Biology subjects

Ge, S.

Publications and source records attributed to Ge, S..

At least 19 recordsLinked to original sources

bioRxiv2026

An information-borrowing Bayesian method for inferring high-dimensional population-level brain connectivity from small samples

Estimating population-level dependence between brain regions is challenging when the number of regions greatly exceeds the number of individuals. We introduce an information-borrowing generalized Bayesian method for high-dimensional covariance estimation from region-level brain measurements. A divide-and-conquer parameterization separates marginal variances from correlation calibrations: an empirical \emph{pilot} correlation matrix--by default, obtained by standardizing a sample covariance matrix (SCM)--provides a reference structure that is adaptively calibrated through region-specific parameters shared across pairwise correlations, reducing the number of free parameters from $\mathcal{O}(d^2)$ to $\mathcal{O}(d)$ ($d$ is the number of regions) while guaranteeing a positive-definite covariance through the inclusion of a ridge term. Horseshoe priors on the calibration parameters enable selective borrowing and shrinkage of pilot information. Under suitable conditions on pilot accuracy, we establish theoretical guarantees for covariance estimation while retaining scalability in ultra-high-dimensional settings via a low-rank likelihood representation and a Woodbury-accelerated Hamiltonian Monte Carlo algorithm. Simulations showed lower estimation error than the SCM, bagging and iteratively refined principal orthogonal complement thresholding (POETRY). Applications to a semi-synthetic cortical-thickness and ADHD resting-state fMRI data revealed significant group differences in the default-mode--visual network and somatomotor network--limbic network blocks after Benjamini--Hochberg correction.

neuroscience↗

bioRxiv2026

Mechanistic Decoding of Pharmacodynamic Intratumoral Heterogeneity: A Pan-Cancer Regulatory Framework

Intratumoral heterogeneity (ITH) drives therapy failure through divergent drug response propensities among coexisting subclones - a phenomenon we term pharmacodynamic intratumoral heterogeneity (PharmITH). While single-cell technologies have catalogued transcriptional heterogeneity, which molecular programs shape differential drug responses remains unresolved. Here we decode PharmITH by linking single-cell transcriptomes from 1,279 samples across 24 cancer types with drug-response profiles for 1,218 compounds. We identify 425 hub genes consistently associated with predicted treatment-response heterogeneity and with potential therapeutic relevance and perform multi-omics network analysis in lung adenocarcinoma (LUAD) integrating transcriptional regulation, somatic mutations, cell-cell communication (CCC), and alternative splicing. This analysis identifies four classes of candidate regulatory associations observed in the LUAD case study: 3 transcription factors establishing pre-existing drug-response programs, 41 genomic alterations amplifying pathway signaling through intermediate networks, 5 ligand-receptor pairs coupling microenvironmental cues to intracellular circuits, and 8 genes producing opposing isoforms via differential splicing. These findings converge on 6 core pathways, revealing that PharmITH arises from tumor-intrinsic mechanisms. Experimental validation and an online platform PharmITH for pan-cancer therapy enable state-resolved drug-response analysis and candidate-gene exploration. This approach transforms ITH from descriptive phenotyping to mechanistic dissection, providing actionable targets to overcome therapy resistance.

cancer biology↗

bioRxiv2026

Deficiency in MICOS component Chchd3 Compromises Drosophila Heart Function via mitophagy, ROS and ER Stress

A pair of paralogs, Chchd3 and Chchd6, two components of mitochondrial contact site and cristae organizing system (MICOS), have been identified to be candidate pathogenetic genes in congenital heart disease (CHD). Previous research found that knockdown (KD) of the single Chchd3/6 (Chchd3) gene and other MICOS components in Drosophila impaired heart function, likely due to a deficit in mitochondrial organization, ATP production, actomyosin levels, and thus severely diminished contractility. However, the underlying mechanisms of how MICOS deficiency leads to these defects are not clear. Here, we performed genetic manipulations in the Drosophila heart to probe for possible interactions between MICOS-compromised mitochondria and other organelles and processes. We found that moderate reduction in Pink1/parkin-mediated mitophagy synergistically aggravated cardiac Chchd3 KD phenotypes, indicating a major interaction. Further, Chchd3 KD increased the level of reactive oxygen species (ROS) and endoplasmic reticulum (ER) stress. Interestingly, KD of catalase (CAT) also elevated cardiac ROS levels, but surprisingly did not compromise contractility either by itself or in combination with Chchd3 KD to aggravate the cardiac phenotype. However, CAT overexpression (OE) in Chchd3 KD hearts restored contractility, but only partially, even though elevated ROS due to Chchd3 KD was fully normalized. Similarly, counteracting ER stress by overexpressing Xbp1 (or spliced mouse Xbp1) also partially rescued the heart function defects induced by Chchd3 KD. Overall, these data indicate a critical role of mitophagy and ER/oxidative stress in cardiac homeostasis involving Chchd3, which suggests that deficiency of MICOS function contributes to heart dysfunction via multiple stress responsive pathways.

