Impact of body mass index on adverse kidney events in diabetes mellitus patients: A systematic-review and meta-analysis.

BACKGROUND: The incidence of chronic kidney disease among patients with diabetes mellitus (DM) remains a global concern. Long-term obesity is known to possibly influence the development of type 2 diabetes mellitus. However, no previous meta-analysis has assessed the effects of body mass index (BMI) on adverse kidney events in patients with DM. AIM: To determine the impact of BMI on adverse kidney events in patients with DM. METHODS: A systematic literature search was performed on the PubMed, ISI Web of Science, Scopus, Ovid, Google Scholar, EMBASE, and BMJ databases. We included trials with the following characteristics: (1) Type of study: Prospective, retrospective, randomized, and non-randomized in design; (2) participants: Restricted to patients with DM aged ≥ 18 years; (3) intervention: No intervention; and (4) kidney adverse events: Onset of diabetic kidney disease [estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 and/or microalbuminuria value of ≥ 30 mg/g Cr], serum creatinine increase of more than double the baseline or end-stage renal disease (eGFR < 15 mL/min/1.73 m2 or dialysis), or death. RESULTS: Overall, 11 studies involving 801 patients with DM were included. High BMI (≥ 25 kg/m2) was significantly associated with higher blood pressure (BP) [systolic BP by 0.20, 95% confidence interval (CI): 0.15-0.25, P < 0.00001; diastolic BP by 0.21 mmHg, 95%CI: 0.04-0.37, P = 0.010], serum albumin, triglycerides [standard mean difference (SMD) = 0.35, 95%CI: 0.29-0.41, P < 0.00001], low-density lipoprotein (SMD = 0.12, 95%CI: 0.04-0.20, P = 0.030), and lower high-density lipoprotein (SMD = -0.36, 95%CI: -0.51 to -0.21, P < 0.00001) in patients with DM compared with those with low BMIs (< 25 kg/m2). Our analysis showed that high BMI was associated with a higher risk ratio of adverse kidney events than low BMI (RR: 1.22, 95%CI: 1.01-1.43, P = 0.036). CONCLUSION: The present analysis suggested that high BMI was a risk factor for adverse kidney events in patients with DM.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.

BACKGROUND: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS: The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS: We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Identification of associated risk factors for serological distribution of hepatitis B virus via machine learning models.

BACKGROUND: The provincial-level sero-survey was launched to learn the updated seroprevalence of hepatitis B virus (HBV) infection in the general population aged 1-69 years in Chongqing and to assess the risk factors for HBV infection to effectively screen persons with chronic hepatitis B (CHB). METHODS: A total of 1828 individuals aged 1-69 years were investigated, and hepatitis B surface antigen (HBsAg), antibody to HBsAg (HBsAb), and antibody to B core antigen (HBcAb) were detected. Logistic regression and three machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), and stochastic gradient boosting (SGB), were developed for analysis. RESULTS: The HBsAg prevalence of the total population was 3.83%, and among persons aged 1-14 years and 15-69 years, it was 0.24% and 4.89%, respectively. A large figure of 95.18% (770/809) of adults was unaware of their occult HBV infection. Age, region, and immunization history were found to be statistically associated with HBcAb prevalence with a logistic regression model. The prediction accuracies were 0.717, 0.727, and 0.725 for the proposed RF, SVM, and SGB models, respectively. CONCLUSIONS: The logistic regression integrated with ML models could helpfully screen the risk factors for HBV infection and identify high-risk populations with CHB.

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.

Background: Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods: The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results: Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion: The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China.

OBJECTIVE: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS: The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION: This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.

PURPOSE: Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models. METHODS: A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data. RESULTS: FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917 to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis. CONCLUSION: A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.

The associated factors of earphone usage on hearing impairment among students aged 14 to 28 / 中国学校卫生

