Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 163 records · Page 9Linked to original sources

bioRxiv2026

Repurposing of the assembly chaperone PAC1-PAC2 defines a specialized proteasome landscape in mature mammalian sperm

Mature mammalian sperm rely on active proteasomes containing the testis-specific subunit 4s for capacitation, acrosomal exocytosis and fertilization, but how these proteasomes are structurally specialized remains unclear. We determine the endogenous proteasome landscape of mature bovine spermatozoa and testis using cryo-electron microscopy. We resolve five sperm proteasome assemblies: PA200-20S, free 20S, a previously unrecognized PA200-20S-PAC1/2 hybrid, and PAC1/2-20S complexes capped at one or both ends. Unexpectedly, the PAC1/2-bound complexes are mature and POMP-free, selectively incorporated for 4s, lack 2, and adopt a PAC1/2 orientation and gate configuration distinct from known assembly intermediates. Together with elevated proteolytic activity, these features indicate that PAC1/2 is repurposed from a transient assembly chaperone into a stable post-assembly regulator in mature sperm. 4s-containing proteasomes assemble through the canonical PAC1-PAC4/POMP pathway yet may follow {beta}2-{beta}3-{beta}1 incorporation ordering; functionally, 4s incorporation remodels the sCP surface and enhances peptidase activities, definining the spermatoproteasomes as a hyperactive 20S variant. PAC1/2-bound proteasomes are absent from testis, where PA200-20S and 26S proteasomes are present, while 26S proteasomes are nearly lost in mature sperm. Our findings define a sperm-specific, ATP-independent proteasome landscape, expand the functional repertoires of 4s and PAC1/2, and provide a structural framework for proteasome specialization in male fertility.

cell biology↗

bioRxiv2026

Hierarchical refinements of cis-regulatory inputs improve scalable gene expression prediction

Deciphering the relationships between cis-regulatory elements (CREs) and target gene expression has long been a challenging problem in molecular biology. However, predicting gene expression from hundreds of candidate cis-regulatory elements (cCREs) requires models that scale to long, noisy inputs while retaining interpretable regulatory structure. Existing Transformer-based approaches typically attend over all nucleotides and all surrounding cCREs, diluting causal signals when hundreds of elements compete for limited model capacity. Here we introduce a two-stage selective framework (TSSF) that performs hierarchical refinements: nucleotide-level masking within each cCRE, followed by cCRE-level selection around each gene, implemented with information-bottleneck priors and a fully Transformer-based architecture. Across 70 human cell types and tissues, TSSF and lightweight variants improve expression prediction and enhancer-gene prioritization relative to strong baselines, including on cross-cell-line and cell-type-specific benchmarks. Prediction-stratified analysis motivates a distance-decay prior that aligns attention with long-range regulatory geometry, and chromatin-contact augmentation improves recovery of distal links. Motif analyses of high-confidence predictions recover proximal and distal regulatory programs, supporting mechanistic interpretability. TSSF offers a general strategy for scalable, interpretable modeling of high-dimensional regulatory inputs in genomics.

bioinformatics↗

bioRxiv2026

FINDER converts zero-background kinetic fingerprinting into area-scalable attomolar biomarker detection

Background constrains analytical sensitivity: surveying larger sensor areas samples more analyte molecules but also accumulates false positives, limiting gains in detection performance. Here we introduce FINDER--Fluorogenic INstantaneous Digital Enumeration and Recognition--a single-molecule platform that combines kinetic fingerprinting with fluorogenic transient probes for rapid molecular classification under near-zero-background conditions. By suppressing both solution and surface-associated background at micromolar probe concentrations, FINDER classifies individual molecules within seconds-scale observation windows per field of view. This regime allows sensitivity to scale with surveyed sensor area, enabling amplification-free quantification of the miRNA cancer biomarker hsa-miR-16 with an 11 aM detection limit. FINDER further generalizes to HPV16 DNA biomarker detection, two-color RNA/DNA co-profiling, and rapid discrimination of clinically relevant EGFR single-nucleotide variants using multidimensional kinetic filtering. Rapid per-field classification permits tens of fields to be surveyed within minutes. By converting kinetic specificity into area-scalable sensitivity, FINDER enables semi-automated attomolar biomarker counting without amplification in practical workflows.

