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.

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.

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.

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.

Propranolol induces hemangioma endothelial cell apoptosis via a p53­BAX mediated pathway.

The use of propranolol for the treatment of infantile hemangioma (IH) has been widely investigated in recent years. However, the underlying therapeutic mechanism of propranolol for the treatment of IH remains poorly understood. The aim of the present study was to investigate the expression of proteins regulated by cellular tumor antigen p53 (p53) in associated apoptosis pathways in IH endothelial cells (HemECs) treated with propranolol. Furthermore, the present study aimed to investigate the exact apoptotic pathway underlying the therapeutic effect of propranolol against IH. In the present study, HemECs were subcultured and investigated using an inverted phase contrast microscope, immunocytochemical staining and a scanning electron microscope (SEM). Experimental groups and blank control groups were prepared. All groups were subjected to drug treatment. A high p53 expression model of HemECs was successfully established via transfection, and a low p53 expression model of HemECs was established using pifithrin­α. The apoptosis rate of each group was determined using Annexin V­fluorescein isothiocyanate/propidium iodide double staining and flow cytometry. The expression levels of downstream proteins regulated by p53 [tumour necrosis factor receptor superfamily member 6 (FAS), p53­induced death domain­containing protein (PIDD), death receptor 5 (DR5), BH3­interacting domain death agonist (BID), apoptosis regulator BAX (BAX), p53 unregulated modulator of apoptosis (PUMA), phosphatidylinositol­glycan biosynthesis class S protein (PIGS), and insulin­like growth factor­binding protein 3 (IGF­BP3)] were revealed in the experimental and control groups via western blotting. Microscopic observation revealed the growth of an adherent monolayer of cells, which were closely packed and exhibited contact inhibition. Immunocytochemical staining demonstrated increased expression of clotting factor VIII. SEM analysis revealed presence of Weibel­Palade bodies. The results of the analyses verified that the cultured cells were HemECs. The staining of the samples resulted in a significantly increased rate of apoptosis in experimental groups compared with the blank control group. This result suggested that there is an association between p53 expression and the rate of apoptosis of propranolol­treated HemECs. The results of the western blot analysis demonstrated an upregulation of BAX expression and a downregulation of IGF­BP3 expression in the HemECs treated with propranolol. There were no significant differences in the expression levels of FAS, DR5, PIDD, BID, PUMA and PIGS between experimental and control groups. This result suggests that p53 has an important role in HemEC apoptosis. The results of the present study additionally suggest that the propranolol­induced HemEC apoptosis pathway is a mitochondrial apoptosis pathway and is regulated by p53­BAX signaling.

A 30-InDel Assay for Genetic Variation and Population Structure Analysis of Chinese Tujia Group.

In the present study, thirty autosomal insertion and deletion polymorphic loci were simultaneously amplified and genotyped in a multiplex system, and their allelic frequencies as well as several forensic parameters were obtained in a sample of 236 unrelated healthy Tujia individuals. All the loci were in Hardy-Weinberg equilibrium after applying a Bonferroni correction and all pair-wise loci showed no significant linkage disequilibrium. These loci were observed to be relatively informative and discriminating, quite efficient for forensic applications. Allelic frequencies of 30 loci were compared between the Tujia group and other reference populations, and the results of analysis of molecular variance indicated the Tujia group showed the least significant differences with the Shanghai Han at one locus, and the most with Central Spanish population at 22 loci. We analyzed the population genetic structure by the principal component analysis, the clustering of STRUCTURE program and a Neighbor-Joining tree, and then evaluated the genetic relationships among Tujia and other 15 populations.

Genetic polymorphism analyses of a novel panel of 19 X-STR loci in the Chinese Uygur ethnic minority.

The population genetic data and forensic parameters of 19 X-chromosome short tandem repeat (X-STR) loci in Chinese Uygur ethnic minority are presented. These loci were detected in a sample of 233 (94 males and 139 females) unrelated healthy individuals. We observed 238 alleles at the 19 X-STR loci, with the corresponding gene frequencies spanning the range from 0.0021 to 0.5644. After Bonferroni correction (P>0.0026), there were no significant deviations from Hardy-Weinberg equilibrium. The cumulative power of discrimination in females and males, and the probability of exclusion of the 19 X-STR loci were 0.999 999 999 999 999 999 998 091, 0.999 999 999 999 966, and 0.999 999 986 35, respectively. The cumulative mean exclusion chance was 0.999 999 992 849 in deficiency cases, 0.999 999 999 999 628 in normal trios, and 0.999 999 998 722 in duo cases. The high value of the forensic parameters mentioned above revealed that the novel panel of 19 loci had important values for forensic applications in the Uygur group.

[Effects of NGF and estrogens on human hair follicle in vitro].

OBJECTIVE: To observe the effects of NGF, estrogens and minoxidil on the growth of human hair follicle in vitro. METHODS: In a model of human hair follicle in vitro, the follicle was separately treated with the NGF, estrogens and minoxidil. The growth of the hair follicle was measured in length with an eyepiece micrometer. The effects of the NGF, estrogens and minoxidil were evaluated by measuring the rates of incorporation of 3H-TdR of DNA synthesis. RESULTS: The growth of the human hair follicle was showing significantly faster in the 100 ng/ml NGF and 125 micrograms/ml minoxidil groups, compared with the control (P < 0.05), but the growth was significantly inhibited in the 0.5 microgram/ml 17 beta-E2 group (P < 0.05). There was no difference shown for the growth of the hair follicle in the group mixed with 100 ng/ml NGF and 0.5 microgram/ml 17 beta-E2 (P > 0.05). The rates of incorporation of 3H-TdR in the groups were shown that the results just correlated with the results of the above-mentioned method. CONCLUSIONS: The 100 ng/ml NGF and 125 micrograms/ml minoxidil could increase the growth of human hair follicle while the 0.5 microgram/ml 17 beta-E2 could inhibit it. The 100 ng/ml NGF could neutralized the effect of the 0.5 microgram/ml 17 beta-E2.