Nevin Manimala

Hepatobiliary Surg Nutr. 2026 Feb 1;15(1):7. doi: 10.21037/hbsn-24-417. Epub 2025 Feb 11.

ABSTRACT

BACKGROUND: In the past decade, diabetes prevalence in China increased significantly, with many cases undiagnosed, particularly in rural areas. Despite the limitations of body mass index (BMI) in assessing visceral fat, obesity indices like waist circumference (WC) and waist-to-height ratio (WHtR) have shown stronger associations with the risk of type 2 diabetes mellitus (T2DM). Few cohort studies, especially in rural China, have examined the predictive power of these obesity indices and their changes on T2DM risk. We aimed to assess sex and age difference in the association between obesity, long-term waist circumference and weight changes with the risk of T2DM among rural Chinese adults.

METHODS: Population-based cohort study of 15,076 adult participants was conducted from 2006.08.11 to 2014.07.19 in rural Deqing, China. Participants were annually followed up for the occurrence of major chronic diseases and vital status through the Deqing electronic health records system from enrollment to December 31st, 2021. Cox proportional hazards model was used to estimate adjusted HR (aHR) and 95% confidence intervals (CIs) for the association of obesity indices and their long-term changes with incident T2DM.

RESULTS: A total of 1,888 cases of incident T2DM were found (incidence: 12.35/1,000 person-years) during the mean follow-up of 10.14±3.64 years. Baseline WC (aHR =1.37, 95% CI: 1.31, 1.43), WHtR (aHR =1.38, 95% CI: 1.32, 1.44), BMI (aHR =1.27, 95% CI: 1.21, 1.32), WHtHR (aHR =1.34, 95% CI: 1.25, 1.43) exhibited statistically significant associations with increased risk of T2DM. Per SD increment of WC and weight changes were associated with a 15% and 13% higher risk of incident T2DM, respectively. Stratification analysis revealed that abdominal obesity indices presented stronger associations among males and those under 60 years, while general obesity markers among females and the elderly.

CONCLUSIONS: Obesity, long-term WC and weight changes increase the risk of incident T2DM among rural adults of eastern China, with modification by sex and age.

PMID:41676777 | PMC:PMC12887344 | DOI:10.21037/hbsn-24-417

Hepatobiliary Surg Nutr. 2026 Feb 1;15(1):3. doi: 10.21037/hbsn-24-300. Epub 2024 Dec 17.

ABSTRACT

BACKGROUND: Highly urgent living donor liver transplantation (HU-LDLT) is vital for treating acute liver failure (ALF), acute-on-chronic liver failure (ACLF), and critically ill cirrhotic patients in life-threatening scenarios. The purpose of our study was to identify the characteristics of HU-LDLT patients, compare the outcomes of HU-LDLT patients with those of elective LDLT patients, and determine the risk factors that can influence the outcomes of HU-LDLT.

METHODS: We retrospectively analyzed Korean Network for Organ Sharing (KONOS) data for consecutive HU-LDLT patients between 2017 and 2021. For comparison with the HU-LDLT group, patients who received elective LDLT except HU-LDLT at Samsung Medical Center during the same period were analyzed as the control group.

RESULTS: The most common reasons for HU-LDLT were hepatic encephalopathy, a model for end-stage liver disease (MELD) score ≥35, and uncontrolled varix bleeding. Among the 419 HU-LDLT patients, 53 (12.6%) were pediatric. The cumulative 1-, 3-, and 5-year overall survival rates were 82.4%, 78.3%, and 74.8%, respectively, in the adult HU-LDLT group. The 1-year overall survival rate was 86.1% in the pediatric HU-LDLT group. The presence of chronic kidney disease, pre-transplant ventilator care, high pre-transplant MELD score, and re-transplantation were closely related to mortality in the adult group. Only hepatorenal syndrome (HRS) was a strong risk factor for graft failure in the adult group. The graft and overall survival in the adult HU-LDLT group were significantly lower than those in the control group.

CONCLUSIONS: High MELD scores, hepatic encephalopathy, and bleeding are the main reasons for HU-LDLT applications in Korea. Graft and overall survival curves in the HU-LDLT group are lower than in the elective LDLT group, but the HU-LDLT outcomes are considered acceptable.