genetics↗

bioRxiv2026

Phenotype-driven de novo molecular design from gene expression signatures

AO_SCPLOWBSTRACTC_SCPLOWTarget-based and structure-guided drug design remain central to modern drug discovery, but complementary strategies are needed when predefined targets or binding pockets do not fully capture disease biology. Gene-expression signatures provide scalable system-level readouts of disease and perturbation states, making them attractive inputs for phenotype-guided molecular design. However, preserving phenotypic information during molecular generation remains challenging, and chemically plausible molecules may lose connection to the intended biological response. Here, we present Tx2Mol, a transcriptome-guided framework that translates gene-expression signatures into candidate molecules while maintaining biological guidance throughout generation. We evaluated Tx2Mol across three biological settings: bulk gene perturbation, single-cell perturbation, and patient-derived disease signatures; and three validation dimensions: chemical plausibility, structural compatibility, and phenotypic preservation. Across 10 cancer-relevant bulk gene-perturbation benchmarks, Tx2Mol outperformed 9 transcriptome-guided baselines, improving maximum Tanimoto similarity to known ligands by 24.10% on average and by 50.67% on HDAC1. Structure-based analyses further supported structurally novel candidates with favorable predicted target binding. Tx2Mol also generalized to noisy single-cell perturbation profiles and preserved drug-induced transcriptional responses through in silico drug-perturbation validation. Patient-derived disease signatures further guided molecular generation toward approved-drug chemical space. Together, these results support gene-expression phenotypes as actionable guidance signals for phenotype-directed molecular design and candidate prioritization.

bioinformatics↗

bioRxiv2026

An Integrated Framework for Transcriptomic Characterization and Lorentzian Hyperbolic Visualization of a High-Risk Topological Branch in Alzheimer's Disease

AO_SCPLOWBSTRACTC_SCPLOWAlzheimers disease (AD) is a highly heterogeneous brain disorder in which molecular alterations vary across brain regions, disease stages, and patient subgroups. This study introduces an integrated analytical framework for characterizing transcriptomic variation associated with a high-risk topological branch, which was identified based on Lorentz distance in postmortem Brodmann area 36 samples from the Mount Sinai Brain Bank cohort, where over 70% of samples were in Braak stages V--VI. The framework integrates weighted gene co-expression network analysis, repeated stability-based differential expression analysis, network-level gene filtering, Gene Ontology enrichment, and nested stratified cross-validation to evaluate whether topological branch-associated genes capture biologically meaningful signals and carry predictive information for high-Braak group status. The identified gene sets were functionally enriched for neuronal development, neuron projection organization, synaptic signaling, vesicle fusion, and regulated synaptic release, suggesting that the high-risk topological branch reflects biologically relevant transcriptomic programs linked to neurodegenerative progression. Nested cross-validation further showed that the selected genes achieved measurable internal predictive performance for distinguishing high-Braak samples. As a second methodological contribution, we introduced a Lorentzian hyperbolic variant of t-distributed stochastic neighbor embedding (Lorentz t-SNE) to explore latent non-Euclidean structure in transcriptomic data. This method embeds samples in hyperbolic space, providing an alternative to Euclidean embeddings for representing hierarchical or nonlinear structures. Compared with conventional Euclidean embeddings, the proposed Lorentz t-SNE revealed a more localized organization of high-Braak samples. Together, these results demonstrate the utility of the proposed analytical framework and Lorentz t-SNE for investigating heterogeneous, potentially non-Euclidean organization in AD transcriptomes.

bioinformatics↗

bioRxiv2026

Helminth infection induces RELMa-dependent adipose tissue transcriptional reprogramming and protection against diet-induced obesity

