Emerging tick-borne viruses are considered a significant public health threat. The understanding of the tick virome has been revolutionized by high-throughput meta-transcriptomic sequencing, and numerous novel pathogens have been unveiled. However, effective data integration and comparative analyses have been hindered by inconsistent research protocols. In response, standardized protocols are proposed, covering field surveys, sample processing, sequencing library construction, virus assembly, phylogenetic analysis, and classification, offering a unified framework for tick-virome research. Key challenges such as sampling representations, sequencing contamination, and classification discrepancies are also discussed. Furthermore, future perspectives on leveraging bioinformatics and machine learning to trace virus evolution, elucidate virus–tick–host interactions, and enhance surveillance in high-risk areas are outlined in our work. Ultimately, special emphasis is placed on the vital significance of worldwide cooperation and data sharing, which is essential for refining methodologies and guiding targeted interventions to mitigate emerging tick-borne diseases.

DeepMicroFinder is a deep learning framework designed to update the existing disease diagnosis model to generate a transfer model by leveraging region-specific microbiome datasets and transfer learning approach. This framework effectively overcomes the limitation of regional effects in the gut microbiome, enabling accurate cross-regional disease detection. Microbial markers related to type 2 diabetes (T2D) were identified by DeepMicroFinder, and subsequently validated in independent T2D cohorts undergoing dietary fiber interventions.

This study investigates the risk of thrombotic microangiopathy (TMA) induced by vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) inhibitors in cancer therapy. Using data from the FDA Adverse Event Reporting System (FAERS), the WHO Global Database for Adverse Drug Reactions (Vigibase), and The Cancer Genome Atlas (TCGA), along with clinical analysis of 1,698 patients and animal experiments, it reveals that VEGFR inhibitors cause rapid endothelial damage through complement activation, while VEGF inhibitors trigger delayed TMA via suppressed VEGF signaling. The analysis of TCGA pan-cancer transcriptomic data identified critical pathways involved, such as platelet activation and complement cascade, contributing to TMA development. Animal model studies further confirmed these findings, with hematoxylin and eosin (HE) staining of kidney tissues showing significant endothelial damage, thrombus formation, and mesangial cell proliferation, reinforcing the biological mechanisms. This comprehensive approach underscores the need for regular monitoring of renal function and platelet counts in patients receiving VEGF/VEGFR inhibitors to mitigate TMA risks.

This study has elucidated the role of three swine acute diarrhea syndrome coronavirus (SADS-CoV) accessory proteins in influencing viral pathogenicity and preliminarily explored the host molecular targets and pathways affected by the NS3a, NS7a, and NS7b proteins of SADS-CoV. Our findings suggest that SADS-CoV accessory proteins may modulate viral pathogenicity by affecting host metabolic pathways and immunity.

Researchers recruited 623 participants, including 270 females, and used 16S rDNA sequencing to investigate gender differences in hyperuricemia. In healthy females, Roseburia was enriched, positively correlating with estrogen and negatively with uric acid, suggesting it is a potential biomarker affecting uric acid levels in females.

It has been 40 years since the first discovery of the important human pathogen, Helicobacter pylori (H. pylori). Eradication of the infection continues to pose significant challenges in part because of the global increase in antibiotic resistance. Generally, the “addition” strategy has failed to deliver the desired results for more than a short period and often contributed to the increase in the prevalence of resistant strains. As such, optimization of each component of the treatment strategy has become an increasingly critical focus of research. In this study, we summarized the evolution of H. pylori eradication regimens and the emergence of promising regimens (e.g., high-dose pronto pump inhibitor dual therapy and potassium-competitive acid blocker dual therapy) in clinical practice. Moreover, we conducted an updated meta-analysis incorporating a total of 29 clinical studies regarding potassium-competitive acid blocker dual therapy. Additionally, we also conducted a network meta-analysis regarding the evaluation of first-line regimens for eradicating H. pylori among patients with penicillin allergy. We concluded that the strategy for the first-line treatment of H. pylori infection needs to shift from the traditional “addition” approach to an optimized “subtraction” approach in this era of increasing antibiotic resistance. Dual therapies, particularly those involving vonoprazan and low-dose amoxicillin, have demonstrated both satisfactory eradication rates and patient adherence.

This study focuses on porcine epidemic diarrhea virus (PEDV), a highly virulent and rapidly evolving coronavirus threatening global swine production. We established a novel genotype system of PEDV targeting site-specific polymorphisms in the spike (S) protein. The system demonstrates that frequent recombination events at both termini of the S gene serve as a critical mechanism driving genotypic diversity in PEDV. Together with adaptive evolution, these processes collectively shape the evolutionary dynamics of global PEDV strains. The research provides critical theoretical foundations for understanding PEDV genome evolution patterns and developing prevention and control measures.