biophysics↗

bioRxiv2026

Immune context unmasks regulatory effects of Neanderthal and Denisovan introgression

Neanderthal and Denisovan introgression have left a pervasive footprint in the human genome, yet its regulatory consequences remain poorly understood. Here we use a massively parallel reporter assay to characterize the cis-regulatory activity of 4,161 high-frequency introgressed variants across respiratory (A549), hepatic (HepG2), and hematopoietic (K562) cells exposed to immune and infectious stimuli. We find that ~18% of variants show differential activity between archaic and modern alleles, including 94 whose effects are revealed or modulated by stimulation, often in a cell type-specific manner. We identify loci, including STAT2, IL23A, and RNF41, where clusters of introgressed alleles exert coordinated regulatory effects consistent with adaptive programs. Finally, we dissect the mechanisms underlying the association between Neanderthal introgression and COVID-19 severity and show that the risk allele rs17713054-A, which displays the strongest effect in our assay, increases activity of a TNF--responsive enhancer in lung epithelial cells, directly upregulating SLC6A20.Together, these findings reveal widespread context-dependent regulatory effects of archaic introgression, with broad evolutionary and biomedical implications.

evolutionary biology↗

bioRxiv2026

Ultrasensitive voltage imaging reveals distinct electrical microdomains in neurons

For the brain to compute, electrical signals must propagate over the membranes of individual neurons, connecting synaptic inputs to synaptic outputs1. Complex neuronal morphologies coupled with the spatial organization of synaptic inputs and outputs enable diverse voltage transformations that underlie cell-type specific computations2,3. However, measuring these transformations in vivo has remained challenging, leaving a crucial gap in our mechanistic understanding of single neuron computation. Here, we develop ASAP7y, a genetically encoded voltage indicator with unprecedented subthreshold sensitivity and expanded excitation compatibility in both mice and flies. We leveraged ASAP7y combined with two-photon random-access microscopy to record sensory stimulus-evoked voltage dynamics with millisecond, subcellular, and subthreshold resolution along the neurites of individual neurons in Drosophila. We found remarkable heterogeneity in voltage propagation across cell-types, delineating a fundamental axis of electrical diversity. Leveraging a nanoscale EM reconstruction of the visual system4, we modeled the electrotonic properties of single neurons spanning 717 cell types, revealing how morphology shapes voltage transformations. Finally, we demonstrate that confined voltage propagation creates substrates for local computation, producing subcellular domains with distinct feature selectivity across multiple cell types. These results provide mechanistic insight into how critical single neuron computations arise and reveal parallel processing in single neurons.

neuroscience↗

bioRxiv2026

Alignment-free phylogenetic inference via hyperbolic protein language models

Conventional phylogenetic methods rely on multiple sequence alignments which are computationally intensive and often fail for highly divergent lineages. Here, we introduce LucaPhylo, an alignment-free framework that infers evolutionary relationships directly from unaligned sequences. Through a cascaded learning strategy LucaPhylo integrates protein language models with hyperbolic geometry, a representation space naturally suited to hierarchical branching, to capture deep evolutionary constraints without explicit homology matching. Using highly divergent RNA virosphere as a test case, LucaPhylo places unaligned sequences into phylogenetic trees with an accuracy comparable to leading alignment-based tree construction tools, while retaining divergent sequences that conventional pipelines frequently discard. It further enables the integration of divergent viral lineages into phylogenetic trees, thereby expanding the evolutionary landscape of RNA viruses. Together, LucaPhylo establishes an AI-driven, alignment-free paradigm for phylogenetic inference and provides a robust computational foundation for resolving deep evolutionary relationships among RNA viruses and other biological systems.