Obesity, a rising epidemic in the United States, is causally associated with adipose tissue inflammation, therefore targeting this immune dysfunction offers promising therapeutic avenues. Chronic helminth infection can prevent obesity pathogenesis but there remain opportunities to determine the immune factors that mediate protection and whether long-term protection is associated with changes in the visceral adipose tissue. We optimized a mouse model of western diet-induced obesity and investigated if transient helminth infection was protective through the immunoregulatory protein RELM. Wild-type (WT) or RELM knockout (KO) C57BL/6J mice were fed a western diet of high glucose and high fat followed by infection with helminth Nippostrongylus brasiliensis, which infects the lung and small intestine but is cleared within two weeks. Infection attenuated weight gain and improved glucose tolerance even after the parasite was expelled in WT but not RELM deficient mice. This protection was associated with reduced adipocyte hypertrophy in WT mice. Adipose tissue bulk RNA sequencing and digital cell quantification indicated that RELM promoted enrichment of eosinophils and M2 macrophages and upregulated pathways associated with fatty acid oxidation, mitochondrial function, and thermogenesis, whereas RELM deficiency led to pro-inflammatory, fibrotic, and lipid accumulation transcriptional profiles. Specific genes that were changed with infection and RELM deficiency included collagen and serpin genes associated with tissue remodeling and fibrosis, and changes within the adipose tissue was confirmed by immunofluorescent staining. Together, these findings establish that helminth-induced RELM critically protects against western diet-driven obesity and associated adipose transcriptional reprogramming and tissue remodeling.

immunology↗

bioRxiv2026

Extra-lineage tissue programs define the transcription states of human pancreatic cancer

Cancers acquire alternate transcriptional states as they evolve, but the origins, timing and determinants of this plasticity are poorly understood in many tumours. We investigated the transcriptional states of pancreatic cancer by integrating [~]1000 tumour-enriched genomes and transcriptomes from 464 patients combined with scRNA-seq, multiome profiling, and spatial proteomics. Four epithelial states covering the spectrum of lineage plasticity were identified (Classical-1, Classical-2, Basal-1, Basal-2). Comparing these states to normal and pan-cancer human single cell atlases showed each state reflects distinct tissue programs found in other malignancies. Single cell analysis uncovered that the main transcription state of this disease (Classical-1) emerges before KRAS mutations. Spatial proteomics from patients and cancer-free donors showed that the Classical-1 program emerges during acinar-to-ductal metaplasia, and also unexpectedly, in normal ducts without disrupting their morphology. Overall, these findings link the extensive lineage plasticity potential of this organ to the origins of the transcriptional states.

cancer biology↗

bioRxiv2026

PROTOTYPE-BASED CONTINUAL LEARNING FOR SINGLE-CELL ANNOTATION

Large-scale single-cell atlases provide an increasingly comprehensive view of cellular diversity, but their continued expansion across studies poses a fundamental challenge: preserving consistent cell identities while capturing biological variation in cellular states. Most existing annotation frameworks are built around static references, making it difficult to incorporate newly generated datasets into established cellular representations without retraining on historical data. When updated sequentially, these methods are further constrained by catastrophic forgetting and batch-specific biases, limiting scalability and the continuity of knowledge integration. Here we introduce scEvolver, a continual learning framework for single-cell annotation that incrementally accumulates knowledge through memory-guided refinement of cell-type prototypes without revisiting historical data. Across sequencing platforms, tissue contexts and molecular modalities, scEvolver supports robust annotation and external query mapping with substantially fewer labelled reference cells. By preserving consistent cell-type semantics across datasets while capturing biologically meaningful within-class heterogeneity, scEvolver enables the identification of epithelial cell-state transitions in inflammatory gut disease. External mapping to the healthy Human Lung Cell Atlas further reveals shared cell-state deviations across multiple diseases, including an FCGR3A+ inflammatory monocyte programme in sarcoidosis, chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis, highlighting scEvolvers potential to characterize context-specific cellular dynamics in complex disease settings.

bioinformatics↗

bioRxiv2026

Integrated Multi-omics Analysis Identifies Molecular Signatures of Regular Physical Activity in Human Peripheral Blood

