Elevated incidence of somatic mutations at prevalent genetic sites.

The common loci represent a distinct set of the human genome sites that harbor genetic variants found in at least 1% of the population. Small somatic mutations occur at the common loci and non-common loci, i.e. csmVariants and ncsmVariants, are presumed with similar probabilities. However, our work revealed that within the coding region, common loci constituted only 1.03% of all loci, yet they accounted for 5.14% of TCGA somatic mutations. Furthermore, the small somatic mutation incidence rate at these common loci was 2.7 times that observed in the non-common. Notably, the csmVariants exhibited an impressive recurrent rate of 36.14%, which was 2.59 times of the ncsmVariants. The C-to-T transition at the CpG sites accounted for 32.41% of the csmVariants, which was 2.93 times for the ncsmVariants. Interestingly, the aging-related mutational signature contributed to 13.87% of the csmVariants, 5.5 times that of ncsmVariants. Moreover, 35.93% of the csmVariants contexts exhibited palindromic features, outperforming ncsmVariant contexts by 1.84 times. Notably, cancer patients with higher csmVariants rates had better progression-free survival. Furthermore, cancer patients with high-frequency csmVariants enriched with mismatch repair deficiency were also associated with better progression-free survival. The accumulation of csmVariants during cancerogenesis is a complex process influenced by various factors. These include the presence of a substantial percentage of palindromic sequences at csmVariants sites, the impact of aging and DNA mismatch repair deficiency. Together, these factors contribute to the higher somatic mutation incidence rates of common loci and the overall accumulation of csmVariants in cancer development.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

Translational informatics for human microbiota: data resources, models and applications.

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.

scFed: federated learning for cell type classification with scRNA-seq.

The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and complexity in biological tissues. However, the nature of large, sparse scRNA-seq datasets and privacy regulations present challenges for efficient cell identification. Federated learning provides a solution, allowing efficient and private data use. Here, we introduce scFed, a unified federated learning framework that allows for benchmarking of four classification algorithms without violating data privacy, including single-cell-specific and general-purpose classifiers. We evaluated scFed using eight publicly available scRNA-seq datasets with diverse sizes, species and technologies, assessing its performance via intra-dataset and inter-dataset experimental setups. We find that scFed performs well on a variety of datasets with competitive accuracy to centralized models. Though Transformer-based model excels in centralized training, its performance slightly lags behind single-cell-specific model within the scFed framework, coupled with a notable time complexity concern. Our study not only helps select suitable cell identification methods but also highlights federated learning's potential for privacy-preserving, collaborative biomedical research.

Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

Pro- and anti-inflammatory cytokines are the game-changers in childhood obesity-associated metabolic disorders (diabetes and non-alcoholic fatty liver diseases).

Childhood obesity is a chronic inflammatory epidemic that affects children worldwide. Obesity affects approximately 1 in 5 children worldwide. Obesity in children can worsen weight gain and raise the risk of obesity-related comorbidities like diabetes and non-alcoholic fatty liver disease (NAFLD). It can also negatively impact the quality of life for these children. Obesity disrupts immune system function, influencing cytokine (interleukins) balance and expression levels, adipokines, and innate and adaptive immune cells. The altered expression of immune system mediators, including interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-17 (IL-17), interleukin-18 (IL-18), transforming growth factor (TGF), tumor necrosis factor (TNF), and others, caused inflammation, progression, and the development of pediatric obesity and linked illnesses such as diabetes and NAFLD. Furthermore, anti-inflammatory cytokines, including interleukin-2 (IL-2), have been shown to have anti-diabetes and IL-1 receptor antagonist (IL-1Ra) anti-diabetic and pro-NAFLFD properties, and interleukin-10 (IL-10) has been shown to have a dual role in managing diabetes and anti-NAFLD. In light of the substantial increase in childhood obesity-associated disorders such as diabetes and NAFLD and the absence of an effective pharmaceutical intervention to inhibit immune modulation factors, it is critical to consider the alteration of immune system components as a preventive and therapeutic approach. Thus, the current review focuses on the most recent information regarding the influence of pro- and anti-inflammatory cytokines (interleukins) and their molecular mechanisms on pediatric obesity-associated disorders (diabetes and NAFLD). Furthermore, we discussed the current therapeutic clinical trials in childhood obesity-associated diseases, diabetes, and NAFLD.

Pro-inflammatory cytokines and CXC chemokines as game-changer in age-associated prostate cancer and ovarian cancer: Insights from preclinical and clinical studies' outcomes.

Prostate cancer (PC) and Ovarian cancer (OC) are two of the most common types of cancer that affect the reproductive systems of older men and women. These cancers are associated with a poor quality of life among the aged population. Therefore, finding new and innovative ways to detect, treat, and prevent these cancers in older patients is essential. Finding biomarkers for these malignancies will increase the chance of early detection and effective treatment, subsequently improving the survival rate. Studies have shown that the prevalence and health of some illnesses are linked to an impaired immune system. However, the age-associated changes in the immune system during malignancies such as PC and OC are poorly understood. Recent research has suggested that the excessive production of inflammatory immune mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor (TGF), tumor necrosis factor (TNF), CXC motif chemokine ligand 1 (CXCL1), CXC motif chemokine ligand 12 (CXCL12), and CXC motif chemokine ligand 13 (CXCL13), etc., significantly impact the development of PC and OC in elderly patients. Our review focuses on the latest functional studies of pro-inflammatory cytokines (interleukins) and CXC chemokines, which serve as biomarkers in elderly patients with PC and OC. Thus, we aim to shed light on how these biomarkers affect the development of PC and OC in elderly patients. We also examine the current status and future perspective of cytokines (interleukins) and CXC chemokines-based therapeutic targets in OC and PC treatment for elderly patients.

The role of proinflammatory cytokines and CXC chemokines (CXCL1-CXCL16) in the progression of prostate cancer: insights on their therapeutic management.

Reproductive cancers are malignancies that develop in the reproductive organs. One of the leading cancers affecting the male reproductive system on a global scale is prostate cancer (PCa). The negative consequences of PCa metastases endure and are severe, significantly affecting mortality and life quality for those who are affected. The association between inflammation and PCa has captured interest for a while. Inflammatory cells, cytokines, CXC chemokines, signaling pathways, and other elements make up the tumor microenvironment (TME), which is characterized by inflammation. Inflammatory cytokines and CXC chemokines are especially crucial for PCa development and prognosis. Cytokines (interleukins) and CXC chemokines such as IL-1, IL-6, IL-7, IL-17, TGF-ß, TNF-α, CXCL1-CXCL6, and CXCL8-CXCL16 are thought to be responsible for the pleiotropic effects of PCa, which include inflammation, progression, angiogenesis, leukocyte infiltration in advanced PCa, and therapeutic resistance. The inflammatory cytokine and CXC chemokines systems are also promising candidates for PCa suppression and immunotherapy. Therefore, the purpose of this work is to provide insight on how the spectra of inflammatory cytokines and CXC chemokines evolve as PCa develops and spreads. We also discussed recent developments in our awareness of the diverse molecular signaling pathways of these circulating cytokines and CXC chemokines, as well as their associated receptors, which may one day serve as PCa-targeted therapies. Moreover, the current status and potential of theranostic PCa therapies based on cytokines, CXC chemokines, and CXC receptors (CXCRs) are examined.

Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses.

BACKGROUND: Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE). METHODS: We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions. RESULTS: ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT's performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening. CONCLUSIONS: These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications.

MOTIVATION: In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance. RESULTS: The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions. AVAILABILITY AND IMPLEMENTATION: CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prematurely delivering mothers show reductions of lachnospiraceae in their gut microbiomes.

