MicroRNA (miRNA) regulation plays a pivotal role in intracellular gene expression. Analysis of miRNA profiles can provide critical insights into disease states. As cancer-associated molecules reported in previous studies, miRNAs may serve as candidate classificatory features for exploratory cancer classification. This research analyzed serum miRNA data from patients with 13 solid cancer types and individuals without cancer. The study comprised two distinct analyses: first, stratifying the dataset into cancer and non-cancer groups to identify miRNAs differentially represented in cancer patients; and second, subdividing the cancer patient data into 13 predefined solid-cancer types to identify candidate miRNA features that discriminate among these cancer types. We employed seven feature-ranking algorithms to evaluate miRNA contributions in both analyses and generate feature lists. Each list was examined using an incremental feature selection method to extract essential miRNAs and build good-performing classification models. Several candidate miRNAs were identified for distinguishing pan-cancer samples from non-cancer ones: miR-4783-3p has been linked to associated with the regulation of endocrine cell differentiation, and miR-663a has been reported in hepatocellular carcinoma and thyroid carcinoma. The analysis also highlighted miRNAs that differentiate solid cancer types, including miR-629-3p, reported to be upregulated in lung and breast cancer, and miR-6087, reported to be downregulated in osteosarcoma and bladder cancer.

Introduction Children with Coronavirus Disease 2019 (COVID-19) and those with Multisystem Inflammatory Syndrome in Children (MIS-C) exhibit similar inflammatory responses, yet distinct differences exist, particularly in immune cell reactions. This study aimed to uncover the differences between them.Methods This study analyzed plasma cell-free ribonucleic acid [cfRNA] and whole blood RNA [wbRNA] from children with COVID-19, MIS-C, and a healthy control group. Pediatric blood and plasma samples were collected from three hospital systems, including patients with PCR-confirmed COVID-19 and those meeting CDC-defined criteria for MIS-C. COVID-19 cases required SARS-CoV-2 positivity within 14 days of sampling, while MIS-C diagnoses were adjudicated by multidisciplinary teams based on clinical and inflammatory features. Each sample was represented by 60,708 gene expressions. Ten advanced feature ranking algorithms were first applied to yield feature lists. Then, these lists were analyzed by incremental feature selection method, which contained four classification algorithms and synthetic minority oversampling technique, to extract essential genes and build efficient prediction models and classification rules.Results Several important genes were discovered, which were identified by multiple feature ranking algorithms. The optimal models on cfRNA and wbRNA achieved weighted F1 scores exceeding 0.9.Discussion Analysis of the cfRNA dataset revealed low EPSTI1 expression and low SNHG6 expression in MIS-C patients, while the wbRNA dataset indicated high IFI27 expression in COVID-19 patients and high JUN expression in COVID-19 and MIS-C patients compared with non-inflammatory controls.Conclusion The newly found genes can serve as potential qualitative markers for COVID-19 or MIS-C, expanding the scope of biomarkers for COVID-19 or MIS-C. These findings will be helpful in investigating the pathogenic mechanisms of MIS-C.

Gastric adenocarcinoma is a significant global health concern. Among the myriad histologic classification methods for this cancer, the Lauren classification stands out as a pivotal tool. Nonetheless, the precision and detail of current histological staging techniques frequently face scrutiny. Utilizing high-resolution single-cell transcriptomic data, this research delves into the distinctive gene expression patterns in diffuse, intestinal, and mixed gastric cancers by deploying various machine learning algorithms. The main goal was to recognize important gene markers and establish efficient classification models. The data was derived from the tumor microenvironment, with cells categorized into six groups according to two locations: tumoral and normal, and three histology types: diffuse, intestinal, and mixed. Every cell describes the expression level of 56,265 genes. We integrated seven feature selection algorithms and four classification algorithms, which increased the accuracy of classification. Importantly, our approach detected intricate expression patterns realized for the first time-for example, high expression of CLDN4 in intestinal-type gastric cancers and CCL4 and CXCR4 in diffuse-type gastric cancers. The identified gene markers and gene expression patterns provide insights into subtype-specific molecular characteristics of gastric cancer. These candidate markers may serve as a foundation for future studies aimed at validating their utility in subtype classification and clinical stratification.

