Kraig Biocraft Labs Creates Immortalized Silk Gland Cell Line

Kraig Biocraft Laboratories reports that company scientists created an immortalized silk gland cell line which Kraig officials say could form the foundation for a next-generation biotech platform with potential applications in biopharmaceutical manufacturing, therapeutic peptides, biologically active proteins, and advanced biomaterials.

This development significantly expands the potential commercial reach of the company’s core technologies beyond recombinant spider silk fibers and textiles, according to Kim Thompson, founder and CEO.

“This scientific achievement opens the potential for entirely new markets,” notes Thompson. “While our research team is expanding our portfolio and creating exciting new opportunities, management remains focused on the ongoing expansion of recombinant spider silk production and commercialization.”

The company’s research team successfully isolated and established immortalized silk gland cells that demonstrate strong proliferative capacity, stable serial passaging, and robust long-term viability in vitro, he adds. Early testing has shown exceptionally strong recombinant protein expression and production capabilities, positioning the platform as a promising candidate for scalable industrial bioprocessing and recombinant protein manufacturing, continues Thompson.

“The potential applications for this technology are extraordinarily broad,” maintains Xiaoli Zhang, PhD, Kraig Labs’ CSO. “We believe these immortalized silk gland cells could become the basis for a highly versatile biotechnology platform capable of supporting future work in therapeutics, vaccines, recombinant proteins, and next-generation biomaterials.”

The immortalized cell line has also reportedly demonstrated adaptability toward suspension culture systems, which are critical for large-scale industrial manufacturing and modern bioprocessing. This capability could allow the platform to integrate with conventional bioprocessing infrastructure and support more efficient, scalable, and cost-effective production systems, points out Zhang.

 

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Smart Wristband Detects Cardiac Arrest with 92% Sensitivity in Early Clinical Study

A wearable smart wristband that continuously measures blood flow at the wrist may be able to detect cardiac arrest and automatically trigger emergency alerts, according to findings from the DETECT-1b study published in the journal Circulation: Arrhythmia and Electrophysiology. Researchers at Radboud University Medical Center found that the technology detected shockable cardiac arrest events with 92% sensitivity during controlled clinical procedures in patients with ventricular arrhythmias. The findings are an early indication that monitoring the condition via a wearable device technology could eventually help reduce delays in providing care for out-of-hospital cardiac arrest by automatically alerting emergency medical systems or nearby responders.

“Our findings are important because many out-of-hospital cardiac arrests are unwitnessed. A smart technology wristband capable of automatically detecting cardiac arrest and triggering an alert could function as a digital witness,” said senior author Judith Bonnes, MD, PhD, a cardiologist at Radboud University Medical Center in the Netherlands. “With the device automatically notifying emergency services or nearby trained responders, help could arrive sooner, which may significantly improve survival chances.”

The DETECT-1b study was designed to evaluate a medically certified smart wristband that uses a light-based sensing method called photoplethysmography (PPG) that is capable of measuring blood flow changes in the wrist. The wearable device, called CardioWatch, continuously monitored pulse-related blood flow signals during procedures in which dangerous heart rhythms were intentionally induced under controlled conditions in people known to have abnormal heart rhythms.

The research is a follow-on of the DETECT-1a study. “In the DETECT-1a, we developed a wrist-derived photoplethysmography (PPG) algorithm for cardiac arrest detection using patient data from induced short-lasting circulatory arrests, achieving 98% sensitivity,” the researchers wrote.

DETECT-1b was developed as a prospective multicenter validation study involving adults undergoing ventricular tachycardia ablation or subcutaneous implantable cardioverter-defibrillator implantation. During these procedures, pulseless ventricular tachycardia or ventricular fibrillation was induced briefly to test cardiac function and device response. While this was occurring, the researchers recorded electrocardiogram and invasive blood pressure measurements as reference standards while patients wore the CardioWatch.

