline resulted in a mean bioactive concentration of 15.9 mg/L across the three-week treatment regimen. However, levels of sarecycline became undetectable three days into the microbiota recovery phase. During treatment, microbial patterns indicated a significant decline in the Shannon diversity index, a baseline model for biodiversity. After week one, microbial diversity remained stable for the following two weeks of treatment. During the recovery phase, biodiversity increased, with many bacterial families returning to a healthy abundance.

Single daily dosages of minocycline resulted in a mean bioactive concentration of 27.4 mg/L. Significant decreases in microbial biodiversity were evident after three weeks of minocycline treatment. Dysbiosis was characterized primarily by the decrease of Bifidobacteriaceae and Lactobacillacea populations, coupled with an increase in Enterococcaceae and Enterobacteriaceae populations. During antibiotic withdrawal in the microbiota recovery phase, biodiversity was slow to recover, with several bacterial families failing to return to pre-treatment levels. Corynebacteriaceae, Planococcaceae, and Ruminococcaceae were nonexistent or marginally detectable at the end of the microbiota recovery phase.

Single daily dosages of doxycycline resulted in a mean bioactive concentration of 23.8 mg/L. Inconsistencies in microbe populations and decreased diversity were evident during treatment. Lower numbers of Lactobacillaceae and Bacteroidaceae especially characterized dysbiosis during treatment. Conversely, an increased abundance of Burkholderiaceae and Enterobacteriaceae was observed during treatment. Return of Lactobacillaceae, Enterobacteriaceae, and Burkholderiaceae populations to pre-treatment levels was not observed during the recovery phase.

Analyzing results from the experiment, it can be concluded that broad-spectrum antibiotics may significantly impact long-term composition, diversity, and equilibrium of the gut microbiome. Considering experimental data, sarecycline treatment results in the least disruption to the gut microbiome, in comparison to minocycline and doxycycline. Following sarecycline withdrawal, the profile of the gut microbiome returned to pre-sarecycline levels, in contrast to the significant long-term disruption following minocycline and doxycycline treatment (Moura et al., 2022). Further research, conducted by Elvers and colleagues, suggests that antibiotic-induced dysbiosis may last as long as several months or years, sometimes never returning to its original profile (Elvers et al., 2020).

Considering the importance of a healthy and diverse gut microbiome for skin health, the use of broad-spectrum antibiotics such as minocycline and doxycycline can pose a long-term risk of both gut and skin dysbiosis. Choosing antibiotic treatments such as sarecycline, which have more targeted bacterial effects, may prove beneficial for long-term microbial homeostasis. Moreover, abstaining from antibiotic treatment for acne vulgaris unless absolutely necessary may be more favorable in the consideration of long-term gut and skin health.

 

References:

Campos, M. (2023). Leaky gut: What is it, and what does it mean for you? Harvard Health Publishing. https://www.health.harvard.edu/blog/leaky-gut-what-is-it-and-what-does-it-mean-for-you-2017092212451

Elvers, K. T., Wilson, V. J., Hammond, A., Duncan, L., Huntley, A. L., Hay, A. D., & Van Der Werf, E. T. (2020). Antibiotic-induced changes in the human gut microbiota for the most commonly prescribed antibiotics in primary care in the UK: a systematic review. BMJ Open, 10(9). https://doi.org/10.1136/bmjopen-2019-035677.

Moura, I. B., Grada, A., Spittal, W., Clark, E., Ewin, D., Altringham, J., Fumero, E., Wilcox, M. H., & Buckley, A. M. (2022). Profiling the Effects of Systemic Antibiotics for Acne, Including the Narrow-Spectrum Antibiotic Sarecycline, on the Human Gut Microbiota. Frontiers in Microbiology, 13. https://doi.org/10.3389/fmicb.2022.901911

Munteanu, C., Turti, S., & Marza, S. M. (2025). Unraveling the Gut–Skin Axis: The Role of Microbiota in Skin Health and Disease. Cosmetics, 12(4), 167. https://doi.org/10.3390/cosmetics12040167

 

Effect of Dental Malocclusions on Posture in Children

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It is estimated that over six million patients seek orthodontic treatment every year to improve their malocclusion, or misalignment of teeth (Hung et al. 2023). Seeing as many people value this treatment, it is not surprising to learn that the way our teeth fit into one another affects the way we eat, talk, breathe, and even our posture. Musculoskeletal (shoulders, spine, muscles) and stomatognathic (teeth, jaws, chewing muscles, tongue, lips) are separate systems of our bodies that interact in intricate ways. For example, a misalignment of teeth alters the muscle-use patterns in our cheeks to compensate for this disparity, which in turn affects the neck muscles which are connected to our face muscles. Through a slight discrepancy in teeth-alignment, the whole head can shift into a different position, impacting one’s health (Bardellini et al. 2022). Unfortunately, the intersection of posture and dental malocclusions is a scarcely researched field. Seeing how impactful dental alignment is to the rest of the body, it is important to research and understand the factors that influence it.    

One study published in 2022 by a group of Italian researchers (Bardellini et al.) examined how these systems work together, and the effects of correcting dental malocclusions through orthodontic treatment on the posture of children. While there are many different classifications and types of dental malocclusions, this article specifically analyzes patients using Angle’s classification. Angle’s classification shows three types of malocclusions: class I, II, or III (Fig. 1). Each is described by the position of the lower (mandible) and upper (maxillary) molars. Class I is defined as the molars fitting together in a standard way, however, malocclusions are still present in other teeth besides the molars. In Angle’s class II, the lower molar is farther back (distal) than the upper molar. Lastly, class III shows the lower molar too far in front of the upper molar (Campbell and Goldstein 2021).  

Angle's classification of occlusion illustrated with dental diagrams and hand analogies: normal occlusion, Class I, Class II, and Class III malocclusions.
Figure 1: Simulate Angle’s classification of malocclusion by hands. Xie, Zhiwei, Fuying Yang, Sujuan Liu, and Min Zong. 2023. “The ‘Hand as Foot’ Teaching Method in Angle’s Classification of Malocclusion.” Asian Journal of Surgery 46 (2): 1063

The patients that participated in the study were assessed by two clinicians who evaluated their dental occlusions according to Angle’s classification. While deciding which patients to include in the study, the type of dental-skeletal malocclusion within Angle’s classification did not play a role. Most patients observed in this study exhibited a class II malocclusion, followed by class I and III. Patients that had scoliosis, required physical therapy, chronic diseases affecting balance, macro trauma, cleft lip or palate were excluded to ensure that the improvement in posture depended only on malocclusions and orthodontic treatment. Since this study aimed to find a connection between misalignment of teeth and posture in children, the patients belonged to the age group of 9-12 (Bardellini et al. 2022).    

Bardellini and her team investigated the postures and weight distribution of patients before and after the treatment using multiple methods, such as vertical laser line (VLL) and stabilo-baropodometric analysis.   

