Explore the vast range of titles available.

The significance of N 6 -methyladenosine (m6A) RNA modifications in the progression of breast cancer (BC) has been recognised. However, their potential role and mechanism of action in the tumour microenvironment (TME) and immune response has not been demonstrated. Thus, the role of m6A regulators and their downstream target gene components in BC remain to be explored. In this study, we used a series of bioinformatics methods and experiments to conduct exploratory research on the possible role of m6A regulators in BC. First, two regulatory modes of immune activation and inactivation were determined by tumour classification. The TME, immune cell infiltration, and gene set variation analysis results confirmed the reliability of this pattern. The prognostic model of the m6A regulator was established by the least absolute shrinkage and selection operator and univariate and multivariate Cox analyses, with the two regulators most closely related to survival verified by real-time quantitative reverse transcription polymerase chain reaction. Next, the prognostic m6A regulator identified in the model was crossed with the differential copy number of variant genes in invasive BC (IBC), and it was determined that YTHDF1 was a hub regulator. Subsequently, single-cell analysis revealed the expression patterns of m6A regulators in different IBC cell populations and found that YTHDF1 had significantly higher expression in immune-related IBC cells. Therefore, we selected the intersection of the BC differential expression gene set and the differential expression gene set of a cell line with knocked-down YTHDF1 in literature to identify downstream target genes of YTHDF1, in which we found IFI6, EIR, and SPTBN1. A polymerase chain reaction was conducted to verify the results. Finally, we confirmed the role of YTHDF1 as a potential prognostic biomarker through pan-cancer analysis. Furthermore, our findings revealed that YTHDF1 can serve as a new molecular marker for BC immunotherapy.

Acute kidney injury is a common and severe complication following total hip arthroplasty, particularly in elderly or high-risk patients with chronic conditions, significantly increasing morbidity and mortality rates. Traditional prediction methods often struggle with the complexity of multidimensional healthcare data. To address this, we developed a machine learning-based prediction model using multidimensional data from 4601 total hip arthroplasty patients, encompassing 16 general variables (e.g., demographic characteristics, surgical duration, and hospital stay) and 53 laboratory indicators (e.g., Cystatin C, D-dimer, and glucose). Feature selection was performed using Random Forest, Lasso regression, and mutual information analysis, with clinically relevant features such as Cystatin C, glucose, and N-terminal proBNP retained to enhance model interpretability and predictive power. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors. Among multiple models, CatBoost achieved the best performance, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93–0.96), an accuracy of 0.88 (95% CI 0.85–0.90), and an F1-score of 0.79 (95% CI 0.75–0.84) in the internal validation set. External validation using an independent hospital dataset ( n  = 240) further confirmed the model’s robustness, with an AUC of 0.65 (95% CI 0.57–0.73). However, the substantial performance decline in external validation underscores the need for cautious interpretation of performance metrics and institution-specific validation prior to clinical deployment. Shapley Additive Explanations analysis identified Cystatin C, surgical duration, and creatinine as key predictors, demonstrating the model’s transparency and clinical relevance. A real-time prediction system, developed using the Flask framework, was validated externally, confirming its utility for acute kidney injury risk assessment and personalized postoperative management. These findings highlight the model’s potential to improve clinical decision-making and outcomes for high-risk patients undergoing total hip arthroplasty.

Dengue, a viral infection transmitted between people by mosquitoes, is one of the most rapidly spreading diseases in the world. Here, we report the analyses covering 11 y (2005–2015) from the city of Guangzhou in southern China. Using the first 8 y of data to develop an ecologically based model for the dengue system, we reliably predict the following 3 y of dengue dynamics—years with exceptionally extensive dengue outbreaks. We demonstrate that climate conditions, through the effects of rainfall and temperature on mosquito abundance and dengue transmission rate, play key roles in explaining the temporal dynamics of dengue incidence in the human population. Our study thus contributes to a better understanding of dengue dynamics and provides a predictive tool for preventive dengue reduction strategies.

Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilitation assessment. Acute spinal cord injury (ASCI) is a sudden trauma that frequently results in severe neurological deficits and a significant decline in quality of life. Early prediction of neurological recovery is crucial for the personalized treatment planning. While extensively explored in other medical fields, this study is the first to apply multiple machine learning methods and Shapley Additive Explanations (SHAP) analysis specifically to ASCI for predicting neurological recovery. A total of 387 ASCI patients were included, with clinical, imaging, and laboratory data collected. Key features were selected using univariate analysis, Lasso regression, and other feature selection techniques, integrating clinical, radiomics, and laboratory data. A range of machine learning models, including XGBoost, Logistic Regression, KNN, SVM, Decision Tree, Random Forest, LightGBM, ExtraTrees, Gradient Boosting, and Gaussian Naive Bayes, were evaluated, with Gaussian Naive Bayes exhibiting the best performance. Radiomics features extracted from T2-weighted fat-suppressed MRI scans, such as original_glszm_SizeZoneNonUniformity and wavelet-HLL_glcm_SumEntropy, significantly enhanced predictive accuracy. SHAP analysis identified critical clinical features, including IMLL, INR, BMI, Cys C, and RDW-CV, in the predictive model. The model was validated and demonstrated excellent performance across multiple metrics. The clinical utility and interpretability of the model were further enhanced through the application of patient clustering and nomogram analysis. This model has the potential to serve as a reliable tool for clinicians in the formulation of personalized treatment plans and prognosis assessment.

Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Every hospital manager aims to build harmonious, mutually beneficial, and steady-state departments. Therefore, it is important to explore a hospital department development assessment model based on objective hospital data. This study aims to use a novel machine learning algorithm to identify key evaluation indexes for hospital departments, offering insights for strategic planning and resource allocation in hospital management. Data related to the development of a hospital department over the past 3 years were extracted from various hospital information systems. The resulting data set was mined using neural machine algorithms to assess the possible role of hospital departments in the development of a hospital. A questionnaire was used to consult senior experts familiar with the hospital to assess the actual work in each hospital department and the impact of each department's development on overall hospital discipline. We used the results from this questionnaire to verify the accuracy of the departmental risk scores calculated by the machine learning algorithm. Deep machine learning was performed and modeled on the hospital system training data set. The model successfully leveraged the hospital's training data set to learn, predict, and evaluate the working and development of hospital departments. A comparison of the questionnaire results with the risk ranking set from the departments machine learning algorithm using the cosine similarity algorithm and Pearson correlation analysis showed a good match. This indicates that the department development assessment model and risk score based on the objective data of hospital systems are relatively accurate and objective. This study demonstrated that our machine learning algorithm provides an accurate and objective assessment model for hospital department development. The strong alignment of the model's risk assessments with expert opinions, validated through statistical analysis, highlights its reliability and potential to guide strategic hospital management decisions.

High altitude acclimatization and adaptation mechanisms have been well clarified, however, high altitude de-adaptation mechanism remains unclear. In this study, we conducted a controlled study on cardiac functions in 96 healthy young male who rapidly entered the high altitude (3700 m) and returned to the plains (1500 m) after 50 days. Ninety eight healthy male who remained at low altitude were recruited as control group. The mean pulmonary arterial pressure (mPAP), left ventricular ejection fraction (LVEF), left ventricular fraction shortening (LVFS), cardiac function index (Tei index) were tested. Levels of serum creatine kinase isoform MB (CK-MB), lactate dehydrogenase isoenzyme-1 (LDH-1), endothelin-1 (ET-1), nitrogen oxide (NO), serum hypoxia-inducible factor-1α (HIF-1α), 8-iso-prostaglandin F(2α) (8-iso PGF(2α)), superoxide dismutase (SOD) and malonaldehyde (MDA) were measured at an altitude of 3700 m and 1500 m respectively. The results showed that after short-term exposure to high altitude mPAP and Tei index increased significantly, while LVEF and LVFS decreased significantly. These changes were positively correlated with altitude. On the 15(th) day after the subjects returned to low altitude, mPAP, LVEF and LVFS levels returned to the same level as those of the control subjects, but the Tei index in the returned subjects was still significantly higher than that in the control subjects (P<0.01). We also found that changes in Tei index was positively correlated with mPAP, ET-1, HIF-1α and 8-iso PGF(2α) levels, and negatively correlated with the level of NO, LVEF, LVFS, CK-MB and LDH-1. These findings suggest that cardiac function de-adapts when returning to the plains after short-term exposure to high altitude and the function recovery takes a relatively long time.

