Neural Networks Are Already Clean

A working hypothesis about neurons, generalization, and the ecology of circuits.

already-clean.goodfire.com

FDA Expedited Investigational New Drug Pilot Program

Accelerating Early-Stage Clinical Research in the U.S.

fda.gov

An Integrated Single-Nucleus Atlas Resolves Cell-Type-Specific Programs and Molecular Subtypes in Alzheimer’s Disease

Interindividual heterogeneity in Alzheimer’s disease (AD) remains poorly understood, as disparate single-cell studies leave it unclear whether findings reflect shared architecture or dataset-specific ...

biorxiv.org

Cell-type-resolved somatic variant discovery from bulk long-read sequencing

Somatic mutations arise throughout life, with functional consequences tied to the cell populations in which they occur. Genome-wide studies measure somatic variations in bulk tissue, whereas single-ce...

medrxiv.org

CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

Understanding how different cell types assemble into tissues and organs, as well as how they interact to transmit and receive biological signals, is essential for advancing biomedical and biological r...

biorxiv.org

Ensembl

The new website of the Ensembl project

ensembl.org

AMR Portal Release 2026_07 is live

Antimicrobial resistance (AMR) Portal updated with new search, genome browser and annotations

ebi.ac.uk

FDA Seeks Public Feedback to Inform Regulatory Approach for Generative AI-Enabled Medical Devices

The U.S. Food and Drug Administration today issued a discussion paper on considerations for the regulation of generative artificial intelligence (GenAI)-enabled medical devices, seeking feedback from ...

fda.gov

Tech giants plunge into life sciences - Nature Biotechnology

Nature Biotechnology - Tech giants plunge into life sciences

nature.com

PubMed Central: PMC Article Datasets

pmc.ncbi.nlm.nih.gov

Beyond Injection Detection: A Positive-Security Prompt Firewall that Closes the Scope and PHI Gap SOTA Classifiers Miss in Healthcare

Large language models embedded in autonomous agents process trusted instructions and untrusted data in one context window, leaving them open to direct and indirect prompt injection. In healthcare this is not hypothetical: a 2025 JAMA Network Open study found commercial medical LLMs followed injected instructions in 94.4% of simulated patient encounters, including life threatening recommendations . Yet the clinically decisive problem we quantify here is different. Most real clinical threats protected health information PHI exfiltration, cross patient access, bulk export, out of scope advice are fluent, legitimate looking requests that carry no attack signal, so even a state of the art injection detector passes them. Existing runtime guardrails trade safety against latency: model based auditors are accurate but add hundreds of milliseconds of Python inference, while lexical filters are fast but blind to obfuscated or semantically disguised payloads. We present QFIRE, an inline, provider

medrxiv.org

Analytical Validation of Minimally Invasive Capillary Blood Microsampling using Tasso+ for Multiplexed Neurological Biomarkers

Blood-based biomarkers are increasingly used to investigate brain health, but collecting venous blood is difficult in remote and field settings. Capillary microsampling offers a practical alternative, although the ability to delay processing and its agreement with gold-standard venous blood require validation. We evaluated Tasso+, a minimally invasive upper-arm capillary blood collection system, for measuring neurological and host-response biomarkers in plasma and serum during an exercise-based protocol. Sampling occurred before, immediately after, and approximately 24-to-36 hours after exercise; Tasso+ samples were processed with or without a 72-hour room-temperature delay. Tasso+ samples were compared with matched venous blood, and Capitainer SEP10 dried plasma spots were also evaluated, using Quanterix Simoa and Alamar Biosciences NULISAseq CNS panel. Tasso+ enabled reliable measurement of several key biomarkers, including GFAP and NfL, even after delayed processing. These findings