To examine the posture through VLL, the patients were positioned in a standardized position (relaxed posture and arms at side) in front of a white wall. A singular vertical laser line (VLL) was projected onto the patients (Bardellini et al. 2022). The posture was then examined for two factors, the position of the head in relation to the VLL and an excess of extension or flexion. A standard position means the head is centered so that it crosses the tragus—the pointy piece of cartilage close to the cheek (Fig. 2).

Anatomical illustration of human ear with labeled pin above the tragus.
Figure 2: Tragus – anatomical structures. Source: IMAIOS, “Tragus – Anatomical Structures,” accessed November 14, 2025.

If the cartilage did not cross the VLL, the patients’ head was either in a forward or backwards position. Extension and flexion were examined by asking the patients to open their mouths as wide as possible. If the head moved away from the VLL line, it indicated either excess of extension—head bent backwards—or of flexion—the head bent forwards (Fig. 3, Bardellini et al. 2022).

Orthodontic treatment outcomes displayed as paired lateral profile photographs of six patients labeled a through f. Each pair shows pre-treatment (left) and post-treatment (right) views with a vertical line for reference. Arrows on some cases indicate anterior or posterior shifts in facial profile. The images demonstrate improvements in head posture following treatment.
Figure 3: Improvement of the head position (evidenced with the “open mouth test”) in six patients (a-b-c-d-e-f). Bardellini, Elena, Maria Gabriella Gulino, Stefania Fontana, Francesca Amadori, Massimo Febbrari, and Alessandra Majorana. 2022. “Can the Treatment of Dental Malocclusions Affect the Posture in Children?” May 1, 2022: 245

The VLL test indicated that 16 out of 60 patients had a backwards position of the head, 29 a forward position, 10 showed excess of extension while opening their mouths, and 31 an excess of flexion. Only seven patients already had a correct position, meaning that in 75% of patients, dental misalignment influenced head position in relation to VLL line, and 68.33% either flexion or extension.  

After determining the posture of the head, the researchers then examined the weight distribution of the participants using a stabilo-paropodometric platform. The patients were asked to stand on a carpet under which a stabilo-paropodometric platform (40x40cm) was placed. The platform measured the typology of the foot and weight distribution across the two feet. The typology of feet can be divided into three kinds: normal, cavus (extreme arch), or flat (underdeveloped arch). Typology can differ between feet, with either both feet showing the same type or different types. The ideal distribution of body weight between feet should be symmetrical at about 50% on each foot (Bardellini et al. 2022).  

Through measurements obtained with the stabilo-baropodometric platform, the study found 45 cases (both or one side) with cavus feet, and 6 with flat feet (both sides). Hence, 85% of patients had a typology that incorrectly supported their body. Additionally, about 70% of patients had an unequal weight distribution between their two feet, exacerbating bad posture. An incorrect spread of body weight can be identical on both feet—either too much pressure on the ball of the foot or heel—or it can vary between feet (i.e. one foot shows increased pressure at heel, and the other at the ball of the foot) (Bardellini et al. 2022).  

After the classification of malocclusion was identified and the posture (VLL) and weight distributions (Stabilo-baropodometric platform) were measured, the patients were treated with an individually prepared Mouth Slow Balance (Fig. 4), which works by repositioning the tongue, widening the maxilla (upper jaw), and keeping the mandible’s (lower jaw) relation to the maxilla (Bardellini et al. 2019, Bardellini et al. 2022). They describe the MSB device as a “evolution of the Binator”, a retainer like appliance adjusting the bite (Fig. 5, Bardellini et al. 2019 p. 243).  

Photograph of a Mouth Slow Balance (MSB) device, an orthodontic appliance made of acrylic resin and wire that resembles a traditional retainer.
Figure 4: The MBS (mouth slow balance) Class III device Bardellini, E., M. G. Gulino, S. Fontana, J. Merlo, M. Febbrari, and A. Majorana. 2019. “Long-term evaluation of the efficacy on the podalic support and postural control of a new elastic functional orthopaedic device for the correction of Class III malocclusion.” European Journal of Paediatric Dentistry, no. 3: 200.
Photograph of a Binator device, an orthodontic appliance made of acrylic resin and wire that resembles a traditional retainer.
Figure 5: The Binator appliance. Pakshir, Hamidreza, Ali Mokhtar, Alireza Darnahal, Zinat Kamali, Mohammad Hadi Behesti, and Abdolreza Jamilian. 2017. “Effect of Bionator and Farmand Appliance on the Treatment of Mandibular Deficiency in Prepubertal Stage.” Turkish Journal of Orthodontics 30 (1): 16

The patients were observed during their treatments for four years (2014-2018), and by the end, 51 out of 60 patients exhibited a correction of malocclusions, either fully aligned or class I (Bardellini et al. 2022). Other patients either dropped out of the study (3 patients) or reached a correction after the observed time frame (6 patients). 

Of the 53 patients, 23 obtained the ideal position and 19 saw an improvement but did not complete correction of head-position. In 10 cases, patients were found to have been overcorrected.  In the beginning of the four-year observation period, 15 patients had a correct position regarding VLL posture assessment. After treatment, 7 kept their correct position, while 8 now developed a forward position. Additionally, two patients that showed a backwards position before treatment developed a forward position by the end (Bardellini et al. 2022).  

Bardellini et al. (2022) also found significant improvements of the posture in VLL open mouth exams. 53.3% now kept their tragus on the laser line while opening their mouths, when they used to hyper-extend or –flex.  

53 participants (88%) improved their foot typology, of which 17 achieved a complete correction. Before treatment, only 15% of participants had a “normal” typology, which increased to 28% after treatment. However, weight distribution that varied between feet significantly increased from 18 to 37, of which seven patients developed a weight distribution imbalance they previously didn’t show. Overall, cases also exhibited an improvement without complete correction which decreased the median of support discrepancies over the course of the treatment (Bardellini et al. 2022).  

These findings provide evidence for Bardellini et al.’s hypothesis that posture is in fact altered by dental malocclusions. They explain that through a complex chain of muscles across different systems, muscles alter their patterns which disturb the posture, specifically in the position of the head and support of feet.  Muscles around our cheeks (masticatory) and neck (cervical) were already discovered to have a connection in previous research (Bardellini et al. 2022). Furthermore, trunk muscles (abdomen, chest, back) are also connected to these muscles. Since the misalignment of teeth affects the so-called mandibular elevator muscles that are a part of our cheek muscles, this change flows over into other muscle systems (cervical and trunk) acting on our posture. Our strategies for balancing are primarily spread across the trunk, head, and pelvis, which means that the misposition of the head leads our body to try and balance it using other methods (trunk and pelvis) (Bardellini et al. 2022). So, the wrong posture shifts the center of gravity. 

Although Bardellini et al. have found significant evidence that there is a correlation between dental malocclusions and posture, they acknowledge that they are one of few studies that focus on this specific alteration in posture, hence emphasizing that more research needs to be done.  