ABSTRACT Metagenomic next-generation sequencing (mNGS) is a promising tool for diagnosing challenging infections like tuberculosis (TB). However, previous studies largely focused on case-specific application of mNGS in TB diagnosis. Thus, we conducted a retrospective observational study to first systematically evaluate the diagnostic performance and cost-effectiveness of mNGS for TB diagnosis. We retrieved a total of 16,776 results of the seven TB diagnostic assays, including mNGS, tuberculosis IgG antibody, TB interferon-γ release assay (TB-IGRA), TB-DNA, Xpert MTB/RIF (Xpert), culture, and acid-fast bacilli staining (AFS) from 3,757 participants with suspected TB infection at Sichuan Provincial People’s Hospital from September 2021 to July 2024. Diagnostic metrics were compared against a composite reference standard. Microbial composition and a cost-utility analysis were performed. Among seven TB assays studied, the World Health Organization (WHO)-recommended assays AFS, culture, and Xpert, as well as TB-IGRA, were requested most frequently for TB diagnosis, whereas mNGS ranked last. mNGS demonstrated the highest specificity (100%), accuracy (72.3%), and area under the curve (AUC) (0.795). Its sensitivity in bronchoalveolar lavage fluid and tissue was 71.0% and 72.7%, respectively. Sequential use of mNGS after initial WHO-recommended tests (Xpert/Culture/AFS) significantly improved diagnostic performance (sensitivity, 70.4%; AUC, 0.823). Microbial analysis associated Candida albicans with TB. Cost-utility analysis showed sequential mNGS became cost-effective at higher willingness-to-pay thresholds (>200,000 RMB per correct diagnosis). mNGS offers superior specificity for TB diagnosis. A sequential strategy applying mNGS to conventional-test-negative cases provides enhanced diagnostic performance and is cost-effective at higher healthcare investment values, supporting its utility for diagnostically challenging TB. IMPORTANCE This study systematically assesses the diagnostic performance and cost utility of metagenomic next-generation sequencing (mNGS) for tuberculosis (TB) in a large real-world cohort of 3,757 suspected patients, comparing it against six conventional assays (tuberculosis IgG antibody, TB interferon-γ release assay, TB-DNA, Xpert, culture, and acid-fast bacilli staining). mNGS demonstrated the highest specificity (100%), accuracy (72.3%), and area under the curve (AUC) (0.795), with sensitivities of 71.0% in bronchoalveolar lavage fluid and 72.7% in tissue. Notably, sequential use of mNGS after the World Health Organization-recommended tests significantly improved sensitivity to 70.4% and AUC to 0.823. Candida albicans showed significant differences among the three groups. The sequential mNGS strategy was cost-effective compared with no mNGS, and its cost-effectiveness increased with a rising willingness-to-pay threshold. Overall, these results highlight mNGS as a valuable supplementary tool for challenging TB cases, especially when conventional tests are inconclusive, and provide strong evidence for integrating it into diagnostic algorithms to optimize clinical decision-making and resource allocation.

Coronavirus Disease 2019 (COVID-19) and other lower respiratory tract diseases (LRTDs), including bacterial pneumonia and acute respiratory distress syndrome, share overlapping clinical features but arise from distinct pathophysiological mechanisms. The molecular signatures that distinguish these diseases remain insufficiently characterized in African populations, where genetic background, endemic infections, and environmental exposures may substantially shape immune responses. We integrated spatially resolved single-cell transcriptomic profiles from lung autopsy specimens of 30 Malawian patients, including 10 with COVID-19, 12 with other LRTDs, and 8 non-LRTD controls. In total, 61,391 cells representing 15 cell types and 36,602 gene expression features were analyzed. Using an integrated machine learning framework that combined nine feature-ranking algorithms with incremental feature selection, we identified potential molecular signatures that could discriminate among disease states within this cohort. The optimal classification models achieved weighted F1 scores greater than 0.94, demonstrating a robust capacity to differentiate COVID-19 from other LRTDs in our dataset. Notably, the macrophage-associated state in COVID-19 was dominated by an IFN-γ response with upregulation of CD163 and HLA-DQA2, contrasting sharply with the type I/III interferon signature reported in European cohorts. In addition, we observed cell-type-specific COVID-19 signatures, including downregulation of CAV1 in AT1 cells, consistent with epithelial damage; dysregulation of SFTPC in AT2 cells, suggesting surfactant dysfunction; and upregulation of NFKBIA in neutrophils, indicating altered inflammatory regulation. Gene Ontology enrichment further revealed universal disruption of protein synthesis machinery, along with cell-type-specific alterations in immune activation, epithelial repair, and inflammatory signaling pathways.