medrxiv.org

Calibrating trust in AI-assisted pituitary surgery

Background: Endoscopic endonasal transsphenoidal surgery (EETS) requires navigation around neurocritical anatomy. Today, artificial intelligence clinical decision support systems (AI-CDSSs) can orientate surgeons, but clinician trust in AI remains unclear, limiting safe deployment. This study evaluates how modifiable design affects trust and performance in a real-world pituitary surgery AI-CDSS. Method: Online, 70 clinicians with pituitary surgery experience were randomised evenly to a Basic or Enhanced AI-CDSS which outline the sella on EETS operative video. The Enhanced group additionally received explanation of the model and previous publications, alongside confidence labels depicting outline reliability. Both groups annotated the sella on six video clips, first alone then with the optional AI-CDSS. Clips were ordered by declining AI performance, except for the final clip. Self-reported trust was measured using a 1-7 scale after each annotation, and performance was the DICE overlap

medrxiv.org

Real-time Computer Vision Assisted Navigation for Endoscopic Pituitary Surgery: Iterative Development and Comparative Preclinical Evaluation

Background Endoscopic pituitary surgery involves navigating high-stakes anatomy where complications, such as carotid artery injury, cause devastating morbidity. While computer vision AI offers potential for real-time anatomical recognition to mitigate these risks, successful translation requires rigorous human-factors and performance evaluation. We present the iterative development and preclinical evaluation of a surgeon-controlled, real-time AI-assisted navigation system. Methods Guided by IDEAL Stage 0 and DECIDE-AI frameworks, the study was conducted in two phases. Phase 1 was an exploratory study where surgeons used the system during high-fidelity simulated surgery and provided feedback via "Think Aloud" protocols and surveys. Following prototype iteration, a Phase 2 randomized crossover comparative trial was conducted with 19 neurosurgeons (15 trainees, 4 experts) performing high-fidelity simulated tumour resections with and without AI assistance, separated by a minimum 2-week was

medrxiv.org

Don't stop the heart: a performance analysis of large language models and potassium dosing

Background: Electrolyte replacement is ubiquitous in the acute care setting, but its familiarity cannot belie that even small dosing errors with potassium can cause lethal cardiac arrhythmias. Recently, MedAgentBench offered a benchmark for agentic artificial intelligence (AI) including the ability to correctly dose potassium based on a single rule; however, this does not adequately reflect the clinical complexity or safety concerns of an agent that has been used as the lethal injection. The purpose of this analysis was to a probe leaderboard large language model (LLM) capabilities to follow basic dosing rules to safely replace potassium in a series of clinician-annotated cases. Methods: Using a clinician panel, we developed a series of dosing principles and 20 clinical cases reflective of the complexity of potassium replacement. External clinicians were surveyed to assess practice variability and agreement to clinician panel answers. We tested GPT-5-chat with each case in triplicate,

medrxiv.org

Assessing and Optimizing Low-Frequency Somatic Mutation Detection: A Multi-Platform High-Throughput Sequencing Perspective

The availability of multiple commercial short-read sequencing platforms necessitates systematic cross-platform performance comparisons, particularly for challenging applications such as low-frequency somatic mutation detection. Here, a large-scale targeted sequencing dataset from five Genome in a Bottle (GIAB) human genomic DNA reference standards, HG001 to HG005, alongside Twist Biosciences cfDNA reference standards featuring 1% variant allele frequency (VAF), was generated by six platforms (NovaSeq 6000, NovaSeq X, FASTASeq 300, GenoLab M, SURFSeq 5000, and MGISEQ-T7). To build a realistic benchmark while keeping authentic sequencing backgrounds, we developed PosMix, a simulating tool that generates position-specific VAFs. To overcome the limitations of conventional variant callers (high recall with poor precision for VarScan2, higher precision with lower recall for Strelka2/Mutect2), we developed SomaticXGB, a machine learning-based caller. In this study, SURFSeq 5000 consistently e

biorxiv.org

Self-Reported Side Effects Among Reddit Users Taking Unapproved Retatrutide

Gray-market retatrutide use is increasing, but patient safety experiences remain poorly characterized. This cross-sectional analysis examined Reddit posts and comments from retatrutide-specific and broader peptide or weight-management communities through December 2025. A validated large language model classified self-reported retatrutide use and extracted author-attributed symptoms mapped to MedDRA Preferred Terms. Among 13,589 users reporting current use, 7,823 had at least one mapped symptom after exclusions. Unlike phase 2 trial findings dominated by gastrointestinal events, Reddit reports most often described appetite increase, fatigue, increased energy, nausea, food craving, insomnia, and elevated heart rate. Findings are hypothesis-generating and warrant pharmacovigilance attention. ### Competing Interest Statement JST reports receiving an investigator-initiated grant, on behalf of the University of Pennsylvania, from Novo Nordisk and receiving consulting fees from Currax Pharm

