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Component identification and concentration estimation of a gas mixture component are important for gas detection. However, the accuracy of traditional gas identification will decrease if the sample is not balanced or the number of samples is too few. In this paper, a method based on sample expansion is proposed to solve the aforementioned problem. Firstly, the ADASYN-ELM method is proposed to identify the composition of a gas mixture component. The KPCA is used to extract the feature of the sensor signal and the ADASYN method is used to expand the samples. The PSO and GA algorithms were used to optimize the parameters of the ELM classification model to complete the qualitative analysis. Secondly, the S-SMOTE-MLSSVR method was put forward to quantitatively estimate. The S-SMOTE method was used to expand the samples, and the regression model MLSSVR was optimized by PSO and GA algorithms to complete the quantitative analysis. The results show that the accuracy rate after sample expansion is generally higher and the MAPE and RMSE are generally lower than before sample expansion, indicating that the sample expansion method has a positive effect on classification and concentration estimation of mixed gases with extremely unbalanced samples and too few samples.

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cancer progression and are potential biomarkers for diagnosis and treatment. This study investigates the role of RARA Antisense RNA 1 (RARA-AS1) in cancer and its implications for diagnosis and treatment. Various bioinformatics tools were conducted to analyze the expression patterns, immune-related functions, methylation, and gene expression correlations of RARA-AS1, mainly including the comparisons of different subgroups and correlation analyses between RARA-AS1 expression and other factors. Furthermore, we used short hairpin RNA to perform knockdown experiments, investigating the effects of RARA-AS1 on cell proliferation, invasion, and migration in glioblastoma. Our results revealed that RARA-AS1 has distinct expression patterns in different cancers and exhibits notable correlation with prognosis. Additionally, RARA-AS1 is highly correlated with certain immune checkpoints and mismatch repair genes, indicating its potential role in immune infiltration and related immunotherapy. Further analysis identified potential effective drugs for RARA-AS1 and demonstrated its potential RNA binding protein (RBP) mechanism in glioblastoma. Besides, a series of functional experiments indicated inhibiting RARA-AS1 could decrease cell proliferation, invasion, and migration of glioblastoma cell lines. Finally, RARA-AS1 could act as an independent prognostic factor for glioblastoma patients and may serve as a promising therapeutic target. All in all, Our study provides a comprehensive understanding of the functions and implications of RARA-AS1 in pan-cancer, highlighting it as a promising biomarker for survival. It is also an independent risk factor affecting prognosis in glioblastoma and an important factor affecting proliferation and migration in glioblastoma, setting the stage for further mechanistic investigations.

Acute myeloid leukemia (AML) is a malignant tumor of the immature myeloid hematopoietic cells in the bone marrow (BM). It is a highly heterogeneous disease, with rising morbidity and mortality in older patients. Although researches over the past decades have improved our understanding of AML, its pathogenesis has not yet been fully elucidated. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) are three noncoding RNA (ncRNA) molecules that regulate DNA transcription and translation. With the development of RNA-Seq technology, more and more ncRNAs that are closely related to AML leukemogenesis have been discovered. Numerous studies have found that these ncRNAs play an important role in leukemia cell proliferation, differentiation, and apoptosis. Some may potentially be used as prognostic biomarkers. In this systematic review, we briefly described the characteristics and molecular functions of three groups of ncRNAs, including lncRNAs, miRNAs, and circRNAs, and discussed their relationships with AML in detail.

Background Established prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR). Methods Data from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model. Results The LightGBM model had the highest AUC (0.815, 95% CI 0.747–0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years. Conclusions This study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era.