The study enrolled 49 adults in the Netherlands. Participants had a median age of 66 years, 84% were men, and many had existing cardiovascular disease. Seven participants had a prior history of cardiac arrest, 69% had experienced myocardial infarction, and approximately half had moderately or severely reduced left ventricular function.

For this study, the researchers evaluated 125.5 hours of data from the wristbands of the participant and identified 59 shockable cardiac arrest events among 26 patients. Fifty of the identified events were pulseless ventricular tachycardia and nine involved ventricular fibrillation. The algorithm detected 92% of all cardiac arrest events overall, including 100% of ventricular fibrillation episodes and 90% of pulseless ventricular tachycardia events.

“This is the first external validation in patients of a wearable-based cardiac arrest detection model, demonstrating that wrist-derived PPG reliably detects shockable cardiac arrest, with 100% sensitivity for VF,” the researcher wrote.

The algorithm developed to detect these events works by continuously monitoring the amplitude of PPG signals collected at the wrist. When signal amplitude decreases, the embedded algorithm evaluates signal quality to determine whether pulse-related peaks remain detectable. If pulse signals disappear, the algorithm triggers a cardiac arrest alarm.

The researchers also uncovered a number of false-positive alerts. Thirty-three alerts occurred in the absence of pulseless ventricular tachycardia or ventricular fibrillation. Nevertheless, of the 33 alert, 24 occurred during hemodynamically tolerated ventricular tachycardia and were considered clinically relevant. In total, nine of the alerts from 125.5 hours of monitoring were classified as false positives.

Lead study author Roos Edgar, a technical physician at Radboud, noted that the technology differs from prior cardiac arrest detection methods because the wrist-based design allows continuous monitoring during routine daily life. While many commercially available smart watches contain similar PPG sensors, they are not equipped to detect cardiac arrest.

The technology has the potential to be used for patients at elevated risk for cardiac arrest, including patients with ventricular arrhythmias or implantable cardioverter-defibrillators. The researchers said future deployment could also involve directly integrating the wristband’s alerts with emergency dispatch systems.

“The goal is to connect the wristband to emergency dispatch centers and volunteer responder networks in the Netherlands so that nearby rescuers and ambulance services can be alerted immediately when cardiac arrest is detected,” Bonnes said.

Future studies are expected to evaluate how the system performs during everyday activities, exercise and sleep, conditions that may create additional signal noise and complicate detection.

The post Smart Wristband Detects Cardiac Arrest with 92% Sensitivity in Early Clinical Study appeared first on Inside Precision Medicine.

Barriers and Facilitators for the Implementation of an Online Portal in Hospital Mental Health Care: Implementation Study

Background: In recent years, digital patient portals have become an increasingly common feature of care in various medical fields. Despite growing scientific evidence of their effectiveness and the benefits they offer to patients and caregivers, their implementation, especially in hospital mental health settings, lags behind expectations. Objective: The study aimed to identify the barriers and facilitators to implementing a patient portal in a public mental health hospital setting in Germany. Moreover, it aimed to develop recommendations for implementing a patient portal. Methods: Three psychiatric clinics in the early stages of implementing an online portal for patients participated in this implementation study. We assessed objective usage data (log data from the patient portal) and performed qualitative interviews with professionals and questionnaire surveys with both patients and professionals. We combined the results to develop generic recommendations for the implementation of patient portals in a mental hospital setting using a 2-stage Delphi method with a group of professionals and patients. Results: Portal log data from 71 patients indicated variation in the use of the portal functions. On average, users logged in 9.5 (SD 14.9) times (median 4, IQR 2-7 times). The variability in the number of logins per patient, ranging from 1 to 72, indicated a high variance in the frequency of use. On average, the portal was used for 47 (SD 59) days (median 27, IQR 2-62 days). Questionnaire data from 27 patients showed satisfaction with the portal and elucidated perceived barriers to usage. Qualitative interview data from 15 professionals revealed patient-related, professional-related, organizational, structural, and technical facilitators and barriers to the implementation process. We developed 10 actionable recommendations for the implementation of digital patient portals in psychiatric hospitals, which were rated by an expert group on different dimensions. Conclusions: To our knowledge, this is the first implementation study in a German mental health hospital setting that provides experience-based recommendations for advancing the implementation of digital patient portals in hospital mental health care. The next steps will include the analysis of a larger number of users and functions, which will help to specify recommendations for different target groups and settings. Trial Registration: German Clinical Trials Register DRKS00036894; https://drks.de/search/en/trial/DRKS00036894
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Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study

