Hypothesis Droplets ejected from the host can directly settle on a substrate as fomite. In industrial environments, especially the food processing industries, the components maintained at specific temperatures can act as a substrate, leading to the fomite mode of infection. We hypothesize that substrate temperature influences the desiccation dynamics, bacterial deposition patterns, and bacterial viability and infectivity. Experiments We conducted a novel study on the desiccation behaviour of bacteria-laden droplets on hydrophilic substrates at different temperatures, an area rarely explored. Such studies have been rarely attempted. We analysed bacterial deposition patterns, mass transport dynamics, and viability across various base fluids used in food industry, such as Milli-Q water, LB media, and meat extract. Thermal imaging, confocal microscopy, scanning electron microscopy, atomic force microscopy, and optical profilometry characterized pattern formations, while bacterial viability and infectivity were assessed post-desiccation Findings Our results indicate that substrate temperature significantly affects bacterial deposition and viability. With Milli-Q water, lower temperature
Disease psoriasis occurs as chronic inflammation of skin and appears as scaly red lesions on skin surface. Advent of several immunosuppressive drugs established that the disease stems from immuno-pathogenic disorder in human blood. Cell biological as well as clinical research on the disease reveals that the helper T-cells and other Leucocytes, responsible for human immunity, may lead to psoriasis pathogenesis if produced in plenty at locations close to the dermal region. Research findings also showed that a complex, self-sustaining (cytokine and related) proteins network play important role in disease maturation by actually leading to a huge proliferation of epidermal keratinocytes. Disease pathogenesis is identified with such hyperproliferation leading to flaking of skin surface (psoriatic plaques). An excessive generation of nitric oxide by proliferated keratinocytes, through a complex chain of bio-chemical events, is causal to the scaliness of psoriatic plaques. Considering these immunopathogenic mechanisms, we propose and analyse a mathematical time differential model for the disease psoriasis. Outcomes of analysis are consistent with existing cell biological and clinical findi
Recent studies emphasized the necessity to identify key (human) biological processes and pathways targeted by the Coronaviridae family of viruses, especially SARS-CoV-2. COVID-19 caused up to 33-55\% death rates in COVID-19 patients with malignant neoplasms and Alzheimer's disease. Given this scenario, we identified biological processes and pathways which are most likely affected by COVID-19. The associations between various diseases and human genes known to interact with viruses from Coronaviridae family were obtained from the IntAct COVID-19 data set annotated with DisGeNET data. We constructed the disease-gene network to identify genes that are involved in various comorbid diseased states. Communities from the disease-gene network through Louvain method were identified and functional enrichment through over-representation analysis methodology was used to discover significant biological processes and pathways shared between COVID-19 and other diseases. The IntAct COVID-19 data set comprised of 828 human genes and 10,473 diseases that together constituted nodes in the disease-gene network. Each of the 70,210 edges connects a human gene with an associated disease. The top 10 genes
The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research int
Significantly higher transmissibility of SARS CoV-2 (2019) compared to SARS CoV (2003) can be attributed to mutations of structural proteins (Spike S, Nucleocapsid N, Membrane M, and Envelope E) and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly and shedding. The non-structural proteins (nsps) avail host protein synthesis machinery to initiate viral replication, along with neutralization of host immune defense. The key protein out of the 16 nsps, is the non-structural protein nsp1, also known as the leader protein. Nsp1 leads the process of hijacking host resources by blocking host translation. This paper concentrates on the analysis of nsps of SARS covid (CoV-2, CoV) and MERS covid based on Cellular Automata enhanced Machine Learning (CAML) model developed for study of biological strings. This computational model compares deviation of structure - function of CoV-2 from that of CoV employing CAML model parameters derived out of CA evolution of amino acid chains of nsps. This comparative analysis points to - (i) higher transmissibility of CoV-2 compared to CoV for major nsps, and (ii) deviation of MERS covid from SARS
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by joint inflammation and joint pain. Much of RA treatment is focused on suppressing inflammation, with the idea being that if inflammation is controlled other symptoms, such as pain, will disappear. However, pain is the most common complaint of RA patients, is often still present following the resolution of inflammation, and can develop prior to the onset of inflammation. Thus, further research is needed to better understand RA-associated pain mechanisms. A number of preclinical rodent models commonly used in rheumatology research have been developed based on bedside-to-bench and reverse translational approaches. These models include collagen-induced arthritis, antigen-induced arthritis, streptococcal cell wall-induced arthritis, collagen antibody-induced arthritis, serum transfer from K/BxN transgenic mice and tumor necrosis factor (TNF)-transgene mice. They have led to increased understanding of RA pathogenesis and have aided the development of successful RA treatments. More recently, these models have been used to elucidate the complexities of RA associated pain. Several potentially modifiable mechanisms,