evolutionary biology↗

bioRxiv2026

WTAP-mediated epitranscriptomic program in alveolar macrophages confers prolonged protection against postinfluenza bacterial pneumonia

Secondary bacterial infections remain an intractable problem of seasonal influenza, primarily due to the depletion and defunctionalization of alveolar macrophages (AMs). Here, we described that N6-methyladenosine (m6A), the most prevalent RNA modification in eukaryotes, critically orchestrates trained immunity (TRIM) establishment in AMs. Influenza A virus (IAV)-trained AMs maintained low Wilms tumor 1-associated protein (WTAP) expression and reduced global m6A deposition for over two months. Mechanistically, this IAV-induced m6A decrease promotes TRIM by enhancing the RNA stability of phagocytic and metabolic genes, thereby boosting antibacterial function. Mimicking this m6A reduction pharmacologically or genetically recapitulates the IAV-trained TRIM phenotype, which improves phagocytosis and protects mice from secondary infection. Clinically, elevated WTAP in AMs correlates with impaired phagocytosis and disease severity in COVID-19 and COPD. These findings for the first time unveil how respiratory virus infection shapes AM TRIM via epitranscriptomic reprogramming, and offer prospective strategies for the prevention and treatment of post-viral bacterial complications.

immunology↗

bioRxiv2026

The in vivo inhibitory function of the MHC-I alpha3 domain/CD8alpha interaction

The interaction between the Major Histocompatibility Complex Class I (MHC-I) 3 domain and CD8 has classically been viewed as a positive coreceptor interaction that stabilizes TCR signaling during antigen recognition. However, its physiological function in mature peripheral CD8+ T cells in vivo remains incompletely understood. Here, we identify the MHC-I 3 domain-CD8 interaction as a previously unrecognized inhibitory pathway that tonically restrains peripheral CD8+ T-cell activation and maintains T-cell tolerance in vivo. Antibody-mediated disruption of the MHC-I 3 domain-CD8 interaction induced spontaneous activation of peripheral CD8+ T cells without impairing their survival, lowered the threshold for antigen-induced activation, and enhanced responsiveness to cognate peptide stimulation. In peptide-induced OT-I T-cell anergy models, blockade of either H-2Db or H-2Kb 3 domain interactions with CD8 prevented the induction of anergy and restored responsiveness of previously anergic T cells. Notably, blockade of the H-2Kb 3 domain enhanced OT-I responses despite simultaneously disrupting the classical positive coreceptor interaction within the TCR-peptide-MHC complex, indicating that tonic inhibitory signaling mediated by the MHC-I 3 domain predominates under these conditions. Together, these findings redefine the classical MHC-I-CD8 interaction as a bidirectional pathway that not only supports antigen recognition but also imposes tonic inhibitory control over peripheral CD8+ T cells. These results identify the MHC-I 3 domain-CD8 axis as a potential target for reversing T-cell tolerance and enhancing antitumor or antiviral immunity.

immunology↗

bioRxiv2026

SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization

Proteins function through a complex interplay of structural and biochemical properties, and mutations can reshape these properties to generate fitness landscapes spanning multiple functional objectives. A central challenge in protein engineering is the need to simultaneously optimize multiple properties. In biocatalysis, for example, practical enzyme development routinely requires the concurrent optimization of catalytic activity, selectivity, stability, and substrate generality. However, despite recent advances in computational protein design and fitness prediction, most existing approaches treat these properties independently and do not explicitly capture the dependencies and trade-offs that govern real-world protein performance. We present SynFit, a multi-objective learning framework that integrates pretrained protein language models with experimental fitness measurements for protein fitness prediction and engineering. SynFit learns both shared and property-specific protein sequence representations through a synergistic contrastive learning strategy, enabling the identification of variants that simultaneously optimize multiple functional properties. Across a large-scale multi-fitness deep mutational scanning benchmark, SynFit consistently outperforms state-of-the-art supervised models trained on individual objectives and more accurately identifies variants that balance competing functional constraints. We further applied SynFit to multi-objective enzyme design for a new-to-nature biocatalytic enantioselective borylation reaction, providing a diverse array of novel cytochrome c sextuple variants in a single round of design with simultaneously improved catalytic activity and enantioselectivity that rival the best variants obtained through directed evolution. Together, these results establish SynFit as a general framework for multidimensional protein fitness prediction and highlight its potential to enable efficient multi-objective optimization in protein engineering, particularly in biocatalysis.