BACKGROUND: Preterm birth is the leading cause of perinatal morbidity and mortality. Despite evidence shows that imbalances in the maternal microbiome associates to the risk of preterm birth, the mechanisms underlying the association between a perturbed microbiota and preterm birth remain poorly understood. METHOD: Applying shotgun metagenomic analysis on 80 gut microbiotas of 43 mothers, we analyzed the taxonomic composition and metabolic function in gut microbial communities between preterm and term mothers. RESULTS: Gut microbiome of mothers delivering prematurely showed decreased alpha diversity and underwent significant reorganization, especially during pregnancy. SFCA-producing microbiomes, particularly species of Lachnospiraceae, Ruminococcaceae, and Eubacteriaceae, were significantly depleted in preterm mothers. Lachnospiraceae and its species were the main bacteria contributing to species' differences and metabolic pathways. CONCLUSION: Gut microbiome of mothers delivering prematurely has altered and demonstrates the reduction of Lachnospiraceae.

The panda-derived Lactiplantibacillus plantarum BSG201683 improves LPS-induced intestinal inflammation and epithelial barrier disruption in vitro.

Captive pandas are suffering from intestinal infection due to intestinal microbiota characterized by a high abundance of Enterobacteriaceae induced by long-term captivity. Probiotic supplements showed improvement in intestinal barrier function and inflammation. However, the effects of panda-derived probiotics on the intestinal epithelium and inflammation have not been elucidated. In the present study, lipopolysaccharide (LPS) impaired Caco-2 and RAW264.7 inflammatory models were applied to assess the protection of Lactiplantibacillus plantarum BSG201683 (L. plantarum G83) on barrier disruption and inflammation. The results showed that treatment with L. plantarum G83 significantly decreased the paracellular permeability to fluorescein isothiocyanate conjugated dextran (MW 4000, FITC-D4) after LPS induction. Meanwhile, L. plantarum G83 alleviated the reduction in tight junction (TJ) proteins and downregulated proinflammatory cytokines caused by LPS in Caco-2 cells. L. plantarum G83 also significantly decreased the expression and secretion of pro-inflammatory cytokines in LPS-induced RAW264.7 cells. In addition, the IL-10 increased in both Caco-2 and RAW264.7 cells after L. plantarum G83 treatment. The phagocytosis activity of RAW264.7 cells was significantly increased after L. plantarum G83 treatment. Toll-like receptor 4/ nuclear factor kappa-B (TLR4/NF-κB) signaling pathways were significantly down-regulated after L. plantarum G83 intervention, and the phosphorylation of NF-κB/p65 was consistent with this result. Our findings suggest that L. plantarum G83 improves intestinal inflammation and epithelial barrier disruption in vitro.

A narrative review: CXC chemokines influence immune surveillance in obesity and obesity-related diseases: Type 2 diabetes and nonalcoholic fatty liver disease.

Adipose tissue develops lipids, aberrant adipokines, chemokines, and pro-inflammatory cytokines as a consequence of the low-grade systemic inflammation that characterizes obesity. This low-grade systemic inflammation can lead to insulin resistance (IR) and metabolic complications, such as type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD). Although the CXC chemokines consists of numerous regulators of inflammation, cellular function, and cellular migration, it is still unknown that how CXC chemokines and chemokine receptors contribute to the development of metabolic diseases (such as T2D and NAFLD) during obesity. In light of recent research, the objective of this review is to provide an update on the linkage between the CXC chemokine, obesity, and obesity-related metabolic diseases (T2D and NAFLD). We explore the differential migratory and immunomodulatory potential of CXC chemokines and their mechanisms of action to better understand their role in clinical and laboratory contexts. Besides that, because CXC chemokine profiling is strongly linked to leukocyte recruitment, macrophage recruitment, and immunomodulatory potential, we hypothesize that it could be used to predict the therapeutic potential for obesity and obesity-related diseases (T2D and NAFLD).

Whole-genome sequencing identifies rare missense variants of WNT16 and ERVW-1 causing the systemic lupus erythematosus.