PMID:41676776 | PMC:PMC12887345 | DOI:10.21037/hbsn-24-300

Hepatobiliary Surg Nutr. 2026 Feb 1;15(1):2. doi: 10.21037/hbsn-24-276. Epub 2024 Nov 19.

ABSTRACT

BACKGROUND: Postoperative pulmonary complications (PPCs) can impact patient recovery and long-term oncological outcomes after hepatectomy. This study aimed to define whether laparoscopic approach was associated with a reduced incidence of PPCs compared with open approach for patients undergoing hepatectomy for hepatocellular carcinoma (HCC).

METHODS: A multicenter, retrospective cohort study was conducted at 12 Chinese centers between January 2010 and December 2021. Patients underwent either laparoscopic hepatectomy (LH) or open hepatectomy (OH) for HCC. The primary outcome was the incidence of PPCs including symptomatic pleural effusion, respiratory insufficiency, acute respiratory distress syndrome (ARDS), pulmonary infection, and pulmonary embolism. Statistical analysis was performed using propensity score analysis with inverse probability of treatment-weighing (IPTW), multivariable logistic regression, and subgroup analysis to adjust for potential confounders and explore the robustness of the findings.

RESULTS: Among 4,694 patients, 766 (16.3%) patients underwent LH while 3,928 (83.7%) underwent OH for HCC. The overall incidence of PPCs was 10.9%. Among the entire cohort, the incidence of PPCs among patients who underwent LH was significantly lower than individuals who underwent OH (7.3% vs. 11.6%, P=0.001); IPTW analysis demonstrated similar findings (7.4% vs. 11.6%, P=0.01). On multivariable analysis, laparoscopic approach remained independently associated with a lower risk of PPCs [adjusted odds ratio (OR) 0.63, 95% confidence interval (CI): 0.42-0.92, P=0.02]. Subgroup analyses demonstrated similar results relative to different patient and tumor characteristics.

CONCLUSIONS: Laparoscopic approach was associated with improved postoperative pulmonary outcomes and a lower incidence of PPCs than open approach the following hepatectomy for HCC. These findings have potentially important implications in selecting optimal surgical management for HCC.

PMID:41676768 | PMC:PMC12887286 | DOI:10.21037/hbsn-24-276

bioRxiv [Preprint]. 2026 Feb 6:2026.02.04.703681. doi: 10.64898/2026.02.04.703681.

ABSTRACT

Point process generalized linear models (GLMs) have been a major tool for studying coordinated activity across populations of neurons. These models typically quantify how the spiking of a single neuron depends on the past activity of other neurons at multiple time lags, and the resulting neuron-to-neuron interactions are then aggregated to obtain population-coupling effects. However, when neurons within the same population exhibit similar spiking patterns, explicitly modeling individual interactions can be redundant and can unnecessarily increase model complexity. In such cases, population-level formulations may offer a more efficient alternative. For example, biophysical population models often characterize circuit dynamics using the average firing rate across neurons within a population, and recent data-driven approaches have similarly demonstrated the utility of population-level statistics for capturing cross-population interactions. Motivated by this consideration, we reformulate the GLM framework to operate directly at the population level. The resulting model, which we call pop-GLM, provides a computationally efficient method for estimating coupling between populations. In a simulated dataset, we show that pop-GLM achieves greater sensitivity in detecting coupling effects and can account for trial-to-trial variation in stimulus drive, which would otherwise introduce bias. We also note that moving from single-neuron to population-level modeling requires a specific modification of the traditional GLM framework. We then apply pop-GLM to real data and find reduced functional connectivity from primary visual cortex (V1) to a higher visual area during locomotion, a change not detected by single-neuron GLMs.

AUTHOR SUMMARY: A central goal of systems neuroscience is to understand how multiple populations of neurons across different brain areas interact as a coordinated circuit to produce perception and behavior. We formulated and investigated a new method for estimating functional interactions between two populations of spiking neurons, and we show that it can be more sensitive and robust than previous approaches. To illustrate, we discovered decreased interaction between two mouse visual areas during locomotion, a result that previous techniques did not detect. The method should aid investigators in searching for important functional relationships across populations of neurons, with precise time scale resolution.

PMID:41676710 | PMC:PMC12889648 | DOI:10.64898/2026.02.04.703681

bioRxiv [Preprint]. 2026 Feb 2:2026.01.30.702623. doi: 10.64898/2026.01.30.702623.