Objective Cardiometabolic multimorbidity (CMM) significantly contributes to the economic burden in China, particularly in rural areas. This study aimed to analyze the spatiotemporal distribution of CMM and identify its primary influencing factors in different townships in Lingwu City, Ningxia, to inform public health policies in Northwest China. Methods The standardized prevalence of CMM was investigated using data from Cardiovascular Disease High-Risk Group Early Screening and Comprehensive Intervention Program (2017–2022) conducted in Lingwu City, Ningxia. We applied spatial autocorrelation, cluster analysis, and spatiotemporal scanning to explore the spatiotemporal distribution characteristics of CMM and identify high-risk clusters. Four machine learning algorithms, logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were developed using 15 major cardiovascular disease influence factors. The performance of these models was evaluated based on accuracy, precision, recall, and AUC to determine their applicability across different townships in Lingwu City. The optimal model was selected for further analysis using interpretable machine learning algorithms (SHAP analysis) to identify common and key influence factors influencing CMM prevalence across townships. Results Among the 11,353 participants, 1,334 individuals (11.8%, 95% CI: 11.2–12.4%) were diagnosed with CMM, with significant variations in influence factors observed across townships ( P  < 0.05). Trend surface analysis revealed a parabolic geographic distribution of CMM prevalence in Lingwu City, increasing from north to south. Dongta Township exhibited the highest prevalence (16.6%), followed by Chongxing Township and Wutongshu Township. Spatiotemporal scanning identified four high-incidence clusters. The random forest algorithm outperformed others in predicting CMM prevalence across townships. SHAP analysis highlighted differences in the geographic distribution of 15 influence factors. Age, waist circumference, and hypertension were significant influence factors across Lingwu City. Township-specific influence factors included TG and BMI in Dongta; HDL and TG in Chongxing, Haojiaqiao; HDL and TC in Wutongshu and TC, TG, HDL and BMI in Baitugang. Conclusion CMM prevalence shows significant geographic variation within Lingwu City, with distinct risk factors across townships. Tailored interventions, based on local needs, should be implemented to reduce CMM prevalence effectively, optimize health resource allocation, and inform public health policies in rural areas of Northwest China. Graphical abstract

Acute myeloid leukemia (AML) is a malignancy caused by the uncontrolled and dysregulated clonal expansion of abnormal myeloid primordial cells. In general, the prognosis of AML remains poor despite new discoveries in its pathogenesis and treatment. It is crucial to find early and sensitive biomarkers and continue to explore active targeted treatments. Interferon-induced transmembrane protein (IFITM) family is an important part of the interferon signaling pathway and participate in the regulation of immune cell signaling, adhesion, cancer, and liver cell migration. However, the clinical and prognostic value of the IFITM family in AML has rarely been studied. We screened The Cancer Genome Atlas database and found 155 AML patients with IFITM family (IFITM1–5) expression data. In patients who only received chemotherapy, those with high IFITM3 expression had significantly shorter event-free survival (EFS) and overall survival (OS) than patients with low expression (all P < 0.05). Multivariate analysis demonstrated that high IFITM3 expression was an independent risk factor for EFS and OS in patients only received chemotherapy (all P < 0.05). In patients who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT), however, all IFITM members had no impact on either EFS or OS. In conclusion, our study elucidated that high IFITM3 expression could be an adverse prognostic factor for AML, whose effect might be overcome by allo-HSCT.

Background Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed. Methods In this study, we investigated the microbiome profile of 610 healthy young men from three different places in China, grouped by altitude, duration of residence, and ethnicity. We conducted widely targeted metabolomic profiling and clinical testing to explore metabolic characteristics. Results Our findings revealed that as the Han individuals migrated from low altitude to high latitude, the gut microbiota gradually converged towards that of the Tibetan populations but reversed upon returning to lower altitude. Across different cohorts, we identified 51 species specifically enriched during acclimatization and 57 species enriched during deacclimatization to high altitude. Notably, Prevotella copri was found to be the most enriched taxon in both Tibetan and Han populations after ascending to high altitude. Furthermore, significant variations in host plasma metabolome and clinical indices at high altitude could be largely explained by changes in gut microbiota composition. Similar to Tibetans, 41 plasma metabolites, such as lactic acid, sphingosine-1-phosphate, taurine, and inositol, were significantly elevated in Han populations after ascending to high altitude. Germ-free animal experiments demonstrated that certain species, such as Escherichia coli and Klebsiella pneumoniae , which exhibited altitude-dependent variations in human populations, might play crucial roles in host purine metabolism. Conclusions This study provides insights into the dynamics of gut microbiota and host plasma metabolome with respect to altitude changes, indicating that their dynamics may have implications for host health at high altitude and contribute to host adaptation. 88DwbBo5LkUszxYCpXiUg_ Video Abstract

Introduction: With the outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the interaction between the host and SARS-CoV-2 was widely studied. However, it is unclear whether and how SARS-CoV-2 infection affects lung microflora, which contribute to COVID-19 complications. Methods: Here, we analyzed the metatranscriptomic data of bronchoalveolar lavage fluid (BALF) of 19 COVID-19 patients and 23 healthy controls from 6 independent projects and detailed the active microbiota landscape in both healthy individuals and COVID-19 patients. Results: The infection of SARS-CoV-2 could deeply change the lung microbiota, evidenced by the α-diversity, β-diversity, and species composition analysis based on bacterial microbiota and virome. Pathogens (e.g., Klebsiella oxytoca causing pneumonia as well), immunomodulatory probiotics (e.g., lactic acid bacteria and Faecalibacterium prausnitzii, a butyrate producer), and Tobacco mosaic virus (TMV) were enriched in the COVID-19 group, suggesting a severe microbiota dysbiosis. The significant correlation between Rothia mucilaginosa, TMV, and SARS-CoV-2 revealed drastic inflammatory battles between the host, SARS-CoV-2, and other microbes in the lungs. Notably, TMV only existed in the COVID-19 group, while human respirovirus 3 (HRV 3) only existed in the healthy group. Our study provides insights into the active microbiota in the lungs of COVID-19 patients and would contribute to the understanding of the infection mechanism of SARS-CoV-2 and the treatment of the disease and complications. Conclusion: SARS-COV-2 infection deeply altered the lung microbiota of COVID-19 patients. The enrichment of several other pathogens, immunomodulatory probiotics (lactic acid or butyrate producers), and TMV in the COVID-19 group suggests a complex and active lung microbiota disorder.

The novel coronavirus pneumonia (COVID-19) outbreak that emerged in late 2019 has posed a severe threat to human health and social and economic development, and thus has become a major public health crisis affecting the world. The spread of COVID-19 in population and regions is a typical geographical process, which is worth discussing from the geographical perspective. This paper focuses on Shandong province, which has a high incidence, though the first Chinese confirmed case was reported from Hubei province. Based on the data of reported confirmed cases and the detailed information of cases collected manually, we used text analysis, mathematical statistics and spatial analysis to reveal the demographic characteristics of confirmed cases and the spatio-temporal evolution process of the epidemic, and to explore the comprehensive mechanism of epidemic evolution and prevention and control. The results show that: (1) the incidence rate of COVID-19 in Shandong is 0.76/100,000. The majority of confirmed cases are old and middle-aged people who are infected by the intra-province diffusion, followed by young and middle-aged people who are infected outside the province. (2) Up to February 5, the number of daily confirmed cases shows a trend of “rapid increase before slowing down”, among which, the changes of age and gender are closely related to population migration, epidemic characteristics and intervention measures. (3) Affected by the regional economy and population, the spatial distribution of the confirmed cases is obviously unbalanced, with the cluster pattern of “high–low” and “low–high”. (4) The evolution of the migration pattern, affected by the geographical location of Wuhan and Chinese traditional culture, is dominated by “cross-provincial” and “intra-provincial” direct flow, and generally shows the trend of “southwest → northeast”. Finally, combined with the targeted countermeasures of “source-flow-sink”, the comprehensive mechanism of COVID-19 epidemic evolution and prevention and control in Shandong is revealed. External and internal prevention and control measures are also figured out.

Acute myeloid leukemia (AML) is a hematological malignancy characterized by the proliferation of immature myeloid cells, with impaired differentiation and maturation. Pyruvate dehydrogenase kinase (PDK) is a pyruvate dehydrogenase complex (PDC) phosphatase inhibitor that enhances cell glycolysis and facilitates tumor cell proliferation. Inhibition of its activity can induce apoptosis of tumor cells. Currently, little is known about the role of PDKs in AML. Therefore, we screened The Cancer Genome Atlas (TCGA) database for de novo AML patients with complete clinical information and PDK family expression data, and 84 patients were included for the study. These patients did not undergo allogeneic hematopoietic stem cell transplantation (allo-HSCT). Univariate analysis showed that high expression of PDK2 was associated with shorter EFS (P = 0.047), and high expression of PDK3 was associated with shorter OS (P = 0.026). In multivariate analysis, high expression of PDK3 was an independent risk factor for EFS and OS (P < 0.05). In another TCGA cohort of AML patients who underwent allo-HSCT (n = 71), PDK expression was not associated with OS (all P > 0.05). Our results indicated that high expressions of PDK2 and PDK3, especially the latter, were poor prognostic factors of AML, and the effect could be overcome by allo-HSCT.