Background: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a shortage of nursing staff, it is essential to identify the early warning signs of adverse conditions among children as quickly as possible. Current targeted care needs to be improved. Objective: This study aimed to investigate the application of an artificial intelligence (AI)–based version of the Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit, analyze the relationship between PEWS and disease severity, and assess its impact on the length of hospital stay and hospitalization costs after admission, thereby providing a reference for targeted nursing care. Methods: We performed a retrospective study. A total of 1233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. The patients were divided according to whether they triggered a PEWS early warning into an early warning group (PEWS ≥1) and a non–early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non–early warning group. Differences between groups were assessed using the Mann-Whitney test. We performed multivariable logistic regression to discuss the association of resource use metrics and PEWS status, adjusted by age, sex, and disease category (respiratory, neurological, and hematologic). Results: Of 1233 patients, 597 (48.4%) triggered the PEWS early warning (mean score 2.44, SD 1.41), and 636 (51.6%) did not. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 (SD 1.73). Compared with the non–early warning group, the early warning group had a longer hospital stay (=−5.180; <.001) and higher hospitalization costs (=−6.500; <.001), and the differences between groups were statistically significant (<.001). Among the top 3 admission categories—respiratory, neurological, and hematologic diseases—children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all <.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald ²=5.533; odds ratio 1.055, 95% CI 1.035‐1.075), while the β coefficient for hospitalization costs was 0.001 (SE 0.000; Wald ²=6.075; odds ratio 1.001, 95% CI 1.001‐1.001). Conclusions: Compared with the non–early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all <.01). These findings show differences between children who triggered the warning and children who did not, providing a reference for identifying critically ill children for targeted care.

Wisdom and Life Purpose as Predictors of Mental Well-Being Among Middle-Aged to Older Adults: Cross-Sectional Study

Background: Positive aging, a concept found in positive psychology, serves as the theoretical foundation for this study. To age positively, one must manage hidden or unrecognized challenges, show flexibility in behavior and thought, adopt a positive outlook on problems involving regression, and make decisions that promote one’s well-being. Objective: This study examined the role of wisdom and life purpose in the mental well-being of middle-aged and older adults. More specifically, we tested 4 hypotheses: wisdom would exhibit a positive correlation with mental well-being, quality of life would exhibit a positive correlation with mental well-being, meaning and purpose would exhibit a positive correlation with mental well-being, and freedom would exhibit a positive correlation with mental well-being. Methods: The research used a multianalytical methodology combining covariance-based structural equation modeling and artificial neural network techniques to analyze data from 377 individuals aged 50 to 102 years. Results: Results from the covariance-based structural equation modeling indicate that meaning and purpose, wisdom, and quality of life were significantly associated with the mental well-being, accounting for 71% of the explained variance. Additionally, the artificial network analysis yielded exact forecasts of mental well-being. The artificial network model achieved an accuracy of 82.1% and 73% on the training and test sets, respectively, for predicting mental well-being. Sensitivity analysis revealed that meaning and purpose were the most critical factors in explaining participants’ mental well-being. Conclusions: These findings have prominent theoretical implications for social psychology researchers and practical consequences for authorities involved in the care of older adults, who can use the results to develop strategic plans and take necessary actions.
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How Gut Bacteria Apply Reversible Epigenetic “Bet-Hedging” Strategy to Adapt to Change

Researchers headed by a team at Icahn School of Medicine at Mount Sinai have discovered that many gut bacteria use a flexible survival strategy—known as epigenetic “bet-hedging”—to withstand disruptions such as antibiotics and diet changes.

