Objective:To screen bile acid-related characteristic genes in IgA nephropathy (IgAN) based on the feature gene selection algorithm in the machine learning method, aiming to exploring the molecular biological mechanisms and biomarkers of IgAN.Methods:The gene expression data and sample grouping information of GSE93798, GSE116626 and GSE35487 were downloaded from the Gene Expression Omnibus (GEO). Bile acid-related gene sequences were obtained from the Molecular Signatures Database (MSigDB). R language was used to identify differentially expressed genes between IgAN samples and healthy control samples. Candidate genes were obtained by intersecting differentially expressed genes and bile acid-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm in machine learning was used to screen the feature genes in the candidate genes as biomarkers, and the feature genes in the training set and validation set were analyzed by the rate of change index. Receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic value of identified bile acid related characteristic genes for IgAN. Gene set enrichment analysis (GSEA) was used to analyze the Spearman correlation between the characteristic genes and all other genes and their related metabolic pathways. The expression of disease-characteristic genes in the kidney tissues of IgAN rats was validated by real-time PCR.Results:Gene expression information from kidney tissue samples of 20 IgAN cases and 22 healthy controls were obtained from GEO database. A total of 204 bile acid-related genes including 24 pathways were obtained from MSigDB. The results of gene differential expression analysis showed that 333 genes in the kidney tissues of IgAN patients were differentially expressed compared with those of healthy controls, including 102 up-regulated genes and 231 down-regulated genes, among which 12 differentially expressed genes were related to bile acid genes, as follows: NR1H4,SLC23A1, ALDH8A1, FABP1, ALB, SLC27A2, DIO1, CYP8B1, BBOX1, PIPOX, AKR1C1 and SLC10A2. Five characteristic genes ( NR1H4, SLC23A1, FABP1, ALB and AKR1C1) were screened by LASSO regression algorithm.ROC analysis results showed that in GSE93798 cohort genes, the AUC of NR1H4, SLC23A1, FABP1 and ALB genes with differential expression was >0.95 respectively in diagnosing IgAN, and that of AKR1C1 genes with differential expression was >0.85 in diagnosing IgAN. The gene expression data of SLC23A1 in GSE35487 cohort was missing. ROC analysis results of other four genes showed that the AUC of differential expression of ALB gene for IgAN was >0.95 respectively, that of NR1H4 gene was >0.70, and that of both FABP1 and AKR1C1 gene was >0.60. In the GSE116626 cohort genes, the AUC of five disease characteristic genes ( NR1H4, SLC23A1, FABP1, ALB, AKR1C1) for diagnosing IgAN was >0.60, respectively. These results suggested that 5 characteristic genes have certain distinguishing ability between IgAN group and control group. GSEA results were displayed that the characteristic genes were related to butyric acid metabolism, propionic acid metabolism, arginine and proline metabolism, valine leucine and isoleucine degradation, fatty acid metabolism, etc. These results suggested that five characteristic genes might be related to IgAN through the above metabolic mechanisms. The verification results of five bile acid characteristic genes in the rat model of IgAN in the kidney tissue showed that the expressions of four genes, NR1H4, SLC23A1, FABP1 and ALB, were higher than those of the control group, and there was no statistical significance in the expression of AKR1C1 gene between the two groups. Conclusions:The expression of bile acid-related characteristic genes is abnormal in the kidney tissue of IgAN patients. Four bile acid-related differentially expressed genes, NR1H4, SLC23A1, FABP1 and ALB, are expected to be biomarkers for non-invasive diagnosis and therapeutic targets .

