The impacts of a justice-focused body image program for early adolescents

To address gaps in universal, diversity-focused eating disorders prevention with early adolescents, our team co-created an evidence-informed body image intervention through a community-engaged, participatory research process. The Body Justice intervention and associated research were co-created by a team of middle school students and staff and undergraduate students and faculty in the Pacific Northwest United States. The intervention includes eight brief lessons (six hours total) with culturally-tailored content rooted in cognitive dissonance and media literacy (e.g., cultural appearance ideals, diversity representation within media, food culture). The intervention was delivered with 7th grade students over three years (N = 333; 49% students of color; 53% cisgender boys, 36% cisgender girls, 12% gender diverse; 27% sexually diverse) using college student leaders (near peers) and middle school student co-leaders. Student satisfaction immediately after the intervention was moderate overall and higher for students of color, sexually diverse students, and cisgender girls and gender diverse students compared to their peers with majority identities. Across the sample, there was a significant reduction in unhealthy weight control behaviors from baseline to two-month follow-up with similar improvement among subgroups except for students of color, who had smaller reductions over time compared to their white peers. Across the sample, there was a significant reduction in internalized appearance norms from pre to post-intervention and through follow-up. These reductions were similar across gender, but the change was significant only for white students and straight students. There was no overall improvement in perceived appearance pressure from social media over time, but subgroup analyses revealed that students of color experienced improvement over time unlike other subgroups. In general, subgroup analyses should be interpreted cautiously due to concerns about adequate power. These results suggest that the Body Justice curriculum was delivered effectively and was well-liked by middle school students with marginalized identities. While aspects of the intervention were beneficial (e.g., a reduction in unhealthy weight control behaviors over time), findings suggest potentially differential results across identity subgroups. This has implications for collaborative school-based research, body image and eating disorders prevention, and community-engaged methods to foster equity.

TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity

ObjectivesThis study aimed to develop an interpretable multimodal framework for detecting tic events in children with tic disorders by translating model decisions into structured, time-aligned evidence from synchronized video and physiological signals.MethodsTIC-XNet was developed to jointly analyze synchronized video, heart rate, and electrodermal activity signals and to generate structured evidence outputs. Recordings from 417 children with clinically diagnosed tic disorders were collected during structured clinical assessments and home-based observations. TIC-XNet was compared with a prediction-only black-box model and a post-hoc explainable model under matched predictive backbones and identical training settings.ResultsWithin the evaluated internal subject-level split, TIC-XNet achieved the best performance on the pooled shared test set, with a window-level AUC of 0.915 ± 0.019, higher event-level recall and precision, fewer missed events, and lower post-buffering prediction latency than the comparator models. Its translated outputs also showed higher decision fidelity, greater stability under perturbation, and closer temporal alignment with expert-annotated tic onsets. Subject-level translated numerical signals were associated with tic severity.ConclusionsThese findings indicate that evidence translation can support more interpretable multimodal detection of tic events in children with tic disorders while remaining compatible with strong predictive performance.

Gender-specific symptom outcomes on cariprazine treatment: a 12-month naturalistic longitudinal follow-up study in schizophrenia

IntroductionA growing body of literature is focusing on third-generation antipsychotics and their unique characteristics, but few studies have examined gender as a crucial factor in response profiles. The present study aims to address this gap by analyzing the outcomes of 12-month naturalistic treatment with cariprazine to elucidate changes in specific psychopathological domains between men and women.MethodsThe present 12-month longitudinal naturalistic study involved a sample of individuals diagnosed with schizophrenia according to the DSM-5-TR treated with cariprazine at the outpatients’ psychiatric services of a major university and community hospitals in Italy. The assessments conducted included sociodemographic data, the Structured Clinical Interview for the DSM-5 (SCID-5), and the Positive and Negative Symptom Scale (PANSS) Total and Subscale scores, as well as the PANSS-derived Marder factors. The PANSS was administered at three time points: before starting the treatment with cariprazine (T0), after 6 months (T1), and after 12 months (T2).ResultsFifteen male and 17 female subjects were assessed at the three time points. The mean dose of cariprazine was 4.2 ± 1.3 mg for men and 4.0 ± 1.5 mg for women. Both genders exhibited improvements in all PANSS subscale symptoms after 6 and 12 months of cariprazine treatment compared to the baseline, with the only exception of the Uncontrolled hostility/excitement Marder factor among men. Progressive improvements through time points in symptom subscales were found in both sexes, reaching numerical differences in every PANSS subscale in both sexes at T2. Gender specifc response profiles emerged after 6 and 12 months of treatment in the PANSS subscales and items in men and women.DiscussionCariprazine exhibited significant efficacy in both sexes, with no significant differences between men and women despite a gender specific response profile emerged. Additional studies are needed to further investigate the efficacy profile and long-term outcome of cariprazine treatment by gender.

