INTRODUCTION: This study explores the diagnostic accuracy of different biomarkers for Alzheimer’s disease (AD) using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).METHODS: A cross-sectional analysis was performed on individuals with mild cognitive impairment. Key biomarkers assessed included plasma p-tau217, CSF p-tau181, CSF Aβ42, and amyloid PET imaging, alongside clinical assessments using the Clinical Dementia Rating (CDR) and the Alzheimer's Disease Assessment Scale (ADAS).RESULTS: Plasma p-tau217 showed the strongest correlation with amyloid-β deposition and clinical scores (Adjusted R² = 0.53 for ADAS, 0.51 for CDR), outperforming CSF Aβ42 and CSF p-tau181.DISCUSSION: These results suggest that plasma p-tau217 may serve as a more accurate, cost-effective, and non-invasive alternative to CSF biomarkers and PET imaging. Its superior predictive power highlights its potential in routine clinical settings for early diagnosis and monitoring of AD progression, addressing key challenges in accessibility and patient compliance associated with current diagnostic methods.

Diamond Blackfan anemia syndrome (DBAS) is a congenital ribosomopathy caused by haploinsufficiency of ribosomal proteins (RPs), but how RP stoichiometry and activity regulates erythroid development remains enigmatic. Using novel in vivo models, we uncover strikingly divergent functions for the small and large ribosomal subunit proteins RPS19 and RPL5 in fetal hematopoiesis. While RPL5 haploinsufficiency causes hematopoietic stem and progenitor cell (HSPC) accumulation and prenatal lethality via p53-mediated ferroptosis of mature erythroid progenitors, RPS19 haploinsufficiency leads to HSPC depletion and impaired erythroid expansion through p53-dependent apoptosis. The latter is accompanied by translational and transcriptional dysregulation, including the upregulation of RUNX1, which is also observed in RPS- haploinsufficient DBAS patients. Importantly, Runx1 deletion in RPS19-haploinsufficient mice partially rescues HSPC numbers. These findings reveal subunit-specific RP functions in controlling fetal hematopoiesis and demonstrate how imbalanced RP stoichiometry disrupts developmental programs, providing crucial mechanistic insights into DBAS pathogenesis and the basis for its clinical heterogeneity.

Alcohol use disorder is a widespread illness commonly leading to alcoholic liver disease (ALD) and cirrhosis with an increased incidence of hepatocellular carcinoma (HCC), but the mechanisms of alcohol-related oncogenesis in the liver are incompletely understood. We tested the hypothesis that ALD predisposes to HCC via dysregulation of splicing. RNA sequencing was performed on liver biopsies from patients with different stages of ALD: early alcoholic steatohepatitis (eASH), non-severe alcoholic hepatitis (nsAH), and severe alcoholic hepatitis (sAH); furthermore, explants were collected from patients who underwent liver transplantation due to sAH (exAH). We found that alcohol caused widespread changes in transcriptome in all stages of ALD: among ~ 58,000 analyzed genomic features, ~ 4,900 were altered in eASH, ~ 9,100 – in nsAH, 14,100 – in sAH, and ~ 14,300 – in exAH. We observed thousands of missplicing events in all hepatic conditions, with mutually exclusive exons (MEE) being the most common event and exon skipping (ES) – second most common event. Analysis of ~ 600,000 exons revealed that ALD is associated with a genome-wide effect on exon expression, with ~ 50,000 exons being differentially expressed in eASH, ~ 130,000 – in nsAH, ~ 150,000 – in sAH, and ~ 120,000 – in exAH. To determine whether alcohol directly perturbs splicing, we subjected rats to alcohol vapor for 7 weeks and found that the expression of multiple snRNAs was drastically decreased, while expression of splicing factors was not affected. Screening of oncogenes and tumor suppressors, commonly involved in HCC pathogenesis, revealed that ALD affected the hepatic expression and/or splicing of most of these cancer-related genes. In summary, it appears that alcohol causes profound genome-wide changes in gene expression and splicing in the liver, likely via affecting the spliceosome. This results in altered expression and missplicing of key oncogenes and tumor suppressors involved in HCC, suggesting a novel mechanism of oncogenesis in the liver of patients with ALD.

Apolipoprotein E4 (ApoE4) genotype, hypertension, and biological sex are critical risk factors for Alzheimer’s disease and related dementias. Yet, their combined impact on early cerebrovascular dysfunction, brain inflammation, and memory impairment remains poorly understood. We developed a translational mouse model incorporating human ApoE4, hypertension via angiotensin II infusion, and induced accelerated ovarian failure (AOF) to mimic perimenopause in females to investigate these interactions. Hypertensive ApoE4 mice of both sexes exhibited impaired spatial working memory, decreased cerebral blood flow, increased neuroinflammation, and decreased blood brain barrier integrity, recapitulating key early clinical features observed in human populations with these risk factors. Brain blood flow reduction was associated with an increased incidence of capillary stalling, with notable sex differences in the extent and cellular composition of stalls: in males, stalling was strongly elevated and mostly due to red blood cell arrest, while stalling was modestly elevated in peri-AOF females with most stalls including leukocytes. Treatment with prasugrel, a P2Y12 receptor inhibitor, improved memory performance in both sexes but was correlated with different physiological effects – restored cerebral blood flow in males and reduced microglia motility and inflammation in peri-AOF females. Platelet depletion mimicked prasugrel’s blood flow and cognitive benefits in males, while microglia depletion selectively rescued memory in females. Our work emphasizes the necessity of including translationally relevant female mouse models in neurodegenerative disease studies, and our findings highlight the importance of risk profile-specific interventions and demonstrate that early vascular dysfunction may be a key, sex-dependent driver of cognitive decline.