ABSTRACT

Synapses are fundamental building blocks of cortical circuits, yet their geometry is typically regarded as a local property, independent of mesoscale architecture. The prevailing assumption is that synaptic clefts are isotropically oriented in space. Here, this assumption was tested by analyzing approximately 117 million synaptic clefts from two independent 1 mm ³ electron microscopy datasets: the human H01 middle temporal gyrus and the mouse MICrONS primary visual cortex. Across both volumes, synaptic cleft orientations are not randomly distributed, but instead show statistically significant and spatially coherent directional biases across cortical layers. This mesoscale anisotropy is conserved across species, yet is stronger and more consistent in human association cortex than in mouse sensory cortex. These findings reveal an unrecognized dimension of cortical microarchitecture and suggest that synaptic geometry contributes to circuit organization, mesoscale connectivity, and interactions with endogenous or externally applied electric fields.

PMID:41676682 | PMC:PMC12889479 | DOI:10.64898/2026.01.30.702623

bioRxiv [Preprint]. 2026 Feb 6:2026.02.04.703700. doi: 10.64898/2026.02.04.703700.

ABSTRACT

Protein-protein interactions governed by conformationally heterogeneous domains remain difficult to drug because ligand-competent states are often absent from single static structures. Here, we present AtlasNMR, a statistical framework that transforms multi-model NMR ensembles into screening-ready conformational hypotheses for small molecule discovery. Using the neuronal nitric oxide synthase (nNOS) PDZ domain that engages the adaptor protein CAPON (NOS1AP) as a model system, AtlasNMR identified two representative conformational states capturing the dominant and minor populations of the NMR ensemble. Ensemble-based virtual screening followed by consensus ranking yielded MC-3 , a small molecule modulator that disrupts the NOS1-NOS1AP interaction in live cells and directly engages the nNOS PDZ domain. MC-3 produced convergent neuroprotective effects in disease-relevant neuronal models by reducing amyloid-β-induced cytotoxicity, suppressing NMDA-driven nitrosative stress, and attenuating pathological tau phosphorylation, while exhibiting a balanced early lead-like ADME and safety profile. Together, this work establishes a generalizable strategy for exploiting NMR ensemble heterogeneity to enable small molecule discovery against dynamic protein-protein interfaces.

PMID:41676628 | PMC:PMC12889626 | DOI:10.64898/2026.02.04.703700

bioRxiv [Preprint]. 2026 Feb 8:2026.02.05.704068. doi: 10.64898/2026.02.05.704068.

ABSTRACT

Kinase family proteins constitute the second largest protein class targeted in drug development efforts, most prominently to treat cancer, but also several other diseases associated with kinase dysfunction. In this work we focus on type II kinase inhibitors which bind to the “classical” inactive conformation of the protein kinase catalytic domain where the DFG motif has a ″DFG-out″ orientation and the activation loop is folded. Many Tyrosine kinases (TKs) exhibit strong binding affinity with a wide spectrum of type II inhibitors while serine/threonine kinases (STKs) often bind more weakly. Recent work suggests this difference is largely due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. The binding affinity of a type II inhibitor to its kinase target can be decomposed into a sum of two contributions: (1) the free energy cost to reorganize the protein from the active to inactive state, and (2) the binding affinity of the type II inhibitor to the inactive kinase conformation. In previous work we used a Potts statistical energy potential based on sequence co variation to thread sequences over ensembles of active and inactive kinase structures. The threading function was used to estimate the free energy cost to reorganize kinases from the active to classical inactive conformation, and we showed that this estimator is consistent with the results of molecular dynamics free energy simulations for a small set of STKs and TKs. In the current study, we analyze the results of a large-scale study of the binding affinities of 50 type II inhibitors to 348 kinases, of which the results for 16 of the 50 type II inhibitors were reported in an earlier study (the “Davis dataset”). The binding data for the remaining 34 type II inhibitors to the panel of 348 kinases were recently obtained (the “Schrödinger dataset”). We use the Potts statistical energy model to investigate the contribution of protein reorganization to the selectivity of the large kinase panel against the set of 50 type II inhibitors, and find that protein reorganization makes a significant contribution to the selectivity. The AUC of the receiver operator characteristic curve is ≈0.8. We report the results of an internal “blind test”, that shows how Potts threading energies can provide more accurate estimates of kinase selectivity than corresponding predictions using experimental results of small sample size. We discuss why two STK phylogenetic kinase families, STE and CMGC, appear to contain many outliers, and how to improve the ability to predict kinase selectivity with a more complete analysis of the kinase conformational landscape. We compare the performance of Potts threading for predicting binding properties of the large set of (50) Type II inhibitors to 348 kinases, with those of a sequence-based purely machine learning model, DeepDTAGen, a publicly available machine learning model that was trained on the complete Davis dataset, including both Type I and Type II kinase inhibitors. We observe that DeepDTAGen performs well on binding predictions for the 16 type II inhibitors in the Davis dataset, but performs poorly on binding predictions for the 34 type II inhibitors against 348 kinases in the Schrödinger dataset.

