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Li Ma

Publications and source records attributed to Li Ma.

12 recordsLinked to original sources
PubMed2026

SCAN: A sample-to-answer cross-priming isothermal assay for on-site virus detection with RT-qPCR sensitivity and genomically similar virus differentiation specificity.

Genomically similar viruses often differ in pathogenicity and host tropism due to specific mutations, and failure to distinguish them risks misdiagnosis and ineffective control. Molecular methods can differentiate such viruses but require laboratory settings and skilled personnel, while field-deployable immunological methods suffer from cross-reactivity. To address this challenge, we developed SCAN (Sample-to-answer Cross-priming isothermal amplification Assay with Nucleic acid strip), a general framework for on-site detection of genomically similar viruses. Comparative bioinformatics of isolation and sequencing data identifies key conserved differential determinants for primer design, ensuring specificity and reducing non-specific amplification. A one-tube cross-priming isothermal amplification (CPA) enables rapid target amplification without thermal cycling, and the products are visually detected on a nucleic acid strip. All steps are integrated into a handheld, lightweight device (9.9&#x202f;&#xd7;&#x202f;4.4&#x202f;&#xd7;&#x202f;3.3&#x202f;cm, <200&#x202f;g) that also prevents aerosol contamination. Using transmissible gastroenteritis virus (TGEV) and porcine respiratory coronavirus (PRCV), the latter a natural mutant of TGEV, as a model, SCAN achieves a detection limit of 102 copies/&#x3bc;L with sensitivity comparable to RT-qPCR and supports sample-to-answer testing within 80&#x202f;min and simple operations. With verified high sensitivity, specificity, and accuracy, as well as field usability, SCAN provides a generalizable route for developing point-of-care tests (PoCT) that require precise field differentiation of closely related pathogens.

Cross-priming isothermal amplification↗
PubMed2026

Failures to maintain CpG-methylation of CoRSIVs in bovine sperm are associated with low sire conception.

In brief: Correlated regions of systemic interindividual epigenetic variation (CoRSIVs) are genomic regions with CpG-methylation patterns that differ between individuals, yet are consistent between tissues, within the same individual. Analyzing two groups of Holstein bull methylomes-nine with a high sire-conception rate (SCR) and nine with a low SCR-we found that a common type of CoRSIVs was significantly associated with reduced SCR and is thus suggested as a biomarker for SCR because it was highly methylated in sperm, but failed to retain hypermethylation in the gametes of males with low SCR. Abstract: Correlated regions of systemic interindividual epigenetic variation (CoRSIVs) are genomic regions with CpG-methylation patterns that differ between individuals, yet are consistent between tissues, within the same individual; therefore, their methylation can be profiled in bodily fluids that are easily obtained, such as blood and semen. Bearing in mind the simple epigenetic profiling of CoRSIVs, we tested whether this type of differentially methylated region (DMR) is associated with bovine fertility. Sequence Read Archive (SRA) meth BLAST was used to estimate CoRSIVs methylation status in 18 healthy, representative, and age-matched Holstein bulls, among which nine had high (H) sire-conception rate (SCR), and the other nine had low (L) SCR (group averages of SCR: 3.3&#x2009;&#xb1;&#x2009;0.6 and -3.8&#x2009;&#xb1;&#x2009;1.8, respectively). This method was also applied to morula and trophoblast SRA methylomes. Analysis with meth BLAST was effective for most (80%) CoRSIVs and showed that CoRSIVs are reprogrammed during blastocyst formation, although this method was incapable of specifically determining the methylation level in CoRSIVs with retrotransposons. In sperm, the effect of global methylation was evident in a common (25%) type of CoRSIVs that is highly (94.5%&#x2009;&#xb1;&#x2009;4.3%) methylated in sperm. Specifically, a failure to retain hypermethylation in the sperm plus strand was significantly (p&#x2009;<&#x2009;0.00025) indicative of low SCR. Comparing global DNA methylation using the latter type of CoRSIVs between sperm and blood can be used as a better biomarker for fertility than using other differentially methylated regions with more complex epigenetics.

Animals↗
PubMed2026

A High-Resolution Stereo-Seq Spatial Transcriptomic Resource for Adult Holstein Cattle Liver.

