Science Bulletin

Every known life form senses and reacts to mechanical forces. These mechanical stimuli can be converted into electrical signals by mechanically gated ion channels, a transduction cascade pivotal to numerous physiological functions including touch, hearing, mechanical pain, circulation, gastrointestinal function, and mechanical loading in various tissues. Despite continuous efforts, numerous mechanically gated ion channels with the mechanotransduction process underlying these physiological functions remain unidentified. Here, we focused on the transmembrane channel-like (TMC) protein family expressed in the cultured cells to identify those with potential mechanosensitive activity. Remarkably, in contrast to human TMC1/2 (HsTMC1/2), human TMC3-8 (HsTMC3-8) proteins are localized to the plasma membrane when heterologously expressed in the cultured cells. Further experiments revealed that mechanical poking stimuli can effectively activate HsTMC3-8. In addition, HsTMC3-8 induced stretch-activated currents and elicited well-resolved single-channel activities in response to negative pressure stimulation. The mutants near the putative pore region altered reversal potentials (Erev) of HsTMC3-8, suggesting that TMC3-8 are likely pore-forming subunits of ion channels. In summary, we proposed that TMC proteins are the largest mammalian mechanically gated ion channel family.

BACKGROUND: Coronary angiography remains the reference standard for diagnosing coronary artery disease and guiding revascularization, yet its interpretation requires expert integration of multi-view anatomy, lesion morphology and procedural context. Existing artificial intelligence approaches are largely task-specific, annotation-dependent and limited in capturing the semantic relationship between angiographic findings and interventional decision-making. Whether large-scale vision-language pretraining can enable transferable foundation-model representations for invasive coronary imaging remains unknown. METHODS We developed CAG-MIND, a domain-specific vision-language foundation model for coronary angiography, using 135,475 CAG examinations paired with procedural reports, comprising 812,850 angiographic videos from Zhongshan Hospital and Shanghai Geriatric Medical Center. Each case consisted of standardized six-view angiographic acquisitions paired with structured procedural semantics extracted from routine reports using a large language model-assisted pipeline. The model was pretrained by aligning multi-view angiographic representations with report-derived semantic embeddings through bidirectional contrastive learning. Performance was evaluated under zero-shot and supervised fine-tuning settings across 11 downstream tasks grouped into structural abnormality detection, atherosclerotic plaque assessment, and interventional decision prediction, using both an internal validation cohort and an independent external test cohort. RESULTS CAG-MIND demonstrated robust performance across all three task categories. In the zero-shot setting, the model achieved mean AUROCs of 0.686 in the internal validation cohort and 0.745 in the external test cohort, indicating transferable multimodal representations without task-specific supervision. Following supervised fine-tuning, the mean AUROC increased to 0.827 and 0.846, respectively, with excellent performance for coronary stenosis detection (AUROC 0.940 in both cohorts), balloon/stent prediction (0.900 and 0.907), and CABG recommendation (0.877 and 0.875). Compared with representative biomedical vision-language models and conventional image-based architectures, CAG-MIND consistently achieved superior performance in both zero-shot and supervised settings and remained superior to fully fine-tuned competing models when trained with only 10% of the labelled data. Grad-CAM visualization demonstrated anatomically plausible lesion-focused attention, supporting the interpretability of the learned representations. CONCLUSIONS CAG-MIND is, to our knowledge, the first large-scale vision-language foundation model for coronary angiography trained at more than 100,000-patient scale. By aligning standardized multi-view angiographic videos with report-derived procedural semantics, CAG-MIND enables robust zero-shot transfer, data-efficient fine-tuning and cross-center generalization. These findings support domain-aligned multimodal pretraining as a scalable foundation-model paradigm for invasive cardiovascular imaging and future cath-lab decision support.