Furthermore, the results may have been skewed because the team did not consider that the natural changes occurring in growing children may also influence their posture, weight distribution, and more. However, for this specific study it would have been unethical to have a control group of untreated children to compare the effects of treatment vs no treatment (Bardellini et al. 2022).   

Bardellini and her team are one of the few trailblazing research articles that examine the impact of malocclusions on posture, specifically targeting the head and feet. As mentioned before, not much research has been done in this field that examines this topic especially, yet it can prove to be vital for child development. Correcting posture early on can improve a person’s life-quality for the rest of their lives, impacting everyday tasks. Hopefully, in the future more researchers will recognize the importance of this subject and contribute new findings.

References:

Bardellini, E., M. G. Gulino, S. Fontana, J. Merlo, M. Febbrari, and A. Majorana. 2019. “Long-term evaluation of the efficacy on the podalic support and postural control of a new elastic functional orthopaedic device for the correction of Class III malocclusion.” European Journal of Paediatric Dentistry, no. 3: 199–203. https://doi.org/10.23804/ejpd.2019.20.03.06. 

Bardellini, Elena, Maria Gabriella Gulino, Stefania Fontana, Francesca Amadori, Massimo Febbrari, and Alessandra Majorana. 2022. “Can the Treatment of Dental Malocclusions Affect the Posture in Children?” May 1. DOI: 10.17796/1053-4625-46.3.11 

Campbell, Stephen, and Gary Goldstein. 2021. “Angle’s Classification–A Prosthodontic Consideration: Best Evidence Consensus Statement.” Journal of Prosthodontics (United States) 30 (S1): 67–71. https://doi.org/10.1111/jopr.13307. 

Hung, Man, Golnoush Zakeri, Sharon Su, and Amir Mohajeri. 2023. “Profile of Orthodontic Use across Demographics.” Dentistry Journal 11 (12): 291. https://doi.org/10.3390/dj11120291. 

IMAIOS. “Tragus.” e-Anatomy, accessed November 20, 2025. https://www.imaios.com/en/e-anatomy/anatomical-structures/tragus-1536888748. 

Pakshir, Hamidreza, Ali Mokhtar, Alireza Darnahal, Zinat Kamali, Mohammad Hadi Behesti, and Abdolreza Jamilian. 2017. “Effect of Bionator and Farmand Appliance on the Treatment of Mandibular Deficiency in Prepubertal Stage.” Turkish Journal of Orthodontics 30 (1): 15–20. https://doi.org/10.5152/TurkJOrthod.2017.1604. 

Xie, Zhiwei, Fuying Yang, Sujuan Liu, and Min Zong. 2023. “The ‘Hand as Foot’ Teaching Method in Angle’s Classification of Malocclusion.” Asian Journal of Surgery 46 (2): 1062–64. https://doi.org/10.1016/j.asjsur.2022.07.130. 

A novel therapeutic strategy for treating Alzheimer’s disease

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Context

As of 2021, over 57 million people worldwide were living with dementia (World Health Organization, 2025). Alzheimer’s disease (AD) is the most common cause of dementia, representing 60–70% of its cases (World Health Organization, 2025). Most people develop AD in their sixties, and the disease is mostly characterized by a person’s difficulty in remembering recent events, yet symptoms also include problems with mood swings and language (National Institute on Aging, 2022). However, the causes of AD remain poorly understood (Knopman et al., 2021); although significant progress has been made in finding genetic factors, the environmental factors are not as clear. One biological definition of AD underlines the presence of malformed protein deposits in the brain, called amyloid-beta plaques and neurofibrillary tangles (“waste proteins”), which accumulate and damage neurons (Knopman et al., 2021).

No treatments are publicly available to stop or reverse the progression of AD. General advice, such as maintaining a healthy diet and physical activity, may temporarily improve symptoms. It has only been a few years since it has been shown for the first time that medications like lecanemab and donanemab can slow the progression of AD, yet these drugs are controversial due to their potentially dangerous side effects, such as brain swelling (Bitar et al., 2025).

Recent research

However, recent research focusing on the blood-brain barrier (BBB) has revealed that it might, after all, be possible to stop and reverse AD. The BBB, a highly selective permeability barrier that protects the central nervous system (CNS) from harmful substances and regulates the transport of essential molecules like glucose, helps move things in and out of the brain (Abbott, 2010). Dysfunction of the BBB is increasingly being seen as a factor in development of AD, as poor performance of the BBB means that it clears out the amyloid-beta plaques at a slower rate. Chen et al. present a novel therapeutic strategy that targets one specific receptor, the low-density lipoprotein receptor-related protein 1 (LRP1), in the BBB. LRP1 is a protein that helps clear amyloid-beta from the brain by facilitating its transport (Shinohara et al., 2017). It also influences amyloid-beta production by regulating certain enzymes (Shinohara et al., 2017). It is known that LRP1 levels are low in AD patients and low levels correlate with cognitive decline.

Methods

Polymersome design

The researchers engineered nanoscale vesicles (polymersomes) that bind LRP1 receptors. They synthesized four different versions carrying 0, 1, 40, and 200 peptides (ligands) to test how multivalency influences BBB transport. Multivalency increases the binding strength with the receptor. However, when binding is too strong, the LRP1 receptors degrade. Even though LRP1 levels are lower in AD patients, the goal of this method is to take advantage of what’s available by having these nanoparticles (vesicles) display just the right number of ligands to promote LRP1-mediated endocytosis (when the cell membrane engulfs substances to take them in) of the polymersome. This is why four different versions with varying amounts of peptides were tested; the ideal number of peptides was unknown. (Ultimately, the 40-peptide version performed the best.)

Post-binding

Once the polymersome binds to the LRP1 receptor, the receptor induces endocytosis and transports the polymersome inside the cell. Therefore, because the nanoparticle (polymersome) increases the number of LRP1 transport cycles per receptor, amyloid-beta also get to move via the LRP1-induced endocytosis (for example, soluble amyloid-beta may attach to the polymersome). This can solve the problem of having insufficient levels of LRP1 receptors (which leads to slower amyloid-beta removal, which can lead to AD).

Evaluation

  • In vitro (outside a living organism) BBB models: the authors compared multivalent and monovalent constructs and found that multivalent ligands (molecules that bind to receptors) bound the LRP1 receptor more effectively.
  • In vivo (inside a living organism) AD mouse models: after the administration of the treatment, biochemical assays confirmed that brain amyloid-beta plaque levels decreased. Imaging confirmed reduced brain amyloid-beta signals, and cognitive testing showed learning and memory improvements.

Results

The results in this study are significant. In AD model mice, this strategy reduced the level of brain amyloid plaques by nearly 45% and increased soluble plasma amyloid plaques 8 times in two hours (measured by ELISA, a laboratory blood test). This increase means that the plaque material is being “exported” out of the brain into the blood rather than accumulating in the brain. The imaging techniques used confirmed that brain amyloid-β signals had reduced after the intervention. Also, cognitive assessments showed that these AD model mice had improved in spatial learning and memory for up to 6 months post-treatment.

