A chimeric mRNA vaccine of S-RBD with HA conferring broad protection against influenza and COVID-19 variants.

Influenza and coronavirus disease 2019 (COVID-19) represent two respiratory diseases that have significantly impacted global health, resulting in substantial disease burden and mortality. An optimal solution would be a combined vaccine capable of addressing both diseases, thereby obviating the need for multiple vaccinations. Previously, we conceived a chimeric protein subunit vaccine targeting both influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), utilizing the receptor binding domain of spike protein (S-RBD) and the stalk region of hemagglutinin protein (HA-stalk) components. By integrating the S-RBD from the SARS-CoV-2 Delta variant with the headless hemagglutinin (HA) from H1N1 influenza virus, we constructed stable trimeric structures that remain accessible to neutralizing antibodies. This vaccine has demonstrated its potential by conferring protection against a spectrum of strains in mouse models. In this study, we designed an mRNA vaccine candidate encoding the chimeric antigen. The resultant humoral and cellular immune responses were meticulously evaluated in mouse models. Furthermore, the protective efficacy of the vaccine was rigorously examined through challenges with either homologous or heterologous influenza viruses or SARS-CoV-2 strains. Our findings reveal that the mRNA vaccine exhibited robust immunogenicity, engendering high and sustained levels of neutralizing antibodies accompanied by robust and persistent cellular immunity. Notably, this vaccine effectively afforded complete protection to mice against H1N1 or heterosubtypic H5N8 subtypes, as well as the SARS-CoV-2 Delta and Omicron BA.2 variants. Additionally, our mRNA vaccine design can be easily adapted from Delta RBD to Omicron RBD antigens, providing protection against emerging variants. The development of two-in-one vaccine targeting both influenza and COVID-19, incorporating the mRNA platform, may provide a versatile approach to combating future pandemics.

An expanded database and analytical toolkit for identifying bacterial virulence factors and their associations with chronic diseases.

Virulence factor genes (VFGs) play pivotal roles in bacterial infections and have been identified within the human gut microbiota. However, their involvement in chronic diseases remains poorly understood. Here, we establish an expanded VFG database (VFDB 2.0) consisting of 62,332 nonredundant orthologues and alleles of VFGs using species-specific average nucleotide identity ( https://github.com/Wanting-Dong/MetaVF_toolkit/tree/main/databases ). We further develop the MetaVF toolkit, facilitating the precise identification of pathobiont-carried VFGs at the species level. A thorough characterization of VFGs for 5452 commensal isolates from healthy individuals reveals that only 11 of 301 species harbour these factors. Further analyses of VFGs within the gut microbiomes of nine chronic diseases reveal both common and disease-specific VFG features. Notably, in type 2 diabetes patients, long HiFi sequencing confirms that shared VF features are carried by pathobiont strains of Escherichia coli and Klebsiella pneumoniae. These findings underscore the critical importance of identifying and understanding VFGs in microbiome-associated diseases.

Differential contribution of microbial and plant-derived organic matter to soil organic carbon sequestration over two decades of natural revegetation and cropping.

Both natural revegetation and cropping have great impact on long-term soil carbon (C) sequestration, yet the differences in their underlying mechanisms remain unclear. In this study, we investigated trends in soil organic C (SOC) accumulation during natural revegetation (VR) and cropping processes over 24 years, and explored the contributions of microbial necromass and plant-derived C to SOC formation and their primary controls. Over the course of 24 years of land use/cover change (LUCC) from 1995, SOC content exhibited a more substantial increase in VR (0.31 g kg-1 a-1) than in cropland (0.14 g kg-1 a-1) during Stage II (>10 y after LUCC), and recalcitrant organic carbon explained more of the SOC variation than easily oxidizable carbon. The higher SOC content in VR was attributed to a greater contribution of plant-derived C (14-28 %) than that in cropland (3-11 %) to SOC and a consistently lower ratio of cinnamyl (C)- to vanillyl (V)-type phenols in VR across all the assessed years. Although there were higher proportion of microbial necromass of SOC (41-84 %) in cropland than in VR, the differences were not significant. The dominant bacterial phylum of Chloroflexi and soil nitrogen content were the primary biotic and abiotic factors regulating microbial-derived and plant-derived C in both cropland and VR. However, soil phosphorus content was the main factor in cropland, while climatic factors such as mean annual precipitation were more important in VR. These results provided evidence that long-term natural revegetation enhanced SOC sequestration by greater contribution of plant-derived C to SOC formation compared to cropping. These findings underscore the synergistic contribution of vegetation and microorganisms to long-term SOC sequestration, offering insights into the different mechanisms of carbon formation during VR and cropping processes, and providing support for optimizing land management to achieve global carbon neutrality goals.