Akkermansia muciniphila (A. muciniphila), regarded as a promising candidate for next-generation probiotic applications, predominantly inhabits the intestinal mucus layer, where it plays a crucial role in maintaining gut barrier integrity and modulating immune responses. Recently, the bacterium has been recognized for its ambivalent influence on cancer, impacting both tumor progression and therapeutic interventions. Research indicates that A. muciniphila might possess both tumorigenic and anticancer capabilities, influenced by factors such as the composition of the gut microbiota, dietary modifications, and immune modulation. There is a compelling need for further studies to uncover the precise mechanisms and optimal use of A. muciniphila in oncology and beyond.

The skin microbiome plays a crucial role in skin health, influencing barrier integrity, immune responses, and disease susceptibility. Various interventions can reshape the microbiome, broadly categorized into targeted and untargeted approaches. Targeted strategies, such as phage therapy, engineered bacteria, and phage lysins, selectively modulate specific microbial populations, restoring balance and mitigating pathogenic influences. Untargeted interventions, including probiotics, prebiotics, skin microbiome transplantation, antibiotics, and lifestyle modifications, aim to enhance overall microbial diversity and stability. These strategies hold promise for innovative dermatological therapies, paving the way for personalized microbiome-based treatments. However, challenges remain in standardization, safety, and long-term ecological impact, necessitating further research for effective clinical translation.

We systematically compared and assessed the commonly used methods for studying live soil microbes, including alkaline buffer washing, propidium monoazide (PMA) treatment, DNase pre-digestion, and rRNA-based analysis, based on soils collected across the western region of China. The results showed that the elimination of extracellular DNA largely affected the analysis of soil prokaryotic abundance, diversity, community profiles, and co-occurrence patterns, but the effects varied considerably across different methods. In contrast, extracellular DNA removal exerted negligible effects on the determination of community assembly mechanisms. We also observed substantial variations in the performance of different methods in characterizing soil live prokaryotic communities, and DNase pre-digestion was recommended for studying soil live prokaryotic communities.

In this study, we explored the therapeutic effects of Parabacteroides goldsteinii (P. goldsteinii) on colitis and its underlying mechanisms. Our results indicate a significant reduction of P. goldsteinii in the stools of inflammatory bowel disease patients. We demonstrate the therapeutic potential of P. goldsteinii in mitigating dextran sulfate sodium-induced colitis and restraining tumorigenesis in the azoxymethane (AOM)/DSS mouse model under a fiber-free diet. Metabolomic profiling further revealed an enrichment of fecal secondary bile acids in response to P. goldsteinii. By employing bile salt hydrolase inhibitors and Takeda G-protein-coupled receptor 5 (Tgr5) knockout mice, we established a connection between the anti-inflammatory effect of P. goldsteinii and fecal bile acids. Furthermore, our findings highlight the modulatory role of diet in enhancing P. goldsteinii's therapeutic potential, underlining the importance of diet in future probiotic research.

The pork microbiome was investigated using an integrated approach combining isolation and metagenomic sequencing methods to comprehensively analyze the pathogens and resistome on pork surfaces. The study revealed a large number and diversity of pathogens and resistance genes, potentially originating from air, transportation, water, or cross-contamination. These findings underscore the importance of implementing multifaceted food surveillance strategies to monitor and mitigate these risks effectively.

Traditional fermented foods in synthetic microbial communities (SynComs). We simulated the production of zha-chili, a traditional fermented food, in the laboratory, intending to address the following questions: How are SynComs constructed, and what is the effect of inoculation with SynComs on the quality of zha-chilli? How does inoculation with SynComs affect microbial community succession?

This study indicated that biocontrol bacterium B. subtilis R31 is likely to prevent and control banana Fusarium wilt through regulating the structure and function of banana rhizosphere microorganism flora (especially increasing the microbial abundance of Actinomyces), and help potential biocontrol bacteria grow into the plant roots. There is a convergent secondary succession in microbial communities between naturally suppressive soil and biocontrol R31-treated soil.