IntroductionWhile placental perfusion and pathology jointly affect pregnancy outcomes, cotyledon-specific perfusion across gestation and its correlation with local injury is not yet well understood. Ferumoxytol dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers a promising way to noninvasively identify cotyledons across gestation and quantify longitudinal cotyledon-specific perfusion changes. Additionally, intraplacental injection of bioactive fibrin sealant allows us to model thrombotic placental injury and further assess cotyledon-level relationships between perfusion and significant injury. MethodsPregnant rhesus macaques (N=13) received intrauterine saline or fibrin sealant injections at gestational day (GD) [~]101 and underwent ferumoxytol DCE-MRI at GDs [~]100, 115, and 145. Placental perfusion domains derived from contrast arrival time were segmented at each imaging time point and matched to cotyledons identified following tissue collection by cesarean section, with cotyledon perfusion quantified longitudinally and correlated with cotyledon-specific quantitative histopathology. ResultsAll pregnancies were successfully carried to term. Fibrin sealant injections induced significantly higher levels of placental pathology compared to saline controls. MRI-derived perfusion domains were largely consistent across gestation and showed predominantly one-to-one correspondence with term cotyledons, with successful perfusion-pathology pairing achieved in 153 cotyledons. Longitudinal cotyledon perfusion changes showed significant positive correlations with villous agglutination injuries. ConclusionsFeasibility of noninvasively tracking placental cotyledon perfusion using ferumoxytol DCE-MRI was demonstrated, and the efficacy of the rhesus macaque thrombotic injury model was confirmed. The positive perfusion-pathology correlations suggested intrinsic placental regulatory mechanisms and functional plasticity. This new framework is promising for future translational studies and validation of ex vivo cotyledon perfusion models. HighlightsO_LILongitudinal tracking of placental perfusion domains with ferumoxytol MRI C_LIO_LISuccessful matching of cotyledons and MRI-derived perfusion domains C_LIO_LIConfirmed thrombotic injury-model induced cotyledon pathology C_LIO_LIMaternal perfusion compensation in presence of villous pathology C_LI

Connected speech is essential for everyday communication, but clinical constraints and patient fatigue limit detailed evaluation in acute stroke (<1-week post-stroke). Bedside assessments may sample discourse but rarely quantify language impairment (LI) in connected speech, leaving patient communication poorly characterized. We analyzed brief story retellings from 86 patients with left-hemisphere stroke (~4 days post-stroke; 63 classified with LI using composite clinical and naming criteria). From transcripts generated with automatic speech recognition, we derived discrete linguistic features and embeddings with Large Language Models (LLMs). An ensemble of embedding-based classifiers distinguished patients with and without LI with 90% balanced accuracy (79% sensitivity, 100% specificity), outperforming independent embedding and discrete-linguistic-based classifiers, showing distinct LLMs contributed complementary information. Adding the discrete-linguistic-based classifier to the ensemble did not improve balanced accuracy but modestly increased sensitivity at the expense of specificity. We provide proof of concept for a fast, largely automated discourse screener of acute LI.

Kyasanur Forest Disease Virus (KFDV) NS5 methyltransferase (MTase) protein is the essential enzyme that is involved in the cap methylation of viral RNA, viral replication, and immune evasion, and therefore it is an important protein of interest for antiviral research and drug design. In the present work, we successfully resolved the three-dimensional crystal structures of KFDV NS5 MTase co-crystallised with SAH and GTP at resolutions of 2.2 [A] and 2.6 [A], respectively. In previous studies, HC (Herbacetin) and CAPE (Caffeic acid phenethyl ester) have shown inhibitory activity against SAM-dependent viral MTase. To evaluate the inhibitory potential of HC and CAPE against KFDV NS5 MTase, we have performed isothermal titration calorimetry (ITC) and tryptophan fluorescence spectroscopy (TFS) to validate protein interaction with target compounds. MTase inhibition assay was performed using capillary electrophoresis (CE) assays. Additionally, fluorescence polarisation (FP) confirmed RNA binding inhibition by CAPE and HC. Together, these experiments suggest that HC and CAPE are promising inhibitors against KFDV NS5 MTase and could potentially act as lead compounds to design broad-spectrum anti-Orthoflavivirus drugs.