Objective@#To investigate the relationship between the use of earphone and hearing impairment and its influencing factors among students aged 14-28, so as to provide a reference for appropriate earphone usage and hearing impairment prevention.@*Methods@#A cross sectional survey was conducted through the questionnaire star platform, and 983 students aged 14 to 28 were recruited across China by snowball sampling during April 3 to May 1, 2022. The χ 2 test was used to identify indicators affecting hearing, the Logistic regression model was used to further selection.@*Results@#There were 366 students with hearing impairment, accounting for 37.23%. Univariate analysis showed significant differences in hearing impairment by gender, earphone usage duration and volume, wearing during sleep, and replacement frequency ( χ 2=6.03, 6.86, 14.87, 12.22, 11.15, P <0.05). The Logistic regression model analysis showed that girls ( OR=1.43, 95%CI =1.10-1.88), maximum earphone volume ( OR=3.08, 95%CI = 1.56- 6.08), earphone usage for >1.5-3 h each time ( OR=1.44, 95%CI =1.04-1.99), sleep with headphone ( OR= 1.53 , 95%CI = 1.11- 2.11) were positively associated with hearing impairment ( P <0.05), earphone replacement every 4-<6 months ( OR= 0.38, 95%CI =0.17-0.86) and earphone replacement every six months or longer ( OR=0.39, 95%CI =0.18-0.85) were negatively associated with hearing impairment ( P < 0.05 ).@*Conclusion@#Students aged 14 to 28 earphone usage shows adverse impact on hearing. When using earphone, it is recommended to limit time spent on earphone usage, low the volume of earphone, avoid sleeping with earphone and replace earphone frequently.

E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation.

BACKGROUND: U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. OBJECTIVE: We use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. METHODS: Before combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. RESULTS: We performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. CONCLUSION: Our proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.

Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.

Background: Factors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic. Methods: A retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery. Results: Of the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6-10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003-1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004-1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194-0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016-0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202-0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939-4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63-0.861; p < 0.001), age (30-70) (OR = 0.738, 95% CI = 0.594-0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292-0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12-1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075-2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306-2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction. Conclusion: Age and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a "sampling-feature-hyperparameter-weight" synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R2 of BSEE2 fused by the "nonlinear + linear" method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R2 of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.

Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging.

Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system. Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function. Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions. Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.

N-mytistoyltransferase 1 and 2 are potential tumor suppressors and novel targets of miR-182 in human non-small cell lung carcinomas.

BACKGROUND & AIMS: Non-small cell lung cancer (NSCLC) accounts for about 80% of lung cancer diagnoses across the world. Despite recent appreciable improvements in treatment plans for patients with NSCLC, the prognosis for those with the cancer still remains poor. Recently, a growing number of studies have shown that N-myristoyltransferases (NMTs) may be critical in carcinogenesis, however, the functional and clinical significance of this pathway in NSCLC remains unclear and requires further research. METHODS: Initially, we evaluated the expression levels of NMT1 or NMT2 in a clinical cohort comprising of 303 paired primary NSCLC tissues and matched normal mucosae by using ELISA. We subsequently performed a tissue microarray analysis (TMA) to confirm its expression pattern in an independent validation cohort (n = 78). Then, we used a publicly available KM plotter database (n = 1921) to evaluate the prognostic impact of NMT1 and NMT2 in NSCLC. Lastly, a series of in-vitro molecular/cellular and animal experiments were performed for mechanistic understanding of the role of N-myristoyltransferases in NSCLC. RESULTS: Our ELISA data revealed that the expression level of NMT1 and NMT2 was down-regulated in tumor tissues (n = 303, P < 0.0001), which was confirmed in an independent validation cohort by TMA (n = 78, P = 0.014 for NMT1 and P < 0.0001 for NMT2). On the other hand, patients with low expression of NMT1 or NMT2 had shorter overall survival (P = 0.013, HR = 0.85 for NMT1; P = 0.00059, HR = 0.8, for NMT2). Mechanistically, we revealed that the interaction and co-localization of NMT1 and NMT2 in NSCLC, and N-terminus of NMT1 and NMT2 was observed to be crucial for their interaction as well as for their catalytic activity. Moreover, we found that NMT1 can significantly promote the expression of NMT2 by enhancing its stability. We corroborated these findings by performing functional assays in which the knockout of NMT1 and NMT2 resulted in enhanced cell proliferation, migration and invasion as well as increased tumorxenograftgrowth. In addition, we identified miR-182 as a novel regulator of both NMT1 and NMT2. More specifically, the overexpression or inhibition of miR-182 modulated globe N-myristoylation level, contributed to phenotypic alterations in NSCLS cells. CONCLUSIONS: NMT1 and NMT2 can act as potential tumor suppressors in NSCLC, and the inhibition of miR-182 expression or therapeutic NMTs replenishment may be a promising treatment option for patients with NSCLC.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion.