The bovine liver is a highly compartmentalized organ that plays essential roles in continuous gluconeogenesis and nitrogen recycling; however, its spatial molecular architecture has remained largely uncharacterized due to the limitations of traditional bulk and single-cell approaches. To address this gap, Spatial Enhanced Resolution Omics-sequencing (Stereo-seq) was utilized to generate a subcellular-resolution (500 nm) transcriptomic map of an adult Holstein cattle liver, and a refined reference-guided workflow was implemented to overcome standard annotation limitations in livestock. Raw sequencing data were processed using the Stereo-seq Analysis Workflow and analyzed with Stereopy, Seurat, SingleR, and reference-guided workflows. Spatial aggregation was evaluated at Bin20, Bin50, Bin100, Bin150, and Bin200. Increasing bin size increased molecular identifier counts and detected-gene complexity while progressively reducing spatial granularity. Bin50, corresponding to 50 &#xd7; 50 DNA nanoballs and an approximate nominal footprint of 25 &#xd7; 25 &#xb5;m, was therefore selected as a practical intermediate aggregation level for the primary analyses. Quality-control assessment, Leiden clustering, UMAP visualization, reference-based cell-type annotation, cluster-marker analysis, and spatial mapping of canonical hepatic genes demonstrated preservation of biologically interpretable liver transcriptional organization. Raw sequencing data processed spatial matrices, annotated objects, and analysis code are publicly available to support reanalysis and computational benchmarking. In summary, we present a Stereo-seq spatial transcriptomic resource generated from liver tissue of an adult Holstein cow. This initial resource provides a valuable foundation for future studies of bovine liver biology, comparative genomics, and the spatial basis of livestock health and production traits.

Animals↗
PubMed2026

Influence of Repeated-Sprint Bout Duration in Sprint Interval Training Intervention on Physical Performance Adaptations of Young Volleyball Players.

The objective of this study was to examine the effects of repeated-sprint training (RST) with varying bout durations on the physical fitness adaptations of young male volleyball players. Forty athletes were randomly allocated to one of three intervention groups performing RST with varying bout durations and similar repetition volumes, all executed at maximal effort. The 3-sec group (n = 10) completed two sets of 30 bouts, the 6-sec group (n = 10) performed two sets of 15 bouts, and the 9-sec group (n = 10) carried out two sets of 10 bouts, each adhering to a 1:3 work to rest ratio. An active control group (n = 10) engaged solely in regular volleyball training without the RST intervention. Physical fitness measures-including countermovement vertical jump (CMVJ), 10-m and 20-m linear sprints, T-test change-of-direction speed (T-CODS), reactive strength index (RSI), and the Wingate anaerobic power test-were assessed pre- and post-a 6-week training intervention (i.e., 18 sessions). All RST groups showed significant post-intervention improvements in physical fitness (main effect of time, p = 0.001), with greater adaptations compared with the control group and effect sizes ranging from small to very large. The 3-sec bout group demonstrated greater gains in CMVJ, 10-m and 20-m sprint performance, RSI, and peak power output compared with the 9-sec group (all, p < 0.05). Conversely, the 9-sec group exhibited superior adaptations in T-CODS and mean power output relative to the 3-sec group (all, p < 0.05). In conclusion, the 3-sec group experienced greater enhancements in explosive and sprint performances, while the 9-sec group showed superior gains in change of direction and mean power output. These findings indicate that manipulation of sprint-bout duration in RST can be used to optimize distinct performance adaptations in young volleyball players.

Humans↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMed2026

Exploratory single-nucleus multiomics analysis of myeloid cell states associated with neoadjuvant chemotherapy response in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) continues to be one of the most lethal human malignancies, with the vast majority of patients ineligible for immunotherapy. Tumour-associated macrophages (TAMs) are key regulators of the PDAC tumour microenvironment (TME), yet their transcriptional and epigenomic heterogeneity in the context of chemotherapy response is poorly understood. Therefore, we performed an exploratory single nucleus multiomics analysis of PDAC tumors stratified by histopathologic response to neoadjuvant chemotherapy. METHODS: Surgical resection specimens from PDAC patients were classified as responders or non-responders using the American College of Pathologists (CAP) histopathologic criteria. Frozen tissue underwent simultaneous snRNA-seq and snATAC-seq on the 10x Genomics Chromium Single Cell Multiome platform, followed by downstream analyses such as differential gene expression, GO and hallmark pathway enrichment, pseudotime trajectory inference and ChromVAR transcription factor motif analysis. RESULTS: Multiomics profiling of 30&#xa0;840 high-quality nuclei revealed a myeloid compartment that differed in composition and transcriptional state between CAP-defined responders and non-responders in this small cohort. We observed a trend toward higher LAM-like state proportions in the responders than non-responders (38.4%&#xa0;vs. 26.7%), although this disparity did not achieve statistical significance. The transcriptional programs of the responder myeloid cells are associated with phagocytosis and lipid handling. Chromatin accessibility analysis further suggested candidate response-associated transcription factor motif accessibility patterns. CONCLUSIONS: Neoadjuvant-treated PDAC tumours from CAP-defined responders in this cohort myeloid landscape with apparent enrichment of LAM-like states and immune-activating transcriptional/epigenetic programs. However, these findings are preliminary and hypothesis-generating because of the small cohort size, heterogeneous treatment regimens, absence of matched pre-treatment biopsies, and lack of knockout validation. Larger treatment cohorts and functional/mechanistic studies are needed to determine whether LAM-like myeloid programs contribute to chemotherapy response or reflect a consequence of chemotherapy treatment.