The PANDA pipeline for colorectal cancer (CRC) analysis integrates metabolomic data from LC-MS with machine learning techniques to classify CRC stages and predict biomarkers. Initially, the raw metabolomic data is processed using partial least squares discriminant analysis (PLS-DA) to reduce dimensionality and highlight key features. These selected features are then used to train a neural network, which learns to classify CRC stages (T1–T4) and predict relevant biomarkers. This approach allows for a more accurate diagnosis, early detection, and identification of potential therapeutic targets, contributing to personalized treatment strategies for CRC patients.

Understanding the skin microbiome is crucial for elucidating its role in health and disease. However, comprehensive studies on the genetic and functional diversity of the skin microbiome in individuals living in extreme environments remain scarce. This work explores the unique characteristics and functional adaptations of the plateau skin microbiome, highlighting its response to environmental stressors, distinct biogeographical patterns, and interactions with host phenotypes.

We generated the first comprehensive view of RNA viral auxiliary metabolic genes (AMGs), expanding the known functional type of AMGs by 75%. RNA viruses encode a remarkably high diversity of AMGs, spanning 25 distinct functional categories. Most of these genes are linked to environmental regulation and genetic information processing, with fewer associated with nutrient cycling. Additionally, RNA viruses carrying AMGs are capable of infecting both eukaryotes and prokaryotes, and may acquire AMGs from organisms beyond their predicted host range.

We selected participants from the NHANES database from 2005 to 2016 for this cross-sectional analysis. Logistic regression and other analytical methods were utilized to analyze the relationship between the intake of dietary live microbes and nondietary prebiotic/probiotic and the prevalence of osteoarthritis (OA) and rheumatoid arthritis (RA). The findings demonstrated a direct relationship between the consumption of nondietary prebiotic/probiotic and the prevalence of developing OA, whereas a greater consumption of dietary live microbes is associated with a lower occurrence of RA. (Graphical abstract was created with BioRender.com).

The longitudinal multi-omics cohort of patients with acute coronary syndrome (LM-ACS) is designed as a real-world prospective cohort of patients with ACS requiring coronary angiography. This study aims to enroll 50,000 participants, with a thorough collection of phenotypic data and multi-omics analyses performed on biological samples. Additionally, long-term follow-up will be conducted to track the incidence of major adverse cardiovascular events (MACEs) over the participants' lifetimes. For this purpose, three follow-up scenarios have been established for ACS patients, differentiated based on whether the patients had acute myocardial infarction (AMI) or unstable angina (UA), and whether they underwent percutaneous coronary intervention (PCI) surgery. The LM-ACS cohort seeks to create a unique resource to advance our understanding of the etiology and clinical outcomes of ACS.

Little has been reported on the effect of arbuscular mycorrhizal fungi (AMF) inoculants from in vitro dual culture system on the growth of maize and its endophytic microbial community. Our results suggest that AMF inoculants play an important role in influencing maize growth and diversity of endophytic bacterial communities. AMF inoculants significantly promote maize growth, especially AMF inoculants with modified Strullu-Romand (MSR) medium. These inoculants also significantly increased the diversity of endophytic microbial communities, especially the abundance of beneficial bacterial flora, thus positively affecting maize growth. This study reveals the utility of AMF inoculants from in vitro dual culture system, which provides a basis for the development of environmentally friendly inoculants.

Breast cancer (BC), specifically HER2-positives subtype, has a poor prognosis. Nevertheless, the development of anti-HER2 therapy yielded satisfactory outcomes. Therefore, evaluating patient HER2 status and ascertaining responsiveness to anti-HER2 therapy is crucial. The advent of deep learning has propelled the artificial intelligence (AI) revolution, leading to an increased applicability of AI in predictive models. In the field of medicine, AI is an emerging modality that is gaining momentum for facilitating cancer diagnosis and treatment, particularly in the effective management of breast cancer. This study aims to provide a comprehensive review of current diagnostic and predictive models that utilize data obtained from histopathological slides, radiomics, and HER2 binding sites. Advancements and practical applications of these models were also evaluated. Additionally, we examined existing obstacles that AI encounters for anti-HER2 therapy. We also proposed future directions for integrating AI in assessing and managing anti-HER2 therapy. The findings of this study offer valuable insights into the evaluation of AI-based anti-HER2 therapy, emphasizing key concepts and obstacles that, if addressed, could facilitate the integration of AI-assisted anti-HER2 therapy. The integration of AI has the potential to enhance the precision and customization of screening and treatment protocols for HER2+ breast cancer.

TCfinder is a tumor cell identification tool, based on pathway activity and deep neural network (DNN). Across different platforms of scRNA-seq datasets, TCfinder demonstrates robust identification efficiency. It outperforms existing tumor cell identification tools and performs under sparse data. TCfinder is freely available as an R package at: https://github.com/XSLiuLab/TCfinder.