Systemic lupus erythematosus (SLE, OMIM 152700) is a rare autoimmune disease with high heritability that affects ~0.1% of the population. Previous studies have revealed several common variants with small effects in European and East Asian SLE patients. However, there is still no rare variant study on Chinese SLE patients using the whole-genome sequencing technology (WGS). Here, we designed a family based WGS study to identify novel rare variants with large effects. Based on large-scale allele frequency data from the gnomAD database, we identified rare protein-coding gene variants with disruptive and sequence-altering impacts in SLE patients. We found that the burden of rare variants was significantly higher than that of common variants in patients, suggesting a larger effect of rare variants on the SLE pathogenesis. We identified the pathogenic risk of rare missense variants with significant odds ratios (p < 0.05) in two genes, including WNT16 (NC_000007.14:g.121329757G > C, NP_057171.2:p.(Ala86Pro) and 7 g.121329760G > C, NP_057171.2:p.(Ala87Pro)), which explains five out of seven patients covering all three families but are absent from all controls, and ERVW-1 (NC_000007.14:g.92469882A > G, NP_001124397.1:p.(Leu167Pro), rs74545114; NC_000007.14:g.92469907G > A, NP_001124397.1:p.(Arg159Cys), rs201142302; NC_000007.14:g.92469919G > A, NP_001124397.1:p.(His155Tyr), rs199552228), which explains the other two patients. None of these variants were identified in any of the controls. These associations are supported by known gene expression studies in SLE patients based on literature review. We further tested the wild and mutant types using the luciferase assays and qPCR in cells. We found that WNT16 can activate the canonical Wnt/ß-catenin pathway while the mutant cannot. Additionally, the wild ERVW-1 expression can be significantly up-regulated by cAMP while the mutant cannot. Our study provides the first direct genetic and in vitro evidence for the pathogenic risk of mutant WNT16 and ERVW-1, which may facilitate the design of precision therapy for SLE.

[Ontologies Applied in Clinical Decision Support Systems for Diabetes].

A clinical decision support system (CDSS) integrated with electronic health records helps physicians at the grassroots make patient-appropriate and evidence-based treatment decisions and improves the efficiency of diagnosis and treatment. Furthermore, using ontologies to build up the medical knowledge base and patient data for CDSS enhances the automation and transparency of the reasoning process of CDSS and helps generate interpretable and accurate treatment recommendations. Herein, we reviewed the relevant ontologies in the field of diabetes treatment and the progress and challenges concerning ontology-based CDSSs. Firstly, we elaborated on the current status and challenges of diabetes treatment in China, highlighting the urgent need to improve the efficiency and quality of medical services. Then, we presented background information about ontologies and gave an overview of the framework, methodology, and features of using ontologies to construct CDSS. After that, we reviewed the ontologies and instances of ontology-based CDSS in the field of diabetes treatment in China and abroad and summarized their construction methods and features. Last but not the least, we discussed the future prospects of the field, suggesting that integrating evidence-based medicine with ontologies to build a reliable clinical recommendation system should be the current focus of CDSS development.

Decoding competing endogenous RNA networks for cancer biomarker discovery.

Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system. Therefore, discovering biomarkers that indicate cancer initiation, development, and/or therapeutic responses via reconstructing and analyzing ceRNA networks is of clinical significance. In this review, the regulatory function of ceRNAs in cancer and crucial determinants of ceRNA crosstalk are firstly discussed to gain a global understanding of ceRNA-mediated carcinogenesis. Then, computational and experimental approaches for ceRNA network reconstruction and ceRNA validation, respectively, are described from a systems biology perspective. We focus on strategies for biomarker identification based on analyzing ceRNA networks and highlight the translational applications of ceRNA biomarkers for cancer management. This article will shed light on the significance of miRNA-mediated ceRNA interactions and provide important clues for discovering ceRNA network-based biomarker in cancer biology, thereby accelerating the pace of precision medicine and healthcare for cancer patients.

HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications.

MOTIVATION: Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary. RESULTS: In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine. AVAILABILITY AND IMPLEMENTATION: The platform is openly available at http://sysbio.org.cn/HFBD/.