Regular physical activity is well established to protect against metabolic disorders and bolster immunity; yet, the underlying molecular and cellular mechanisms remain incompletely understood. We integrated plasma metabolomics and lipidomics with single-cell transcriptomic and chromatin accessibility profiles to decode the systemic impact of physical activity on human immunity and metabolism. Our data reveal that regular physical activity is linked to a coordinated metabolic signature marked by enhanced fatty acid oxidation and antioxidant defense. In circulating immune cells, regularly active individuals exhibited synchronous enhancement at both the chromatin accessibility and transcriptional levels for antigen presentation-related genes in antigen-presenting cells (APCs), particularly in classical monocytes, naive B cells, and switched memory B cells. Meanwhile, cytotoxic programs in CD8+ cytotoxic T and mature NK cells showed epigenetic pre-activation of effector function regulators. Intercellular communication analysis further revealed that regular exercise enhanced MHC-I/II signaling between APCs and T cells and suppressed inflammatory signaling networks. Together, these findings elucidate molecular mechanisms underlying the health benefits of regular exercise and offer a theoretical basis for enhancing public health and preventing chronic diseases.

bioinformatics↗

bioRxiv2025

Characterization of adult hippocampal neurogenesis in the novel AppSAA Knock-in Alzheimer's disease mouse model

Adult hippocampal neurogenesis (AHN) declines with age and is thought to be severely exacerbated in neurodegenerative disorders like Alzheimers disease (AD). Despite numerous efforts to understand how AHN is altered in AD mouse models, results have been inconsistent, largely due to limitations of first-generation transgenic AD mouse models. The newly developed App Knock-in models address many of these limitations. Here, we provide the first in-depth characterization of hippocampal cell populations and AHN across different ages in the novel AppSAAKnock-in (AppKI) mouse model. Our findings reveal that AppKI mice show no early deficits in excitatory dentate granule cells or inhibitory interneurons at 2 and 4 months, but significant loss of both populations emerges by 6 months of age. We also identified a progressive decline in AHN with the survival of newborn neurons being impaired first at 4 months, followed by a deficit in proliferation at 6 months. Furthermore, we demonstrate that exposure to enriched environment, a form of hippocampus-engaged exploration, robustly enhances AHN in AppKI mice, primarily by promoting survival. In conclusion, our study provides a foundational characterization of the AppKI model, establishing a timeline for cellular and AHN deficits.

neuroscience↗

bioRxiv2025

Homodimerized Arpins: Binding to Arp2/3 Complexes with Positive Synergy to Inhibit Lamellipodia-Dependent Tumor Metastasis

The Arp2/3 complex is a central regulator of actin polymerization dynamics and cancer cell motility, with its dysregulation implicated in metastasis and therapeutic resistance. Despite its therapeutic potential, targeting Arp2/3 remains challenging due to the lack of precise molecular insights into endogenous inhibitors. Arpin, a lamellipodia-localized Arp2/3 inhibitor, has emerged as a critical modulator of cancer migration; however, the structural and mechanistic basis of its inhibitory function remains unresolved. Here, we report the 1.65-[A] crystal structure of Arpins N-terminal globular domain, revealing a unique structural fold distinct from all known Arp2/3-binding factors. Structural and biophysical analyses demonstrate that Arpin forms homodimers via a conserved interface, which is essential for cooperative inhibition of Arp2/3-dependent actin polymerization. Functional studies in cancer models reveal that Arpin homodimerization potently suppresses lamellipodia-driven migration and invasion, effects further enhanced by engineered dimeric constructs or artificially designed dual-tailed peptides. These findings elucidate a structural basis for Arpin-mediated suppression of cancer cell motility and identify homodimeric cooperativity as a druggable mechanism for targeting metastatic progression. Our work provides a molecular framework for designing Arpin-inspired inhibitors to counteract therapeutic resistance and metastasis in cancers reliant on Arp2/3-driven actin dynamics.

cancer biology↗

bioRxiv2025

Single and Multiple Buds Form characteristics in Amorphophallus muelleri

This study compared the physiological characteristics and transcriptomic profiles of single-budded and multi-budded corms, as well as developmental stages of foliar bulbils in Amorphophallus muelleri, to reveal regulatory mechanisms underlying bud development. Hormonal analysis showed that the IAA/CTK ratio was significantly higher in single-budded corms (1.9060 vs 1.5753), while multi-budded corms exhibited higher CTK (0.2766 g/g), BR (0.8692 p mol/g), and SLs (0.5090 ng/g) levels. During foliar bulbils development, ABA content peaked at 41.54 ng/g in the G2 stage, and sucrose reached maximum levels (6.86 mg/g) in the G1 stage. Transcriptomic sequencing identified 742 differentially expressed transcription factors, with over 80% of CRE1 and AHP genes in CTK signaling pathways significantly upregulated. KEGG pathway analysis highlighted starch/sucrose metabolism (215 DEGs) and plant hormone signal transduction (243 DEGs) as key regulatory networks. The study demonstrated that CTK signaling activation and carbohydrate metabolic reprogramming promoted multi-bud formation, while foliar bulbils development displayed stage-specific gene expression patterns, providing theoretical insights for improving konjac propagation coefficient.