Prognostic significance of exportin-5 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. As liver cancer often presents no noticeable symptoms in its early stages, most patients are diagnosed at an advanced stage, complicating treatment. Therefore, the identification of new biomarkers is crucial for the early detection and treatment of HCC. Research on exportin-5 (XPO5) could offer new avenues for early diagnosis and improve treatment strategies. AIM: To explore the role of XPO5 in HCC progression and its potential as a prognostic biomarker. METHODS: This study assessed XPO5 mRNA expression in HCC using The Cancer Genome Atlas, TIMER, and International Cancer Genome Consortium databases, correlating it with clinical profiles and disease progression. We performed in vitro experiments to examine the effect of XPO5 on liver cell growth. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were used to elucidate the biological roles and signaling pathways. We also evaluated XPO5's impact on immune cell infiltration and validated its prognostic potential using machine learning. RESULTS: XPO5 was significantly upregulated in HCC tissues, correlating with tumor grade, T-stage, and overall survival, indicating poor prognosis. Enrichment analyses linked high XPO5 expression with tumor immunity, particularly CD4 T cell memory activation and macrophage M0 infiltration. Drug sensitivity tests identified potential therapeutic agents such as MG-132, paclitaxel, and WH-4-023. Overexpression of XPO5 in HCC cells, compared to normal liver cells, was confirmed by western blotting and quantitative real-time polymerase chain reaction. The lentiviral transduction-mediated knockdown of XPO5 significantly reduced cell proliferation and metastasis. Among the various machine learning algorithms, the C5.0 decision tree algorithm achieved accuracy rates of 95.5% in the training set and 92.0% in the validation set. CONCLUSION: Our analysis shows that XPO5 expression is a reliable prognostic indicator for patients with HCC and is significantly associated with immune cell infiltration.

Metabolomics of Benzene Exposure and Development of Biomarkers for Exposure Hazard Assessment.

Benzene, a common industrial solvent, poses significant health risks including poisoning and hematopoietic diseases. However, its precise toxicity mechanisms remain unclear. To assess the health impact of prolonged benzene exposure through metabolomic analyses of exposed workers and benzene-poisoned mice, aiming to identify biomarkers and minimize occupational hazards. This study compared 18 benzene-exposed workers with 18 non-exposed workers, matching for age, lifestyle, and BMI. The metabolites in the workers' samples were analyzed using ultra-high-performance liquid chromatography and mass spectrometry. A larger study included 118 exposed and 158 non-exposed workers, incorporating surveys and routine blood and urine tests with differential metabolites targeted via an enzyme-linked immunosorbent assay. The animal studies consisted of two 15- and 60-day benzene staining and control experiments on 28 C57BL/6J mice, followed by sample collection and organ analysis. The data analysis employed eXtensible Computational Mass Spectrometry (XCMS), Python, MetaboAnalyst 6.0, and SPSS24.0. The exposed workers exhibited altered metabolites indicating external benzene exposure, lower glucose levels, and changes in white blood cell counts and urinary ketone bodies. The plasma metabolomics revealed disturbances in energy and lipid metabolism. The benzene-exposed mice displayed reduced weight gain, behavioral changes, and organ damage. Oxidative stress and abnormal purine and lipid metabolism were observed in both the long-term benzene-exposed workers and benzene-exposed mice. Metabolic markers for the early detection of benzene exposure hazards were identified, underscoring the need to mitigate occupational risks.

UBE2L3 promotes benzene-induced hematotoxicity via autophagy-dependent ferroptosis.