It is important to remember that the BBB of mice is not nearly as complex as the BBB of humans. Therefore, this intervention does not necessarily apply to humans. The study does not explore the off-target uptake in organs like the liver. The next steps for this research may be to repeat it in different AD models, perhaps even with ex vivo human brain microvessels, or to explore the effects of the intervention in different parts of the organism.

Recent research in Alzheimer’s disease has shown that slowing the progression of the disease may indeed be possible. This paper outlines just one of the methodologies that try to slow down the progression of AD. However, slowing down the progression of AD is not the same as reversing the effects of it. This research paper, as well as most research in AD, does not focus on reversing cognitive decline caused by AD; this is because to reverse such effects, we must first understand how to slow down the progression of the disease.

References

Abbott, N. J., Patabendige, A. A. K., Dolman, D. E. M., Yusof, S. R., & Begley, D. J. (2010). Structure and function of the blood–brain barrier. Neurobiology of Disease, 37(1), 13–25. https://doi.org/10.1016/j.nbd.2009.07.030

Bitar, I., Alabdalrazzak, M., Zamzam, M., Desai, Y., & Abushaban, K. (2025). Clinically silent amyloid-related imaging abnormality with edema following lecanemab therapy: A case report. Cureus, 17(8). https://doi.org/10.7759/cureus.91230

Knopman, D. S., Amieva, H., Petersen, R. C., Chételat, G., Holtzman, D. M., Hyman, B. T., Nixon, R. A., & Jones, D. T. (2021). Alzheimer disease. Nature Reviews Disease Primers, 7, 33. https://doi.org/10.1038/s41572-021-00269-y

National Institute on Aging. (2022, October 18). What Are the Signs of Alzheimer’s Disease? https://www.nia.nih.gov/health/alzheimers-symptoms-and-diagnosis/what-are-signs-alzheimers-disease

Shinohara, M., Tachibana, M., Kanekiyo, T., & Bu, G. (2017). Role of LRP1 in the pathogenesis of Alzheimer’s disease: evidence from clinical and preclinical studies. Journal of Lipid Research, 58(7), 1267–1281. https://doi.org/10.1194/jlr.R075796

World Health Organization. (2025, March 31). Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia

Cover image by Robina Weermeijer on Unsplash. https://unsplash.com/photos/brown-brain-decor-in-selective-focus-photography-3KGF9R_0oHs

Ethical ramifications of AI-powered medical diagnoses

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Incredible advancements in artificial intelligence (AI) have recently paved the way for the use of AI in healthcare settings. Implementation of AI has the potential to address worker shortages in the medical field, lead to discovery of new drugs, or improve diagnoses (Bajwa et al., 2021). A writer for the American Medical Association, Benji Feldheim applauds AI for restoring the “human side” in medicine. For example, AI scribes in particular ease the documentation burden doctors face—reducing burnout and improving doctors’ interactions with patients as a result (Feldheim, 2025). Another example is the AI model developed by Shmatko et al. (2025), known as Delphi-2M, which is capable of accurately predicting a patient’s next 20 years of disease burden (i.e., what diseases they would contract and when). Evidently, AI is a very promising technology already capable of improving lives, however, there are reasons to be skeptical. While these advances are promising, these uses of AI also raise concerns about fairness and clinical safety. After a brief synopsis of Shmatko et al.’s Delphi-2M, I evaluate the ethical ramifications of AI-powered diagnoses and related clinical tools.

Delphi-2M is an AI model trained on over 400,000 patient histories from a UK database to forecast an individual’s 20-year disease trajectory. Similar to chatbots like ChatGPT, Delphi-2M is a large language model (LLM), a type of AI that can recognize and reproduce patterns from large amounts of data. Similar to how chatbots pick up on what words are likely to appear with other words in order to form sentences, Delphi-2M learns from its vast training set of medical records to predict a patient’s disease trajectory from realworld patterns. As Yonghui Wu puts it in her summary of Shmatko et al.’s work, it’s just how becoming a smoker may be followed by a future diagnosis of lung cancer—these are patterns Delphi-2M recognize. To do this, Delphi-2M is fed “tokens” that link diseases or health factors to specific times in a person’s life, like chickenpox at age 2 or smoking at age 41 (Figure 1). Then, Delphi-2M outputs new tokens that predict what diseases and when they will occur in an individual’s life, like the onset of respiratory disorders at age 71 as a result of smoking. Delphi-2M, after being trained, was tested by predicting the medical histories of 1.9 million patients not included in the original training set. Shmatko et al. demonstrate this AI to have great success in accurately predicting disease trajectory, as it partially predicts patterns in individuals’ diagnoses in 97% of cases.

Visualization of Delphi-2M input and output (Wu, 2025).

Nonetheless, we must hold AI used to diagnose patients to a higher level of scrutiny compared to AI used commercially. LLMs are not perfect as they are subject to algorithmic bias and misuse, beginning before their creation. Shmatko et al. (2025), for example, address some shortcomings of the training data used for Delphi-2M. Notably, they explain the data from a mostly-white, older subset of the UK population isn’t entirely generalizable to very different demographics. Though Shmatko et al. found successes testing the model against a Danish database after training it on UK patients, I’m still concerned how Delphi-2M would perform on non-European and younger demographics, or those underrepresented in training data. Facial recognition is a prime example of where AI underperforms when training datasets lack diverse representation. AI designed to recognize faces historically underperform on individuals with feminine features or darker skin due to unrepresentative training data (Hardesty, 2018). With this in mind, it’s important that training data for diagnostic AI is representative of all demographics prior to widespread implementation.

Furthermore, Cabitza et al. (2017) wrote on some of the unintended consequences of machine learning in healthcare, postulating that widespread implementation of these tools also has the potential to reduce the skill of physicians. Though convenient in the short run, Cabitza et al. raise concerns with overreliance on AI—as studies show physicians aided by AI were less sensitive and accurate in diagnosing patients. Mammogram readers, for instance, were 14% less sensitive in their diagnostics when presented with images marked by computer-aided detection (Povyakalo et al., 2013). Though this study focused on image diagnoses, it’s clear how widespread use of Delphi-2M would lead to the same problems of deskilling in physicians. Delphi-2M is also exclusively a text-based model, which as Cabitza et al. detail, means that these diagnosis algorithms do not incorporate crucial contextual elements that are “psychological, relational, social, and organizational” in nature. A realworld example that Cabitza et al. described was an instance in which an AI model predicted a lower mortality risk for patients with pneumonia and asthma compared to those with pneumonia and without asthma. Understanding that asthma is not a protective factor for pneumonia patients, the involved researchers found the discrepant AI output was the result of hospital procedures that admitted pneumonia patients with asthma directly to intensive care, giving them better health outcomes. This missing piece of crucial information, which was difficult to represent in these prognostic models, led to an error a physician would not make. Thus, AI is limited in what information it can train on.