physiology↗

bioRxiv2024

The Glutamatergic Projection from the Substantia Nigra Pars Reticulata to the Dorsal Raphe Nucleus Facilitates Social Hierarchy in Mice

Social hierarchy constitutes a fundamental organizational characteristic among various social species, significantly influencing individual survival, health, and reproductive success within these societies. Neurons in the substantia nigra pars reticulata (SNr) exhibit extensive connectivity with the dorsal raphe nucleus (DRN), a critical structure implicated in social interaction, reward processing, and the establishment of social rank. However, the specific neuronal types within the SNr, as well as the associated neural circuits that regulate social dominance, remain inadequately characterized. This study aims to elucidate the crucial role of SNr glutamatergic neurons in the establishment and maintenance of social hierarchy in mice. Employing fiber photometry, we observed that the activation of SNr glutamatergic neurons increased during the initiation of effortful behaviors in the tube test. Further investigations revealed that optogenetic activation or chemogenetic inhibition of the glutamatergic neural circuit connecting the SNr to the DRN induced upward or downward shifts in social ranks, respectively. Additionally, our findings indicate that the activation of SNr glutamatergic terminals in the DRN reduces anxiety levels in mice. Collectively, these results underscore the critical role of the glutamatergic pathway from the SNr to the DRN in regulating social hierarchy. This work enhances our understanding of the functions of SNr glutamatergic neurons in both physiological contexts and neurological disorders.

neuroscience↗

bioRxiv2024

A Multi-Modal Genomic Knowledge Distillation Framework for Drug Response Prediction

Precision oncology utilizes genomic data to tailor treatment to individuals. Cancer drug sensitivity studies can predict the response levels of different drugs for the same cultured cancer cell line, which is beneficial for personalized medicine. Recent studies have demonstrated that integrating multi-modal genomic data, e.g., gene expression, mutation, copy number alteration, methylation, can provide comprehensive knowledge and improve drug response prediction. Although multimodal genomic profiles are generally available from public datasets, only gene expression data is commonly used in clinical settings. In this study, we propose a framework for privileged information knowledge distillation to transfer knowledge from a multi-modal genomic teacher network, using only gene expression for inference. Specifically, we train a teacher network by feature re-weighting based on inter-modality dependencies and align the inter-sample correlations through our proposed relation-aware differentiation distillation. Experiments on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset demonstrate that our framework improves drug response prediction by about 6% compared to the baseline and outperforms state-of-the-art methods. Transferable studies performed on missing GDSC data and clinical datasets further confirm the feasibility of our model for predicting drug responses using only gene expression data.

bioinformatics↗

bioRxiv2024

Decoding multicellular niche formation in the tumour microenvironment from nonspatial single-cell expression data

Accurate inference of granular cell states that co-occur within the tumour microenvironment (TME) is central to defining pro- and anti-tumour environments. Here, we describe how ecosystems of cell populations can be robustly inferred from nonspatial single-cell RNA-seq (scRNA-seq) atlases. Leveraging a unique discovery-validation setup across eight scRNA-seq datasets profiling pancreatic ductal adenocarcinoma (PDAC), we show highly consistent co-occurrence of fine-grained cell states across patients and characterize the positive predictive value of such analyses. Building on this, we develop a novel probabilistic model to quantify multi-cellular ecosystems directly from such atlas-scale scRNA-seq datasets. By mapping these ecosystems to spatial transcriptomics data, we demonstrate that such ecosystems represent bona fide spatial variation within individual tumours despite being learned from nonspatial data. Importantly, through mapping these ecosystems across two large clinical cohorts, we show they are more predictive of therapy response than any individual cell state and are associated with specific tumour somatic mutations. Together, this work lays the foundation for inferring reproducible multicellular ecosystems directly from large nonspatial scRNA-seq atlases.

bioinformatics↗

bioRxiv2024

SASH1 impairs melanin synthesis and metastasis by down-regulating the TGF-β signaling pathway