Heterosis, or hybrid vigor, is characterized by the enhanced performance of F1 progeny in terms of yield, biomass, and environmental adaptation compared to their parental lines. Recent studies underscore the significant influence of soil microbes on heterosis, revealing that plant genotypes shape microbial communities which, in turn, have the potential to support plant growth through complex host-microbe interactions. The deeper insight into microbial roles suggests innovative ways to boost crop performance and sustainability by managing the plant microbiome to further enhance heterosis

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor with a range of risk factors, including viral infections, alcoholic liver disease, exposure to fungal toxins, obesity, and type 2 diabetes. Despite advancements in early detection and treatment, HCC continues to exhibit a high rate of recurrence. Patients in advanced stages have a poor prognosis, and the survival rate after cancer metastasis is notably low. Consequently, there exists an urgent necessity for novel treatment strategies. Nanomedicine possesses superior attributes, such as targeted administration and responsive drug release within the tumor microenvironment. These systems demonstrate potential in HCC treatment by facilitating the transportation of diverse therapeutic drugs. This comprehensive review aims to delve into the application of nanoparticle drug delivery systems in the management of liver tumors, with particular emphasis on formulation, targeted strategies specific to liver tumors, and various methods of drug release. By offering insights into the utilization of nanoparticle drug delivery systems in the realm of liver tumor treatment, this review endeavors to assist readers in exploring their vast potential.

Antibiotic pretreatment is routine for chronic dacryocystitis (DC) patients. Herein, the longitudinal effects of antibiotic pretreatment before dacryocystorhinostomy for DC patients were evaluated. Conjunctival and nasal swabs were collected longitudinally from 33 DC patients with and without antibiotic pretreatment, both before dacryocystorhinostomy and at 1, 2, and 4 weeks postdacryocystorhinostomy. Additionally, conjunctival sac swabs were collected from 46 healthy volunteers and 14 other ocular diseases patients. Comparisons focused on ocular/nasal microbiota and recovery outcomes. Compared to healthy participants, DC patients without antibiotic pretreatment exhibited greater ocular microbiota diversity before dacryocystorhinostomy. Although clinical recovery rates were comparable, our results suggest that, after antibiotic pretreatment, the ocular microbiota richness and diversity, and the composition alteration tendency, significantly changed 4 weeks after surgery. This implies that the ocular microbiota was more disturbed in patients who underwent antibiotic pretreatment compared to those without such treatment. Furthermore, two types of ocular microbiota and three types of nasal microbiota were identified in ocular diseases. This study provides comprehensive data on the ocular and nasal microbiota in DC patients with and without antibiotic pretreatment, along with other ocular diseases. This finding suggested that antibiotic pretreatment may not be necessary before dacryocystorhinostomy for DC patients, especially for nonsevere cases.

Complete T2T genome assembly of sweet cherry. Chromosome doubled sweet cherry. Mutations that affecting fruit color related genes..

Fine-needle aspiration cytology and imaging examinations are commonly used diagnostic tools for papillary thyroid carcinoma (PTC). However, these methods have limitations. Inflammatory proteins have the potential to serve as diagnostic and prognostic markers, as well as treatment targets. The expression profile and diagnosis effect of inflammatory proteins in PTC are not well understood. Here, 18 healthy volunteers (as healthy control), 12 patients with nodular goiter, and 34 patients with PTC were collected to analyze serum inflammatory proteins by proximity extension assay. Receiver operating characteristic curve analysis was used to evaluate the diagnostic potential of differential expression of proteins via the area under the curve (AUC) analysis. A total of 36 differentially expressed inflammatory proteins were found among PTC, nodular goiter, and healthy control. The combination diagnosis derived from the logistic regression analysis exhibited promising diagnostic capabilities in distinguishing nodular goiter from healthy control (AUC = 0.88), distinguishing PTC from healthy control (AUC = 0.89), and distinguishing PTC from nodular goiter (AUC = 0.87). Whereas the combination diagnosis derived from the least absolute shrinkage and selection operator (LASSO) exhibited promising diagnostic capabilities in distinguishing nodular goiter from healthy control (AUC = 0.92), distinguishing PTC from healthy control (AUC = 0.93), and distinguishing PTC from nodular goiter (AUC = 0.93). Overall, this study offers potential biomarkers for distinguishing between PTC and nodular goiter in clinical practice. The combination derived from the LASSO algorithm outperforms logistic regression.

