Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.

Objectives: The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods: PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results: The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion: Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412851, identifier CRD42023412851.

Digital therapeutics as an emerging new therapy for diabetes mellitus: potentials and concerns.

The global burden of controlling and managing diabetes mellitus (DM) is a significant challenge. Despite the advancements in conventional DM therapy, there remain hurdles to overcome, such as enhancing medication adherence and improving patient prognosis. Digital therapeutics (DTx), an innovative digital application, has been proposed to augment the traditional disease management workflow, particularly in managing chronic diseases like DM. Several studies have explored DTx, yielding promising results. However, certain concerns about this innovation persist. In this review, we aim to encapsulate the potential of DTx and its applications in DM management, thereby providing a comprehensive overview of this technique for public health policymakers.

Awareness of diabetic retinopathy among patients with type 2 diabetes mellitus attending Donga Clinic in Francistown, Botswana.

Context: Diabetic retinopathy (DR) prevalence is steadily increasing in the country and by raising patient awareness, health providers can educate on regular eye exams, stimulate collaboration with experts, enhance monitoring and follow-up, and improve the patient's overall condition. Aim: To assess the awareness of diabetic retinopathy (DR) among patients with type 2 diabetes mellitus (T2DM) during their new/follow-up visit in a diabetes clinic. Settings and Design: Patients were given a questionnaire for 4 weeks. Methods and Material: A facility-based cross-sectional study was conducted, and data were analyzed with SPSS. Results: A total of 160 patients were enrolled (59.08 study was conductedents wite females. 42% had DM duration of less than 5 years. Hypertension was a comorbidity at 83%. Blood sugar control was good among 53%. 96.3% were nonsmokers, 1.9% quit smoking, and 1.9% smoked. 100% believed diabetes may affect their eyes, 83.1% stated eye exams were necessary even when diabetes was well managed, 96.9% believed eye exams were necessary when diabetes was poorly controlled. Majority (43%) felt they should go for eye checkups every 6 months. 75% were unaware of the treatments available for DR. Patients were aware of blindness, cataract, glaucoma, DR, at 63%, 14%, 10%, and 13%, respectively. The primary reason for undergoing eye examination was doctor's referral at 94%. Healthcare provider was the common source of information on DM complications (79%). Conclusion: The need arises to raise DR awareness to increase case detection thus reduce the strain of DR's sight-threatening complications.

The potential of the Medical Digital Twin in diabetes management: a review.

Diabetes is a chronic prevalent disease that must be managed to improve the patient's quality of life. However, the limited healthcare management resources compared to the large diabetes mellitus (DM) population are an obstacle that needs modern information technology to improve. Digital twin (DT) is a relatively new approach that has emerged as a viable tool in several sectors of healthcare, and there have been some publications on DT in disease management. The systematic summary of the use of DTs and its potential applications in DM is less reported. In this review, we summarized the key techniques of DTs, proposed the potentials of DTs in DM management from different aspects, and discussed the concerns of this novel technique in DM management.

Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features.

The intelligent classification of heart-sound signals can assist clinicians in the rapid diagnosis of cardiovascular diseases. Mel-frequency cepstral coefficients (MelSpectrums) and log Mel-frequency cepstral coefficients (Log-MelSpectrums) based on a short-time Fourier transform (STFT) can represent the temporal and spectral structures of original heart-sound signals. Recently, various systems based on convolutional neural networks (CNNs) trained on the MelSpectrum and Log-MelSpectrum of segmental heart-sound frames that outperform systems using handcrafted features have been presented and classified heart-sound signals accurately. However, there is no a priori evidence of the best input representation for classifying heart sounds when using CNN models. Therefore, in this study, the MelSpectrum and Log-MelSpectrum features of heart-sound signals combined with a mathematical model of cardiac-sound acquisition were analysed theoretically. Both the experimental results and theoretical analysis demonstrated that the Log-MelSpectrum features can reduce the classification difference between domains and improve the performance of CNNs for heart-sound classification.

Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients.