Benzene is a common environmental pollutant and significant health hazard. Low-dose benzene exposure is common in most industrial settings, and some workers exhibit hematotoxicity characterized by impaired hematopoietic function. Consequently, understanding the early hematopoietic damage and biomarkers associated with low-dose benzene exposure is of critical importance for health risk assessment. Using data from a 5-year prospective cohort study on benzene exposure and the National Center for Biotechnology Information's Gene Expression Omnibus database, we detected significant downregulation of the ubiquitin-conjugating enzyme UBE2L3 (E2) in benzene-exposed subjects compared to control subjects. Liquid chromatography tandem mass spectrometry and co-immunoprecipitation experiments illustrated the binding interaction between UBE2L3 and the ubiquitin-protein ligase ZNF598 (E3). We applied deep learning algorithms to predict candidate interacting proteins and then conducted validation via co-immunoprecipitation experiments, which showed that ZNF598 engages in binding with the autophagy protein LAMP-2. Subsequent overexpression and knockdown of UBE2L3 coupled with immunofluorescence experiments and transmission electron microscopy revealed that UBE2L3 disrupts the ubiquitination-degradation of LAMP-2 by ZNF598, reduces GPX4 expression levels, and activates an autophagy-dependent ferroptosis pathway. It also leads to increased lipid peroxidation, thereby promoting ferroptosis and contributing to the hematotoxicity induced by benzene. In summary, our results suggest that UBE2L3 may be involved in early hematopoietic damage by modulating the autophagy-dependent ferroptosis signaling pathway in benzene-induced hematotoxicity.

Colonization of bacterial and viral respiratory pathogens among healthcare workers in China during COVID-19 pandemic.

Background: Healthcare settings may amplify transmission of respiratory pathogens, however empirical evidence is lacking. We aimed to describe the spectrum and distribution of respiratory pathogens among healthcare workers in eastern China. Methods: Healthcare workers were recruited from October 2020 to November 2021 in Jiangsu province. Participants were interviewed regarding demographic and hospital-based protective measures. Thirty-seven common respiratory pathogens were tested using real-time PCR/RT-PCR (Probe qPCR). The role of demographic and hospital-based protective measures on pathogens colonization using multivariable logistic regression models. Results: Among 316 enrolled healthcare workers, a total of 21 pathogens were detected. In total, 212 (67.1%) healthcare workers had at least one respiratory pathogen; 195 (61.7%) and 70 (22.2%) with a bacterial and viral pathogen. The most commonly detected pathogen was streptococcus pneumoniae (47.5%) followed by Haemophilus influenzae (21.2%). One hundred and five (33.2%) healthcare workers with copathogens had at least two respiratory pathogens. Both bacterial and viral colonization were more common in 2020 compared to 2021. A decreased risk of colonization was seen in participants with infection prevention and control training and suitable hand hygiene. Conclusions: Colonization of respiratory pathogens in healthcare workers from eastern China was high. Differential risk was impacted only by hospital-based protective measures and not demographic factors.

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: results from a prospective cohort study.

BACKGROUND: The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. RESEARCH DESIGN AND METHODS: This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. RESULTS: Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while ß-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. CONCLUSIONS: Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

Characteristics of Vaginal Microbiota of Women of Reproductive Age with Infections.

The vaginal microbiota can be classified into five major community state types (CSTs) based on the bacterial content. However, the link between different CST subtypes and vaginal infection remains unclear. Here, we analyzed 2017 vaginal microbiota samples from women of a reproductive age with vaginal infections that were published in the last decade. We found that L. iners was the most dominant in 34.8% of the vaginal samples, followed by L. crispatus (21.2%). CST I was common in healthy individuals, whereas CST III and IV were associated with dysbiosis and infection. CST III-B, IV-A, IV-B, and IV-C0 were prevalent in patients with bacterial vaginosis (BV). Based on the relative abundance of bacteria at the (sub)genus level, a random forest classifier was developed to predict vaginal infections with an area under the curve of 0.83. We further identified four modules of co-occurring bacterial taxa: L. crispatus, Gardnerella, Prevotella, and Bacteroides. The functional prediction revealed that nucleotide biosynthesis pathways were upregulated in patients with human papilloma virus, and carbohydrate degradation pathways were downregulated in patients with BV. Overall, our study identified the bacterial signatures of healthy and infected vaginal microbiota, providing unique insights into the clinical diagnosis and health status prediction of women of a reproductive age.