medrxiv.org

Promise vs. Proof in Digital Interventions for Antimicrobial Stewardship: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Digital antimicrobial stewardship (AMS) interventions, such as clinical decision support systems, audit and feedback platforms, and electronic prescribing tools, have been increasingly adopted to improve antibiotic use. However, the effectiveness of these interventions across healthcare settings remains uncertain, and the certainty of the evidence has not been comprehensively evaluated. The objective of this study was to provide a comprehensive understanding of the role of digital interventions in optimizing antimicrobial use and improving clinical outcomes within a broad spectrum of healthcare settings. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials evaluating digital AMS interventions that followed PRISMA 2020 guidelines and registered in PROSPERO CRD420251178854 and funded by the Wellcome Trust CAMO Net programme. Searches were performed across major databases. Primary outcomes included the appropriateness of antibiotic prescr

medrxiv.org

PatientEvent: An Event-Based Ontology for Patient-Initiated Portal Communication

Patient portal messaging has become a primary channel for asynchronous clinical communication, it spans a wide range of content, from symptom reports and medication concerns to administrative requests. Despite this volume and diversity, there is no formal representation for what a portal message contains: no vocabulary for the clinical and administrative events it describes, or for the attributes of those events that the patient has actually disclosed. Without such a representation, it is difficult to systematically analyze portal communication, assess message completeness, or build downstream tools that depend on structured input, such as automated triage, response drafting, and follow-up question generation. A clinical event schema, grounded in real portal messages and reviewed by clinicians, would provide this missing foundation. We introduce a clinical event ontology for patient portal messages, containing 8 event types and 70 roles that span clinical content (symptoms, medications

medrxiv.org

AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS) - npj Digital Medicine

npj Digital Medicine - AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS)

nature.com

A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction - npj Digital Medicine

npj Digital Medicine - A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction

nature.com

Automated evaluation can distinguish the good and bad AI responses to patient questions about hospitalization - npj Digital Medicine

npj Digital Medicine - Automated evaluation can distinguish the good and bad AI responses to patient questions about hospitalization

nature.com

Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach - npj Digital Medicine

npj Digital Medicine - Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach

nature.com

Feasibility and impact of a large language model pipeline for surgical trial abstracts - npj Digital Medicine

npj Digital Medicine - Feasibility and impact of a large language model pipeline for surgical trial abstracts

nature.com

Neonatal EEG network activity associates with 2-year neurodevelopment after perinatal asphyxia

Background Prediction of long-term neurodevelopmental outcomes remains challenging after perinatal asphyxia. Here, we studied whether computational metrics of brain function derived from neonatal EEG are associated with long-term neurodevelopment in infants with perinatal asphyxia. Methods Total of 36 term-born infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy were studied with neonatal multichannel electroencephalography (EEG). We computed local EEG amplitudes and phase-amplitude coupling (PAC), as well as large-scale functional cortical networks estimated using amplitude-amplitude correlations (AAC) and phase-phase correlations (PPC). These EEG-derived markers were tested for associations with neurodevelopmental outcomes at two years, assessed using the Griffiths Scales of Child Development, 3rd edition (GMDS-III). Results EEG amplitudes showed positive associations with GMDS-III Foundations of Learning and General Development scores across most electr

medrxiv.org

Deep learning-based decoding of axonal ultrastructure in gene-edited mice using electron microscopy imaging

Myelin forms an insulating sheath around axons enabling both rapid and energy-efficient conduction of action potentials and myelin abnormalities or loss can lead to severe motor, sensory, and cognitive impairment. While electron microscopy can resolve multiple axonal components that are affected myelin, their large-scale quantitative analysis is both difficult and time consuming. To overcome such limitations, we developed a machine learning framework that automatically recognizes and quantifies multiple features of axons and myelin including axonal mitochondrial density and periaxonal area. Applying that framework to fibers in the spinal cord of variably hypomyelinated mice, we show here that reduction in the thickness and length of myelin sheaths results in correlating changes in mitochondrial density and periaxonal area. The machine learning framework introduced here should contribute to future insight into the axon, myelin, and mitochondrial relationships that change during neurolog

biorxiv.org

Wearable and Interview-based Assessment of Psychological Risk in Alzheimer’s Caregivers: Machine Learning vs. Large Language Models