Though these new advancements in healthcare AI are promising, they have their limits. Tools like Delphi-2M spot patterns across vast clinical histories that no single clinician could feasibly track, yet the benefits depend on who is represented in the data, how predictions are explained and used, and whether safeguards are in place when they fail. Before AI is implemented in healthcare, we must demand representative training sets, validation across diverse populations, clear disclosures of uncertainty and limitations, and constant human involvement in the process that resists automation bias and deskilling. In short, diagnostic AI should supplmenent—not replace—clinical judgment, and it should be developed with privacy, equity, and patient trust at the forefront. Only then will these systems reliably improve care rather than merely appear to.

 

References

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095

Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517. https://doi.org/10.1001/jama.2017.7797

Feldheim, B. (2025, June 12). AI scribes save 15,000 hours—and restore the human side of medicine. American Medical Association. https://www.ama-assn.org/practice-management/digital-health/ai-scribes-save-15000-hours-and-restore-human-side-medicine

Hardesty, L. (2018, February 11). Study finds gender and skin-type bias in commercial artificial-intelligence systems. MIT News. https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212

Povyakalo, A. A., Alberdi, E., Strigini, L., & Ayton, P. (2013). How to Discriminate between Computer-Aided and Computer-Hindered Decisions. Medical Decision Making, 33(1), 98–107. https://doi.org/10.1177/0272989×12465490

Wu, Y. (2025). AI uses medical records to accurately predict onset of disease 20 years into the future. Nature, 647(8088), 44–45. https://doi.org/10.1038/d41586-025-02971-3

Phytoplankton and Ocean Warming: Uneven Adaptations at the Base of the Marine Food Web

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Global warming is steadily transforming Earth’s oceans. Between 1901 and 2023, sea surface temperatures have increased at an average rate of 0.14℉ per decade (US EPA, 2016). This seemingly small thermal shift is enough to disrupt circulation patterns, alter nutrient availability, and restructure entire marine communities. As oceans absorb over 90% of excess atmospheric heat, they become both a buffer against and a victim of climate change (Climate Change, 2025). Among the many organisms affected by these changes, phytoplankton—the microscopic, photosynthetic organisms that drift near the ocean’s surface—serve as a critical case study. These single-celled producers are responsible for about half of Earth’s oxygen production, and they form the foundation of aquatic food webs, converting sunlight into chemical energy that sustains nearly all marine life (Hook, 2023). Therefore, understanding how phytoplankton respond to warming is essential for predicting the future of marine ecosystems.

Phytoplankton are highly sensitive to temperature fluctuations. Since their metabolic processes, growth rates, and enzymatic activities are temperature-dependent, even minor thermal changes can reshape their abundance and distribution. When waters warm beyond a species’ thermal tolerance, populations may decline or shift toward cooler regions (Barton et al., 2016). At the microscopic level, these shifts can cascade upward through the food web, reducing food availability for zooplankton, fish, and the higher-level predators that feed on them, such as sharks, whales, and seals. However,  one key question remains: can phytoplankton adapt to rising temperatures, or will their thermal limits determine the structure of future marine ecosystems?

Huertas et al. (2011) directly addressed this question through controlled laboratory experiments designed to measure the capacity of phytoplankton to evolve under warming. The researchers selected twelve species representing a range of environments—freshwater, coastal, open-ocean, and coral symbiotic systems—to test whether thermal tolerance varied among ecological types. To simulate long-term warming, they employed a “ratchet technique,” in which phytoplankton populations were gradually exposed to higher temperatures. Each population started from a single cloned cell to remove preexisting genetic variation. Then, the cell cultures were repeatedly grown and transferred into warmer conditions, forcing the populations to either adapt to the changes through genetic mutations or face extinction.

The results revealed striking differences among species. Freshwater species, such as Scenedesmus intermedius, exhibited remarkable resilience, adapting to temperatures as high as 40°C. Coastal species like Tetraselmis suecica and Dictyosphaerium chlorelloides tolerated up to 35°C, while open-ocean species such as Emiliania huxleyi and Monochrysis lutheri showed little to no capacity for adaptation. Coral symbionts (Symbiodinium species) demonstrated limited but detectable resistance, reflecting the thermal stress already observed in coral reef environments. Importantly, adaptation was not simply a case of short-term acclimation. The researchers found that resistant populations arose at different times across replicate cultures. This serves as evidence that adaptation stemmed from rare, spontaneous genetic mutations instead of physiological flexibility. Growth rates of adapted populations diverged significantly from their ancestral strains, confirming that true evolutionary change had occurred.

These findings carry major implications for understanding the ecological future of the oceans. If phytoplankton species differ so widely in their ability to adapt, warming will likely reorganize marine communities from the bottom up. Species capable of rapid genetic adaptation may dominate, while others could decline or disappear. This uneven resilience could favor smaller, faster-growing species, altering nutrient cycling and potentially weakening the ocean’s ability to sequester carbon. Because phytoplankton drive roughly half of global primary production, any restructuring of these communities could ripple through food webs, climate regulation, and fisheries.

While Huertas et al. focused on individual species in controlled conditions, Poloczanska et al. (2016) broadens this picture to the scale of global ecosystems. Their review synthesized nearly 2,000 observations of marine organisms responding to climate change, confirming that uneven adaptation is already occurring across taxa and ocean regions. On average, species distributions are shifting towards the north and south poles by about 72 kilometers per decade, and spring life-cycle events such as breeding or migration are advancing by four days per decade. Warm-water species are becoming more abundant, while cold-water species decline. Coral calcification, the process by which corals take in calcium and carbonate ions to build their exoskeletons, is weakening under combined warming and acidification stress. These patterns mirror the interspecific variability observed by Huertas et al.; some organisms adjust successfully to changing conditions, while others falter. Here, the broader conclusion is that climate change does not affect marine life uniformly—it selectively reshapes communities based on biological flexibility, dispersal ability, and evolutionary potential.

Fig 1. Global distribution of documented marine biological responses to climate change across major ocean regions (Poloczanska et al., 2016). Bars show the proportion of observed responses as consistent (dark blue), equivocal (light blue), or no change (yellow). Numbers indicate total observations per region; symbols identify taxa with ≥10 observations. Background colors represent regional sea-surface warming from 1950–2009 (yellow: low; orange: medium; red: high). Regions are defined by ecological structure and oceanographic features. eveal that climate-driven shifts in abundance, distribution, and phenology vary sharply across ocean basins—mirroring the uneven adaptive capacities described by Huertas et al. (2011).

Together, these studies illustrate both the mechanisms and the consequences of ocean warming. Huertas et al. provides mechanistic insight—showing that adaptation in phytoplankton depends on genetic change, and that some species are inherently more capable than others. Building off of this, Poloczanska et al. reveals how these species-level differences scale up, driving global shifts in abundance, distribution, and ecosystem structure. The two perspectives complement one another; laboratory experiments explain how adaptation might occur, while global syntheses show where and to what extent it already has.