Our previous work applied a combination of bioinformatics approaches and in vitro assays to identify the dengue-2 virus (DENV-2)-specific B- and T-cell epitopes. In this report, we first evaluated the antigenicity of both B- and T-cell epitopes reacting with different sera against DENV-2 by ELISA as well as the ability of T-cell epitope to activate CD4+ T-cell producing IFN-γ using ELISPOT, which showed a specific reactivity between either B- or T-cell epitope and DENV-2 antisera, and a significant increase of IFN-γ producing cells in DENV-2 infected mice. Then, a multi-epitope peptide containing the above B-, T-cell epitopes of envelope domain III (EDIII) of DENV-2 and pan-DR epitope (PADRE) was bioinformatically designed and synthesized. The verification of its immunogenicity and protective effect was performed in in vitro and in vivo experiments. The results showed that a high level of antibody in mice elicited by the multi-epitope peptide was detected by ELISA and the anti-peptide sera binding to the vero cells infected with DEN-2 was observed with immunofluorescence test. More importantly, the peptide could induce lymphoproliferation in vitro and a predominant Th1 type of immune response was examined by flow cytometry. We also found that the virus replication in the mice vaccinated with the multi-epitope peptide was obviously less than that of the control groups. These results may provide some important information for the development of dengue vaccine.

The radiation-based sterile insect technique (SIT) has successfully suppressed field populations of several insect pest species, but its effect on mosquito vector control has been limited. The related incompatible insect technique (IIT)—which uses sterilization caused by the maternally inherited endosymbiotic bacteria Wolbachia —is a promising alternative, but can be undermined by accidental release of females infected with the same Wolbachia strain as the released males. Here we show that combining incompatible and sterile insect techniques (IIT–SIT) enables near elimination of field populations of the world’s most invasive mosquito species, Aedes albopictus . Millions of factory-reared adult males with an artificial triple- Wolbachia infection were released, with prior pupal irradiation of the released mosquitoes to prevent unintentionally released triply infected females from successfully reproducing in the field. This successful field trial demonstrates the feasibility of area-wide application of combined IIT–SIT for mosquito vector control. A field trial succeeded in eliminating populations of the mosquito Aedes albopictus through inundative mass release of incompatible Wolbachia -infected males, which were also irradiated to sterilize any accidentally-released females, and so prevent population replacement.

The significance of N -methyladenosine (m6A) RNA modifications in the progression of breast cancer (BC) has been recognised. However, their potential role and mechanism of action in the tumour microenvironment (TME) and immune response has not been demonstrated. Thus, the role of m6A regulators and their downstream target gene components in BC remain to be explored. In this study, we used a series of bioinformatics methods and experiments to conduct exploratory research on the possible role of m6A regulators in BC. First, two regulatory modes of immune activation and inactivation were determined by tumour classification. The TME, immune cell infiltration, and gene set variation analysis results confirmed the reliability of this pattern. The prognostic model of the m6A regulator was established by the least absolute shrinkage and selection operator and univariate and multivariate Cox analyses, with the two regulators most closely related to survival verified by real-time quantitative reverse transcription polymerase chain reaction. Next, the prognostic m6A regulator identified in the model was crossed with the differential copy number of variant genes in invasive BC (IBC), and it was determined that YTHDF1 was a hub regulator. Subsequently, single-cell analysis revealed the expression patterns of m6A regulators in different IBC cell populations and found that YTHDF1 had significantly higher expression in immune-related IBC cells. Therefore, we selected the intersection of the BC differential expression gene set and the differential expression gene set of a cell line with knocked-down YTHDF1 in literature to identify downstream target genes of YTHDF1, in which we found IFI6, EIR, and SPTBN1. A polymerase chain reaction was conducted to verify the results. Finally, we confirmed the role of YTHDF1 as a potential prognostic biomarker through pan-cancer analysis. Furthermore, our findings revealed that YTHDF1 can serve as a new molecular marker for BC immunotherapy.