The development of therapeutic targets for COVID-19 treatment is based on the understanding of the molecular mechanism of pathogenesis. The identification of genes and proteins involved in the infection mechanism is the key to shed out light into the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced the accumulation of both protein and genetic interactions. In this work we integrate these available results and we obtain an host protein-protein interaction network composed by 1432 human proteins. We calculate network centrality measures to identify key proteins. Then we perform functional enrichment of central proteins. We observed that the identified proteins are mostly associated with several crucial pathways, including cellular process, signalling transduction, neurodegenerative disease. Finally, we focused on proteins involved in causing disease in the human respiratory tract. We conclude that COVID19 is a complex disease, and we highlighted many potential therapeutic targets including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, HSPA1A, which are central and also associated with multiple di
Analytical study of results of experimental and theoretical works on pathogenesis of psoriatic disease was conducted. Psoriasis is dermal implication of systemic psoriatic process (SPP). New SPP model explaining results of clinical and laboratory experiments was formulated. According to Y-model there are two main factors: hyperpermeability of small intestine for bacterial products and colonization of its walls by Gram+ bacteria (incl. psoriagenic bacteria PsB) and Gram(-) TLR4-active bacteria. Inside SPP there is a vicious cycle which is supported by disturbance of production and-or circulation of bile acids. SPP central subprocess is PAMP-nemia, namely chronic kPAMP-load on blood phagocytes (neutrophiles, monocytes and dendritic cells). The load results in increase of blood kPAMP level. The major key PAMP (kPAMP) are LPS and PG (incl. PG-Y - peptidoglycan of psoriagenic bacteria). Chronically increased kPAMP-load possibly provides tolerization of some neutrophils Neu, monocytes Mo and dendritic cells DC in blood flow. The chemostatus of tolerized blood Neu-T in process of their aging changes similarly to chemostatus nonactivated Neu and, hence, they carry endocytosed content from
Background: The COVID-19 pandemic clock is ticking and the survival of many of mankind's modern institutions and or survival of many individuals is at stake. There is a need for treatments to significantly reduce the morbidity and mortality of COVID-19. Hence, we delved deep into the SARS-CoV-2 genome, which is the virus that has caused COVID-19. SARS-CoV-2 is from the same family as SARS-CoV in which three small viral RNAs (svRNA) were recently identified; those svRNAs play a significant role in the virus pathogenesis in mice. Contribution: In this paper, we report potential orthologs of those three svRNAs in the SARS-CoV-2 genome. Instead of off-the-shelf search and alignment algorithms, which failed to discover the orthologs, we used a special alignment scoring that does not penalize C/T and A/G mismatches as much as the other mutations. RNA bases C and U both can bind to G; similarly, A and G both can bind to U, hence, our scoring. We also validate this hypothesis using a novel, independent computational experiment. To validate our results, we confirmed the discovered orthologs are fully conserved in all the tested publicly available genomes of various strains of SARS-CoV-2; th
Oxidation-associated malondialdehyde (MDA) modification of proteins can generate immunogenic neo-epitopes that are recognized by autoantibodies. In health, IgM antibodies to MDA-adducts are part of the natural antibody pool, while elevated levels of IgG anti-MDA are associated with inflammatory conditions. Yet, in human autoimmune disease IgG anti-MDA responses have not been well characterized and their potential contribution to disease pathogenesis is not known. Here, we investigate MDA-modifications and anti-MDA-modified protein autoreactivity in rheumatoid arthritis (RA). While RA is primarily associated with autoreactivity to citrullinated antigens, we also observed increases in serum IgG anti-MDA in RA patients compared to controls. IgG anti-MDA levels significantly correlated with disease activity by DAS28-ESR and serum TNF-alpha, IL-6, and CRP. Mass spectrometry analysis of RA synovial tissue identified MDA-modified proteins and revealed shared peptides between MDA-modified and citrullinated actin and vimentin. Furthermore, anti-MDA autoreactivity among synovial B cells was discovered when investigating recombinant monoclonal antibodies (mAbs) cloned from single B cells. Sev