Investigating the transcriptional signatures of immune cells in various cancer types is crucial for understanding their roles in the tumor microenvironment and developing effective immunotherapeutic strategies. In this study, we employed machine learning methods to analyze RNA-seq data from patients with four different types of cancers and two immune cell types, including T cell and CD45+CD3- leukocyte cell types. We processed seven datasets, each divided into three groups on the basis of cell source: tumor, normal adjacent tissue, and peripheral blood. The datasets were downscaled by using the Boruta method, and the remaining genes were ranked for criticality in a list through the max-relevance and min-redundancy method. The obtained list of genes was fed into incremental feature selection (IFS), which employed decision tree or random forest to distinguish cells, for the identification of key genes associated with immune cell function in different cancer types and construction of efficient classifiers and classification rules (special patterns for different groups). Our results revealed distinct expression patterns of key genes, such as the downregulation of CST7 in T cells from tumor tissues and differential expression of CD2 in non-tumor sites. Furthermore, we identified LCP1, CD27, and MAL as immunologically relevant genes in T cells across different tissue origins, whereas IFI30, CXCR4, and FOSB played various roles in CD45+CD3- leukocytes. The identified key genes were supported by evidence in the literature, highlighting their involvement in antitumor processes in T cells and other immune cells. Our findings provide valuable insights into the transcriptional signatures of immune cells in different cancer types and lay the foundation for the development of novel diagnostic, prognostic, and therapeutic strategies in cancer immunology.

Mucosal melanoma (MM), an aggressive melanoma subtype arising in mucosal tissues, displays resistance to therapies effective in cutaneous melanoma. To understand how mucosal microenvironment contributes to treatment nonresponsiveness, we performed integrative analysis of single-cell and bulk messenger RNA sequencing data derived from oral mucosa-originated melanoma and revealed that mucosa-specific inflammation induces enrichment of low-pigmented neural crest-like cancer cell, mediated by COX2+ macrophages and their secretome. Maintenance of this inflammation-induced neural crest-like state in cancer cells depends on HER2 and HER3 activation. Inhibition of HER2/3 by pan-HER inhibitors blocks cell state plasticity and overcomes chemoresistance in primary MM cell lines and patient-derived xenograft (PDX) models. These findings provide insights into how the tissue of origin determines cancer aggressiveness, highlight the role of mucosal inflammation in driving melanoma stemness and chemoresistance, and advance the identification of effective treatment options currently lacking for patients with MM.

Introduction Non-small Cell Lung Cancer (NSCLC) is characterized by key gene mutations, such as EGFR, KRAS, and ALK. ALK rearrangement occurs in 3-5% of patients with non-small cell lung adenocarcinoma and is related to different clinical characteristics. Although ALK tyrosine kinase inhibitors have shown efficacy, drug resistance remains a challenge. This current study aims to determine the unique molecular characteristics of ALK-positive lung adenocarcinoma to improve detection and prognosis.Methods GSE128311 integrates expression profiling data by array from GSE128309 and noncoding RNA profiling data by array from GSE128310, including 42 patients with ALK-positive lung adenocarcinoma and 35 patients with ALK-negative lung adenocarcinoma. This data was analyzed by eight feature ranking algorithms, yielding eight feature lists. These lists were fed into incremental feature selection to extract essential features.Results Key differentially expressed genes and miRNAs were identified, and functional enrichment analysis was carried out.Discussion Results of the imbalance of the cell cycle pathway, FOXM1 transcription factor network, and immune response process in ALK-positive tumors were emphasized. It is worth noting that CX3CL1, MMS22L, DSG3, RUFY1, miR-652-5p, and miR-1288 are potentially important markers. Gene set enrichment analysis revealed the low expression of the cell cycle pathway in ALK-positive samples.Conclusion This comprehensive computational analysis provides new insights into the molecular basis of ALK-positive lung adenocarcinoma and determines promising biomarkers for further research.