bioinformatics↗

bioRxiv2026

SpatialClaw: A Memory-Augmented Autonomous Ecosystem for Spatial Omics Analysis

While the expansion of spatial omics has revolutionized our ability to dissect tissue architecture, the accumulation of incompatible computational methods has heavily fragmented end-to-end analysis, rendering complex workflows irreproducible. Generic conversational agents lack the domain-specific precision necessary to navigate the intricate biological pipelines. To overcome this, we present SpatialClaw, a memory-augmented autonomous ecosystem to unify spatial omics analysis under a single natural-language interaction. SpatialClaw integrates 30 specialized skills, spanning raw data preprocessing, spatial domain identification, deconvolution, spatially variable gene detection, cell-cell communication analysis, multi-sample and cross-modality integration. Distinct from existing agents, SpatialClaw introduces a graph-based persistent memory architecture that stores dataset metadata, analysis lineage, biological insights, and user preferences as versioned nodes and edges across three hierarchical layers (Session, Episodic, and Semantic), governed by a deterministic promotion policy. A Memory-Augmented Reasoning (MAR) Operator bridges the memory store and the main agent, synthesizing retrieved experiences into task-specific guidance for each query. In rigorous benchmarking spanning three memory-sensitive scenarios across 10 spatialomics skills, SpatialClaw outperforms both a standard large language model and the memory-only configuration. Furthermore, we demonstrate its robust biological utility by dissecting the complex tumor microenvironment of a 15-section human triple-negative breast cancer cohort. In merely three conversational turns and with zero direct scripting, SpatialClaw executes a comprehensive end-to-end workflow, yielding standardized output bundles. Ultimately, by synergizing comprehensive analytical tools with structured persistent memory, SpatialClaw elevates spatial omics from disjointed computational stitching to a fully traceable, reproducible, and self-improving discovery ecosystem. SpatialClaw is ready to use at https://github.com/ShangBioLab/SpatialClaw.

bioinformatics↗

bioRxiv2026

Genos-m: a foundation model for human-associated microbial genomes

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations in-corporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and Hugging-Face (https://huggingface.co/BGI-HangzhouAI/Genos-m).

bioinformatics↗

bioRxiv2026

Force-regulated catch bonds and fusion peptide exposure drive coronavirus entry

Coronaviruses invade human cells within dynamic mechanical environments through endocytosis and membrane fusion, both mediated by the class I fusion protein spike. In SARS-CoV and SARS-CoV-2, the spike engages the human ACE2 receptor through a catch bond--an interaction whose lifetime increases under tensile force. Concurrently, mechanical pulling facilitates disruption of the S1/S2 subunits of spike, a critical step for membrane fusion. To elucidate how mechanical cues coordinate these processes, we developed a unified elastic-stochastic model that integrates theoretical analysis and computational simulations to trace viral entry. Our results identify the force-regulated catch bond between spike and ACE2 as a key determinant of successful invasion. This catch bond not only enhances receptor-mediated endocytosis but also increases the probability of S1/S2 disengagement, thereby promoting membrane fusion. Importantly, under conditions of strong catch bonding, the force-accelerated separation of S1 and S2 fine-tunes the balance between entry pathways. These findings uncover a potential mechanobiological mechanism that mediates viral cell entry by coupling receptor binding strength with spike disassembly under force. By characterizing these mechanical regulations, this work facilitates the assessment of emerging viral threats and inspires the design of drug delivery systems that leverage catch-bond kinetics for enhanced targeting.