Studying infant and gut microbiomes, the investigators showed that microbes can switch between functional states, rather than relying solely on genetic mutations, to try to survive shifting conditions. While bet-hedging has been observed in disease-causing bacteria, this is the first study to show that it is widespread among the beneficial microbes that make up the healthy human gut.

The findings shed light on a previously hidden layer of microbiome biology and may help explain why probiotics and fecal microbiota transplantation (FMT) produce inconsistent benefits across individuals.

Gang Fang, PhD, professor of genetics and genomic sciences and director of the Center for Genomic AI and Microbiome Medicine at the Icahn School of Medicine at Mount Sinai, is senior and corresponding author of the team’s published paper in Cell Host & Microbe, titled “Epigenetic phase variation in the gut microbiome enhances bacterial adaptation.”

The human gut microbiome is constantly being disturbed—by medications, illness, and shifts in diet. Yet it often rebounds, the investigators noted. “In response to these alterations, the gut microbiome shows a remarkable adaptive capacity,” they wrote. “Characterizing this adaptive capacity is crucial for understanding the dynamic relationship between the gut microbiome and host physiology, especially in the context of human health and disease.”

Until now, scientists largely attributed this resilience to genetic mutations that accumulate over time.  But, as the authors continued, “Another mechanism of bacterial adaptation involves DNA methylation, which can regulate gene expression, enhance clonal heterogeneity, and mediate epigenetic phase variation (ePV, intra-strain epigenetic variation that leads to phenotypic differences … ePVs have been characterized in human pathogens, but their roles in commensals remain unclear.”

Fang continued, “Our study shows that there is another mechanism at work. Even within a single group of genetically identical bacteria, a small subset of cells exists in a different epigenetic state—where chemical tags on the DNA change how genes are turned on or off without altering the genetic code itself. That means some cells are essentially preprogrammed to respond differently to stress, giving the population a built-in survival advantage when conditions suddenly change.”

So when a stressor such as an antibiotic is introduced, this small subgroup can quickly become dominant because it is already primed to survive. When conditions change again, the population can shift back. This reversible strategy, known as “bet-hedging,” allows microbial communities to adapt rapidly to uncertainty.

To carry out their work, the researchers combined advanced DNA sequencing, large-scale data analysis, and laboratory experiments. They used long-read sequencing technology to analyze stool samples from infants before and after antibiotic treatment, as well as from FMT donor-recipient pairs. This approach allowed them to detect both genetic structure and epigenetic modifications simultaneously.

The scientists then analyzed more than 2,300 microbiome samples from previously published studies to determine how common this phenomenon is across individuals and bacterial species. To understand the mechanism in detail, the team isolated a beneficial gut bacterium, Akkermansia muciniphila, and tracked how its epigenetic states shifted in response to different antibiotics—identifying a specific gene involved in the process.

“Focusing on an Akkermansia muciniphila isolate, we find a specific ePV regulating mucC, a gene of unknown function but whose heterologous expression enhances bacterial tolerance to antibiotics via a bet-hedging strategy,” they stated. “Our results indicate that in the human gut, ePVs may help bacterial populations regain heterogeneity after bottlenecks encountered during colonization of a new host or severe perturbations due to antibiotic exposures.”

“Our work is the first to systematically demonstrate epigenetic bet-hedging across the human gut microbiome,” Fang noted. “It also identifies a specific gene that controls this switch in a beneficial bacterium and shows that the process is reversible—shifting in different directions depending on the type of antibiotic exposure. We were struck by how quickly small subpopulations could take over. In some cases, bacteria representing less than one percent of a population became dominant under changing conditions.”