Spousal caregivers of individuals with Alzheimer’s disease and related dementias frequently experience elevated perceived stress, caregiver burden, and loneliness, which are associated with adverse health outcomes. Early identification is therefore critical for timely intervention. Existing approaches commonly rely on wearable sensor data and standardized psychological questionnaires, while recent multimodal methods aim to improve prediction by integrating behavioral and linguistic information. In this study, we explored three modality configurations, wearable-derived features, interview-based text, and their combination, to classify caregiver psychological risk using the Perceived Stress Scale (PSS), Zarit Burden Interview, and UCLA Loneliness Scale. We compared traditional machine learning models and large language models (LLMs) (Gemini 2.0, Llama 4, and GPT-4o) under psychometrician-centered and caregiver-centered prompting strategies. Traditional machine learning models performed

medrxiv.org

Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma

Purpose To predict retinal nerve fiber layer thickness (RNFLT) norms from fundus images. Methods We selected 18,000 OCT scans and visual fields (VF) from the Massachusetts Eye and Ear Glaucoma Service. A U-Net-based deep learning model was developed to predict RNFLT norms from OCT en face fundus images. A total of 10,000 OCT scans with normal VFs (mean deviation [MD] ≥ -1 dB, glaucoma hemifield test within normal limits, and pattern standard deviation probability > 5%) tested within 30 days were used for training, while the remaining 8,000 OCT scans (mean VF MD: −3.3 ± 4.9 dB), including 2,419 scans with normal VFs, were used for evaluation. Structure-function correlations between RNFLT maps and VFs were assessed using linear regression and VGG-16 across original RNFLT maps, deviation maps, and their combination. Performance was evaluated using correlation coefficients, mean absolute error (MAE), and R2. Results Predicted RNFLT norm maps showed agreement with baseline RNFLT maps in e

medrxiv.org

Normative Speech Modeling for ALS Diagnosis with Application to Other Neurodegenerative Diseases

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease affecting more than 450,000 individuals worldwide and is frequently diagnosed more than 12 months after symptom onset, delaying intervention during a critical early window. Because up to 80% of patients develop dysarthria within two years, subtle changes in speech provide a signal of early bulbar motor neuron degeneration. However, existing speech-based systems rely on supervised classification trained on limited datasets, achieving moderate sensitivity and depending heavily on labeled disease examples, which restrict scalability and early detection. This study introduces SPEAK-NORM, the first-ever normative speech modeling framework for early ALS diagnosis, which learns age- and sex-conditioned motor-speech distributions exclusively from healthy individuals. A conditional variational autoencoder models coordination of hypoglossal, laryngeal, and respiratory motor pathways, and deviation from this healthy ma

medrxiv.org

Grounding Language Models in Behavioral Science to Scale Physical Activity Interventions for Hispanic/Latinx Populations

Objective: Hispanic/Latinx populations in the U.S. experience higher rates of chronic disease linked to physical inactivity, yet digital health interventions remain largely inaccessible to more than 16 million Hispanic/Latinx adults with limited English proficiency. While large language models (LLMs) offer scalable personalization, their use in non-English behavioral coaching is unexplored. This study introduces MHC-Coach-ES, a Spanish-language LLM fine-tuned on the Transtheoretical Model (TTM) of behavior change. Materials and Methods: We fine-tuned Llama 3-70B-Instruct using a two-stage pipeline. First, the model was adapted to Spanish health and motivational language using a 2.21-million-token corpus. Second, it was instruction-tuned on 3,268 translated human written messages to align the model with the Transtheoretical Model (TTM) of Behavioral Change. We compared MHC-Coach-ES with Llama 3-70B-Instruct and translated human-expert messages using a forced-choice preference survey (N

medrxiv.org