biophysics↗

bioRxiv2026

Widespread use of invalid statistical tests in biomedical machine learning

Cross-validation is routinely used to compare performance in biomedical artificial intelligence. Standard tests ignore correlation across cross-validation folds, inflating false-positive rates. In a PRISMA-guided meta-analysis of 184 studies (impact factor [≥] 15) across 30 biomedical fields, 97% use invalid tests. Among studies with abstract-level claims supported by invalid tests, 59% rely on a spurious comparison: one that loses significance after correcting for this correlation. On average, regaining significance requires a 43% larger effect. Extending these findings to the broader literature, we estimate that spurious comparisons occur in nearly two in three studies and support abstract-level claims in nearly one in three studies. Simulations confirm false-positive rates paradoxically approach 100% when cross-validation is repeated to improve stability. We introduce SHARP, a redesign of cross-validation, which best balances false-positive control and power among 13 benchmarked tests. These results reveal widespread fragility in biomedical artificial intelligence and offer a practical route to valid comparisons.

bioinformatics↗

bioRxiv2026

Cellular stemness identifies high-risk ductal carcinoma in situ and offers a therapeutic interception opportunity

Ductal carcinoma in situ (DCIS) exhibits substantial heterogeneity in its risk of progression to invasive breast cancer, yet the cellular and molecular determinants of high-risk lesions remain incompletely defined. Using spatially resolved single-cell transcriptomic and epigenomic profiling of 43 patient-derived DCIS and DCIS/invasive ductal carcinoma (IDC) samples, we delineate cellular programs, spatial organization, and epigenetic regulatory mechanisms associated with invasive potential. We identify an epithelial population with stemness features within luminal hormone-responsive (LumHR) cells that progressively expands from benign tissue to DCIS and IDC, and is strongly associated with invasive progression and recurrence-linked transcriptional programs. Spatial mapping reveals discrete DCIS niches enriched for stem-like LumHR cells, characterized by elevated CEACAM6 expression and enhanced ligand-receptor interactions, including CEACAM6-EGFR signaling between epithelial and stromal compartments, including cancer-associated fibroblasts, macrophages (APOC1-positive) and perivascular cells. These niches define a microenvironmental context that supports stemness and invasive potential. Epigenomic analyses implicate FOXA1 as a key regulator of these stem-like transcriptional states. Pharmacologic disruption of FOXA1-regulatory network using LSD1 inhibition suppresses stemness-associated transcriptional programs in vitro and significantly restrains tumor growth in vivo. Collectively, these findings define high-risk DCIS as a stemness-driven disease embedded within specialized microenvironments, and identify associated regulatory networks as candidate biomarkers and therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

A Panel of Human Monoclonal Antibodies for Tracking the Antigenic Evolution of Influenza H5N1 Clade 2.3.4.4b

The ongoing panzootic of clade 2.3.4.4b H5N1 influenza has resulted in widespread infection of birds, mammals, and livestock, underscoring the need for tools to interpret its real-time evolution. Here, we describe the isolation and characterization of a panel of 19 human monoclonal antibodies that potently neutralize current isolates. Competition immunoassays and cryo-electron microscopy analyses revealed their collective near-complete epitope coverage of the H5-hemagglutinin surface. Neutralization profiling across multiple historical and contemporary H5 viruses defined their epitope-specific patterns of virus neutralization. One cluster of antibodies potently neutralized only clade 2.3.4.4b viruses, while many others exhibited broadly neutralizing activity against diverse H5N1 clades. Application of this structurally calibrated antibody panel to recent North American human isolates revealed genotype-specific antigenic divergence between lineages that have spread among cattle (B3.13) and poultry (D1.1). Together, the findings of this study establish a structurally grounded antibody reference panel spanning major vulnerable sites of H5 hemagglutinin and provide a toolbox for interpreting emergent mutations, monitoring ongoing antigenic drift, and anticipating the evolutionary trajectory of circulating H5N1 influenza viruses.