Protein-protein interactions (PPIs) between a virus and its host govern infection, replication, and pathogenesis. While high-throughput mapping has identified thousands of virus-host associations, much of the virus-host interactome remains uncharacterized due to the labor-intensive nature of experimental screens, the inherent difficulty in capturing transient interactions, and the limited sequence homology across divergent viral families. Here, we introduce ViraHinter, a dual-modal deep learning framework for the precise prediction of virus-host interactions and large-scale inference of interaction landscapes. ViraHinter couples a structure-generation branch with a sequence-representation branch, integrating structure-informed pair representations with ESM-derived embeddings to learn generalizable interaction rules across unseen viruses. We benchmark ViraHinter on pathogenic coronaviruses and influenza A viruses and show that it consistently outperforms RoseTTAFold2-PPI, AlphaFold 3 and RoseTTAFold2-Lite in prioritizing high-confidence candidates even under severe class imbalance and across diverse interface regimes. Notably, it successfully identifies novel functionally relevant h
Background: Predicting the efficacy of combination therapies is a critical challenge in clinical decision-making, particularly for diseases requiring multi-drug regimens. Traditional evidence synthesis methods, such as component network meta-analysis (cNMA), often face parameter explosion and limited interpretability, especially when modeling interaction effects between components. Objective: This article introduces a general Efficacy Comparison Framework (ECF), a mechanistically grounded system for predicting combination therapy outcomes. ECF integrates biological pathway-based abstractions with expert knowledge, optimized with quasi-rules derived from clinical trial data to overcome the limitations of traditional methods. Methods: ECF employs a disease pathogenesis graph to encode domain knowledge, reducing the parameter space through mechanistic functions and sparse network structures. Optimization may be performed using a loss function inspired by the Thurstone-Mosteller model, focusing on pairwise regimen comparisons. A pilot study was conducted for acne vulgaris to evaluate ECF's ability in both tested and untested comparisons. Results: In the acne vulgaris case study, the EC
Many rare genetic diseases exhibit recognizable facial phenotypes, which are often used as diagnostic clues. However, current facial phenotype diagnostic models, which are trained on image datasets, have high accuracy but often suffer from an inability to explain their predictions, which reduces physicians' confidence in the model output.In this paper, we constructed a dataset, called FGDD, which was collected from 509 publications and contains 1147 data records, in which each data record represents a patient group and contains patient information, variation information, and facial phenotype information. To verify the availability of the dataset, we evaluated the performance of commonly used classification algorithms on the dataset and analyzed the explainability from global and local perspectives. FGDD aims to support the training of disease diagnostic models, provide explainable results, and increase physicians' confidence with solid evidence. It also allows us to explore the complex relationship between genes, diseases, and facial phenotypes, to gain a deeper understanding of the pathogenesis and clinical manifestations of rare genetic diseases.
Trigeminal neuralgia (TN) is the most common neuropathic disorder; however, its pathogenesis remains unclear. A prevailing theory suggests that nitric oxide (NO) may induce nerve compression and irritation via vascular dilation, thereby being responsible for the condition, making real-time detection of generated NO critical. However, traditional evaluations of NO rely on indirect colorimetric or chemiluminescence techniques, which offer limited sensitivity and spatial resolution for its real-time assessment in biological environments. Herein, we reported the development of a highly sensitive NO electrochemical biosensor based cerium single-atom nanozyme (Ce1-CN) with ultrawide linear range from 1.08 nM to 143.9 μM, and ultralow detection limit of 0.36 nM, which enables efficient and real-time evaluation of NO in TN rats. In-situ attenuated total reflection surface-enhanced infrared spectroscopy combined with density functional theory calculations revealed the high-performance biosensing mechanism, whereby the Ce centers in Ce1-CN nanoenzymes adsorb NO and subsequently react with OH- to form *HNO2. Results demonstrated that NO concentration was associated with TN onset. Following ca
Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads to memory and behavior impairment severe enough to interfere with daily life activities. Understanding this disease pathogenesis can drive the development of new targets and strategies to prevent and treat AD. Recent advances in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of massive amounts of transcriptomic data at the single-cell level provided remarkable insights into understanding the molecular pathogenesis of Alzheimer's disease. In this study, we introduce ScAtt, an innovative Attention-based architecture, devised specifically for the concurrent identification of cell-type specific AD-related genes and their associated gene regulatory network. ScAtt incorporates a flexible model capable of capturing nonlinear effects, leading to the detection of AD-associated genes that might be overlooked by traditional differentially expressed gene (DEG) analyses. Moreover, ScAtt effectively infers a gene regulatory network depicting the combined influences of genes on the targeted disease, as opposed to examining correlations among genes in conventional gene co-ex