The research team also found significant diversity within what had been considered a single bacterial strain. Even closely related cells could behave differently, with distinct gene activity and stress responses—highlighting how much remains to be understood about the microbiome at a deeper level. The findings help explain why the microbiome is resilient yet difficult to predict, and why microbiome-based treatments can produce variable results.

“At the same time, our study does not suggest that people should avoid antibiotics when they are medically necessary, nor does it recommend for or against any specific probiotic. Our research is aimed at understanding fundamental biology, not changing current medical care,” added Fang.

“Compared with genetic phase variation, ePV offers several advantages in enhancing clonal heterogeneity,” the team noted. “The reversibility of ePV, without altering DNA sequence or incurring mutation costs, serves as an additional way for individual bacterial strains to adapt to diverse stresses … Our results indicate that in the human gut, ePVs may help bacterial populations regain heterogeneity after bottlenecks encountered during colonization of a new host or severe perturbations  due to antibiotic exposures.”

The discoveries have several important implications for human health. In the field of probiotics, it may be that bacteria in a probiotic capsule are not in the same functional state as those that successfully establish themselves in the gut—potentially explaining inconsistent results. “Ultimately, our goal is to design probiotics that are better equipped to establish themselves in the gut and to develop therapies that support beneficial microbes while limiting harmful ones,” Fang said.

For FMT-based treatments, differences in these epigenetic states between donors and recipients may influence how well microbiota transplants work. And when considering antibiotic recovery, some gut bacteria may survive antibiotic treatment not because they are genetically resistant, but because a subset of cells is already in a protective epigenetic state that allows rapid rebound after treatment ends.

The research team plans to study larger groups of patients over time, particularly during and after antibiotic treatment and FMT. They also aim to explore whether similar mechanisms exist in other gut bacteria and to investigate how these epigenetic switches might be harnessed. In the longer term, understanding and potentially controlling these reversible switches could lead to more effective microbiome-based therapies, the investigators suggest.

“These ePV-driven regulatory mechanisms open new opportunities for targeted epigenetic interventions to improve the desired functions of beneficial bacteria,” the scientists stated. “For example, by manipulating ePV, we may strategically boost the resilience and functional capabilities of beneficial bacteria, which might improve the success rates of probiotic engraftment and the efficacy of treatments for microbiota-associated conditions.”

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Google DeepMind and Edison Are Building the AI Scientist

Google DeepMind and Edison Scientific are on an ambitious mission to build the AI scientistThese platforms propose to automate the scientific method using reasoning systems that connect hypothesis generation, experimental design, and data interpretation in one platform. In drug discovery, where traditional development timelines can stretch beyond a decade, such systems promise to dramatically accelerate the pace of biomedical research.

The AlphaFold developer and the nonprofit home organization behind Edison, FutureHouse, originally introduced their respective systems, Co-Scientist and Robin, as bioRxiv preprints in early 2025. Those studies have now been published in Nature, marking another step toward a growing ecosystem of specialized AI agents for life science research. 

Led by Demis Hassabis, PhD, CEO, and 2024 Nobel laureate in Chemistry, DeepMind is no stranger to expanding biomedicine. The team published a January Nature paper describing AlphaGenome, a unifying DNA sequence model for regulatory variant-effect prediction to support understanding of genome function and disease biology. 

Additionally, DeepMind drug discovery spinout, Isomorphic Labs, recently made waves after securing a whopping $2.1 billion Series B led by Thrive Capital, signaling the industry’s growing investment in AI-driven therapeutics. 

I’ve always believed the No.1 application of AI should be to improve human health,” wrote Hassabis on LinkedIn when announcing Isomorphic’s blockbuster raise. 

DeepMind’s newly published AI assistant, Co-Scientist, is a general-purpose multi-agent system built with Google’s Gemini and driven by natural language prompts. The platform demonstrated initial validation across three biomedical applications: drug repurposing for acute myeloid leukemia, novel target discovery for liver fibrosis, and explaining mechanisms of anti-microbial resistance. 