microbiology↗

bioRxiv2026

A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort

Lung neuroendocrine tumours (lung NETs) are rare neoplasms comprising approximately 2% of lung cancers. Recent studies have identified distinct molecular groups based on transcriptome and methylome data, but genomic and morphological features remain underexplored due to limited whole-genome and imaging data. We have generated the largest multi-omic dataset of lung NETs to date (201 participants, for a total of n = 294 tumours), including RNA sequencing, EPIC 850K methylation arrays, and whole-genome sequencing. This multiomic dataset also include multi-regional whole-genome sequencing for 41 participants, allowing for the quantification of intra-tumoural heterogeneity. We additionally generated spatial proteomics (64 participants), spatial transcriptomics (4 participants) and whole-slide histopathology images for 212 cases. This dataset enables a comprehensive characterization of lung NET molecular groups and the identification of group-specific morphological features using deep learning algorithms. All quality control analyses, processed data, and scripts are provided to ensure reproducibility. This dataset is available as a basis for further molecular and morphological analysis of lung NETs, and for future research on multi-scale integration.

bioinformatics↗

bioRxiv2026

Evolutionary history and genomic vulnerability of the extinct giant deer Megaloceros giganteus

The extinct giant deer (Megaloceros giganteus) was one of the most striking megafaunal species of the Late Quaternary, distinguished by its enormous palmated antlers reaching up to 3.5 m across, the largest known among both living and extinct cervids. Despite its iconic status, little is known about its genomic history prior to extinction [~]8 thousand years ago (kya). We generated the first nuclear palaeogenomes for Megaloceros, represented by nine individuals from Germany ([~]40 kya) and Ireland ([~]11 kya), mapped to a new chromosome-level reference genome of the fallow deer (Dama dama). Phylogenomic analyses placed Megaloceros as sister to Dama (divergence [~]3.5 Ma) and revealed evidence of gene flow with ancestral Cervus lineages. Population analyses identified clear differentiation between German and Irish lineages, with higher genetic diversity in the German individuals. Two genes under strong positive selection, BNIPL and SLC10A7, are associated with apoptosis regulation and skeletal development/bone mineralisation, respectively, and may relate to the species large body and antler size. Demographic reconstructions indicate a long-term decline in effective population size, extremely low heterozygosity, little evidence of extensive runs of homozygosity, and an elevated burden of predicted deleterious alleles. Together, these results suggest that Megaloceros entered the terminal Pleistocene in a genomically fragile state, offering new insight into the biology and evolutionary legacy of one of the largest and most distinctive cervids that ever lived.

evolutionary biology↗

bioRxiv2026

Quantitative Analysis of External Urethral Sphincter Stimulation Parameters for Modulating Urinary Output

ObjectiveNeurogenic lower urinary tract dysfunction (NLUTD) impairs bladder control and remains difficult to treat. We aim to define how electrical stimulation (ES) parameters of the external urethral sphincter (EUS) affect urinary leakage thresholds to guide neuromodulation strategies for NLUTD. MethodsWe performed direct EUS stimulation in anesthetized rats using charge-balanced biphasic pulses while systematically varying current amplitude (0.5-3.0 mA), frequency (20-100 Hz), and pulse duration (0.5-3 ms). Urine leakage thresholds were mapped across the multidimensional parameter space. ResultsStimulation parameters exhibited strong nonlinear interdependence in determining leakage onset. At a fixed pulse duration, higher current amplitudes required lower stimulation frequencies to evoke leakage. Increasing pulse duration substantially reduced both current and frequency thresholds. Age and sex caused modest shifts in absolute thresholds but did not alter the fundamental parameter-response relationships. ConclusionPulse duration, current amplitude, and frequency jointly govern urinary leakage thresholds, with pulse duration serving as the dominant modulator of stimulation efficiency. SignificanceThis work establishes a quantitative framework for charge-efficient stimulation parameter selection, enabling the design of energy-aware, precision neuromodulation protocols and implantable systems for NLUTD rehabilitation.

neuroscience↗