As climate change accelerates, understanding the adaptability of foundational organisms like phytoplankton becomes increasingly urgent. Their evolutionary potential will determine not only the structure of marine ecosystems, but also the ocean’s capacity to regulate the planet’s climate. By linking experimental evidence with global ecological trends, researchers are beginning to map out a future ocean defined by winners and losers—a mosaic of adaptation, migration, and loss. The challenge ahead lies in predicting how these microscopic shifts will ripple through the web of life that depends on them.

References:

Barton, A. D., Irwin, A. J., Finkel, Z. V., & Stock, C. A. (2016). Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proceedings of the National Academy of Sciences, 113(11), 2964–2969. https://doi.org/10.1073/pnas.1519080113 

Climate Change: Ocean Heat Content | NOAA Climate.gov. (2025, June 26). https://www.climate.gov/news-features/understanding-climate/climate-change-ocean-heat-content 

Hook, B. (2023, May 31). Phenomenal Phytoplankton: Scientists Uncover Cellular Process Behind Oxygen Production | Scripps Institution of Oceanography. https://scripps.ucsd.edu/news/phenomenal-phytoplankton-scientists-uncover-cellular-process-behind-oxygen-production 

Huertas, I. E., Rouco, M., López-Rodas, V., & Costas, E. (2011). Warming will affect phytoplankton differently: Evidence through a mechanistic approach. Proceedings of the Royal Society B: Biological Sciences, 278(1724), 3534–3543. https://doi.org/10.1098/rspb.2011.0160 

Poloczanska, E. S., Burrows, M. T., Brown, C. J., García Molinos, J., Halpern, B. S., Hoegh-Guldberg, O., Kappel, C. V., Moore, P. J., Richardson, A. J., Schoeman, D. S., & Sydeman, W. J. (2016). Responses of Marine Organisms to Climate Change across Oceans. Frontiers in Marine Science, 3. https://doi.org/10.3389/fmars.2016.00062 

US EPA, O. (2016, June 27). Climate Change Indicators: Sea Surface Temperature [Reports and Assessments]. https://www.epa.gov/climate-indicators/climate-change-indicators-sea-surface-temperature 

 

Floating Systems: Jellyfish and Evolving Nervous Systems

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Jellyfish are just one species within the phylum cnidaria. A phylum is a broad level of taxonomic classification that includes many different species, with cnidaria additionally including coral and anemones. Cnidaria provides comparative neuroscience information due to the simple behaviors that the species within the phylum exhibit. Despite their shared phylum that creates nerve cells with similar properties, the species have dramatically different nervous systems, allowing for unique perspectives on the diversity, origins, and evolution of neural systems within species (Cunningham et al., 2024). Comparative neuroscience information is the study of nervous systems across a variety of animal species. Through this research, the evolutionary changes in the brain’s structure can be examined, allowing scientists to see how differences in nervous systems shape certain behaviors (Miller et al., 2019). Neuroscience researchers can use an all-optical interrogation, in which they study and manipulate neural systems using light, upon these species, allowing them to image and photograph the neuronal networks in the creature for further examination. 

Fig 1. Photo of Clytia hemisphaerica (Clytia Hemisphaerica Medusa – 13673149 ❘ Science Photo Library, n.d.)

Jellyfish are major contributors to ocean ecosystems. Their reproductive, foraging, and defensive behaviors all uniquely impact the ecosystem at large. What is notable about jellyfish, however, is that these behaviors are shaped out of decentralized, regenerative nervous systems. Rather than the creature being controlled by the neurons in its brain, the jellyfish’s neurons are spread throughout the body (“Thinking without a Centralized Brain,” n.d.). This allows the various parts of its body to have a role in controlling and processing information. Additionally, the nervous system itself has the ability to repair and restore itself, allowing damaged nerves to be replaced by new ones (Gaskill, 2018). 

Jellyfish are the most complicated species of Cnidaria, due to various behaviors that demonstrate their higher level functioning compared to other species in the phylum. They have the ability to move in 3-dimensions, capture and consume other creatures, and the ability to escape from predators and other potential threats. Notably, jellyfish also exhibit courtship behaviors and sleep states, despite lacking a central brain. These behaviors are  due to their sensory structures, made up by two nervous systems: one which controls their swimming and another that controls all other behaviors. The jellyfish’s nervous systems can respond to each other, despite the lack of a central controller (Cunningham et al., 2024). 

A recent scientific investigation conducted at Caltech by Anderson et al. sought to explore how the jellyfish can be used to conduct neuroscience research. Clytia hemisphaerica is a species of jellyfish that has recently been adopted into a genetic neuroscience model. It has previously been used as a model to study evolution, embryology, regeneration, and other fields. This species of jellyfish is a particularly useful model for neuroscience research because its genome is already sequenced and assembled from the birth of the creature, with whole-animal single-celled Ribonucleic Acid (RNA) sequences formed within the species. Rather than using multiple animals to sequence the RNA, Clytia hemisphaerica has the capability to provide the necessary amounts of cells needed to be examined. Using multiplexed single-cell RNA sequencing, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run (Chari et al., 2021). Clytia hemisphaerica is the only jellyfish whose RNA sequences are being used to rapidly develop genomic tools. These tools can be tested and utilized by researchers, allowing them to explore brain function and neurological disorders through this model (Cunningham et al., 2024).

The last common ancestor of Clytia hemisphaerica was a hydrozoan jellyfish, which are able to perform specific behaviors even if certain body parts are detached from the body. Hydrozoan jellyfish, notably, have the ability to cycle back and forth between various stages of their life–allowing them to live for large expanses of time. When the body parts exist in an intact organism, they also have the ability to perform more complex behaviors. These include different feeding behaviors and mechanisms. The swimming behavior of the Clytia hemisphaerica also reveals key information about the neuromechanics behind different behaviors of jellyfish. As an example, although jellyfish spend a majority of their lives swimming, there are periods where they may start and stop. These periods are only exhibited when there is food passing or defensive behaviors are exhibited. When these behaviors are examined, neuroscientists can ponder and develop further conclusions about multi-sensory integration, motor control, and the mechanisms that underlie behavioral states (Cunningham et al., 2024). Figure 1

Fig 2. The Evolution, Life Cycle, and Genetic Tools of Clytia hemisphaerica (Cunningham et al., 2024)

Through using neural population imaging, in which researchers have the ability to monitor large groups of neurons through calcium and voltage imaging, on the whole-organism scale through the Clytia hemisphaerica, emergent properties of function networks can be uncovered (Zhu et al., 2022). Without this model, scientists would have to use traditional single-cell unit recordings, requiring using fine tools just to see the individual activity of a single neutron, or anatomical studies, which would not provide the same amount of potential discoveries that new techniques with Clytia hemisphaerica provide. Through using this species as a model, researchers can uncover more knowledge and data about nervous system evolution and function, particularly for neural regeneration.