Acute myeloid leukemia (AML) is a severe hematological malignancy characterized by high recurrence rates, especially in pediatric patients, highlighting the need for reliable prognostic markers. This study proposes methylation signatures associated with AML recurrence using computational methods. DNA methylation data from 696 newly diagnosed and 194 relapsed pediatric AML patients were analyzed. Feature selection algorithms, including Boruta, least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection, were employed to screen and rank methylation sites strongly correlated with AML recurrence. Incremental Feature Selection was performed to evaluate these results, and optimal subsets were identified using Decision Tree and Random Forest methods. Several important methylation features, such as modifications in SLC45A4, S100PBP, TSPAN9, PTPRG, ERBB4, and PRKCZ, emerged from the intersection of all feature selection algorithms. Functional enrichment analysis indicated these genes participate in biological processes, including calcium-mediated signaling and regulation of binding. These findings are consistent with existing literature, suggesting that identified methylation features likely contribute to AML progression through alterations in gene expression levels. Therefore, this study provides a valuable reference for enhancing recurrence risk prediction models in AML and clarifying disease pathogenesis, as well as offering broader insights into mechanisms underlying other major diseases.

Cryoablation therapy for tumors has a long history of clinical application. Its anti-tumor mechanisms and histopathological changes have been well established, with extensive clinical practice demonstrating its safety and efficacy, theoretically making it an ideal modality for tumor treatment. Historically constrained by limitations in cryogenic media and freezing equipment, its therapeutic effectiveness and clinical adoption were significantly restricted. The emergence of new-generation cryoablation systems represented by Argon-Helium cryosurgical systems has achieved substantial advancements in refrigeration efficiency, ablation range precision, and temperature monitoring accuracy, thereby greatly promoting the widespread adoption of tumor cryoablation technology. This consensus systematically summarizes the mechanisms of cryoablation technology, indications for cryotherapy in head and neck mucosal melanoma, standardized clinical treatment protocols, management of adverse reactions, and related principles. It aims to provide authoritative references for standardizing cryoablation therapy in the treatment of head and neck mucosal melanoma.

OBJECTIVE:Betel-chewing-related oral squamous cell carcinoma (BCR-OSCC) has become a global health issue with increasing incidence year by year around the world. Active prevention of the occurrence of BCR-OSCC, monitoring the population exposed to Betel Nuts, and early diagnosis and treatment are very important to maintain and improve the quality of life of patients. However, there is currently no consensus or guideline that provides targeted guidance on the management of BCR-OSCC. SUBJECTS AND METHODS:A consensus panel consisting of 15 leading Chinese experts from multidisciplinary fields was convened, and a roundtable meeting was held to discuss the topics of BCR-OSCC. RESULTS:Based on existing research reports and the experts' clinical experiences, a consensus on staging, diagnosis, and treatment for BCR-OSCC was formed through extensive discussion. CONCLUSION:This manuscript presents consensus recommendations and a summary of evidence supporting each recommendation. This consensus may improve clinical practices about BCR-OSCC in China and propel more clinical trials to provide high-level evidence for BCR-OSCC management.

PURPOSE:Anti-CRISPR (Acr) proteins can evade CRISPR-Cas immunity, yet their molecular determinants remain poorly understood. This study aimed to uncover key features driving Acr activity, thereby advancing both fundamental knowledge and the rational design of robust CRISPR-based tools. EXPERIMENTAL DESIGN:We compiled a binary-encoded matrix of 761 InterPro-annotated domains and binding-site features for known Acr proteins. Seven feature ranking algorithms were applied to prioritize determinant features, and an incremental feature selection strategy, coupled with four distinct classifiers, was used to identify optimal subsets. Consensus key features were defined by intersecting the top subsets across all methods. RESULTS:Key identified features include the DUF2829 domain, the Lambda repressor-like domain and Sulfolobus islandicus virus proteins, the Cro/C1-type helix-turn-helix domain, phage protein, and replication initiator A. These findings illuminate novel structural modules and regulatory motifs that underpin Acr inhibition. CONCLUSIONS:This study provides critical theoretical support for deciphering Acr mechanisms and offers actionable insights for engineering next-generation CRISPR-Cas applications in clinical and biotechnological settings. SUMMARY:The CRISPR system is a part of the antiviral immune defense initially discovered in bacteria and archaea. At present, the CRISPR system has become the cornerstone of genome editing technologies such as CRISPR-Cas9, widely used in clinical, agricultural, and biological research. Anti-CRISPR proteins are a group of proteins that inhibit the normal activity of CRISPR-Cas system in certain bacteria or archaea and avoid having the phages' genomes destroyed by the prokaryotic cells. The anti-CRISPR protein family has various components, but with similar functions to help exogenous DNA escape from the immune system. This study tried to uncover molecular mechanisms for anti-CRISPR proteins.