Long-term chemical and organic fertilization induces distinct variations of microbial associations but unanimous elevation of soil multifunctionality.

Intricate microbial associations contribute greatly to the multiple functions (multifunctionality) of natural ecosystems. However, the relationship between microbial associations and soil multifunctionality (SMF) in artificial ecosystems, particularly in agricultural ecosystem with frequent fertilization, remains unclear. In this study, based on a 28-year paddy field experiment, high-throughput sequencing and networks analysis was performed to investigate changes in soil microbial (archaea, bacteria, fungi, and protists) associations and how these changes correlate with SMF under long-term fertilization. Compared to no fertilization (CK), both chemical fertilization with N, P, and K (CF) and chemical fertilization plus rice straw retention (CFR) treatments showed significantly higher soil nutrient content, grain yield, microbial abundance, and SMF. With the exception of archaeal diversity, the CF treatment exhibited the lowest bacterial, fungal, and protist diversity, and the simplest microbial co-occurrence network. In contrast, the CFR treatment had the lowest archaeal diversity, but the highest bacterial, fungal, and protist diversity. Moreover, the CFR treatment exhibited the most complex microbial co-occurrence network with the highest number of nodes, edges, and interkingdom edges. These results highlight that both chemical fertilization with and without straw retention caused high ecosystem multifunctionality while changing microbial association oppositely. Furthermore, these results indicate that rice straw retention contributes to the development of the soil microbiome and ensures the sustainability of high-level ecosystem multifunctionality.

Bacteriophage-resistant carbapenem-resistant Klebsiella pneumoniae shows reduced antibiotic resistance and virulence.

Phage therapy has shown great promise in the treatment of bacterial infections. However, the effectiveness of phage therapy is compromised by the inevitable emergence of phage-resistant strains. In this study, a phage-resistant carbapenem-resistant Klebsiella pneumoniae strain SWKP1711R, derived from parental carbapenem-resistant K. pneumoniae strain SWKP1711 was identified. The mechanism of bacteriophage resistance in SWKP1711R was investigated and the molecular determinants causing altered growth characteristics, antibiotic resistance, and virulence of SWKP1711R were tested. Compared to SWKP1711, SWKP1711R showed slower growth, smaller colonies, filamentous cells visible under the microscope, reduced production of capsular polysaccharide (CPS) and lipopolysaccharide, and reduced resistance to various antibiotics accompanied by reduced virulence. Adsorption experiments showed that phage vB_kpnM_17-11 lost the ability to adsorb onto SWKP1711R, and the adsorption receptor was identified to be bacterial surface polysaccharides. Genetic variation analysis revealed that, compared to the parental strain, SWKP1711R had only one thymine deletion at position 78 of the open reading frame of the lpcA gene, resulting in a frameshift mutation that caused alteration of the bacterial surface polysaccharide and inhibition of phage adsorption, ultimately leading to phage resistance. Transcriptome analysis and quantitative reverse transcriptase PCR revealed that genes encoding lipopolysaccharide synthesis, ompK35, blaTEM-1, and type II and Hha-TomB toxin-antitoxin systems, were all downregulated in SWKP1711R. Taken together, the evidence presented here indicates that the phenotypic alterations and phage resistance displayed by the mutant may be related to the frameshift mutation of lpcA and altered gene expression. While evolution of phage resistance remains an issue, our study suggests that the reduced antibiotic resistance and virulence of phage-resistant strain derivatives might be beneficial in alleviating the burden caused by multidrug-resistant bacteria.

Electrochemically coupled CH4 and CO2 consumption driven by microbial processes.

The chemical transformations of methane (CH4) and carbon dioxide (CO2) greenhouse gases typically have high energy barriers. Here we present an approach of strategic coupling of CH4 oxidation and CO2 reduction in a switched microbial process governed by redox cycling of iron minerals under temperate conditions. The presence of iron minerals leads to an obvious enhancement of carbon fixation, with the minerals acting as the electron acceptor for CH4 oxidation and the electron donor for CO2 reduction, facilitated by changes in the mineral structure. The electron flow between the two functionally active microbial consortia is tracked through electrochemistry, and the energy metabolism in these consortia is predicted at the genetic level. This study offers a promising strategy for the removal of CH4 and CO2 in the natural environment and proposes an engineering technique for the utilization of major greenhouse gases.