Humans↗
PubMed2026

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction.

Nonalcoholic fatty liver disease (NAFLD) is a complex condition influenced by metabolic and genetic factors, yet the shared genetic architecture underlying its progression remains poorly understood. The aim of this study was to employ genomic structural equation modeling (GSEM) to elucidate the genetic architecture linking NAFLD with key metabolic traits-including insulin resistance, body mass index (BMI), hemoglobin A1c (HbA1c), and liver fibrosis using summary statistics from large-scale genome-wide association studies. By harmonizing 2.18 million variants across five genome-wide association studies (GWAS) datasets, we identified 134 genome-wide significant loci that mapped to 24 genes. GSEM revealed a latent genetic structure composed of two distinct dimensions: a metabolic regulation factor primarily driven by insulin resistance, BMI, and HbA1c; and a structural pathology factor specifically associated with liver fibrosis. These factors explained 65.5% and 78.1% of the genetic variance in BMI and fibrosis, respectively, with minimal correlation (rg = 0:07), indicating their genetic distinctness. Additionally, integrating Mendelian randomization with liver transcriptome profiling, we characterized how the 24 genes contribute to disease and identified mitochondrial glycerol-3-phosphate acyltransferase (GPAM) as the key gene that causally links lipid metabolism to fibrogenesis. In conclusion, we present the first genetically grounded mechanism for the progression of NAFLD to fibrosis. This mechanism encompasssses genetic variants, dysregulated gene expression, metabolic disturbances, and the processes involved in fibrotic remodeling. This research establishes a genetic framework for understanding the pathogenesis of NAFLD and highlights novel therapeutic targets for intervention.

Non-alcoholic Fatty Liver Disease↗
PubMed2025

Resistance Gene-Guided Discovery of a Fungal Spirotetramate as an Acetolactate Synthase Inhibitor.

Biosynthetic gene clusters (BGCs) of bioactive natural products occasionally encode resistant versions of the proteins they inhibit, offering opportunities for resistance gene-guided genome mining to uncover natural products with predictable modes of action. In this study, we developed a genome mining tool designed to identify fungal BGCs harboring putative resistance genes. Applying this tool to approximately 2500 fungal genomes, we identified a BGC designated as the pts cluster, which encodes an acetolactate synthase (ALS) homologue. Functional characterization of the pts cluster resulted in the identification of pterrespiramide A (1), featuring unique spirotetramate and cis-decalin moieties. Consistent with the predicted activity, 1 was confirmed as an ALS inhibitor and exhibited both antifungal and herbicidal activities. This study illuminates the potential of resistance gene-guided genome mining as a powerful strategy for accelerating the discovery of previously undescribed bioactive natural products.

Acetolactate Synthase↗
PubMed2025

Chromosome-level genome assembly of Ampulex clypecomplana Chen & Li (Hymenoptera: Ampulicidae).

Ampulex clypecomplana Chen & Li, 2010 (Hymenoptera: Ampulicidae) is an important predatory insect in Hymenoptera. However, molecular information about this predatory insect is currently limited. In this study, we employed ONT long-read sequencing, MGI-SEQ short-read sequencing, Hi-C sequencing and transcriptomic data to assemble the high-quality genome of A. clypecomplana. The genome assembly length was 338.43&#x2009;Mb, with a Scaffold N50 length of 19.05&#x2009;Mb. Our BUSCO analysis further confirmed the gene coverage completeness of the genome assembly to be 99.2%. Phylogenetic analysis indicated that A. clypecomplana appeared approximately 132 million years ago. We annotated 110.75&#x2009;Mb of repetitive sequences, accounting for 32.72% of the entire genome. In A. clypecomplana, we identified 180 gene expansions and 1029 genes that underwent contraction or loss. The high-quality genome of A. clypecomplana provides a valuable genetic resource for future research in evolution, molecular biology, and applied studies.

Animals↗
PubMed2016

Design and synthesis of a minimal bacterial genome.

We used whole-genome design and complete chemical synthesis to minimize the 1079-kilobase pair synthetic genome of Mycoplasma mycoides JCVI-syn1.0. An initial design, based on collective knowledge of molecular biology combined with limited transposon mutagenesis data, failed to produce a viable cell. Improved transposon mutagenesis methods revealed a class of quasi-essential genes that are needed for robust growth, explaining the failure of our initial design. Three cycles of design, synthesis, and testing, with retention of quasi-essential genes, produced JCVI-syn3.0 (531 kilobase pairs, 473 genes), which has a genome smaller than that of any autonomously replicating cell found in nature. JCVI-syn3.0 retains almost all genes involved in the synthesis and processing of macromolecules. Unexpectedly, it also contains 149 genes with unknown biological functions. JCVI-syn3.0 is a versatile platform for investigating the core functions of life and for exploring whole-genome design.

Artificial Cells↗