Cholesterol is a key component of eukaryotic cell membranes, promoting membrane stability and modulating the function of many membrane proteins, including ion channels. In our previous work using purified human voltage-gated proton channel proteins, we showed that cholesterol inhibits the hHv1 channel by altering the conformational dynamics of its S4 segment, the key element that senses membrane voltage to control proton permeation. In the present work, we examined the effects of cholesterol analogs and potential sites in the hHv1 channel mediating cholesterol inhibition using site-directed mutagenesis and docking simulations. Our results showed that desmosterol, the immediate precursor of cholesterol, markedly attenuates cholesterol inhibition. Using single-molecule Fluorescence Resonance Energy Transfer (smFRET), we showed that desmosterol attenuates cholesterol inhibition by promoting the intermediate and open state conformations of the S4 segment. Moreover, we identified multiple residues in the hHv1 channel that are critical for cholesterol inhibition, including Y141A in the S2 segment, which reduces cholesterol inhibition by nearly 3-fold. Our smFRET results showed that the Y141A mutation promotes the intermediate conformation in the S4 segment, which underlies the attenuation of cholesterol inhibition. Consistently, docking simulations also revealed multiple residues spanning the transmembrane domain, rather than clustered within a single localized pocket. Our work identified the key molecular determinant in the hHv1 channel that mediates cholesterol inhibition and also provided a mechanism linking the conversion between demosterol and cholesterol by DHCR24 to pH homeostasis in many cells, such as phagocytes, cardiomyocytes, neurons and microglial cells.

Echovirus 18 (E18) is a predominant pathogen causing aseptic meningitis in children, and post-E18 infection frequently triggers myasthenia gravis-like autoimmune neurological damage. This pathological process relies on neonatal Fc receptor (FcRn)-mediated IgG transcytosis across mucosal barriers, and FcRn also acts as an essential functional receptor required for E18 attachment and uncoating during host cell invasion. At present, no E18-specific prophylactic vaccine has been clinically approved, and anti-FcRn monoclonal antibodies are the available therapeutics to alleviate autoantibody-mediated tissue injury. We constructed an integrated automated phylogenetic pipeline named evolution_conservation, which enables rapid tracing of the evolutionary position and genetic relatedness of clinical isolates to identify closely related strains from previous outbreaks. Serving as an in silico alternative to animal experiments, this pipeline supports reference-guided vaccine design and longitudinal comparative assessment of vaccine safety and efficacy, facilitates identification of patient populations presenting rare post-viral sequelae, and accelerates clinical trial progression. In this study, we inserted the pre-screened linear epitope E18-3 into a truncated hepatitis B core (HBc) scaffold to generate chimeric virus-like particles (VLPs). A non-invasive floss-based gingival mucosal immunization mouse model was established, with subcutaneous Freunds adjuvant immunization set as the control group. ELISA results confirmed that gingival mucosal delivery of particulate HBc-E18-3 VLPs alone could induce sustained high levels of antigen-specific intestinal IgG in vivo. Drawing on research paradigms of therapeutic neoantigen vaccines for tumor recurrence prevention, the evolution_conservation bioinformatic pipeline and mucosal VLP platform described herein establish an innovative framework for developing antigen-competitive prophylactic and therapeutic vaccines targeting FcRn for myasthenia gravis and autoimmune encephalitis.

The subgenual anterior cingulate cortex (sgACC) is a key node in treatment-resistant depression (TRD), but precise non-invasive neuromodulation of this target is challenging. Preclinical studies of non-ablative stereotactic radiosurgery (SRS) have shown neuromodulatory ("radiomodulation") effects. In this single-center, double-masked, randomized, dose-seeking pilot trial, nine adults with TRD were randomly assigned to bilateral sgACC radiomodulation at a dose of either 15, 20, or 25 Gy per hemispheric target. Primary endpoints were safety and feasibility; the efficacy endpoint was week-4 change in the Montgomery-Asberg Depression Rating Scale (MADRS). Both primary endpoints were met: the only treatment-related adverse event was transient grade 1 dizziness, with no structural MRI abnormality through week 12. Mean MADRS fell from 33.0 to 17.0 (48.5% reduction); 67% responded and 44% remitted, with benefit sustained to week 12. Resting-state fMRI revealed regional connectivity changes correlating with clinical improvement, with tractography showing streamline counts differing by response status. These first-in-human findings support a larger randomized controlled trial of sgACC radiomodulation for TRD. ClinicalTrial.gov registration: NCT07274917.