The reproduction process of Gram-positive protocells.

Protocells are believed to have existed on early Earth prior to the emergence of prokaryotes. Due to their rudimentary nature, it is widely accepted that these protocells lacked intracellular mechanisms to regulate their reproduction, thereby relying heavily on environmental conditions. To understand protocell reproduction, we adopted a top-down approach of transforming a Gram-positive bacterium into a lipid-vesicle-like state. In this state, cells lacked intrinsic mechanisms to regulate their morphology or reproduction, resembling theoretical propositions on protocells. Subsequently, we grew these proxy-protocells under the environmental conditions of early Earth to understand their impact on protocell reproduction. Despite the lack of molecular biological coordination, cells in our study underwent reproduction in an organized manner. The method and the efficiency of their reproduction can be explained by an interplay between the physicochemical properties of cell constituents and environmental conditions. While the overall reproductive efficiency in these top-down modified cells was lower than their counterparts with a cell wall, the process always resulted in viable daughter cells. Given the simplicity and suitability of this reproduction method to early Earth environmental conditions, we propose that primitive protocells likely reproduced by a process like the one we described below.

Distribution of hepatitis C virus in eastern China from 2011 to 2020: a Bayesian spatiotemporal analysis.

Objective: This study aimed to evaluate the spatiotemporal distribution of patients with hepatitis C virus (HCV) and the factors influencing this distribution in Jiangsu Province, China, from 2011 to 2020. Methods: The incidence of reported HCV in Jiangsu Province from 2011 to 2020 was obtained from the Chinese Information System for Disease Control and Prevention (CISDCP). R and GeoDa software were used to visualize the spatiotemporal distribution and the spatial autocorrelation of HCV. A Bayesian spatiotemporal model was constructed to explore the spatiotemporal distribution of HCV in Jiangsu Province and to further analyze the factors related to HCV. Results: A total of 31,778 HCV patients were registered in Jiangsu Province. The registered incidence rate of HCV increased from 2.60/100,000 people in 2011 to 4.96/100,000 people in 2020, an increase of 190.77%. Moran's I ranged from 0.099 to 0.354 (P < 0.05) from 2011 to 2019, indicating a positive spatial correlation overall. The relative risk (RR) of the urbanization rate, the most important factor affecting the spread of HCV in Jiangsu Province, was 1.254 (95% confidence interval: 1.141-1.376), while other factors had no significance. Conclusion: The reported HCV incidence rate integrally increased in the whole Jiangsu Province, whereas the spatial aggregation of HCV incidence was gradually weakening. Our study highlighted the importance of health education for the floating population and reasonable allocation of medical resources in the future health work.

Optimization of benzene exposure risk assessment: An integrated approach utilizing internal and external concentrations with a focus on biomarkers S-PMA & t, t-MA.

In the majority of occupational settings within China, the concentrations of benzene are observed to fall markedly below the demarcated detection thresholds. Employing traditional risk assessment models, the presence of exceptionally low airborne benzene exposure concentrations may infuse heightened degrees of uncertainty. Consequently, the necessity arises to investigate risk assessment methodologies more apt for the prevalent exposure environment among employees. In the present study, a pharmacokinetic model premised on urinary benzene metabolites (S-PMA and t, t-MA) was employed to ascertain a more precise daily airborne benzene exposure concentration per individual. This value was integrated into the linear multistage model as the 'internal exposure concentration'. In conjunction with the U.S National Environmental Protection Agency's (EPA) inhalation risk assessment model predicated on the external exposure concentration, the Singapore Ministry of Manpower's (MOM) model, and the linear multistage (LMS) model, the carcinogenic and non-carcinogenic effects of benzene were evaluated for 1781 benzene-exposed employees across 76 enterprises in Jiangsu Province. Findings suggest that in the linear multilevel model assessment, the cancer risk levels based on t, t-MA and S-PMA were higher in the printing and recording media reproduction industry, automobile manufacturing industry, general equipment manufacturing industry and the furniture manufacturing industry (median 2.842 × 10-4, 2.819 × 10-4, 2.809 × 10-4, and 2.678 × 10-4), which align more consistently with the actual benzene exposure circumstances of each industry's study participants, with overall risk levels calculated by the linear multistage model exceeding those of the EPA inhalation risk assessment model and the MOM model. This implies that the linear multistage model of internal exposure, based on the reciprocal of benzene biomarkers S-PMA and t, t-MA for airborne benzene exposure, presents enhanced sensitivity and suitability for the current occupational health risk assessment of workers. Without doubt, biomarker-based benzene exposure risk assessment emerges as the optimal choice.