Vaccination with ChAdOx1 nCoV-19 is an important countermeasure to fight the COVID-19 pandemic. This vaccine enhances human immunoprotection against SARS-CoV-2 by inducing an immune response against the SARS-CoV-2 S protein. However, the immune-related genes induced by vaccination remain to be identified. This study employs feature ranking algorithms, an incremental feature selection method, and classification algorithms to analyze transcriptomic data from an experimental group vaccinated with the ChAdOx1 nCoV-19 vaccine and a control group vaccinated with the MenACWY meningococcal vaccine. According to different time points, vaccination status, and SARS-CoV-2 infection status, the transcriptomic data was divided into five groups, including a pre-vaccination group, ChAdOx1-onset group, MenACWY-onset group, ChAdOx1-7D group, and MenACWY-7D group. Each group contained samples with 13,383 RNA features and 1662 small RNA features. The results identified key genes that could indicate the efficacy of the ChAdOx1 nCoV-19 vaccine, and a classifier was developed to classify samples into the above groups. Additionally, effective classification rules were established to distinguish between different vaccination statuses. It was found that subjects vaccinated with ChAdOx1 nCoV-19 vaccine and infected with SARS-CoV-2 were characterized by up-regulation of HIST1H3G expression and down-regulation of CASP10 expression. In addition, IGHG1, FOXM1, and CASP10 genes were strongly associated with ChAdOx1 nCoV-19 vaccine efficacy. Compared with previous omics-driven studies, the machine learning algorithms used in this study were able to analyze transcriptome data faster and more comprehensively to identify potential markers associated with vaccine effect and investigate ChAdOx1 nCoV-19 vaccine-induced gene expression changes. These observations contribute to an understanding of the immune protection and inflammatory responses induced by the ChAdOx1 nCoV-19 vaccine during symptomatic episodes and provide a rationale for improving vaccine efficacy.

While combination BRAF/MEK inhibition has improved survival in BRAFV600 mutant melanoma, targeted therapies for BRAFWT melanoma remain limited. Microphthalmia transcription factor (MITF), a lineage-specific transcription factor that regulates melanocyte proliferation and melanin synthesis, represents a promising melanoma-specific drug target. In this study, we evaluated TT-012, a recently identified MITF dimerization specific inhibitor, and surprisingly found that most BRAFWT melanoma lines were resistant to TT-012 due to low MITF transcriptional activity and reduced dependency on MITF for proliferation. High-throughput drug screen identified tivozanib, an FDA-approved drug targeting VEGFR and other receptor tyrosine kinases (RTKs), which sensitized cells to TT-012. Mechanistically, tivozanib induced cell state transition from MITFlow to MITFhigh state via VEGFR2 inhibition followed by NF-κB pathway activation, restoring MITF transcriptional activity and growth dependency. The combination of tivozanib and TT-012 synergistically inhibited melanoma growth both in vitro and in vivo, underscoring its potential as a novel therapeutic strategy for BRAFWT melanoma.

Background:Pulmonary rehabilitation (PR) is essential for airway management after thoracic surgery. Most current PRs are composed of 2-4-week exercises, which require significant consumption of medical resources and concerns about disease progression. Materials and methods:This single-center, prospective, randomized controlled trial enrolled smoking patients with pulmonary masses or nodules suitable for lobectomy, aged 18-80, with a smoking history (>= 20 pack-years). Eligible patients were randomized in a 1:1 ratio into two groups. Patients in the intervention group underwent perioperative breathing exercises based on positive pressure vibration expectoration and 3-day preoperative lower limb endurance training. Patients in the control group received routine perioperative care. The primary outcome was in-hospital incidence of postoperative pulmonary complications. Secondary outcomes included postoperative hospital stay, total hospitalization cost, postoperative drainage time, drainage volume, semiquantitative cough strength score, pain score, Borg scale-assessed fatigue, and walking distance on postoperative days 1 and 2. Results:A total of 194 patients were included in the study, with 94 in the intervention group and 100 in the control group. Our ultrashort PR program potentially reduced pulmonary complications incidence (24.5 vs. 33.0%), but without statistical significance (P=0.190). No significant differences were found in other perioperative outcomes, except for postoperative semiquantitative cough strength score (3 [interquartile range, 3-3.75] vs. 3 [interquartile range, 2-3], P<0.001) and change in walking distance from postoperative days 1 to 2 (60 [interquartile range, 40-82.5] vs. 30 [interquartile range, 10-60], P=0.003). Conclusion:There were no significant differences in postoperative complications and other hospitalizations, but our ultrashort rehabilitation program improved patients' semiquantitative cough strength score and walking distance, indicating the potential for better outcomes. This treatment is a safe and effective means of airway management for thoracic surgery in the era of enhanced recovery (ClinicalTrials.gov Identifier: NCT03010033).