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective:To screen bile acid-related characteristic genes in IgA nephropathy (IgAN) based on the feature gene selection algorithm in the machine learning method, aiming to exploring the molecular biological mechanisms and biomarkers of IgAN.Methods:The gene expression data and sample grouping information of GSE93798, GSE116626 and GSE35487 were downloaded from the Gene Expression Omnibus (GEO). Bile acid-related gene sequences were obtained from the Molecular Signatures Database (MSigDB). R language was used to identify differentially expressed genes between IgAN samples and healthy control samples. Candidate genes were obtained by intersecting differentially expressed genes and bile acid-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm in machine learning was used to screen the feature genes in the candidate genes as biomarkers, and the feature genes in the training set and validation set were analyzed by the rate of change index. Receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic value of identified bile acid related characteristic genes for IgAN. Gene set enrichment analysis (GSEA) was used to analyze the Spearman correlation between the characteristic genes and all other genes and their related metabolic pathways. The expression of disease-characteristic genes in the kidney tissues of IgAN rats was validated by real-time PCR.Results:Gene expression information from kidney tissue samples of 20 IgAN cases and 22 healthy controls were obtained from GEO database. A total of 204 bile acid-related genes including 24 pathways were obtained from MSigDB. The results of gene differential expression analysis showed that 333 genes in the kidney tissues of IgAN patients were differentially expressed compared with those of healthy controls, including 102 up-regulated genes and 231 down-regulated genes, among which 12 differentially expressed genes were related to bile acid genes, as follows: NR1H4,SLC23A1, ALDH8A1, FABP1, ALB, SLC27A2, DIO1, CYP8B1, BBOX1, PIPOX, AKR1C1 and SLC10A2. Five characteristic genes ( NR1H4, SLC23A1, FABP1, ALB and AKR1C1) were screened by LASSO regression algorithm.ROC analysis results showed that in GSE93798 cohort genes, the AUC of NR1H4, SLC23A1, FABP1 and ALB genes with differential expression was >0.95 respectively in diagnosing IgAN, and that of AKR1C1 genes with differential expression was >0.85 in diagnosing IgAN. The gene expression data of SLC23A1 in GSE35487 cohort was missing. ROC analysis results of other four genes showed that the AUC of differential expression of ALB gene for IgAN was >0.95 respectively, that of NR1H4 gene was >0.70, and that of both FABP1 and AKR1C1 gene was >0.60. In the GSE116626 cohort genes, the AUC of five disease characteristic genes ( NR1H4, SLC23A1, FABP1, ALB, AKR1C1) for diagnosing IgAN was >0.60, respectively. These results suggested that 5 characteristic genes have certain distinguishing ability between IgAN group and control group. GSEA results were displayed that the characteristic genes were related to butyric acid metabolism, propionic acid metabolism, arginine and proline metabolism, valine leucine and isoleucine degradation, fatty acid metabolism, etc. These results suggested that five characteristic genes might be related to IgAN through the above metabolic mechanisms. The verification results of five bile acid characteristic genes in the rat model of IgAN in the kidney tissue showed that the expressions of four genes, NR1H4, SLC23A1, FABP1 and ALB, were higher than those of the control group, and there was no statistical significance in the expression of AKR1C1 gene between the two groups. Conclusions:The expression of bile acid-related characteristic genes is abnormal in the kidney tissue of IgAN patients. Four bile acid-related differentially expressed genes, NR1H4, SLC23A1, FABP1 and ALB, are expected to be biomarkers for non-invasive diagnosis and therapeutic targets .

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective:Exploring the effect of radiofrequency ablation treatment to restore sinus rhythm on the improvement of functional mitral regurgitation (FMR) and cardiac structure in patients with atrial fibrillation combined with moderate or severe FMR, compared with drug therapy alone.Methods:This retrospective cohort study consecutively enrolled patients diagnosed with persistent atrial fibrillation and moderate or severe FMR who were admitted to the Third Affiliated Hospital of Sun Yat-sen University from January 2019 to December 2021. Forty-eight patients who were treated with radiofrequency ablation and maintained sinus rhythm were enrolled in the ablation group, and 63 patients who were treated with medication alone during the same period were in the medicine group. Patients in the ablation group and medicine group were matched in a 1∶1 ratio using a propensity score, and 41 patients were finally included in each of the 2 groups. All patients reexamined echocardiography after 3-month of treatment. The proportion of patients with FMR improvement and the differences in changes of cardiac structural and functional parameters were compared between groups.Results:After propensity score matching, the ablation group was aged (69.3±7.1) years with 21 males (51.2%) and the medicine group was aged (71.3±9.4) years with 21 males (51.2%). The echocardiography after 3-month of treatment showed the rate of FMR improvement was significantly higher in the ablation group than in the medicine group (19 (46.3%) vs. 33 (80.5%), P<0.001), and patients in the ablation group showed a significant decrease in FMR extent (Δmitral regurgitation area: (-1.30±2.64) cm 2 vs. (-3.55±2.50) cm 2, P<0.001), left atrial size (Δleft atrial diameter: (-0.17±3.78) mm vs. (-2.46±4.01) mm, P=0.009) and E/e′ (ΔE/e′:-2.54±7.34 vs.-6.34±7.08, P=0.021) compared with the medicine group. There was also a significant decrease in left ventricular size (Δleft ventricular end diastolic diameter: (-3.12±6.62) mm vs. (-0.73±3.62) mm, P=0.046) and significant increase in left ventricular ejection fraction (Δleft ventricular ejection fraction: (2.73±9.69) % vs. (-0.93±5.41) %, P=0.038) in ablation group. Conclusion:Performing radiofrequency ablation to restore sinus rhythm can effectively reduce the severity of mitral regurgitation and improve left atrial and left ventricular remodeling and cardiac function in patients with atrial fibrillation and FMR.