IntroductionThe growing availability of AI tools is transforming health and science communication by streamlining content creation and promotion. This study investigates the impact of AI-assisted research summaries on user engagement with the NIH-funded RECOVER program's website and evaluates the efficiency and readability of the content.MethodsWe analyzed Google Analytics 4 data from two distinct periods: one with entirely human-generated content and a second with AI-assisted content. We measured changes in page views, active users, and average engagement time, and assessed the review time and readability of the AI-enhanced summaries.ResultsThere was no significant change in page views or active users between the two periods. However, average engagement time increased by 4.37 seconds (P = .0461), suggesting AI-assisted content may be more compelling. Human review of AI-drafts averaged 19.88 changes, and readability improved, with the mean Flesch-Kincaid grade level decreasing from 12.28 to 11.56.ConclusionThis study demonstrates that AI can be a valuable tool for accelerating the creation of accessible and engaging content. Our findings highlight a crucial balance: while AI can save effort and reduce cost in public engagement efforts, human oversight remains essential to ensure the accuracy, clarity, and accessibility of vital health communications.

Perineuronal nets (PNNs) are key regulators of neuronal excitability, yet whether they are altered during neurogenic hypertension is unknown. Here, we mapped the developmental trajectory of PNNs in the paraventricular nucleus of the hypothalamus (PVN), a crucial nucleus involved in blood pressure regulation, and examined their modulation in neurogenic hypertension. We show that PNNs in PVN follow a developmental pattern, enwrapping 25% of neuronal nitric oxide synthase (nNOS)-expressing neurons, with sex differences observed only in oxytocin (OXT)-enwrapped populations. In the DOCA-salt mouse model of neurogenic hypertension, males, but not females, exhibit an increased number and area of PNNs in the PVN. Given that PNNs modulate neuronal activity, our findings may implicate recruitment of previously “silent” neurons as potential contributors of PVN hyperactivity in hypertension. These results demonstrate that PNN remodeling is associated with neurogenic hypertension.

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5’s preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open-chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open-chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open-chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

Various mathematical models have been formulated to describe the changes in synaptic strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models include a third factor, dopamine, which interacts with spike timing to contribute to plasticity at specific synapses, notably those from cortex to striatum at the input layer of the basal ganglia.Theoretical work to analyze these plasticity models has largely focused on abstract issues, such as the conditions under which they may promote synchronization and the weight distributions induced by inputs with simple correlation structures, rather than on scenarios associated with specific tasks, and has generally not considered dopamine-dependent forms of STDP.In this paper we introduce forms of dopamine-modulated STDP adapted from previously proposed plasticity rules. We then analyze, mathematically and with simulations, their performance in two biologically relevant scenarios.We test the ability of each of the three models to complete simple value estimation and action selection tasks, studying the learned weight distributions and corresponding task performance in each setting.Interestingly, we find that each plasticity rule is well suited to a subset of the scenarios studied but falls short in others. Different tasks may therefore require different forms of synaptic plasticity, yielding the prediction that the precise form of the STDP mechanism present may vary across regions of the striatum, and other brain areas impacted by dopamine, that are involved in distinct computational functions.

Summary:The ecological and evolutionary consequences of ongoing extinction episodes remain poorly understood1, despite mounting evidence of global biodiversity loss. To assess how human activities are reshaping tetrapod evolution, we estimate species-level extinction probabilities (‘pEX’) over the next ~50–500 years2,3 using time-calibrated phylogenies4–8, 35 ecological and environmental attributes9, and current-day, expert-assessed threats for 33,281 terrestrial vertebrates2. We find a critical, divergent association between extinction risk and macroevolutionary patterns10: in birds, lizards, and snakes, both evolutionary distinct and rapidly diversifying lineages are most imperiled; while in amphibians and mammals, threat is concentrated in less distinct and slowly radiating groups. Overall, species with high fecundity, intermediate body sizes, and broad geographic ranges are more likely to persist through the ongoing extinction crisis11–14. Without intervention, current-day threats alone suggest a 15% decline (~5,000 extinctions) in species richness and a 16% loss in median speciation rate from 0.11 to 0.094 lineages per million years within the next 500 years. Notably, mammals are projected to experience the largest declines in future speciation potential, despite lower overall imperilment than turtles, crocodilians, or amphibians. Across tetrapods, projected evolutionary distinct extinctions are concentrated in tropical regions, whereas faster-radiating lineages face widespread risk across deserts, tropical islands, and temperate zones. These results uncover ecological and evolutionary drivers of the ongoing reorganization of Earth’s biodiversity, underscoring the urgent need for coordinated conservation efforts to preserve both deep evolutionary history and future potential for evolutionary responses.

Medical management of early pregnancy loss (EPL) achieves ~90% efficacy with mifepristone-misoprostol combination versus ~70% with misoprostol alone, yet biomarkers predicting individual treatment response remain unknown. We performed proteomic analysis of serum samples from 59 participants in the PreFaiR trial, comparing pre- and post-treatment protein expression between responders and non-responders. Combination therapy showed 5 baseline predictors (LAP-TGF-β1, IL17RB, MYOC, CDH1, CD97) with higher expression in responders, while misoprostol monotherapy demonstrated 10 predictors with bidirectional patterns. Treatment-induced changes differed markedly: combination therapy showed significant alterations in 4 proteins, while misoprostol triggered changes in 30 proteins. TNNI3 emerged as a molecular efficiency marker, which showed almost no change between pre- and post-treatment levels in successful combination therapy but substantially declining post-treatment with misoprostol monotherapy. DPP7 showed opposite regulation between treatments among responders. These distinct molecular signatures provide evidence for treatment-specific mechanisms and offer potential biomarkers for personalized EPL management.