Background: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. Methods: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C r ). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R t ). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. Results: (1) The overall level of R t in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R t decreased rapidly, and the overall R t level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. Conclusion: Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

Restoring HOXD10 Exhibits Therapeutic Potential for Ameliorating Malignant Progression and 5-Fluorouracil Resistance in Colorectal Cancer.

Emerging evidence suggests that hypermethylation of HOXD10 plays an important role in human cancers. However, the biological and clinical impacts of HOXD10 overmethylation and its downstream targets in colorectal cancer remain unknown. We evaluated the methylation level of HOXD10 in paired cancer and normal tissues (n = 42) by using pyrosequencing, followed by validation of the methylation status of HOXD10 from The Cancer Genome Atlas (TCGA) datasets with 302 cancer tissues and 38 normal tissues. The biological function of HOXD10 was characterized in cell lines. We further evaluated the effects of HOXD10 and its targets on chemoresistance in our established resistant cell lines and clinical cohort (n = 66). HOXD10 was found frequently methylated in colorectal cancer, and its hypermethylation correlates with its low expression level, advanced disease, and lymph node metastasis. Functionally, HOXD10 acts as a tumor suppressor gene, in which HOXD10-expressing cells showed suppressed cell proliferation, colony formation ability, and migration and invasion capacity. Mechanistically, DNMT1, DNMT3B, and MeCP2 were recruited in the HOXD10 promoter, and demethylation by 5-Aza-2'-deoxycytidine (5-Aza-CdR) treatment or MeCP2 knockdown can sufficiently induce HOXD10 expression. HOXD10 regulates the expressions of miR-7 and IGFBP3 in a promoter-dependent manner. Restoration of the expression of HOXD10 in 5-fluorouracil (5-FU)-resistant cells significantly upregulates the expressions of miR-7 and IGFBP3 and enhances chemosensitivity to 5-FU. In conclusion, we provide novel evidence that HOXD10 is frequently methylated, silenced, and contributes to the development of colorectal cancers. Restoration of HOXD10 activates the expressions of miR-7 and IGFBP3 and results in an inhibited phenotype biologically, suggesting its potential therapeutic relevance in colorectal cancer (CRC).

An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

This study aimed to construct and validate an immunoscore nomogram that may be used to predict the prognosis of oesophageal cancer. With the gene expression data of oesophageal cancer in a public database, we used CIBERSORT to estimate the fractions of 22 infiltrating immune cell types. We then built an immunoscore signature based on 12 types of infiltrating immune cells using the least absolute shrinkage and selection operator (LASSO) model. This immunoscore was used as an independent predictor in the prognostic model (training cohort: [hazard ratio (HR), 4.78; 95% confidence interval (CI), 2.64-8.67; P < 0.001], validation cohort: [HR, 2.15; 95% CI, 1.04-4.45; P = 0.040]). Subgroup analysis by clinical features showed that overall survival was significantly different between the high-immunoscore group and the low-immunoscore group. The predictors that constituted the individualized prediction nomogram were immunoscore, age, and tumour stage. The nomogram had good discrimination and calibration. Decision curve analysis showed that the immunoscore nomogram was clinically useful. Therefore, the novel immunoscore signature based on infiltrating immune cells can be used as a reliable predictor of the prognosis of oesophageal cancer, and the immunoscore nomogram is a convenient tool for predicting the survival of individual patients.

High incidence of stroke in COVID-19 patients.

A retrospective analysis of 11 COVID-19 patients complicated with stroke was performed. It was found that the incidence of stroke in patients with COVID-19 was significantly higher than the average level of the general population (P=0.003), and the D-dimer levels of 11 stroke patients were significantly higher than other patients (P=0.004). The significant increase of D-dimer can be used as an early warning indicator of cerebral infarction. It is critical to have a response plan for treating acute stroke in COVID-19 patients.