STAT+: Gene-editing startup launches with $230 million and a Chinese licensing deal

A new gene-editing startup has threaded the needle of negotiating contracts with a Chinese drug company, raising $230 million in funding, and executing a reverse merger with a preexisting biotech company. 

The result? Serapha Bio, which launched Tuesday to develop a one-and-done treatment for a liver and lung disease called Alpha-1 Antitrypsin Deficiency, or AATD, founding investors RA Capital and RTW Investments told STAT exclusively. 

RA and RTW arranged $138 million in Series A funding for Serapha, along with $92 million in additional funds tied to the company’s reverse merger with Boundless Bio. Boundless Bio launched in 2019 to study extrachromosomal DNA and its role in cancer growth but ran into trouble with its lead program.

Continue to STAT+ to read the full story…

Breast and Cervical Cancer Stigma in Rwanda

Conditions: Breast Cancer; Cervical Cancer

Interventions: Behavioral: Behavioral: RISE Intervention; Behavioral: Behavioral: CHW Educational Intervention

Sponsors: Dana-Farber Cancer Institute; Dana-Farber/Harvard Cancer Center (DF/HCC) Boston, MA; American Association for Cancer Research; Breast Cancer Research Foundation

Not yet recruiting

Long‑Range Gene Networks Uncover 641 New Schizophrenia‑Associated Genes

Schizophrenia’s genetic landscape just expanded dramatically. A new study in Nature Genetics identifies 641 previously unrecognized genes associated with schizophrenia, thanks to a modeling framework that captures how distant genetic variants regulate gene expression through co‑expression networks. The work reframes schizophrenia not as a collection of isolated genetic hits, but as a disorder shaped by long‑range regulatory relationships across the brain. The study is titled, “Co‑expression‑based models improve eQTL predictions for transcriptome‑wide association studies and highlight new schizophrenia‑associated genes.”

The research team, led by Giulio Pergola, PhD, at the Lieber Institute for Brain Development (LIBD), developed two trans‑aware predictive models—INGENE and MODULE—that quantify how variants far from a gene influence its expression through co‑regulated partners. Traditional transcriptome‑wide association studies (TWAS) focus almost exclusively on cis‑expression quantitative trait loci (cis–eQTLs), variants within ±1 Mb of a gene. But as the paper noted, “Most transcriptome‑wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained.” By contrast, the new models incorporate distal (trans) regulatory effects, capturing regulatory relationships that behave more like social networks than neighborhood blocks.

Using RNA‑seq data from six human post‑mortem brain regions and genetic data from more than 102,000 individuals, the team integrated cis‑based predictors (CIS, EpiXcan) with their new trans‑based frameworks. The combined approach improved gene‑expression prediction for 18,744 genes, and when applied to Psychiatric Genomics Consortium (PGC3) datasets, it identified 766 schizophrenia‑associated genes, including 641 not previously detected by TWAS.

Pergola said the field has been “looking for the light under the lamppost, focusing only on genes close to disease‑associated DNA variants.” By illuminating long‑range interactions, he explained, “we’ve essentially turned on lights across the entire neighborhood, revealing how distant genetic variants coordinate to build the genetic basis of schizophrenia.”