To establish a risk prediction model and make individualized assessment for the susceptible diabetic retinopathy (DR) population in type 2 diabetic mellitus (T2DM) patients. According to the retrieval strategy, inclusion and exclusion criteria, the relevant meta-analyses on DR risk factors were searched and evaluated. The pooled odds ratio (OR) or relative risk (RR) of each risk factor was obtained and calculated for ß coefficients using logistic regression (LR) model. Besides, an electronic patient-reported outcome questionnaire was developed and 60 cases of DR and non-DR T2DM patients were investigated to validate the developed model. Receiver operating characteristic curve (ROC) was drawn to verify the prediction accuracy of the model. After retrieving, eight meta-analyses with a total of 15,654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, intensive glucose control, course of T2DM, glycated hemoglobin (HbA1c), fasting plasma glucose, hypertension, gender, insulin treatment, residence, and smoking were included for LR modeling. These factors, followed by the respective ß coefficient was bariatric surgery (- 0.942), myopia (- 0.357), lipid-lowering drug follow-up < 3y (- 0.994), lipid-lowering drug follow-up > 3y (- 0.223), course of T2DM (0.174), HbA1c (0.372), fasting plasma glucose (0.223), insulin therapy (0.688), rural residence (0.199), smoking (- 0.083), hypertension (0.405), male (0.548), intensive glycemic control (- 0.400) with constant term α (- 0.949) in the constructed model. The area under receiver operating characteristic curve (AUC) of the model in the external validation was 0.912. An application was presented as an example of use. In conclusion, the risk prediction model of DR is developed, which makes individualized assessment for the susceptible DR population feasible and needs to be further verified with large sample size application.

Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China.

Ambient PM2.5 pollution is a leading environmental health risk factor worldwide. The spatial resolution of PM2.5 concentrations and population strongly impacts PM2.5-related health impact estimates. However, long-term variations and regional differences in this impact have rarely been explored, particularly in China. Here, by aggregating satellite-derived PM2.5 concentration and population datasets at 1-km resolution in China to coarser resolutions (10, 50, and 100 km), we evaluated decadal changes in the impact of resolution on health assessments at national and local scales. For the sensitivity of population-weighted mean (PWM) PM2.5 concentrations to resolution, we found that the national PWM PM2.5 concentration decreased with coarser resolutions; this pattern was widely observed and was more obvious in southern and central China and the Sichuan Basin. The results showed that the sensitivity of national PWM PM2.5 concentrations to resolution continuously weakened from 2010 to 2020, likely due to a reduction in the spatial heterogeneity of PM2.5 concentrations in regions with high sensitivity. This weakness caused a large underestimation of the long-term trend of national PWM PM2.5 using a 100-km resolution, which was 7% lower than the trend at 1 km. Regarding the sensitivity of PM2.5-attributable mortality to resolution, most of China exhibited a pattern in which attributable mortality decreased with coarser resolution. The sensitivity of the estimated PM2.5-attributable mortality to resolution also weakened over time on a national scale and in most parts of China. Nevertheless, the weakness for mortality sensitivity was not as apparent as for PWM PM2.5 sensitivity. This was likely because different drivers played distinct roles in the temporal variation of the mortality sensitivity: population aging enhanced the sensitivity, and variations in PM2.5 concentrations and population distribution both weakened the sensitivity. However, the national attributable mortality trend at a 100-km resolution was still underestimated by 1.75% relative to the 1-km resolution.

Comprehensive bioinformatic analyses of lncRNA-mediated ceRNA network for uterine corpus endometrial carcinoma.

Background: Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through a lncRNA-mediated ceRNA network. Methods: The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via "Edge R" package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell (TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. Results: In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related Differentially Expressed long noncoding RNAs (DElncRNAs) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related Differentially Expressed messenger RNA (DEmRNAs) in two ceRNA networks were further validated in the Human Protein Atlas Portal (HPA) database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC. Conclusions: Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

A Multi-Omics Study on the Effect of Helicobacter Pylori-Related Genes in the Tumor Immunity on Stomach Adenocarcinoma.

Background: Helicobacter pylori (HP), a gram-negative spiral-shaped microaerophilic bacterium, colonizes the stomach of approximately 50% of the world's population, which is considered a risk factor for gastritis, peptic ulcers, gastric cancer, and other malignancies. HP is also considered carcinogenic since it involves the mutation and damage of multiple HP-related genes. Stomach adenocarcinoma (STAD) is a common stom5ach cancer with a poor prognosis and high risk of metastasis in the advanced stage. Therefore, an early diagnosis and targeted therapies are needed to ensure a better prognosis. In this study, a scoring system was constructed based on three HP infection-related candidate genes to enable a more accurate prediction of tumor progression and metastasis and response to immunotherapies. Methods: HP infection-induced mutation patterns of STAD samples from six cohorts were comprehensively assessed based on 73 HP-related genes, which were then correlated with the immune cell-infiltrating characteristics of the tumor microenvironment (TME). The risk signature was constructed to quantify the influence of HP infection on individual tumors. Subsequently, an accurate nomogram was generated to improve the clinical applicability of the risk signature. We conducted immunohistochemical experiments and used the Affiliated Hospital of Youjiang Medical University for Nationalities (AHYMUN) cohort data set with survival information to further verify the clinical value of this risk signature. Results: Two distinct HP-related mutation patterns with different immune cell-infiltrating characteristics (ICIC) and survival possibility were identified. We demonstrated that the evaluation of HP infection-induced mutation patterns of tumor could assist the prediction of stages, phenotypes, stromal activity, genetic diversity, and patient prognosis. A low risk score involved an increased mutation burden and activation of immune responses, with a higher 5-year survival rate and enhanced response to anti-PD-1/L1 immunotherapy, while a high risk score involved stromal activation and poorer survival. The efficiency of the risk signature was further evidenced by the nomogram. Conclusions: STAD patients with a low risk score demonstrated significant therapeutic advantages and clinical benefits. HP infection-induced mutations play a nonnegligible role in STAD development. Quantifying the HP-related mutation patterns of individual tumors will contribute to phenotype classification, guide more effective targeted and personalized therapies, and enable more accurate predictions of metastasis and prognosis.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. OBJECTIVE: The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. METHODS: Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. RESULTS: A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non-logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. CONCLUSIONS: Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Association between cystatin C and diabetic retinopathy among type 2 diabetic patients in China: a Meta-analysis.

AIM: To explore the correlation between cystatin C (Cys-C) and diabetic retinopathy (DR) in those patients with type 2 diabetes mellitus (DM) in China. METHODS: Articles were collected from China National Knowledge Infrastructure (CNKI), Wanfang, VIP, PubMed, EMBASE, Cochrane Library, Clinical Trials.gov, and Google Scholar. Quality and risk of bias within included studies was assessed using the Newcastle-Ottawa scale (NOS). Heterogeneity was determined by using Cochran's Q-test and Higgins I 2 statistics. Mean differences (MDs) and 95% confidence intervals (CIs) of Cys-C within the diabetes without retinopathy (DWR) and DR, DWR and non-proliferative diabetic retinopathy (NPDR), NPDR and proliferative diabetic retinopathy (PDR) were collected by using random-effects model because of high heterogeneity. Meta-analysis was conducted based on 23 articles of 2331 DR including NPDR and PDR patients and 2023 DWR patients through Review Manager 5.3. Subgroup analyses were also performed according to DM duration, body mass index (BMI), total cholesterol (TC), total triglycerides (TG), low-density lipoprotein C (LDL-C), and high-density lipoprotein C (HDL-C), sample origins and methods. Publication bias was assessed by the funnel plot. RESULTS: Cys-C level in DR patients was increased compared with that of DWR (total MD: 0.69, 95%CI: 0.41 to 0.97, Z=4.79, P<0.01). Besides, the synthesized results of the studies showed the similar findings in the DWR vs NPDR group (total MD: 0.29, 95%CI 0.20 to 0.39, Z=6.02, P<0.01) and the NPDR vs PDR group (total MD: 0.63, 95%CI 0.43 to 0.82, Z=6.33, P<0.01). Heterogeneity of most of the subgroup analyses was still obvious (I 2≥50%, P<0.1). Forest plots of different subgroups indicated that there was a slight increase of Cys-C during the period between DWR and DR, DWR and NPDR, NPDR and PDR. Funnel plot showed that there was no significant publication bias. CONCLUSION: The elevated Cys-C is closely related with DR and probably plays a critical role in its progression.

Quercetin inhibits the proliferation of multiple myeloma cells by upregulating PTPRR expression.

Multiple myeloma (MM) is an incurable disease characterized by malignant plasma cell clonal expansion in the bone marrow; therefore, inhibiting the proliferation of plasma cells is an important approach to overcome the progression of MM. Quercetin (Que) is a promising flavonoid with broad-spectrum anti-tumor activity against various cancers, including MM; however, the underlying mechanism is not yet understood. The present study aimed to reveal the gene expression profile of Que-treated MM cells and clarify its potential mechanism. The 30% inhibitory concentration (IC30) of Que against MM cells was calculated, and the proliferation rate was significantly reduced after Que treatment. Next, 495 dysregulated genes were identified via RNA sequencing in Que-treated MM cells. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses indicated that the dysregulated genes were enriched in various apoptosis-related GO terms and amino acid metabolism-related pathways. qPCR validation showed that protein tyrosine phosphatase receptor-type R (PTPRR) had the highest verified log2 FC (abs) among the top 15 dysregulated genes. Overexpression of PTPRR increased the sensitivity of MM cells against Que, significantly inhibiting their proliferation and colony formation ability; silencing of PTPRR showed the opposite results. Furthermore, bioinformatics analyses and PPI network construction of PTPRR indicated that dephosphorylation of ERK might be the potential pathway for the PTPRR-induced inhibition of MM cell proliferation. In summary, our study identified the gene expression profile in Que-treated MM cells and demonstrated that the upregulation of PTPRR was one of the important mechanisms for the Que-induced inhibition of MM cell proliferation.

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers.

BACKGROUND AND OBJECTIVES: Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. METHODS: The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. RESULTS: A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. CONCLUSION: The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.

Deep Learning Methods for Heart Sounds Classification: A Systematic Review.

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

Anti-DLBCL efficacy of DCZ0825 in vitro and in vivo: involvement of the PI3K‒AKT‒mTOR/JNK pathway.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, characterized by high heterogeneity. The poor outcome of a portion of patients who suffer relapsing or resistant to conventional treatment impels the development of novel agents for DLBCL. DCZ0825 is a novel compound derived from pterostilbene and osalmide, whose antitumor activities have drawn our attention. In this study, we found that DCZ0825 exhibited high cytotoxicity toward DLBCL cell lines in a dose- and time-dependent manner, as revealed by cell counting kit-8 assay. Flow cytometry and western blot analysis results showed that DCZ0825 also promoted cell apoptosis via both extrinsic and intrinsic apoptosis pathways mediated by caspase. In addition, DCZ0825 induced cell cycle arrest in the G2/M phase by downregulating Cdc25C, CDK1, and Cyclin B1, thus interfering with cell proliferation. Further investigation showed the involvement of the phosphatidylinositol 3-kinase (PI3K)‒AKT‒mTOR/JNK pathway in the efficacy of DCZ0825 against DLBCL. Remarkably, DCZ0825 also exerted notable cytotoxic effects in vivo as well, with low toxicity to important internal organs such as the liver and kidney. Our results suggest that DCZ0825 may have the potential to become a novel anti-DLBCL agent or to replenish the conventional therapeutic scheme of DLBCL.

Association between retinopathy, nephropathy, and periodontitis in type 2 diabetic patients: a Meta-analysis.

AIM: To verify the association between retinopathy, nephropathy, and periodontitis in type 2 diabetic (T2D) patients. METHODS: Several electronic databases were available for our comprehensive search including China National Knowledge Infrastructure (CNKI), Chinese VIP Information (VIP), Wanfang, Web of Science, ScienceDirect and PubMed and were queried for relevant citations (updated to Mar. 2019). RevMan was utilized to perform Meta analysis and publication bias detection. After evaluation of the methodological quality of included studies, a fixed or random effect model was utilized to analyze data from included studies. RESULTS: A total of eight articles were finally included in this Meta analysis. In all 3987 subjects, there were 1207 T2D patients accompanying with microvascular complications and 1734 patients with periodontitis as well. The Meta forest plot presented little heterogeneity of the eight studies (P<0.00001, I 2=89%). The total effect demonstrated periodontitis was associated with overall microvascular complications (OR: 1.96, 95%CI: 1.67-2.30, Z=8.25, P<0.00001). Subgroup investigations among the studies in Asian (OR: 2.33, 95%CI: 1.91-2.85) and North American (OR: 1.42, 95%CI: 1.08-1.86) populations confirmed the existed association between retinopathy, nephropathy, and periodontitis. While the strength of such associations between periodontitis and diabetic microvascular complications were more obvious in the Asians than North Americans. All the results indicated that periodontitis was associated with diabetic retinopathy (OR: 3.77, 95%CI: 2.71-5.24), diabetic nephropathy (OR: 1.55, 95%CI: 1.24-1.94) in T2D patients. CONCLUSION: The periodontitis is associated with diabetic retinopathy, diabetic nephropathy among T2D patients and further large sample size clinical trials are in need to confirm the findings.

Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences.

A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.

Wide-ranging analysis of survival-related alternative splicing events in invasive breast carcinoma.

Invasive breast carcinoma (BRCA) is a serious disease that threatens the survival time of those affected. Alternative splicing (AS) involved in BRCA pathogenesis may be a potential therapeutic target. However, to the best of our knowledge, a systematic analysis of survival-related alternative splicing events (SREs) has not yet been reported. The aim of the present study was to identify SREs and analyze their potential biological functions as BRCA prognostic biomarkers. An UpSet plot demonstrated AS global characteristics. Cox's proportional hazards regression model quantitatively demonstrated the prognostic relevance of AS events. Functional enrichment analysis investigated the potential pathways through which AS events affect BRCA progression. The receiver operating characteristic curve model determined the clinical significance of AS events represented using percent-spliced-in (PSI) values. The regulatory network of splicing factors (SFs) and AS events laid the foundation for studying the role of SFs in BRCA. The present study identified 1,215 SREs and their distribution characteristics, suggesting that AS events in exon skipping (ES) primarily exerted normal physiological functions, while AS events in alternative terminator sites had the most significant prognostic effect. The present study demonstrated that survival-associated genes are involved primarily in certain biological processes of ribosomal proteins. In the diagnostic model, the alternative acceptor site, alternative donor site, alternative promoter site and ES performed well. ELAVL4 was the key gene associated with prognosis and SREs. In conclusion, a number of AS events affect BRCA initiation, progression and prognosis. The PSI value of AS events has the potential to diagnose BRCA and predict a prognosis; however, this must be confirmed in additional studies.

Association between retinol binding protein 4 and diabetic retinopathy among type 2 diabetic patients: a meta-analysis.

AIMS: The aim of this study was to investigate the association between retinol-binding protein 4 (RBP4) and diabetic retinopathy (DR) among patients with type 2 diabetes mellitus (T2DM). METHODS: Databases PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, VIP, and Wangfang were searched to July 30, 2019. The Newcastle-Ottawa Scale was applied to assess the quality of all identified studies, and those qualified were included in the meta-analysis. The Chi squared Q test and I2 statistics were conducted to evaluate heterogeneity. Standardized mean differences (SMD) and 95% confidence intervals (CI) among RBP4 within the DR and T2DM without retinopathy (DWR) groups were pooled using the random effects model depending on the heterogeneity. Subgroup analyses were conducted among the groups having different diabetes duration, detection methods, body mass index, and total cholesterol and triglyceride levels. The funnel plot was used to assess publication bias. RESULTS: Nineteen observational studies were included in our meta-analysis. RBP4 was significantly higher in both nonproliferative DR (SMD: 0.72, 95% CI 0.48-0.95, P < 0.00001) and proliferative DR (SMD: 2.68, 95% CI 1.69-3.67, P < 0.00001) groups despite high heterogeneity (I2 = 87 and 97% in DR and PDR groups, respectively). Significant differences were noted among most subgroups (P < 0.05). Among those accompanied by hypercholesterolemia, the association between RBP4 and DR were unclear (P = 0.09). CONCLUSIONS: Elevated RBP4 is strongly associated with DR and may play an essential role in its progression. Additional large-scale controlled studies are needed to confirm these findings.