PMID:41676586 | PMC:PMC12889613 | DOI:10.64898/2026.02.05.704068

bioRxiv [Preprint]. 2026 Feb 7:2026.02.04.703900. doi: 10.64898/2026.02.04.703900.

ABSTRACT

Detecting a sex difference in response to a treatment or intervention, often reported as a ‘sex-specific effect,’ requires statistical comparison of the response across sex. Here, we investigated analytical approaches used to test for such effects in the behavioral and brain sciences. Of 200 recent articles containing terms such as ‘sex-specific’ or ‘gender-dependent’ in their titles, only 24% presented appropriate evidence supporting the claim: the effect was compared statistically across sex and results consistent with the claim were reported. In most articles (58%), no test was conducted that could have supported the title claim. Only 15% of studies on non-human animals supported the claim with appropriate evidence, which was significantly less frequently than studies on human participants (34%; p = 0.002). The use of appropriate analytical approaches was unrelated to journal rank or the citation impact of the article. We conclude that claims of sex/gender-dependent effects in the behavioral and brain sciences are only infrequently supported by appropriate evidence.

PMID:41676574 | PMC:PMC12889599 | DOI:10.64898/2026.02.04.703900

bioRxiv [Preprint]. 2026 Feb 6:2026.02.03.703561. doi: 10.64898/2026.02.03.703561.

ABSTRACT

Directed evolution is a powerful and widely used technique for protein engineering, and reducing the cost of iterated experimental observations has become a major priority for practitioners. A number of recent efforts to use machine-learning-based predictors to improve sequence selection have led to remarkable improvements in efficiency, but the sparse data at each experimental iteration restricts these approaches to extremely simple models. Adapting large-scale pre-trained protein language models using experimental data offers an alternative that we show productively leverages the strong inductive biases of the natural distribution of protein sequences to navigate high-dimensional, combinatorially large fitness landscapes. Our approach uses a general-purpose “post-training” algorithm grounded in statistical physics that employs quantitative experimental rankings to directly produce a sampler for diverse, high fitness sequences with fewer data points than competing methods. The resulting adapted protein language model can itself be studied and interpreted, shedding further light on the biophysical characteristics of highly fit sequences and their properties.

PMID:41676563 | PMC:PMC12889597 | DOI:10.64898/2026.02.03.703561

bioRxiv [Preprint]. 2026 Feb 6:2026.02.04.703796. doi: 10.64898/2026.02.04.703796.

ABSTRACT

Recent advances in deep learning have led to the development of sequence-to-omics (S2O) models that predict molecular phenotypes directly from DNA sequences. Here, we systematically evaluate the utility of these models, e.g., AlphaGenome, Borzoi, Enformer, and Sei, for improving the reproducibility of genetic fine-mapping across expression quantitative trait loci (eQTL) datasets from Genotype-Tissue Expression (GTEx), Trans-Omics Precision Medicine (TOPMed), and Multi-Ancestry Analysis of Gene Expression (MAGE) projects. We show that purely statistical fine-mapping often yields high replication failure rates (RFRs), but integrating S2O model predictions substantially reduces RFRs and enhances the accuracy of prioritizing SNPs replicated in other consortia. We describe a generalized framework for functionally informed fine-mapping that combines traditional posterior inclusion probabilities (PIPs) from statistical fine-mapping methods with scores from S2O models to generate functionally informed PIPs (fiPIPs) that improve reproducibility. Our findings demonstrate that S2O models, particularly newer ones like AlphaGenome and Borzoi, enable robust identification of replicated variants across consortia, highlighting their promise for scalable, functionally aware genetic mapping.

PMID:41676556 | PMC:PMC12889643 | DOI:10.64898/2026.02.04.703796