Cerebrospinal fluid (CSF) provides a specialized extracellular environment for the central nervous system, which is predominantly produced by the choroid plexus, a highly vascularized epithelial structure whose ion transport processes are fundamental to CSF secretion, composition, and homeostasis. The mechanisms of Na+ entry into choroid plexus epithelial cells (CPECs) from the interstitial side remain disputed. The slc4a10 gene product encoding the Na+-dependent Cl-/HCO3- exchanger, Ncbe, was suggested as a key transport mechanism based on its impact on the cell's Na+-dependent regulation of intracellular pH and its basolateral membrane expression. The current study was undertaken to directly assess the contribution of Ncbe to the Na+ uptake into CPECs. Intracellular Na+ was recorded by fluorometry using the Na+ probe Sodium Binding Fluorescent Indicator in clusters of CPECs with access to both the luminal and basolateral membranes. Removal of extracellular Na+ reduced the apparent ex vivo intracellular [Na+] to ~5 mM from a baseline of ~43 mM in the absence of CO2/HCO3- and ~54 mM in the presence of CO2/HCO3-. Flame photometry estimated the intracellular [Na+] ex vivo to ~28 mM. The CO2/HCO3--dependent rate of [Na+] recovery amounted to ~53% of the total recovery rate upon re-addition of Na+. Experiments with access to only the luminal membrane show a [Na+] recovery of a similar rate as observed in the absence of CO2/HCO3- in the clusters. The CO2/HCO3--independent [Na+] recovery was inhibited to ~50% by the NKCC1 inhibitor bumetanide and to ~30% by the TRPv4 inhibitor RN1734. NHE contributed to a minor extent to the CO2/HCO3--independent transport. The HCO3- transport inhibitor DIDS, however, inhibited the total [Na+] recovery rate to ~50%, indicating a role for Ncbe rather than NBCn1 in the cellular [Na+] recovery. Indeed, docking of DIDS into Ncbe and NBCn1 indicated that both proteins can accommodate the binding of DIDS. However, the orientation of the DIDS poses in Ncbe suggests a binding mode more similar to that found in the Anion Exchangers (SLC4A1-3), which seems to accommodate the covalent-type docking more than NBCn1. The Ncbe inhibition by DIDS was supported by the rate of [Na+] recovery that was significantly higher in CPECs from Ncbe-wt than Ncbe-ko mice in the presence of CO2/HCO3-. As both NKCC1 and TRPv4 are localized to the luminal membrane, the findings collectively suggest that Ncbe is the most prominent mechanism for Na+ entry into CPECs expressed at the basolateral side. We suggest Ncbe as the rate-limiting mechanism in the vectorial Na+ transport driving CSF secretion.

Pediatric neuroimaging needs age- and sex-appropriate references, yet existing atlases span broad age ranges that blur development or lack sex specificity. We present BRAIN CAST: 28 year-by-year, sex-specific brain MRI templates covering ages 5-18, built from 1,272 quality-screened children in the Healthy Brain Network by an MRIQC-guided pipeline combining reduced-strength denoising, cerebrospinal-fluid-anchored intensity normalization, deep-learning skull stripping and iterative groupwise diffeomorphic registration. We evaluate templates not by image sharpness, which is not comparable across intensity conventions, but by the structural bias they induce downstream. Held-out children align to their matched template with sub-voxel gray-white interface error (1.1 mm); on a direction-symmetric surface-distance metric BRAIN CAST matches the best single-template reference and outperforms an age-specific pediatric atlas in 189 of 189 subjects. Female cortex is fit measurably better by female than by male templates, an effect no sex-neutral reference can provide. Templates, tissue-probability maps and the containerized pipeline are released.

This study aims to quantify the genetic similarity of different species (from fish to humans) to the human reference genome (pp6, Homo sapiens.GRCh38) based on the allele presence/absence patterns of 33 language/cognition related gene SNV loci, identify key breakpoints during evolution, and evaluate the enrichment of language and cognition genes at these breakpoints. We designed a similarity calculation method relying on binary features (four columns for A/T/C/G), adopted five difference/distance measures (Sorensen, Rogers, Nei, Reynolds, and Hellinger), and converted them into similarity values (1/(1+distance)). For each method, samples were independently ranked, the first derivative of similarity was computed, and the top 12 peaks were selected as candidate breakpoints. Results show that the similarity curves from the five methods are highly consistent (correlation coefficients >0.9), with major peaks concentrated at positions 355, 363, 381, 382, 390, 400, etc., where the corresponding samples are predominantly ancient hominins and primates. Furthermore, we defined 13 peak groups (starting positions 355-401). For each peak within a group, pairwise SNV differences between the peak apex sample and its immediate left neighbor were compared, and the intersection F_INTERSECTION (shared differential loci) was obtained. For each F_INTERSECTION, we calculated the proportions of language genes and cognition genes. In addition, we computed the differential sets between adjacent groups' F_INTERSECTION to trace the gradual emergence of new loci. In F_INTERSECTION, language genes accounted for an average of 59.5%, and cognition genes for an average of 62.9%. The proportion of language genes reached a peak at position 383 (61.2%), while cognition genes peaked at position 386 (64.9%). High frequency peak samples include c25, c27, and ja2, suggesting that language cognition genes may have undergone independent intensification during Eurasian evolution. Differential analysis between adjacent F_INTERSECTION revealed a stepwise acquisition of new loci from position 355 to 401, with three bursts of newly added loci along the entire evolutionary axis. This study provides a quantitative framework based on similarity curves, offers a novel molecular perspective for understanding the evolution of language and cognitive abilities, and highlights the potential importance of East Asian archaic hominins in the evolution of language cognition genes.

Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug development is recognized as a cumulative, often synergistic, effect across multiple stages, the autonomous evolution of existing algorithms across the entire pipeline is demanded to achieve a holistic advancement. Here, we present DrugEvolve, a multi-role large language model system for systematic and autonomous algorithm evolution in drug development. DrugEvolve realizes a closed-loop evolution process by incorporating Researcher, Engineer, and Analyst domains, and enables an iterative design, implementation, evaluation, and refinement of algorithm by leveraging scientific knowledge and accumulated evolutionary experience. Across eleven representative tasks spanning target identification, drug discovery, preclinical study, and clinical trial, DrugEvolve autonomously evolved the corresponding task-specific algorithms and achieved substantial performance enhancement on 120 benchmark test sets. Moreover, it showed robust generalizabilities across heterogeneous data modalities (ranging from biological sequence and graph to molecular topology and textual language), and realized gains in both predictive and generative tasks. Collectively, this AI system can serve not only as an algorithmic infrastructure for drug development, but also as a transferable paradigm for broader scientific domains.

Human Dicer (hDicer) is a key enzyme in the RNA interference (RNAi) pathway that generates [~]21-22 nt micro-RNA (miRNAs) and small interfering RNAs (siRNAs). We have previously shown that hDicer also generates tRNA-derived small RNAs (tsRNAs), which mediate nuclear gene silencing and regulate hundreds of disease-associated genes. As powerful and evolutionarily conserved cellular regulators, tsRNAs emerged as an important class of small RNAs. Therefore, it is essential to understand their biogenesis. However, the molecular and structural basis of tRNA cleavage by hDicer, as well as the role of chemical modifications such as 5-methylcytosine (m5C), in this process, remain unknown. Here, we present the first structural insights into hDicer in complex with tRNA, obtained by cryo-electron microscopy (cryo-EM), selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) and molecular dynamics (MD) simulations. Our results reveal that tRNAs adopt alternative conformations that are recognized and processed by hDicer. Furthermore, we show that tRNA cleavage by hDicer is facilitated by the m5C modification deposited by Nop2/SUN RNA methyltransferase 2 (NSUN2). Collectively, our findings redefine tRNAs as bona fide hDicer substrates and uncover a modification-dependent biogenetic pathway that reshapes the current understanding of the origins and regulation of human small RNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/744379v1_ufig1.gif" ALT="Figure 1000"> View larger version (24K): org.highwire.dtl.DTLVardef@ad78aborg.highwire.dtl.DTLVardef@cd3a7dorg.highwire.dtl.DTLVardef@1bb2594org.highwire.dtl.DTLVardef@1a0427e_HPS_FORMAT_FIGEXP M_FIG C_FIG

A protein whose removal or deletion causes significant disruption or collapse of a protein-protein interaction (PPI) network is referred to as a vulnerable protein. Such proteins may serve as valuable therapeutic or diagnostic targets in disease-associated networks. In this study, two PPI networks were constructed, one for HPV-positive and the other for HPV-negative head and neck squamous cell carcinoma (HNSCC), and the vulnerable proteins of these networks were identified by the node deletion approach. After analyzing the networks, 27 unique vulnerable proteins in HPV-positive and 72 unique vulnerable proteins in HPV-negative HNSCC were identified. Among them, one HPV-positive and seven HPV-negative HNSCC vulnerable proteins were further chosen by integrating multi-omics data. To exploit the vulnerabilities of these proteins, candidate synthetic lethal (SL) partners were predicted whose inhibition may selectively impair tumor survival. Subsequently, drug-gene interaction analysis was performed to identify inhibitors targeting the SL partners of these vulnerable proteins. Notably, in HPV-positive HNSCC, TOP2A, CHEK1, and CHEK2 genes were identified as SL partners of TTN, and their inhibitors were already clinically approved. While in HPV-negative HNSCC, ADA and MMP19 were identified as an SL partner of LMO7; TMEM45B, CDH3, and ELF3 genes were identified as an SL partner of CGN; and ZNF433 was identified as an SL partner of FLNC. However, MMP19, ZNF433, and TMEM45B inhibitors were not reported. Thus, these vulnerable proteins, including their SL partners, provide novel avenues to explore and develop more efficient and precise therapeutic and diagnostic strategies.