Combined use of the neutrophil-to-lymphocyte ratio and CRP to predict 7-day disease severity in 84 hospitalized patients with COVID-19 pneumonia: a retrospective cohort study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide from Wuhan. An easy-to-use index capable of the early identification of inpatients who are at risk of becoming critically ill is urgently needed in clinical practice. Hence, the aim of this study was to explore an easy-to-use nomogram and a model to triage patients into risk categories to determine the likelihood of developing a critical illness. METHODS: A retrospective cohort study was conducted. We extracted data from 84 patients with laboratory-confirmed COVID-19 from one designated hospital. The primary endpoint was the development of severe/critical illness within 7 days after admission. Predictive factors of this endpoint were selected by LASSO Cox regression model. A nomogram was developed based on selected variables. The predictive performance of the derived nomogram was evaluated by calibration curves and decision curves. Additionally, the predictive performances of individual and combined variables under study were evaluated by receiver operating characteristic curves. The developed model was also tested in a separate validation set with 71 laboratory-confirmed COVID-19 patients. RESULTS: None of the 84 inpatients were lost to follow-up in this retrospective study. The primary endpoint occurred in 23 inpatients (27.4%). The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were selected as the final prognostic factors. A nomogram was developed based on the NLR and CRP. The calibration curve and decision curve indicated that the constructed nomogram model was clinically useful. The AUCs for the NLR, CRP and Combined Index in both training set and validation sets were 0.685 (95% CI: 0.574-0.783), 0.764 (95% CI: 0.659-0.850), 0.804 (95% CI: 0.702-0.883), and 0.881 (95% CI: 0.782-0.946), respectively. CONCLUSIONS: Our results demonstrated that the nomogram and Combined Index calculated from the NLR and CRP are potential and reliable predictors of COVID-19 prognosis and can triage patients at the time of admission.

Cumulative Evidence for Associations between Genetic Variants and Risk of Esophageal Cancer.

BACKGROUND: A large number of studies have been conducted to investigate associations between genetic variants and esophageal cancer risk in the past several decades. However, findings from these studies have been generally inconsistent. We aimed to provide a summary of the current understanding of the genetic architecture of esophageal cancer susceptibility. METHODS: We performed a comprehensive field synopsis and meta-analysis to evaluate associations between 95 variants in 70 genes or loci and esophageal cancer risk using data from 304 eligible publications, including 104,904 cases and 159,797 controls, through screening a total of 21,328 citations. We graded levels of cumulative epidemiologic evidence of a significant association with esophageal cancer using the Venice criteria and false-positive report probability tests. We constructed functional annotations for these variants using data from the Encyclopedia of DNA Elements Project and other databases. RESULTS: Thirty variants were nominally significantly associated with esophageal cancer risk. Cumulative epidemiologic evidence of a significant association with overall esophageal cancer, esophageal squamous cell carcinoma, or esophageal adenocarcinoma was strong for 13 variants in or near 13 genes (ADH1B, BARX1, CDKN1A, CHEK2, CLPTM1L, CRTC1, CYP1A1, EGF, LTA, MIR34BC, PLCE1, PTEN, and PTGS2). Bioinformatics analysis suggested that these variants and others correlated with them might fall in putative functional regions. CONCLUSIONS: Our study summarizes the current literature on the genetic architecture of esophageal cancer susceptibility and identifies several potential polymorphisms that could be involved in esophageal cancer susceptibility. IMPACT: These findings provide direction for future studies to identify new genetic factors for esophageal cancer.

Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe2 and WO3.

The development of novel synaptic device architectures with a high order of synaptic plasticity can provide a breakthrough toward neuromorphic computing. Herein, through the thermal oxidation of two-dimensional (2D) WSe2, unique memristive synapses based on the lateral heterostructure of 2D WSe2 and WO3, with multi-gate modulation characteristics, are firstly demonstrated. An intermediate transition layer in the heterostructure is observed through transmission electron microscopy. Raman spectroscopy and detailed electrical measurements provide insights into the mechanism of memristive behavior, revealing that the protons injected into/removed from the intermediate transition layer account for the memristive behavior. This novel memristive synapse can be used to emulate two neuron-based synaptic functions, like post-synaptic current, short-term plasticity and long-term plasticity, with remarkable linearity, symmetry, and an ultralow energy consumption of ∼2.7 pJ per spike. More importantly, the synaptic plasticity between the drain and source electrodes can be effectively modulated by the gate voltage and visible light in a four-terminal configuration. Such multi-gate tuning of the synaptic plasticity cannot be accomplished by any previously reported multi-gate synaptic devices that only mimic two neuron-based synapses. This new synaptic architecture with electrical and optical modulation enables a realistic emulation of biological synapses whose synaptic plasticity can be additionally regulated by the surrounding astrocytes, greatly improving the recognition accuracy and processing capacity of artificial neuristors, and paving a new way for highly efficient neuromorphic computation devices.