Neural regeneration is particularly important in the treatment of injury and disease in the nervous system. It aids in cognitive recovery following neurodegeneration, helping rebuild neurons and nervous tissue (Steward et al., 2013). Through neural regeneration, the nervous system may regain its functions, allowing for betterment of quality of life. By continuing to examine species capable of neural regeneration, we may learn to apply this to the human nervous system, allowing us to move forward in curing traumatic brain injuries and degeneration of the brain and its abilities (Neuroregeneration – an Overview | Sciencedirect Topics, n.d.).

 

 

 

 

 

References:

Chari, T., Weissbourd, B., Gehring, J., Ferraioli, A., Leclère, L., Herl, M., Gao, F., Chevalier, S., Copley, R. R., Houliston, E., Anderson, D. J., & Pachter, L. (2021). Whole animal multiplexed single-cell rna-seq reveals plasticity of clytia medusa cell types. bioRxiv. https://doi.org/10.1101/2021.01.22.427844

Cunningham, K., Anderson, D. J., & Weissbourd, B. (2024). Jellyfish for the study of nervous system evolution and function. Current Opinion in Neurobiology, 88, 102903. https://doi.org/10.1016/j.conb.2024.102903

Gaskill, M. (2018, November 20). No brain? For jellyfish, no problem | blog | nature | pbs. Nature. https://www.pbs.org/wnet/nature/blog/no-brain-for-jellyfish-no-problem/

Miller, C. T., Hale, M. E., Okano, H., Okabe, S., & Mitra, P. (2019). Comparative principles for next-generation neuroscience. Frontiers in Behavioral Neuroscience, 13. https://doi.org/10.3389/fnbeh.2019.00012

Neuroregeneration—An overview | sciencedirect topics. (n.d.). Retrieved April 27, 2025, from https://www.sciencedirect.com/topics/neuroscience/neuroregeneration

Steward, M. M., Sridhar, A., & Meyer, J. S. (2013). Neural regeneration. Current Topics in Microbiology and Immunology, 367, 163–191. https://doi.org/10.1007/82_2012_302

Thinking without a centralized brain: The intelligence of the octopus. (n.d.). WHYY. Retrieved April 27, 2025, from https://whyy.org/segments/thinking-without-a-centralized-brain-the-intelligence-of-the-octopus/

Zhu, F., Grier, H. A., Tandon, R., Cai, C., Agarwal, A., Giovannucci, A., Kaufman, M. T., & Pandarinath, C. (2022). A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with sub-frame temporal resolution. Nature Neuroscience, 25(12), 1724–1734. https://doi.org/10.1038/s41593-022-01189-0

Biological ChatGPT: Rewriting Life With Evo 2

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What makes life life? Is there underlying code that, when written or altered, can be used to replicate or even create life? On February 19th 2025, scientists from Arc Institute, NVIDIA, Stanford, Berkeley, and UC San Francisco released Evo 2, a generative machine learning model that may help answer these questions. Unlike its precursor Evo 1, which was released a year earlier, Evo 2 is trained on genomic data of eukaryotes as well as prokaryotes. In total, it is trained on 9.3 trillion nucleotides from over 130,000 genomes, making it the largest AI model in biology. You can think of it as ChatGPT for creating genetic code—only it “thinks” in the language of DNA rather than human language, and it is being used to solve the most pressing health and disease challenges (rather than calculus homework).

Computers, defined broadly, are devices that store, process, and display information. Digital computers, such as your laptop or phone, function based on binary code—the most basic form of computer data composed of 0s and 1s, representing a current that is on or off. Evo 2 centers around the idea that DNA functions as nature’s “code,” which, through protein expression and organismal development, creates “computers” of life. Rather than binary, organisms function according to genetic code, made up of A, T, C, G, and U–the five major nucleotide bases that constitute DNA and RNA.

Although Evo 2 can potentially design code for artificial life, it has not yet designed an entire genome and is not being used to create artificial organisms. Instead, Evo 2 is being used to (1) predict genetic abnormalities and (2) generate genetic code.

11 Functions of Evo 2 in biology at the cellular/organismal, protein, RNA, and epigenome levels.
Functions of Evo 2 at different levels. Adapted from https://www.biorxiv.org/content/10.1101/2025.02.18.638918v1.full

Accurate over 90% of the time, Evo 2 can predict which BRCA1 (a gene central to understanding breast cancer) mutations are benign versus potentially pathogenic. This is big, since each gene is composed of hundreds and thousands of nucleotides, and any mutation in a single nucleotide (termed a Single Nucleotide Variant, or SNV) could have drastic consequences for the protein structure and function. Thus, being able to computationally pinpoint dangerous mutations reduces the amount of time and money spent testing each mutation in a lab, and paves the way for developing more targeted drugs.

Secondly, Evo 2 can design genetic code for highly specialized and controlled proteins which provide many fruitful possibilities for synthetic biology (making synthetic molecules using biological systems), from pharmaceuticals to plastic-degrading enzymes. It can generate entire mitochondrial genomes, minimal bacterial genomes, and entire yeast chromosomes–a feat that had not been done yet.

A notable perplexity of eukaryotic genomes is their many-layered epigenomic interactions: the complex power of the environment in controlling gene expression. Evo 2 works around this by using models of epigenomic structures, made possible through inference-time scaling. Put simply, inference-time scaling is a technique developed by NVIDIA that allows AI models to take time to “think” by evaluating multiple solutions before selecting the best one.

How is Evo 2 so knowledgeable, despite only being one year old? The answer lies in deep learning.

Just as in Large Language Models, or LLMs (think: ChatGPT, Gemini, etc.), Evo 2 decides what genes should look like by “training” on massive amounts of previously known data. Where LLMs train on previous text, Evo 2 trains on entire genomes of over 130,000 organisms. This training—the processing of mass amounts of data—is central to deep learning. In training, individual pieces of data called tokens are fed into a “neural networks”—a fancy name for a collection of software functions that are communicate data to one another. As their name suggests, neural networks are modeled after the human nervous system, which is made up of individual neurons that are analogous to software functions. Just like brain cells, “neurons” in the network can both take in information and produce output by communicating with other neurons. Each neural network has multiple layers, each with a certain number of neurons. Within each layer, each neuron sends information to every neuron in the next layer, allowing the model to process and distill large amounts of data. The more neurons involved, the more fine-tuned the final output will be. 

This neural network then attempts to solve a problem. Since practice makes perfect, the network attempts the problem over and over; each time, it strengthens the successful neural connections while diminishing others. This is called adjusting parameters, which are variables within a model that can be adjusted, dictating how the model behaves and what it produces. This minimizes error and increases accuracy. Evo 2 was trained with 7b and 40b parameters to have a 1 million token context window, meaning the genomic data was fed through many neurons and fine-tuned many times.

Example neural network
Example neural network modeled using tensorflow adapted from playground.tensorflow.org

The idea of anyone being able to create genetic code may spark fear; however, Evo 2 developers have prevented the model from returning productive answers to inquiries about pathogens, and the data set was carefully chosen to not include pathogens that infect humans and complex organisms. Furthermore, the positive possibilities of Evo 2 usage are likely much more than we are currently aware of: scientists believe Evo 2 will advance our understanding of biological systems by generalizing across massive genomic data of known biology. This may reveal higher-level patterns and unearth more biological truths from a birds-eye view.