Cardiac imaging enables quantitative assessment of cardiac structure and function but remains constrained by cost, infrastructure and specialist expertise. In contrast, electrocardiogram (ECG) is widely accessible yet underexploited, despite encoding latent information about cardiac physiology. Here we introduce visionECG, a conditional flow matching framework that learns a probabilistic mapping between two biological distributions - the space of cardiac electrical signals and the space of cardiac geometries. Using 71,132 paired ECG and cardiac mesh sequence datasets from the UK Biobank, with external assessment in 5,000 patients with ECG-echocardiogram pairs, the model reconstructs quantitatively accurate spatiotemporal representations of the left ventricle using ECG inputs and basic demographic information alone. These reconstructions enable discrimination of structural abnormalities and disease labels, provide visualisations of functional abnormalities, and support flexible quantification of both global and regional parameters. By reframing the ECG as a generative source of patient-specific left ventricular geometry and motion, this work establishes a scalable framework for translating low-dimensional signals into high-dimensional, physiologically grounded structured representations.

BACKGROUND: Subarachnoid hemorrhage (SAH) induces inflammatory responses and subsequent immune cell activation, which may contribute in cerebral vasospasm, microcirculatory impairment and poor neurological outcomes. Although cerebral vasospasm has traditionally been considered a major cause of delayed cerebral ischemia after SAH, therapies targeting angiographic vasospasm have not consistently improved functional outcomes. Early inflammatory responses may contribute to microcirculatory impairment, cerebral vasospasm, and subsequent neurological injury. Herein, we investigated whether interleukin-10 (IL-10), an anti-inflammatory cytokine, improves these outcomes in an experimental SAH model. METHODS: Mice received intramuscular injections of either an adeno-associated virus encoding IL-10 (AAV/IL-10) vector or an AAV expressing green fluorescent protein (AAV/GFP) vector (control). India ink angiography was performed to assess the diameter of the sphenoidal segment of the middle cerebral artery (MCA), the total length of the visible cortical arteries, and cortical staining intensity, as indices of cerebral vasospasm, microcirculatory impairment, and cerebral perfusion, respectively. Perivascular inflammatory cell infiltration and cytokine levels were assessed using immunohistochemistry and ELISA. We also evaluated the therapeutic efficacy of the AAV/IL-10 vector when administered immediately after SAH induction. RESULTS: IL-10 overexpression significantly improved neurological outcomes after SAH and was associated with attenuated cerebral vasospasm and microcirculatory impairment, as well as preservation of cerebral perfusion. It also significantly reduced neutrophil and macrophage infiltration around the internal carotid artery and attenuated SAH-induced elevations in IL-6 and matrix metalloproteinase-3 levels. Mice treated with the AAV/IL-10 vector immediately after SAH induction showed significant improvements in neurological scores and cerebral perfusion. CONCLUSIONS: AAV-mediated IL-10 overexpression improves neurological outcomes after SAH, likely by attenuating inflammatory responses, cerebral vasospasm, and microcirculatory impairment. These findings suggest that IL-10-based anti-inflammatory therapy is a promising therapeutic strategy for SAH.