The findings converge on pathways involved in glutamate signaling, neuronal communication, immune processes, and neurodevelopment—biological systems repeatedly implicated in psychiatric risk. MODULE‑derived trans‑single nucleotide polymorphisms (SNPs) showed particularly strong enrichment for schizophrenia‑associated variants, and many overlapped with cis‑eQTLs for transcription factors such as GATAD2A, RERE, IRF3, and SP4, all previously prioritized in schizophrenia GWAS.

Daniel Weinberger, MD, CEO and director of LIBD, emphasized the shift in perspective: “Schizophrenia risk isn’t just about individual genes acting one after another—it’s about how networks of genes work together. Understanding these coordinated genetic programs brings us closer to precision psychiatry.”

By demonstrating that trans‑regulatory architecture is both detectable and biologically meaningful, the study provides a roadmap for expanding TWAS beyond local effects. It also underscores the importance of integrating multi‑region brain transcriptomics with large‑scale genetic cohorts to reveal disease‑relevant regulatory relationships.

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Network analysis of spousal support and fear of childbirth in pregnant women of advanced maternal age

BackgroundFear of childbirth is an important perinatal mental health concern, particularly among women of advanced maternal age. However, the specific interrelations between spousal support and fear of childbirth remain unclear.MethodsThis cross-sectional study recruited 279 pregnant women of advanced maternal age from a tertiary hospital in Henan, China, using convenience sampling. Spousal support and fear of childbirth were assessed using the Spouse Support Inventory and the Childbirth Attitude Questionnaire. A regularized partial-correlation network was estimated using EBICglasso, and central and bridge nodes were identified. Network stability was examined using bootstrap procedures.ResultsThe prevalence of any fear of childbirth, defined as a CAQ score ≥28 and including mild, moderate, and severe categories, was 86.4% (n = 241). Negative associations predominated between the spousal support and fear of childbirth communities. The strongest cross-community association was observed between “teaching you how to do things you do not know how to do” and “concern about fetal health.” The most central nodes were “participating in activities together to reduce your stress” and “providing you with helpful information,” whereas the strongest bridge nodes were “helping you understand why things did not go well” and “giving you encouragement.ConclusionSpecific supportive behaviors, especially informational and cognitive-appraisal support, occupied central positions in the network linking spousal support and fear of childbirth among pregnant women of advanced maternal age. Strengthening these forms of spousal support may inform the development of couple-based interventions to reduce childbirth fear and promote perinatal mental health.

AI Discovers Potential Antimicrobial “Prionin” Peptides

Prions are best known for their role in rare, fatal neurodegenerative diseases. But a new study by researchers at the University of Pennsylvania suggests that proteins in this family may also conceal molecular fragments that can kill bacteria, including drug-resistant strains.

The Penn scientists used a deep learning platform called APEX 1.1 to scan millions of short protein fragments derived from nearly 3,000 prion and prion-like proteins. The search identified more than a thousand candidate antimicrobial peptides, which they called “prionins.” In tests 59 synthesized prionins inhibited bacterial pathogens, and two reduced Acinetobacter baumannii burden in mice.

The discovery is unexpected because prions are usually discussed in the context of misfolding, aggregation, and brain disease—not immunity or antibiotic discovery. The new findings suggest that useful biological activities may be hidden inside proteins whose known roles have little to do with infection, and that artificial intelligence can help reveal them.

“Prions have long been seen almost entirely through the lens of disease,” said César de la Fuente, PhD, associate professor and director of the Machine Biology Group at the University of Pennsylvania. “Our work shows that when AI looks across biology at scale, even proteins with a dark reputation can contain useful molecular instructions. In this case, those instructions point to possible new antibiotics.”

De la Fuente is senior and corresponding author of the researchers’ published paper in Nature Microbiology, titled “Deep learning reveals antimicrobial peptides within prions.”