Human coronavirus (HCoV) causes potentially fatal respiratory disease. Pregnancy is a physiological state that predisposes women to viral infection. In this review, we aim to present advances in the pathogenesis, clinical features, diagnosis, and treatment in HCoV in pregnancy. We retrieved information from the Pubmed database up to June 2020, using various search terms and relevant words, including coronaviruses, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, 2019 coronavirus disease, and pregnancy. Both basic and clinical studies were selected. We found no evidence that pregnant women are more susceptible to HCoV infection or that those with HCoV infection are more prone to developing severe pneumonia. There is also no confirmed evidence of vertical mother-to-child transmission of HcoV infection during maternal HCoV infection. Those diagnosed with infection should be promptly admitted to a negative-pressure isolation ward, preferably in a designated hospital with adequate facilities and multi-disciplinary expertise to manage critically ill obstetric patients. Antiviral treatment has been routinely used to treat pregnant women with HCoV infection. The timing and mode of delivery should be individualized, depending mainly on the clinical status of the patient, gestational age, and fetal condition. Early cord clamping and temporary separation of the newborn for at least 2 weeks is recommended. All medical staff caring for patients with HCoV infection should use personal protective equipment. This review highlights the advances in pathogenesis, maternal-fetal outcome, maternal-fetal transmission, diagnosis and treatment in HCoV including severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, and coronavirus disease 2019 in pregnancy.

Human coronavirus (HCoV) causes potentially fatal respiratory disease. Pregnancy is a physiological state that predisposes women to viral infection. In this review, we aim to present advances in the pathogenesis, clinical features, diagnosis, and treatment in HCoV in pregnancy. We retrieved information from the Pubmed database up to June 2020, using various search terms and relevant words, including coronaviruses, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, 2019 coronavirus disease, and pregnancy. Both basic and clinical studies were selected. We found no evidence that pregnant women are more susceptible to HCoV infection or that those with HCoV infection are more prone to developing severe pneumonia. There is also no confirmed evidence of vertical mother-to-child transmission of HcoV infection during maternal HCoV infection. Those diagnosed with infection should be promptly admitted to a negative-pressure isolation ward, preferably in a designated hospital with adequate facilities and multi-disciplinary expertise to manage critically ill obstetric patients. Antiviral treatment has been routinely used to treat pregnant women with HCoV infection. The timing and mode of delivery should be individualized, depending mainly on the clinical status of the patient, gestational age, and fetal condition. Early cord clamping and temporary separation of the newborn for at least 2 weeks is recommended. All medical staff caring for patients with HCoV infection should use personal protective equipment. This review highlights the advances in pathogenesis, maternal-fetal outcome, maternal-fetal transmission, diagnosis and treatment in HCoV including severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, and coronavirus disease 2019 in pregnancy.

Objective:To investigate the effect of automated nested multiplex PCR system in the detection of children with severe community-acquired pneumonia(CAP), and identify the pathogenic infection of the children with severe CAP in Foshan.Methods:Children with severe CAP, who were admitted to the PICU at Foshan First People′s Hospital from January 2016 to December 2018, were enrolled in the analysis.Nasopharyngeal secretions were collected.And automated nested multiplex PCR was used to detect adenovirus, coronavirus (HKUl type, NL63 type, 229E type, 0C43 type), human metapneumovirus, influenza A virus (H1 subtype, H1-2009 subtype, H3 subtype), influenza B virus, parainfluenza virus (type 1, type 2, type 3, type 4), respiratory syncytial virus, Bacillus pertussis, Chlamydia pneumoniae and Mycoplasma pneumonia.Results:Among the 290 specimens detected by the automated nested multiplex PCR, 246(84.83%) were positive.There were 166 positive samples for a single pathogen, 60 positive samples for two pathogens, 17 positive samples for three pathogens, and three positive samples for four pathogens.Among the virus-positive cases, respiratory syncytial virus was the most common pathogen in children younger than 6 months(62.39%, 73/117). The most common pathogen was human rhinovirus/enterovirus(43.48%, 20/46) from seven months to one year old.Adenovirus(37.50%, 18/48) was the most common pathogen among children aged one to three years old.Rhinovirus/enterovirus(35.00%, 7/20) was the most common pathogen among children aged three to six years old.The most common pathogen in children over six years old was influenza virus(46.67%, 7/15). The adenovirus detection rate was highest in May, the syncytial virus detection rate was highest in August, and the influenza virus detection rate was highest in July.Mycoplasma pneumoniae and pertussis were distributed throughout the year.Conclusion:The automated nested multiplex PCR system can detect multiple pathogens efficiently, quickly and accurately; the common pathogens of severe CAP are diverse in different age groups; the epidemic season of common pathogens is unique in different regions due to different climates.