Natural products derived from medicinal plants and microbes might act as a game-changer in breast cancer: a comprehensive review of preclinical and clinical studies.

Breast cancer (BC) is the most prevalent neoplasm among women. Genetic and environmental factors lead to BC development and on this basis, several preventive - screening and therapeutic interventions have been developed. Hormones, both in the form of endogenous hormonal signaling or hormonal contraceptives, play an important role in BC pathogenesis and progression. On top of these, breast microbiota includes both species with an immunomodulatory activity enhancing the host's response against cancer cells and species producing proinflammatory cytokines associated with BC development. Identification of novel multitargeted therapeutic agents with poly-pharmacological potential is a dire need to combat advanced and metastatic BC. A growing body of research has emphasized the potential of natural compounds derived from medicinal plants and microbial species as complementary BC treatment regimens, including dietary supplements and probiotics. In particular, extracts from plants such as Artemisia monosperma Delile, Origanum dayi Post, Urtica membranacea Poir. ex Savigny, Krameria lappacea (Dombey) Burdet & B.B. Simpson and metabolites extracted from microbes such as Deinococcus radiodurans and Streptomycetes strains as well as probiotics like Bacillus coagulans and Lactobacillus brevis MK05 have exhibited antitumor effects in the form of antiproliferative and cytotoxic activity, increase in tumors' chemosensitivity, antioxidant activity and modulation of BC - associated molecular pathways. Further, bioactive compounds like 3,3'-diindolylmethane, epigallocatechin gallate, genistein, rutin, resveratrol, lycopene, sulforaphane, silibinin, rosmarinic acid, and shikonin are of special interest for the researchers and clinicians because these natural agents have multimodal action and act via multiple ways in managing the BC and most of these agents are regularly available in our food and fruit diets. Evidence from clinical trials suggests that such products had major potential in enhancing the effectiveness of conventional antitumor agents and decreasing their side effects. We here provide a comprehensive review of the therapeutic effects and mechanistic underpinnings of medicinal plants and microbial metabolites in BC management. The future perspectives on the translation of these findings to the personalized treatment of BC are provided and discussed.

Detection and Prevention of Virus Infection.

The pathogenic mechanism of viral infection is a complex process involving viral mutation, viral integration, and various aspects of the interaction between the viral genome and the host. Moreover, the virus mutation will lead to the failure of related vaccines, leading to the increasing of vaccine development costs and difficulties in virus prevention. With the accumulation of various types of data, using bioinformatics methods to mine the potential viral characteristics of the pathogenic process can help virus detection and diagnosis, to take intervention measures to prevent disease development or develop effective antiviral therapies. In this chapter, we first outlined traditional approaches and emerging technologies of virus detection and prevention, and then summarized the latest developments in the bioinformatics methods application in different fields of virus researches. The emergence of artificial intelligence provides advanced analysis techniques for revealing key factors of virus infection and has been widely used in the virology community. In particular, we highlight machine learning and deep learning algorithms to identify factors/categories from complex multidimensional data and uncover novel patterns of virus or disease risk prediction.

5G, Big Data, and AI for Smart City and Prevention of Virus Infection.

With the development of urbanization, artificial intelligence, communication technology, and the Internet of Things, cities have evolved a new ecology from traditional city structures, that is, smart city. Combining 5G and big data, the applications of smart cities have been extended to every aspect of residents' lives. Based on the popularization of communication equipment and sensors, the great improvement in data transmission and processing technology, the production efficiency in medical field, industrial field, and security field has been improved. This chapter introduces the current research related to smart cities, including its architecture, technologies, and equipment involved. Then it discussed the challenges and opportunities of explainable artificial intelligence (XAI), which is the next important development direction of AI, especially in the medical field, where patients and medical personnel have non-negligible needs for the interpretability of AI models. Then, taking COVID-19 as an example, it discussed how smart cities play a role during virus infection and introduced the specific applications designed so far. Finally, it discussed the shortcomings of the current situation and the aspects that can be improved in the future.