The following article for this Special Issue was published in an earlier Issue . Q. L. Ma, Y. H. Zhang, L. Chen, Y. S. Bao, W. Guo, K. Y. Feng, T. Huang, Y.-D. Cai. “Machine Learning-Driven Discovery of Essential Binding Preference in Anti-CRISPR Proteins,” Proteomics. Clinical Applications , 19 , (2025): e70013. https://doi.org/10.1002/prca.70013 . https://onlinelibrary.wiley.com/doi/10.1002/prca.70013

Postoperative pain can significantly impair functional recovery and diminish the quality of life in patients who have undergone thoracoscopic surgery. Virtual reality (VR), by leveraging cognitive-behavioral intervention techniques and redirecting attention from noxious stimuli, holds promise as a modality to alleviate postoperative pain. Despite this potential, current VR software for postoperative care predominantly emphasizes physical therapy and rehabilitation, often overlooking the integration of pain management strategies. The primary objective of our study is to evaluate the effectiveness and safety of an adjunctive VR-based software for pain control following thoracoscopic surgery. This is a prospective, multicenter, open-label, randomized controlled trial involving 215 patients who have undergone thoracoscopic surgery. Participants will be randomly allocated to one of two parallel groups. The experimental group will receive postoperative adjuvant analgesic software in addition to standard postoperative pharmacological analgesia, while the control group will receive only standard postoperative pharmacological analgesia. Pain intensity will be assessed using the numerical rating scale (NRS) at pre-intervention and at 24 and 48 h post-surgery. The primary outcome measure will be the effectiveness of the VR-based adjuvant analgesic software, as assessed by the reduction in NRS scores after the second intervention at 48 h postoperatively compared to pre-intervention baseline at 24 h postoperatively, and the secondary outcome measure will assess its safety profile. Our study marks a pioneering effort to incorporate VR-based adjuvant software into the postoperative pain management regimen. We endeavor to explore an innovative approach to deliver evidence-based pain treatments. The findings of this trial aim to shed light on the potential benefits of VR as a complementary tool to traditional analgesic therapies in the context of postoperative pain management following thoracoscopic procedures. Our protocol was retrospectively registered in the Chinese Clinical Trial Registry on August 2, 2024. The registration number was ChiCTR2400087741.

Background: Significant variations in immune profiles across different age groups manifest distinct clinical symptoms and prognoses in Coronavirus Disease 2019 (COVID-19) patients. Predominantly, severe COVID-19 cases that require hospitalization occur in the elderly, with the risk of severe illness escalating with age among young adults, children, and adolescents. Objective: This study aimed to delineate the unique immune characteristics of COVID-19 across various age groups and evaluate the feasibility of detecting COVID-19-induced immune alterations through peripheral blood analysis. Methods: By employing a machine learning approach, we analyzed gene expression data from nasopharyngeal and peripheral blood samples of COVID-19 patients across different age brackets. Nasopharyngeal data reflected the immune response to COVID-19 in the upper respiratory tract, while peripheral blood samples provided insights into the overall immune system status. Both datasets encompassed COVID-19 patients and healthy controls, with patients divided into children, adolescents, and adult age groups. The analysis included the expression levels of 62,703 genes per patient. Then, 9 feature-sequencing methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, random forest, ridge regression, adaptive boosting, categorical boosting, extremely randomized trees, and extreme gradient boosting) were employed to evaluate the association of the genes with COVID-19. Key genes were then utilized to develop efficient classification models. Results: The findings identified specific markers: insulin-like growth factor binding protein 3 (downregulated in the peripheral blood of COVID-19 patients), interferon alpha-inducible protein 27 (upregulated), and SERPING1 (upregulated in nasopharyngeal tissues). In addition, fibulin-2 was downregulated in adolescent patients, but upregulated in the other groups, while epoxide hydrolase 3 was upregulated in healthy controls, but downregulated in children and adolescents. Conclusion: This study offers valuable insights into the local and systemic immune responses of COVID-19 patients across age groups, aiding in identifying potential therapeutic targets and formulating personalized treatment strategies.

BACKGROUND:Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE:This study aims to discover essential differences for different intestinal bacteria. METHODS:This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS:Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION:This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.