Glycosylation plays a pivotal role in the physiological and pathological processes of male reproduction. It impacts thousands of proteins and is actively ongoing through all stages of reproduction, including spermatogenesis, maturation, capacitation, and fertilization. However, our grasp on glycosylation within male reproductive processes remains limited, largely due to the technical hurdles. Recent advancements have seen the mapping of the glycoproteome of human semen, utilizing cutting-edge glycoproteomic technologies. This breakthrough lays the groundwork for in-depth research into the influence of glycosylation on male reproductive system and related disorders. Nevertheless, the field faces numerous challenges that necessitate further advancements in glycoproteomic methodologies. In this analysis, we evaluated the potential applications of advanced glycoproteomic techniques in the study of male reproduction and summarized the detailed profiling of the human semen glycome and glycoproteome. Our current understanding of glycosylation's role within the male reproductive system alongside recent progress in glycoproteomics may equip biologists with a comprehensive insight. Furthermore, this analysis brought together findings on abnormal glycosylation and its link to male reproductive disorders in the view of glycomics and glycoproteomics. It can facilitate the clinical application of glyco-related biomarkers and targets in the treatment of infertility.

The 45 diploid cotton species identified worldwide exhibit remarkable morphological diversity. Modern cotton breeding is limited by incomplete understanding of the genetic variation in these species, suggesting a need for pan-genomic comprehensive analyses. In this study, a high-quality super pan-genome was built using 22 representative diploid cottons species and their adaptive evolution was investigated. The genomes of the twenty-two species yielded an average of 923,706 transposable elements (TEs) per assembly, with TE proportions ranging from 62.29% to 88.92%. The inferring ancestor genome structure (IAGS) showed that the D5 genome was closer to the ancestor, and the K2 genome accumulated more fissions and fusions. A gene-based super pan-genome identified 67,807 genes, including 22,384 core, 34,093 variable, and 11,330 specific genes. The structural variations (SVs) were unevenly distributed on the chromosomes, and 321 hotspot regions were detected, containing 90 genes associated with fiber initiation and/or elongation. During the eastward diffusion of diploid cotton, the genome size and structure experienced significant changes. We investigated the foliar nectary in 17 diploid cotton species, and identified a 444-bp deletion in the promoter sequence of GoNe that explained the lack of foliar nectary in G. gossypiodes (D6) and G. schwendimanii (D11). This pan-genome construction and comprehensive analysis for diploid cotton provided insight into dynamic genomic variation during diploid cotton expansion and can facilitate effective modern cotton breeding.

Bacteria often exist and function as a community, known as the bacterial microbiota, which consists of vast numbers of bacteria belonging to many bacterial species (taxa). Characterizing the bacterial microbiota needs high-throughput approaches that enable the identification and quantification of many bacterial cells, and such approaches have been under development for more than 30 years. In this review, we describe the history of high-throughput technologies based on 16S ribosomal RNA (rRNA) gene-amplicon sequencing for the characterization of bacterial microbiotas. Then, we summarize the features and applications of current 16S rRNA gene-amplicon sequencing approaches, including a recent achievement that enables the identification of individual cells with single-base accuracy for 16S rRNA genes and the quantification of many identified cells. Furthermore, we present the prospects for further technical development, including the combined use of high-throughput methods and other informative analyses, such as whole-genome sequencing in the common unit of the cell, which enables bacterial microbiota characterization based on both the number of cells and their functions.

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32-item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org.

Dysregulation of the gut microbiota often leads to immune-related disorders, indigestion, or diarrhea. Here, Jiaxing Black (JXB) pig, a local Chinese pig breed known for its great tolerance and digestibility of nutrients, was employed for a metagenomic and transcriptomic integrative analysis to reveal the gut microbiota-genes and gut microbiota-pathway interactions. A total of 452 differentially expressed genes, and 174 phyla were found between the JXB and the Duroc × Landrace × Yorkshire (DLY) pigs. Detailed analysis revealed that the differences in colon gene expression signatures between the JXB and DLY are mainly enriched in metabolic and inflammatory responses, with Lactobacillus and Lachnospiraceae enriched in DLY and JXB, respectively. Notably, Pacebacteria, Streptophyta, and Aerophobetes were found to participate in the PI3K-Akt mediated immune response in both pig breeds; however, they only accelerated the metabolism in the intestines of JXB pigs. Moreover, the host could regulate microbe metabolism and immune response by Ig-like domain-containing protein and ITIH2, PAEP, and TDRD9, respectively. Taken together, our results revealed both common and breed-specific regulations of host genes by gut microbiota in two pig breeds.