The outbreak of coronavirus disease (COVID-19) caused by SARS-CoV-2 virus continually lead to worldwide human infections and deaths. Currently, there is no specific viral protein-targeted therapeutics. Viral nucleocapsid protein is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. However, the structural information of SARS-CoV-2 nucleocapsid protein remains unclear. Herein, we have determined the 2.7 Å crystal structure of the N-terminal RNA binding domain of SARS-CoV-2 nucleocapsid protein. Although the overall structure is similar as other reported coronavirus nucleocapsid protein N-terminal domain, the surface electrostatic potential characteristics between them are distinct. Further comparison with mild virus type HCoV-OC43 equivalent domain demonstrates a unique potential RNA binding pocket alongside the -sheet core. Complemented by binding studies, our data provide several atomic resolution features of SARS-CoV-2 nucleocapsid protein N-terminal domain, guiding the design of novel antiviral agents specific targeting to SARS-CoV-2.

Objective    To compare the effects of transthoracic device closure and traditional surgical repair on atrial septal defect systemically. Methods    A systematic literature search was conducted using the PubMed, EMbase, The Cochrane Library, VIP, CNKI, CBM, Wanfang Database up to July 31, 2018 to identify trials according to the inclusion and exclusion criteria. Quality was assessed and data of included articles were extracted. The meta-analysis was conducted by RevMan 5.3 and Stata 12.0 software. Results    Thirty studies were identified, including 3 randomized controlled trials (RCTs) and 27 cohort studies involving 3 321 patients. For success rate, the transthoracic closure group was lower than that in the surgical repair group (CCT, OR=0.34, 95%CI 0.16 to 0.69, P=0.003). There was no statistical difference in mortality between the two groups (CCT, OR=0.43, 95%CI 0.12 to 1.52, P=0.19). Postoperative complication occurred less frequently in the transthoracic closure group than that in the surgical repair group (RCT, OR=0.30, 95%CI 0.12 to 0.77,  P=0.01; CCT, OR=0.27, 95%CI 0.17 to 0.42, P<0.000 01). The risk of postoperative arrhythmia in the transthoracic closure group was lower than that in the surgical repair group (CCT, OR=0.56, 95%CI 0.34 to 0.90, P=0.02). There was no statistical difference in the incidence of postoperative residual shunt in postoperative one month (CCT, OR=4.52, 95%CI 0.45 to 45.82, P=0.20) and in postoperative one year (CCT, OR=1.03, 95%CI 0.29 to 3.68, P=0.97) between the two groups. Although the duration of operation (RCT MD=–55.90, 95%CI –58.69 to –53.11, P<0.000 01; CCT MD=–71.68, 95%CI -– 79.70 to –63.66, P<0.000 01), hospital stay (CCT, MD=–3.31, 95%CI –4.16, –2.46, P<0.000 01) and ICU stay(CCT, MD=–10.15, 95%CI –14.38 to –5.91, P<0.000 01), mechanical ventilation (CCT, MD=–228.68, 95%CI –247.60 to – 209.77, P<0.000 01) in the transthoracic closure group were lower than those in the traditional surgical repair group, the transthoracic closure costed more than traditional surgical repair during being in the hospital (CCT, MD=1 221.42, 95%CI 1 124.70 to 1 318.14, P<0.000 01). Conclusion    Compared with traditional surgical repair, the transthoracic closure reduces the hospital stay, shortens the length of ICU stay and the duration of ventilator assisted ventilation, while has less postoperative complications. It is safe and reliable for patients with ASD within the scope of indication.