Antibiotic resistance is among the most urgent challenges in medicine, and many existing antibiotics were discovered by searching traditional natural sources. The new study takes a different route: instead of asking where antibiotics usually come from, it asks whether biology has hidden antimicrobial molecules in places scientists would not normally look.

Certain amyloid-associated protein sequences may participate in host defense, the authors wrote. “Several amyloid-associated proteins, including amyloid-β and the cellular prion protein, have been reported to display antimicrobial or host-protective activities, raising the possibility that aggregation-prone proteins may encode cryptic antimicrobial fragments within their primary sequence.” But until now, scientists had not systematically searched prion and prion-like proteins at scale to ask whether they broadly encode hidden antimicrobial peptides.

“Whether such encrypted peptides are broadly embedded across prion and prion-like proteins has not been systematically examined,” the researchers continued. The Penn team took on that task, using AI to move from scattered observations to a global search across millions of possible protein fragments. They mined prion-related proteins with APEX1.1, a deep learning platform for antimicrobial peptide (AMP) discovery. “… using deep learning, we screened 19.3 million fragments from 2,897 curated prion-related proteins and identified 1,179 candidate antimicrobial peptides, which we term prionins,” they stated.

To test the predictions, the researchers synthesized 75 prionins and evaluated them against a panel of clinically relevant bacterial pathogens, including multidrug-resistant strains. Fifty-nine inhibited at least one pathogen, and 42 showed potent activity at concentrations of 16 micromolar or lower against at least one pathogen.

The team then examined how the molecules worked. Many active prionins damaged bacterial membranes, a common mechanism used by antimicrobial peptides. Importantly, several candidates also showed early signs of selectivity: hemolysis was rare, and 16 active peptides showed neither measurable hemolysis nor cytotoxicity at the highest concentrations tested.

Two of the strongest candidates were tested in a mouse skin-infection model caused by Acinetobacter baumannii, a difficult-to-treat pathogen. A single topical dose of each peptide significantly lowered the bacterial burden, with effects comparable to the antibiotic polymyxin B in the model tested. The researchers observed no treatment-associated weight loss. In summary, they wrote, “What makes this exciting is that the predictions held up experimentally,” said Marcelo D T Torres, PhD, co-first author of the study. “We went from millions of hidden protein fragments to synthesized molecules that killed bacteria in the lab, and then to candidates that worked in an animal infection model. That is the difference between an AI screen and a true discovery platform.”

The findings build on the de la Fuente lab’s broader effort to mine the biological world for “encrypted peptides”—short, hidden sequences within larger proteins that can have biological functions when isolated. Previous work from the group has searched human proteins, extinct organisms, archaea, microbiomes, and venoms. The prion study expands that concept into one of biology’s most unexpected protein classes.

The study also raises a provocative possibility at the intersection of neurodegeneration and innate immunity. It does not establish that these peptides are naturally released during infection or function physiologically in host defense, they stated. But it suggests that prion and prion-like proteins may contain cryptic antimicrobial sequences, opening a new way to think about prion biology and its possible links to immunity. “… it establishes prion-related proteins as a productive source space for antibiotic discovery and provides a framework for testing whether cryptic peptides contribute to defense in specific biological contexts.”

The researchers emphasize that this is an early discovery, not a new treatment ready for patients. The study does not change the established role of misfolded prions in devastating neurodegenerative disease. Instead, it suggests prion and prion-like proteins as a rich and previously overlooked source space for antibiotic discovery. “Our findings identify prion and prion-like proteins as an unexpectedly rich reservoir of encrypted AMPs,” the authors concluded. “This expands a growing view that antimicrobial activity can be hidden within proteins not canonically annotated as immune effectors and extends that concept to prion biology … These results connect prion-related sequence space to antimicrobial function and highlight unconventional protein classes as sources of antibiotic leads.”

“For a long time, drug discovery has been limited not only by what we can test, but by where we choose to look,” de la Fuente said. “AI is changing that. It gives us a way to search the hidden layers of biology and ask whether molecules associated with one story—in this case, disease—may also carry another story with therapeutic potential.”

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