Dyschromatosis universalis hereditaria (DUH) is a rare genetic dermatosis characterized by widespread hyperpigmentation and depigmentation. In our previous study, we identified SH3 domain-containing protein 1 (SASH1) mutations associated with the DUH phenotype in Chinese families and predict SASH1/THBS1/TGF-{beta}1 signaling to mediate melanin production and melanocyte transport. We detected SASH1/THBS1/TGF-{beta}1 pathway genes expression and evaluated cell phenotypes and melanin synthesis in A375 and PIG1 cell lines by regulating the expression of SASH1 and THBS1 respectively. Finally, we validated the results of the cellular experiments in nude mice. Results showed that SASH1 inhibited the proliferation, migration, invasion, EMT ability and melanin synthesis via TGF-{beta}1 signaling, and THBS1 reversed the elevation of TGF-{beta}1 caused by SASH1 knockdown. We demonstrated that SASH1 further inhibits TGF-{beta}1 through its regulatory effect on THBS1, thereby inhibiting melanin synthesis and metastasis, which may advance the utilization of TGF-{beta}1 for therapeutic purposes. Plain Language SummaryDyschromatosis universalis hereditaria(DUH) is a genetic disease showing uneven pigment pattern, which greatly affects the appearance. In our previous study, we had reported a SASH1 mutation in a Chinese DUH pedigree. In this study, we mainly focus on the effect of SASH1 gene on pigment synthesis and metabolism at the cell and animal level. The results show SASH1 inhibits the melanin synthesis of melanocytes, it can also hinder cell migration, invasion, and EMT. More than that, SASH1 reduced TGF-{beta}1 mRNA, protein expression and promoter activity, and THBS1 could discharge this effect. In mice, SASH1 inhibits the tumor growth via TGF-{beta}1 signaling. We detected a novel SASH1/THBS1/TGF-{beta}1 pathway in cell phenotypes and melanin synthesis.

cell biology↗

bioRxiv2024

MetaSCDrug: Meta-Transfer Learning for Single-Cell-Level Drug Response Prediction from Transcriptome and Molecular Representations

ObjectiveSingle-cell pharmacogenomic data is crucial for identifying biomarkers and understanding resistance mechanisms. However, the limited availability of those data poses significant challenges for efficient pre-training and thus hinders the generalization capability of the model. This paper aims to develop an efficient predictive model for single-cell drug response inference by leveraging knowledge from a bulk dataset. MethodsTo translate knowledge from bulk cell lines to single-cell analysis, this paper proposes a meta-pretraining and few-shot transfer learning frame-work based on pharmacogenomic embeddings. To enhance feature representation and alignment, genomic information is processed by a position-based feature extraction network to extract contextual features. Simultaneously, a graph-aware Transformer is developed to capture interatomic relations for drug information representation. Underlying this framework, key drug action pathways can be identified at the cellular level through the proposed gene gradient attribution algorithm. ResultsThis model integrates drug response data from 223 drugs across 14 tissues for meta pre-training, followed by transfer and testing on seven single-cell datasets. In comparison to other models, the proposed framework achieves an average accuracy increase of 4.58% for pre-trained drugs and demonstrates a 20% improvement in generalization performance for previously unseen drugs. Case studies further illustrate its capability to differentiate between resistance genes. ConclusionThe proposed framework exhibits enhanced generalization capabilities in predicting cellular responses across multiple drugs, including unseen drugs. This model serves as an effective tool for investigating drug action pathways and elucidating resistance mechanisms from single-cell insights, with significant implications for guiding clinical medication practices.

bioinformatics↗

bioRxiv2024

Five-layered robot (F-robot) based on functionally graded multilayer foam antiviral low-carbon (F-MAX) system for Pathogenic Microorganism Elimination

In this study, a revolutionary air filtration technology, the F-MAX multilayer composite plate, is introduced, offering high efficiency and environmental sustainability. This innovative system is designed to capture a wide range of pollutants, including harmful viruses and bacteria, enhancing air quality significantly. The F-MAX combines multiple layers, each tailored to target specific particles, with features like an electrostatically charged melt-blown fabric and eco-friendly materials like lithium brine by-product magnesia. Its durability, antiviral, and antibacterial properties make it a sustainable choice for air purification, suitable for both commercial and residential use. This system represents healthier living environments, effectively removing airborne contaminants, and demonstrating a commitment to a sustainable future. Additionally, the study introduces the F-robot specifically designed for laboratory environments to ensure pristine air quality. One-Sentence SummaryT-robot air filter, which use F-MAX, a multilayer composite consisted of self-healing cellular coating, Desert Rose (DR) coating, melt-blown cloth, and BMSC with high-efficiency, environmentally sustainable filtration, and antiviral properties, suitable for diverse environments.

microbiology↗