The attention mechanism offers the possibility for data-driven discovery of biological principles. However, for important protein families such as human olfactory receptors, the extent to which attention can associate with biologically meaningful key regions lacks systematic validation. In this study, using human olfactory receptors (ORs) as a model, we constructed CrossVOI, a VOC-OR interaction prediction framework based on protein language models and cross-attention, achieving predictive performance superior to existing methods. Furthermore, we systematically analyzed the attention distributions of CrossVOI and found that attention not only focused on ligand-binding interfaces and evolutionarily conserved sites, but also to some extent identified certain dynamically regulated regions. In summary, we propose CrossVOI, currently the best-performing framework for VOC-OR interaction prediction, and analyze the interpretability of the attention mechanism for human ORs. This study provides insights into the interpretability of protein function prediction methods and is expected to contribute to the exploration of attention mechanisms in biological mechanisms, and provide assistance for large-scale screening and mechanistic analysis of olfactory receptors.

Plant-derived small molecules possess highly diverse physicochemical properties, and the computational design of their protein recognition elements depends not only on the global structural quality of candidate backbones, but also on whether the local binding pocket, ligand-contact pattern, and predefined recognition conformation can be consistently retained after sequence design and structural back-prediction. To explore pocket-design strategies for different types of natural-product small molecules, this study selected capsaicin, (4R)-limonene, and quercetin as model ligands, representing a flexible amphipathic molecule, a compact hydrophobic monoterpene, and a rigid polyphenolic flavonoid scaffold, respectively, and covering the dimensions of pungent sensory flavor, volatile aroma, and flavonoid functional constituents. A ligand- physicochemical-property-guided computational design and multi-stage prioritization framework was established for candidate protein binders. The results showed that candidates with favorable initial global structural scores did not necessarily form reasonable local small-molecule binding pockets, indicating that evaluation of the local ligand environment is essential for candidate prioritization. After screening, 31 partial- pocket candidate backbones for capsaicin, 75 buried hydrophobic-pocket candidate backbones for (4R)-limonene, and 56 pocket-qualified candidate backbones for quercetin were obtained. Further sequence design and structural back-prediction analyses indicated that a subset of candidates could maintain the original pocket geometry and major ligand-contact patterns after sequence realization. Overall, these results suggest that the physicochemical properties of different plant-derived small molecules substantially influence the efficiency of de novo protein pocket formation, with compact hydrophobic ligands being more compatible with buried hydrophobic- pocket strategies, whereas flexible or multipolar ligands require a more refined balance between hydrophobic burial and polar exposure. This study provides a pre- experimental computational prioritization framework for natural-product small- molecule-recognizing proteins and offers candidate resources for subsequent protein expression, in vitro binding validation, active-constituent enrichment, and development of small-molecule biorecognition tools. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/743643v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@8fe6c2org.highwire.dtl.DTLVardef@176cef2org.highwire.dtl.DTLVardef@10c8201org.highwire.dtl.DTLVardef@2b28cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

Accurate assessment of ligand coordinate-density consistency across different resolutions remains challenging in macromolecular crystallography. We introduce the atomic Box Correlation Coefficient (aBCC), an atom-level metric for evaluating the consistency between ligand atomic coordinates and electron density in a resolution-standardized framework. To predict aBCC values from electron-density maps, we developed QAEmap, a machine-learning model based on three-dimensional convolutional neural networks (3D-CNNs). The model was trained using Fourier-truncated electron-density maps and corresponding ligand coordinates generated from high-resolution structures in the Protein Data Bank. It was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures. was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures.The prediction accuracy gradually decreased with decreasing resolution, but remained reliable up to [~]3.5 [A]. These results demonstrate that aBCC enables resolution-standardized atom-wise evaluation of coordinate-density consistency across different resolutions and provide a foundation for further development and refinement of machine learning-based coordinate validation. SynopsisWe introduce the atomic box correlation coefficient (aBCC), a machine learning-based metric for the resolution-standardized atom-level evaluation of ligand coordinate-density consistency in crystallographic structures. aBCC provides a common framework for assessing and communicating the local coordinate reliability between structural biologists and researchers in structure-based drug discovery.

Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in clinical scans. In particular, many tools underperform in infant scans due to morphological and physical changes such as rapid myelination. Here, we introduce ClinSeg: a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast. We leverage existing registration and synthetic data generation tools to construct a training corpus for a 3d U-Net spanning anatomical and contrast diversity, including scans with morphological abnormalities from a pediatric hospital. Validated against manual segmentations, ClinSeg outperforms existing models in infancy while matching them in childhood and adolescence. Finally, ClinSeg enables the construction of reference brain growth trajectories in 11,699 individuals from 0-21 years of age, leading to the detection of more nuanced age-related findings in clinical groups.