It’s important to note that Evo 2 is a foundational model, emphasizing generalist capabilities over task-specific optimization. It was intended to be a foundation for scientists to build upon and alter for their own projects. Being open source, anyone can access the model code and training data. Anyone (even you!) can even generate their own strings of genetic code with Evo Designer. 

Biotechnology is rapidly advancing. For example, DNA origami allows scientists to fold DNA into highly specialized nanostructures of any shape–including smiley faces and China–potentially allowing scientists to use DNA code to design biological robots much smaller than any robot we have today. These tiny robots can target highly specific areas of the body, such as receptors on cancer cells. Evo 2, with its designing abilities, opens up many possibilities for DNA origami design. From gene therapy, to mutation-predictions, to miniature smiley faces, it is clear that computation is becoming increasingly important in understanding the most obscure intricacies of life—and we are just at the start.

 

Garyk Brixi, Matthew G. Durrant, Jerome Ku, Michael Poli, Greg Brockman, Daniel Chang, Gabriel A. Gonzalez, Samuel H. King, David B. Li, Aditi T. Merchant, Mohsen Naghipourfar, Eric Nguyen, Chiara Ricci-Tam, David W. Romero, Gwanggyu Sun, Ali Taghibakshi, Anton Vorontsov, Brandon Yang, Myra Deng, Liv Gorton, Nam Nguyen, Nicholas K. Wang, Etowah Adams, Stephen A. Baccus, Steven Dillmann, Stefano Ermon, Daniel Guo, Rajesh Ilango, Ken Janik, Amy X. Lu, Reshma Mehta, Mohammad R.K. Mofrad, Madelena Y. Ng, Jaspreet Pannu, Christopher Ré, Jonathan C. Schmok, John St. John, Jeremy Sullivan, Kevin Zhu, Greg Zynda, Daniel Balsam, Patrick Collison, Anthony B. Costa, Tina Hernandez-Boussard, Eric Ho, Ming-Yu Liu, Thomas McGrath, Kimberly Powell, Dave P. Burke, Hani Goodarzi, Patrick D. Hsu, Brian L. Hie (2025). Genome modeling and design across all domains of life with Evo 2. bioRxiv preprint doi: https://doi.org/10.1101/2025.02.18.638918.

 

POTS vs Atomoxetine: The Unseen Interaction

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Postural Orthostatic Tachycardia Syndrome (POTS) is a malfunction in the body’s autonomic nervous system. Rather than the blood vessels below their heart compensating by constricting, when a person with POTS goes from a lying to a standing position, a large amount of blood pools in the legs and abdomen. Normally, the blood vessels in the lower extremities constrict to maintain appropriate blood pressure throughout the whole body and help return the blood to the heart and head. The autonomic system (the part of the nervous system that is in charge of the involuntary aspects of the body) responds to low blood pressure by releasing norepinephrine and adrenaline, which cause vasoconstriction and a rise in heart rate. In POTS patients, vessels do not respond to the hormones and remain vasodilated. This combination of high heart rate and insufficient blood flow to the brain causes characteristic dizziness, fainting, and fatigue. POTS can be aggravated by a variety of things, including strenuous exercise, caffeine, hot environments, and certain medications (POTS, n.d.). 

One such class of medications is norepinephrine reuptake inhibitors (NOIs). Used to treat ADHD, major depressive disorder, and narcolepsy, NOIs block the uptake of norepinephrine in the synapses (De Crescenzo et al., 2018). This type of medication allows norepinephrine to stay in the blood longer, elevating mood and energy levels and enhancing focus. A common side effect is an elevated heart rate, which aggravates POTS. 

Green et al. conducted the first study examining the acute effects of atomoxetine on POTS patients. The study was composed of 27 patients and a variety of tests. A baseline was created to manage the patients’ diets. This entailed removing methylxanthines from their diet, which includes caffeine among other compounds, and moderating sodium and potassium intake. Additionally, all long-term medications were suspended for at least 5 half-life periods to ensure no hormonal effects would be present. All of these measures were taken to minimize the exacerbation of POTS symptoms (Green et al., 2013). 

All patients received the atomoxetine and the placebo (on different days). During this time, a posture study was done. Measurements of heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and plasma catecholamines were taken during a lying position and a standing position. This targets the effects of POTS, highlighting the possible impact of the atomoxetine. 

The posture study was paired with the medication study. During the medication study, patients were asked to fill out a symptom feedback form before the experiment, and every hour up to 4 hours after drug administration. This is because peak atomoxetine concentration occurs 1-2 hrs after ingestion. The Vanderbilt Orthostatic Symptom Score (VOSS) was used on the symptom feedback form, where patients are asked to rank the following on a scale from 1-10: mental clouding, brain fog, shortness of breath, palpitations, tremors, headache, tightness in the chest, blurred vision, and nausea. The lowest (1) is no symptom burden, and 10 is the worst. 

Researchers found that when patients took atomoxetine, their symptom burden increased. This presented a statistically significant increase in heart rate and a general upward trend in blood pressure throughout the 4 hours. In the case of the placebo, there was a decrease in symptom burden as the 4-hour period progressed. 

Atomoxetine is a non-stimulant medication used to treat ADHD; unfortunately, the stimulant alternatives are found to have similar effects on POTS patients. Due to a susceptibility to heart rate changes, ADHD medication negatively interacts with the condition and must be administered with exceeding caution. This interaction is important for prescribing professionals to be aware of. As this is a relatively under-researched intersection, consideration of mechanisms and close patient-doctor communication is necessary when considering medication. 

Figure 1: Results of VOSS with and without atomoxetine (Green et al., 2013)

Graph describing the effects of the medication on POTS symptoms

References

De Crescenzo, F., Ziganshina, L. E., Yudina, E. V., Kaplan, Y. C., Ciabattini, M., Wei, Y., & Hoyle, C. H. (2018). Noradrenaline reuptake inhibitors (NRIs) for attention deficit hyperactivity disorder (ADHD) in adults. The Cochrane Database of Systematic Reviews, 2018(6), CD013044. https://doi.org/10.1002/14651858.CD013044

Green, E. A., Raj, V., Shibao, C. A., Biaggioni, I., Black, B. K., Dupont, W. D., Robertson, D., & Raj, S. R. (2013). Effects of norepinephrine reuptake inhibition on postural tachycardia syndrome. Journal of the American Heart Association, 2(5), e000395. https://doi.org/10.1161/JAHA.113.000395

POTS: Causes, Symptoms, Diagnosis & Treatment. (n.d.). Cleveland Clinic. Retrieved April 8, 2025, from https://my.clevelandclinic.org/health/diseases/16560-postural-orthostatic-tachycardia-syndrome-pots