The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it remains a challenging task. This study aims to address this challenge by combining joint graph learning with network analysis. Motivated by this discovery, we develop a Conjoint Graph Representation Learning (CGRL) framework that: a) constructs two networks based on disease coding, including the patient network and the disease difference network. Three comorbidity network features were generated based on the basic difference network to capture the potential relationship between comorbidities and risk diseases; b) incorporates computational structure intervention and learning feature representation, CGRL was developed to predict the risks of diabetes and coronary heart disease in patients; and c) analysis the comorbidity patterns and exploring the pathways of disease progression, the pathological pathogenesis of diabetes and coronary heart disease may be revealed. The results show that the network features extracted based on the difference network are important, and the framework we proposed provides more accurate predictions than other st
Cardiac pulsations in the human brain have received recent interest due to their possible role in the pathogenesis of neurodegenerative diseases. Further interest stems from their possible application as an endogenous signal source that can be utilized for brain imaging in general. The (pulse-)wave describing the blood flow velocity along an intracranial artery consists of a forward (anterograde) and a backward (retrograde, reflected) part, but measurements of this wave usually consist of a superposition of these components. In this paper, we provide a mathematical framework for the inverse problem of estimating the pulse wave velocity, as well as the forward and backward component of the pulse wave separately from MRI measurements on intracranial arteries. After a mathematical analysis of this problem, we consider possible reconstruction approaches, and derive an alternate direction approach for its solution. The resulting methods provide estimates for anterograde/retrograde wave forms and the pulse wave velocity under specified assumptions on a cerebrovascular model system. Numerical experiments on simulated and experimental data demonstrate the applicability and preliminary in v
Chikungunya virus (CHIKV) is one of the most relevant arboviruses affecting public health today. It belongs to the Togaviridae family and alphavirus genus, causing an arthritogenic disease known as Chikungunya fever (CHIKF). This multifaceted disease is distinguished from other arbovirus infections by intense arthralgia, which can persist for months or even years in some individuals. The virus has re-emerged as a global health threat in recent decades, originating in Africa and spreading across Asia and America, leading to widespread outbreaks affecting millions. Despite more than 50 years of research on CHIKV pathogenesis, no drugs or vaccines are available. Current management focuses on supportive care to alleviate symptoms and improve patient's quality of life. The ongoing threat posed by CHIKV highlights the need to understand its pathogenesis better. This review provides a comprehensive overview of CHIKV, focusing on host factors, vector-related factors, and complex viral genetic interactions. By exploring these intricate connections, we aim to offer insights that may lead to more effective strategies for preventing and managing this re-emerging global health threat.
This paper details a technical plan for building a clinical case database for Traditional Chinese Medicine (TCM) using web scraping. Leveraging multiple platforms, including 360doc, we gathered over 5,000 TCM clinical cases, performed data cleaning, and structured the dataset with crucial fields such as patient details, pathogenesis, syndromes, and annotations. Using the $Baidu\_ERNIE\_Speed\_128K$ API, we removed redundant information and generated the final answers through the $DeepSeekv2$ API, outputting results in standard JSON format. We optimized data recall with RAG and rerank techniques during retrieval and developed a hybrid matching scheme. By combining two-stage retrieval method with keyword matching via Jieba, we significantly enhanced the accuracy of model outputs.
The intricate interplay between host organisms and their gut microbiota has catalyzed research into the microbiome's role in disease, shedding light on novel aspects of disease pathogenesis. However, the mechanisms through which the microbiome exerts its influence on disease remain largely unclear. In this study, we first introduce a structural equation model to delineate the pathways connecting the microbiome, metabolome, and disease processes, utilizing a target multiview microbiome data. To mitigate the challenges posed by hidden confounders, we further propose an integrative approach that incorporates data from an external microbiome cohort. This method also supports the identification of disease-specific and microbiome-associated metabolites that are missing in the target cohort. We provide theoretical underpinnings for the estimations derived from our integrative approach, demonstrating estimation consistency and asymptotic normality. The effectiveness of our methodologies is validated through comprehensive simulation studies and an empirical application to inflammatory bowel disease, highlighting their potential to unravel the complex relationships between the microbiome, me