Co-Scientist’s design scales test-time compute to iteratively reason, evolve, and improve the output as it gathers more knowledge. Researchers can also actively steer the system by refining generated ideas or providing feedback through the natural language chat.

Vivek Natarajan, research scientist at DeepMind, emphasizes that time is a valuable commodity when tackling disease. Co-Scientist aims to support humans scientists in reaching answers to their problems much faster than before, from “months and years to minutes and hours.”

“To realize this vision, we need to build in reliability, trustworthiness and ensure a collaborative human-AI interaction paradigm. We have done a lot of research on these aspects and we are continuing to improve,” Natarajan told GEN Edge.

Closing the loop 

Edison is the commercial spinout of FutureHouse, an AI scientist non-profit backed by former Google CEO Eric Schmidt and co-founded by Sam Rodriques, PhD, former group leader at The Francis Crick Institute and Edison’s CEO. The team’s newly published platform, Robin, leverages both OpenAI o4-mini and Anthropic Claude 3.7 to aid biological discovery.  

In research tasks, Robin proposed repurposing Ripasudil, an existing drug for treatment of glaucoma, to address dry age-related macular degeneration (dAMD) via a novel mechanism that enhanced retinal pigment epithelial cell phagocytosis. The platform also suggested a circadian clock modulator, KL001, as an unexpected treatment for dAMD, illustrating the ability to make new connections not found in existing literature. Both insights were experimentally validated in patient-derived retinal pigment epithelium (RPE) cells. 

Since Robin’s May 2025 preprint release, Edison unveiled an updated AI scientist, Kosmos, last November. Kosmos can reason over 175 million full-text papers, clinical trials and patents, and operate interactively as a colleague that can sends updates mid-run. The system is reported to perform hundreds of research tasks in parallel to compress months of work into a single day.  

Today, Edison announced a collaboration with Incyte to employ Kosmos across the global pharma’s discovery and development pipeline. The partnership will focus on enabling continuous learning from translational and clinical data, real-time synthesis of evidence, and predictive models of therapeutic performance.

Michaela Hinks, founding member of technical staff at Edison, says the main bottlenecks for AI scientist adoption are trust, validation, and the gap in end-to-end solutions.  

“Most AI tools accelerate the cheaper and easier upstream work, but not the expensive and regulated downstream stages of scientific research,” Hinks told GEN Edge. 

She also highlights Robin as the first demonstration of an agentic AI scientist generating a hypothesis that is tested and validated in patient-derived cells, not an immortalized cell line, supporting clinically actionable insights for patients in need. 

Whether AI scientists will truly revolutionize discovery remains to be seen, but researchers are already beginning the experiment.  

The post Google DeepMind and Edison Are Building the AI Scientist appeared first on GEN – Genetic Engineering and Biotechnology News.

Automated PACS-integrated pipeline for TractSeg-based segmentation of the arcuate fasciculus in patients with hearing loss

ObjectiveWhile tractography is used to determine the anatomical course of white matter tracts, it can often be imprecise and time-consuming, which can be a problem when comparing large groups of patients. The aim of this study is to compare the automated process of arcuate fasciculus determination using the TractSeg algorithm with manual AF determination in DSI studio software.MethodsThe process of importing the structured MPRAGE sequence and raw diffusion-weighted images from the PACS system, performing the TractSeg algorithm, superimposing the bilateral AFs obtained on the MPRAGE image and exporting this composite image to the PACS system was automated. This procedure was used to segment the arcuate fasciculus in 25 patients with hearing loss.ResultsThe automated algorithm was able to delineate the arcuate fasciculus bilaterally in all 25 patients, while the manual reference method and automated tractography based on DSI Studio software failed in three and one patient, respectively. TractSeg showed a mean distance of 2.0 ± 0.7 mm from manual segmentation, compared with 2.8 ± 1.0 mm for DSI Studio Auto-Tracking. In addition, TractSeg appeared to involve larger portions of the medial AF fibres than the other methods.ConclusionThe TractSeg algorithm has shown high efficacy in segmenting the arcuate fasciculus in patients with hearing loss. The algorithm is fast to run and has great potential to optimise and improve neural pathway delineation.