Synergistic effects of rhizosphere effect and combined organic and chemical fertilizers application on soil bacterial diversity and community structure in oilseed rape cultivation.

The combined application of chemical and organic fertilizers has been recognized to enhance soil fertility and foster the soil microbial ecosystem. However, the optimal ratio of chemical and organic fertilizers in oilseed rape cultivation is still uncertain, and the role of rhizosphere effect is still unclear. Thus, this study aimed to elucidate the impacts of varying ratios of chemical and organic fertilizers on the structure and potential functionalities of rhizosphere and non-rhizosphere soil microbial communities. The interplay of microbial communities with soil properties and oilseed rape root exudates was investigated in controlled pot cultivations receiving varying ratios of chemical and organic fertilizers. Results indicated clear segregation in the soil bacterial community, influenced by both fertilization treatments and rhizosphere effects. The bacterial community structure significantly correlated with nitrate nitrogen, organic acids, and dissolved organic carbon (DOC) content. Rhizosphere effects led to increased bacteria abundance, reduced diversity, and decreased network stability. Notably, F3 treatment receiving 25% chemical and 75% organic fertilizers showed a significantly higher abundance at 1.43 × 1011 copies g-1 dry soil, accompanied by increased species and genetic diversity, and ecological network complexity. This treatment also yielded the highest aboveground biomass of oilseed rape. However, the application of organic fertilizers also increased the risk of plant pathogenicity. This study reveals the impact of fertilizers and rhizosphere effects on soil microbial community structure and function, shedding light on the establishment of more effective fertilization schemes for oilseed rape agriculture.

Predicting coal workers' pneumoconiosis trends: Leveraging historical data with the GARCH model in a Chinese Miner Cohort.

Coal workers' pneumoconiosis (CWP) is one of the most common and severe occupational diseases worldwide. The main risk factor of CWP is exposure to respirable mine dust. Prediction theory was widely applied in the prediction of the epidemic. Here, it was used to identify the characteristics of CWP today and the incidence trends of CWP in the future. Eight thousand nine hundred twenty-eight coal workers from a state-owned coal mine were included during the observation period from 1963 to 2014. In observations, the dust concentration gradually decreased over time, and the incidence of tunnels and mine, transportation, and assistance workers showed an overall downward trend. We choose a better prediction model by comparing the prediction effect of the Auto Regression Integrate Moving Average model and Generalized Autoregressive Conditional Heteroskedasticity model. Compared with the Auto Regression Integrate Moving Average model, the Generalized Autoregressive Conditional Heteroskedasticity model has a better prediction effect. Furthermore, the status quo and future trend of coal miners' CWP are still at a high level.

Diagnosis, Treatment, and Associated Factors Among Patients with HCV Infection - Jiangsu Province, China, 2004-2020.

What is already known on this topic?: The global efforts to address the hepatitis C virus (HCV) are progressing, but there are still significant gaps in the diagnosis and treatment of HCV, leading to an increasing number of deaths related to HCV. What is added by this report?: An extensive investigation was conducted to assess HCV RNA diagnosis, treatment uptake, and associated factors among individuals infected with HCV within Jiangsu Province. The study encompassed a large geographical area and utilized a substantial sample size. What are the implications for public health practice?: Implementing focused interventions to improve the timely diagnosis of HCV RNA and increase the uptake of HCV treatment could effectively reduce the future burden of HCV-related health problems, deaths, and healthcare expenses. This is essential for achieving the global target of eliminating hepatitis C.

G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma.

BACKGROUND: Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood. MATERIALS & METHODS: To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis. RESULTS: Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed. CONCLUSION: The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma.