Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis.

BACKGROUND: The prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity. METHODS: We acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein-protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases. RESULTS: Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions. CONCLUSIONS: Our investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.

Metal-organic frameworks (MOFs) for photoelectrocatalytic (PEC) reducing carbon dioxide (CO2) to hydrocarbon fuels.

MOF-based photoelectrocatalysis (PEC) using CO2 as an electron donor offers a green, clean, and extensible way to make hydrocarbon fuels under more tolerant conditions. Herein, basic principles of PEC reduction of CO2 and the preparation methods and characterization techniques of MOF-based materials are summarized. Furthermore, three applications of MOFs for improving the photoelectrocatalytic performance of CO2 reduction are described: (i) as photoelectrode alone; (ii) as a co-catalyst of semiconductor photoelectrode or as a substrate for loading dyes, quantum dots, and other co-catalysts; (iii) as one of the components of heterojunction structure. Challenges and future wave surrounding the development of robust PEC CO2 systems based on MOF materials are also discussed briefly.

Research progress on photocatalytic reduction of CO2 based on ferroelectric materials.

Transforming CO2 into renewable fuels or valuable carbon compounds could be a practical means to tackle the issues of global warming and energy crisis. Photocatalytic CO2 reduction is more energy-efficient and environmentally friendly, and offers a broader range of potential applications than other CO2 conversion techniques. Ferroelectric materials, which belong to a class of materials with switchable polarization, are attractive candidates as catalysts due to their distinctive and substantial impact on surface physical and chemical characteristics. This review provides a concise overview of the fundamental principles underlying photocatalysis and the mechanism involved in CO2 reduction. Additionally, the composition and properties of ferroelectric materials are introduced. This review expands on the research progress in using ferroelectric materials for photocatalytic reduction of CO2 from three perspectives: directly as a catalyst, by modification, and construction of heterojunctions. Finally, the future potential of ferroelectric materials for photocatalytic CO2 reduction is presented. This review may be a valuable guide for creating reasonable and more effective photocatalysts based on ferroelectric materials.

Constructing an extracellular matrix-related prognostic model for idiopathic pulmonary fibrosis based on machine learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease. Multiple research has revealed that the extracellular matrix (ECM) may be associated with the development and prognosis of IPF, however, the underlying mechanisms remain incompletely understood. METHODS: We included GSE70866 dataset from the GEO database and established an ECM-related prognostic model utilizing LASSO, Random forest and Support vector machines algorithms. To compare immune cell infiltration levels between the high and low risk groups, we employed the ssGSEA algorithm. Enrichment analysis was conducted to explore pathway differences between the high-risk and low-risk groups. Finally, the model genes were validated using an external validation set consisting of IPF cases, as well as single-cell data analysis. RESULTS: Based on machine learning algorithms, we constructed an ECM-related risk model. IPF patients in the high-risk group had a worse overall survival rate than those in the low-risk group. The model's AUC predictive values were 0.786, 0.767, and 0.768 for the 1-, 2-, and 3-year survival rates, respectively. The validation cohort validated these findings, demonstrating our model's effective prognostication. Chemokine-related pathways were enriched through enrichment analysis. Moreover, immune cell infiltration varied significantly between the two groups. Finally, the validation results indicate that the expression levels of all the model genes exhibited significant differential expression. CONCLUSIONS: Based on CST6, PPBP, CSPG4, SEMA3B, LAMB2, SERPINB4 and CTF1, our study developed and validated an ECM-related risk model that accurately predicts the outcome of IPF patients.

Incomplete immune reconstitution and its predictors in people living with HIV in Wuhan, China.

OBJECTIVE: This study aimed to build and validate a nomogram model to predict the risk of incomplete immune reconstitution in people living with HIV (PLWH). METHODS: Totally 3783 individuals with a confirmed diagnosis of HIV/AIDS were included. A predictive model was developed based on a retrospective set (N = 2678) and was validated using the remaining cases (N = 1105). Univariate and multivariate logistic regression analyses were performed to determine valuable predictors among the collected clinical and laboratory variables. The predictive model is presented in the form of a nomogram, which is internally and externally validated with two independent datasets. The discrimination of nomograms was assessed by calculating the area under the curve (AUC). Besides, calibration curve and decision curve (DCA) analyses were performed in the training and validation sets. RESULTS: The final model comprised 5 predictors, including baseline CD4, age at ART initiation, BMI, HZ and TBIL. The AUC of the nomogram model was 0.902, 0.926, 0.851 in the training cohort, internal validation and external cohorts. The calibration accuracy and diagnostic performance were satisfactory in both the training and validation sets. CONCLUSIONS: This predictive model based on a retrospective study was externally validated using 5 readily available clinical indicators. It showed high performance in predicting the risk of incomplete immune reconstitution in people living with HIV.

Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning.

Background: To explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19). Methods: Cuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data. Results: Patients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples. Conclusion: Our study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection.

Development and validation of a scoring system for the prediction of HIV drug resistance in Hubei province, China.

Objective: The present study aimed to build and validate a new nomogram-based scoring system for the prediction of HIV drug resistance (HIVDR). Design and methods: Totally 618 patients with HIV/AIDS were included. The predictive model was created using a retrospective set (N = 427) and internally validated with the remaining cases (N = 191). Multivariable logistic regression analysis was carried out to fit a model using candidate variables selected by Least absolute shrinkage and selection operator (LASSO) regression. The predictive model was first presented as a nomogram, then transformed into a simple and convenient scoring system and tested in the internal validation set. Results: The developed scoring system consisted of age (2 points), duration of ART (5 points), treatment adherence (4 points), CD4 T cells (1 point) and HIV viral load (1 point). With a cutoff value of 7.5 points, the AUC, sensitivity, specificity, PLR and NLR values were 0.812, 82.13%, 64.55%, 2.32 and 0.28, respectively, in the training set. The novel scoring system exhibited a favorable diagnostic performance in both the training and validation sets. Conclusion: The novel scoring system can be used for individualized prediction of HIVDR patients. It has satisfactory accuracy and good calibration, which is beneficial for clinical practice.

The survival analysis of rifampicin/multidrug-resistant tuberculosis patients based on the levels of inflammatory biomarkers: a retrospective cohort study.

Purpose: The development of tuberculosis and inflammatory status are closely related. The aim of this study was to investigate the prognostic value of inflammatory biomarkers in patients with rifampicin/multidrug-resistant tuberculosis (RR/MDR-TB). Patients and methods: This study recruited 504 patients with RR/MDR-TB from Wuhan Jinyintan Hospital. A total of 348 RR/MDR patients from January 2017 to December 2019 were defined as training set, the rest of patients as validation set. The patients were divided into three-risk degrees according to the levels of inflammatory biomarkers (median, 85th percentile). Kaplan-Meier curve and log-rank test were used to assess survival differences among the groups. Cox proportion risk regression was used to identify risk factors for RR/MDR-TB mortality. Results: In training set, cox proportion risk regression analysis showed that high age (≥60 years) [OR (95%CI):1.053(1.03188-1.077)], smoking [OR (95%CI):2.206(1.191-4.085)], and bronchiectasia [OR (95%CI):2.867(1.548-5.311)] were prognostic factors for RR/MDR-TB patients. In addition, lower survival rates were observed in high CAR group [OR (95%CI):1.464(1.275-1.681)], high CPR group[OR (95%CI):1.268(1.101-1.459)], high CLR group[OR (95%CI):1.004(1.002-1.005)], high NLR group[OR (95%CI):1.103(1.069-1.139)], high PLR group[OR (95%CI):1.003(1.002-1.004)], and high MLR group[OR (95%CI):3.471(2.188-5.508)].Furthermore, AUCs of age, smoking, bronchiectasia, CAR, CPR, CLR, NLR, PLR, and MLR for predicting mortality in RR/MDR-TB patients were 0.697(95%CI:0.618-0.775), 0.603(95%CI:0.512-0.695), 0.629(95%CI:0.538-0.721), 0.748(95%CI:0.675-0.821, P<0.05), 0.754(95%CI:0.683-0.824, P<0.05), 0.759(95%CI:0.689-0.828, P<0.05), 0.789(95%CI:0.731-0.846, P<0.05), 0.740(95%CI:0.669-0.812, P<0.05), and 0.752(95%CI:0.685-0.819, P<0.05), respectively. Importantly, the AUC of predicting mortality of combination of six inflammatory biomarkers [0.823 (95%CI:0.769-0.876)] is higher than any single inflammatory biomarkers. Additionally, the similar results are also obtained in the validation set. Conclusion: Inflammatory biomarkers could predict the survival status of RR/MDR-TB patients. Therefore, more attention should be paid to the level of inflammatory biomarkers in clinical practice.

A Scoring System Based on Laboratory Parameters and Clinical Features to Predict Unfavorable Treatment Outcomes in Multidrug- and Rifampicin-Resistant Tuberculosis Patients.

Background: The growth of antibiotic resistance to Mycobacterium TB represents a major barrier to the goal of "Ending the global TB epidemics". This study aimed to develop and validate a simple clinical scoring system to predict the unfavorable treatment outcomes (UTO) in multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB) patients. Methods: A total of 333 MDR/RR-TB patients were recruited retrospectively. The clinical, radiological and laboratory features were gathered and selected by lasso regression. These variables with area under the receiver operating characteristic curve (AUC)>0.6 were subsequently submitted to multivariate logistic analysis. The binomial logistic model was used for establishing a scoring system based on the nomogram at the training set (N = 241). Then, another independent set was used to validate the scoring system (N = 92). Results: The new scoring system consists of age (8 points), education level (10 points), bronchiectasis (4 points), red blood cell distribution width-coefficient of variation (RDW-CV) (7 points), international normalized ratio (INR) (7 points), albumin to globulin ratio (AGR) (5 points), and C-reactive protein to prealbumin ratio (CPR) (6 points). The scoring system identifying UTO has a discriminatory power of 0.887 (95% CI=0.835-0.939) in the training set, and 0.805 (95% CI=0.714-0.896) in the validation set. In addition, the scoring system is used exclusively to predict the death of MDR/RR-TB and has shown excellent performance in both training and validation sets, with AUC of 0.930 (95% CI=0.872-0.989) and 0.872 (95% CI=0.778-0.967), respectively. Conclusion: This novel scoring system based on seven accessible predictors has exhibited good predictive performance in predicting UTO, especially in predicting death risk.

Advancements in MAFLD Modeling with Human Cell and Organoid Models.

Metabolic (dysfunction) associated fatty liver disease (MAFLD) is one of the most prevalent liver diseases and has no approved therapeutics. The high failure rates witnessed in late-phase MAFLD drug trials reflect the complexity of the disease, and how the disease develops and progresses remains to be fully understood. In vitro, human disease models play a pivotal role in mechanistic studies to unravel novel disease drivers and in drug testing studies to evaluate human-specific responses. This review focuses on MAFLD disease modeling using human cell and organoid models. The spectrum of patient-derived primary cells and immortalized cell lines employed to model various liver parenchymal and non-parenchymal cell types essential for MAFLD development and progression is discussed. Diverse forms of cell culture platforms utilized to recapitulate tissue-level pathophysiology in different stages of the disease are also reviewed.

A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis.

Purpose: This study aimed to develop and validate a scoring system based on a nomogram of common clinical metrics to discriminate between active pulmonary tuberculosis (APTB) and inactive pulmonary tuberculosis (IPTB). Patients and methods: A total of 1096 patients with pulmonary tuberculosis (PTB) admitted to Wuhan Jinyintan Hospital between January 2017 and December 2019 were included in this study. Of these patients with PTB, 744 were included in the training cohort (70%; 458 patients with APTB, and 286 patients with IPTB), and 352 were included in the validation cohort (30%; 220 patients with APTB, and 132 patients with IPTB). Data from 744 patients from the training cohort were used to establish the diagnostic model. Routine blood examination indices and biochemical indicators were collected to construct a diagnostic model using the nomogram, which was then transformed into a scoring system. Furthermore, data from 352 patients from the validation cohort were used to validate the scoring system. Results: Six variables were selected to construct the prediction model. In the scoring system, the mean corpuscular volume, erythrocyte sedimentation rate, albumin level, adenosine deaminase level, monocyte-to-high-density lipoprotein ratio, and high-sensitivity C-reactive protein-to-lymphocyte ratio were 6, 4, 7, 5, 5, and 10, respectively. When the cut-off value was 15.5, the scoring system for recognizing APTB and IPTB exhibited excellent diagnostic performance. The area under the curve, specificity, and sensitivity of the training cohort were 0.919, 84.06%, and 86.36%, respectively, whereas those of the validation cohort were 0.900, 82.73, and 86.36%, respectively. Conclusion: This study successfully constructed a scoring system for distinguishing APTB from IPTB that performed well.

The Novel Predictive Biomarkers for Type 2 Diabetes Mellitus in Active Pulmonary Tuberculosis Patients.

Purpose: This study was to explore the predictive value of monocyte to high-density lipoprotein cholesterol ratio (MHR), neutrophils to high-density lipoprotein cholesterol ratio (NHR), C-reactive protein-to-lymphocyte ratio (CLR), and C-reactive protein-to-albumin ratio (CAR) for type 2 diabetes mellitus (T2DM) in patients with active pulmonary tuberculosis (APTB). Patients and Methods: A total of 991 active pulmonary tuberculosis (APTB) patients (201 with T2DM) were hospitalized in the Department of Tuberculosis, Wuhan Jinyintan Hospital, Tongji Medical College, Huazhong University of Science and Technology were included. The routine blood examination indicators and biochemical parameters were collected to calculate MHR, NHR, CLR, and CAR. The Pearson correlation analysis, Univariate Logistic regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to assess the predictive value of MHR, NHR, CLR, and CAR for APTB-T2DM patients. Results: The levels of MHR, NHR, CLR, and CAR in the APTB-T2DM patients were significantly higher than in the APTB-no T2DM patients (P < 0.05). Additionally, the MHR, NHR, CLR, and CAR have a positive correlation with fasting blood glucose in the whole study population. However, in the APTB-T2DM patients, MHR, NHR, and CAR were not correlated with fasting blood glucose, and only CLR was positively correlated with fasting blood glucose. The area under curve (AUC) predicting APTB-T2DM patients of the MHR, NHR, CLR, and CAR was 0.632, 0.72, 0.715, and 0.713, respectively. Further, univariate logistic regression analyses showed that the higher MHR, NHR, CLR, and CAR were independent risk factors for APTB-T2DM (P < 0.01). The MHR, NHR, CLR, and CAR quartiles were used to divide the APTB patients into four groups for further analysis. The prevalence of T2DM was significantly higher in APTB individuals as MHR, NHR, CLR, and CAR values increased (P < 0.05). Conclusion: MHR, NHR, CLR, and CAR are simple and practicable inflammatory parameters that could be used for assessing T2DM in APTB. APTB patients have a greater possibility to be diagnosed with T2DM with the higher MHR, NHR CLR, and CAR values. Therefore, more attention should be given to the indicator in the examination of APTB.

Correction: FTSJ1 regulates tRNA 2'-O-methyladenosine modification and suppresses the malignancy of NSCLC via inhibiting DRAM1 expression.

The original version of this article contained an error in the spelling of the author Yuchen Chen, which was incorrectly given as Yuhuan Chen. This has now been corrected in both the PDF and HTML versions of the article.

FTSJ1 regulates tRNA 2'-O-methyladenosine modification and suppresses the malignancy of NSCLC via inhibiting DRAM1 expression.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. The mechanisms underlying NSCLC tumorigenesis are incompletely understood. Transfer RNA (tRNA) modification is emerging as a novel regulatory mechanism for carcinogenesis. However, the role of tRNA modification in NSCLC remains obscure. In this study, HPLC/MS assay was used to quantify tRNA modification levels in NSCLC tissues and cells. tRNA-modifying enzyme genes were identified by comparative genomics and validated by qRT-PCR analysis. The biological functions of tRNA-modifying gene in NSCLC were investigated in vitro and in vivo. The mechanisms of tRNA-modifying gene in NSCLC were explored by RNA-seq, qRT-PCR, and rescue assays. The results showed that a total of 18 types of tRNA modifications and up to seven tRNA-modifying genes were significantly downregulated in NSCLC tumor tissues compared with that in normal tissues, with the 2'-O-methyladenosine (Am) modification displaying the lowest level in tumor tissues. Loss- and gain-of-function assays revealed that the amount of Am in tRNAs was significantly associated with expression levels of FTSJ1, which was also downregulated in NSCLC tissues and cells. Upregulation of FTSJ1 inhibited proliferation, migration, and promoted apoptosis of NSCLC cells in vitro. Silencing of FTSJ1 resulted in the opposite effects. In vivo assay confirmed that overexpression of FTSJ1 significantly suppressed the growth of NSCLC cells. Mechanistically, overexpression of FTSJ1 led to a decreased expression of DRAM1. Whereas knockdown of FTSJ1 resulted in an increased expression of DRAM1. Furthermore, silencing of DRAM1 substantially augmented the antitumor effect of FTSJ1 on NSCLC cells. Our findings suggested an important mechanism of tRNA modifications in NSCLC and demonstrated novel roles of FTSJ1 as both tRNA Am modifier and tumor suppressor in NSCLC.