Stronger associations of affectivity than social leisure activities with cognitive impairment: a 10-year trajectory study of Chinese older adults

BackgroundBoth positive and negative affectivity (PNA) and social leisure activities (SLA) are significantly associated with cognitive function, yet their relative strength of association with long-term cognitive decline remains unclear. Exploring this issue has significant implications for designing precise and effective cognitive health promotion programs in the future.MethodsThe data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Our analysis included 1,847 older adults (≥65 years) without diagnosed dementia at baseline, who were followed from 2008 to 2018. Using Group-Based Trajectory Models (GBTM), participants were classified based on their PNA and SLA trajectories to identify distinct subgroups. Binomial logistic regression analyzed associations between these groups and cognitive function, with Likelihood Ratio and Wald Tests comparing their relative strengths of association.ResultsThe “High-rapid-increasing PNA” group exhibited a 264% higher risk of cognitive impairment compared to the “Low-stable-increasing PNA” group (OR = 3.64, 95% CI: 2.92, 4.52). The “Low-stable SLA” group was associated with increased cognitive impairment risk (OR = 1.66, 95% CI: 1.32, 2.08). PNA demonstrated a stronger association with cognitive function than SLA [Likelihood Ratio Test: Δχ²(1) = 137.37, p < 0.001]. However, a formal test for multiplicative interaction was not statistically significant (OR = 0.88, 95% CI: 0.57, 1.35).ConclusionPNA demonstrated a stronger association with cognitive function in older adults than SLA. Affective factors may be a critical, and potentially underutilized, target for cognitive health interventions in aging populations.

Determinants of antipsychotic prescription in women detainees admitted to an acute forensic psychiatric unit

BackgroundWomen represent a small proportion of the global prison population but carry a disproportionate burden of mental illness. Evidence indicates high rates of psychotropic medication use among women in prison, often independently of the clinical diagnosis. Antipsychotics, particularly second-generation (SGAs) are widely prescribed in forensic settings. Data focusing on the prescription of these agents to women detainees are still scarce.ObjectivesThis study aimed to investigate whether socio-demographic, clinical and forensic characteristics determine SGA prescription to women admitted in an acute forensic psychiatric ward located in prison.MethodsWe conducted a retrospective study of 166 women admitted between 2014 and 2023 to the sole acute forensic psychiatric unit for detainees in French-speaking Switzerland. Among them, 128 cases received second generation antipsychotic medication during their hospital stay. Sociodemographic data included age, nationality, educational attainment and primary spoken language. Criminological variables included types of offense and detention. Clinical variables included psychiatric outpatient and inpatient history prior to conviction, total number of stays during the study period, main diagnosis, presence of substance use disorder and personality disorders. Psychotropic prescriptions were analyzed with conversion of antipsychotic doses into chlorpromazine equivalents. Regression analyses, including LASSO models were used to identify variables associated with antipsychotic dosage.ResultsPsychotropic use was very high with more than two-thirds receiving two or more psychotropic agents. SGAs were prescribed in 77.1% of cases (128 out of 166 cases), while psychotic disorders were diagnosed in only 28.3% indicating frequent off-label use. Regression analyses showed that higher antipsychotic doses were associated with previous psychiatric history, more inpatient stays and court-ordered treatments. Cluster A personality disorders were associated with lower antipsychotic doses.ConclusionsOur findings reveal an extremely high rate of psychotropic use with very frequent off-label prescription for antipsychotics in acute forensic psychiatry wards. Moreover, they show that clinical variables and not demographic and criminological factors determine the use of antipsychotics in this setting.