OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method. METHODS: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared. RESULTS: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%). CONCLUSION Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
Diabetic Retinopathy/diagnosis* ; Humans ; Machine Learning ; Male ; Female ; Polymorphism, Single Nucleotide ; Middle Aged ; Exome Sequencing ; Aged ; Adult ; Pedigree ; Diabetes Mellitus, Type 2/complications* ; Genetic Predisposition to Disease ; Mutation

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background and Objectives: Osteoblasts are derived from bone marrow mesenchymal stem cells (BMMSCs) and playimportant role in bone remodeling. While our previous studies have investigated the cell subtypes and heterogeneity in osteoblasts and BMMSCs separately, cell-to-cell communications between osteoblasts and BMMSCs in vivo in humans have not been characterized. The aim of this study was to investigate the cellular communication between human primary osteoblasts and bone marrow mesenchymal stem cells. Methods: and Results: To investigate the cell-to-cell communications between osteoblasts and BMMSCs and identifynew cell subtypes, we performed a systematic integration analysis with our single-cell RNA sequencing (scRNA-seq) transcriptomes data from BMMSCs and osteoblasts. We successfully identified a novel preosteoblasts subtype which highly expressed ATF3, CCL2, CXCL2 and IRF1. Biological functional annotations of the transcriptomes suggested that the novel preosteoblasts subtype may inhibit osteoblasts differentiation, maintain cells to a less differentiated status and recruit osteoclasts. Ligand-receptor interaction analysis showed strong interaction between mature osteoblasts and BMMSCs. Meanwhile, we found FZD1 was highly expressed in BMMSCs of osteogenic differentiation direction. WIF1 and SFRP4, which were highly expressed in mature osteoblasts were reported to inhibit osteogenic differentiation. We speculated that WIF1 and sFRP4 expressed in mature osteoblasts inhibited the binding of FZD1 to Wnt ligand in BMMSCs, thereby further inhibiting osteogenic differentiation of BMMSCs. Conclusions Our study provided a more systematic and comprehensive understanding of the heterogeneity of osteogenic cells. At the single cell level, this study provided insights into the cell-to-cell communications between BMMSCs and osteoblasts and mature osteoblasts may mediate negative feedback regulation of osteogenesis process.

Malignant tumors are major diseases that endanger human health. Due to their complex and variable microenvironment, most anti-tumor drugs cannot precisely reach the focal tissue and be released in a controlled manner. Intelligent responsive nano carriers have become a hot spot in the field of anti-tumor drug delivery systems. As an excellent nano material, mesoporous silica has the advantages of non-toxic, stable, adjustable pore volume and pore diameter, and easy functional modification on the surface. By virtue of its perceptive response to the tumor microenvironment or physiological changes, it can achieve the targeted drug release or controlled drug release of the drug delivery system in the tissue, making it an ideal carrier for intelligent response drug delivery system. In this paper, we review the design strategies and current research status of smart responsive anti-tumor drug delivery systems based on mesoporous silica, in order to provide a reference for the development of anti-tumor drug nanoformulations.

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
Infant, Newborn ; Humans ; Male ; Pregnancy ; Female ; Nomograms ; Retrospective Studies ; Cesarean Section ; Risk Factors ; Asphyxia Neonatorum/etiology*

OBJECTIVE: To evaluate the feasibility and tolerability of metoprolol standard dosing pathway (MSDP) in Chinese patients with acute coronary syndrome (ACS). METHODS: In this multicenter, prospective, open label, single-arm and interventional study that was conducted from February 2018 to April 2019 in fifteen Chinese hospitals. A total of 998 hospitalized patients aged ≥ 18 years and diagnosed with ACS were included. The MSDP was applied to all eligible ACS patients based on the standard treatment recommended by international guidelines. The primary endpoint was the percentage of patients achieving the target dose at discharge (V2). The secondary endpoints included the heart rate and blood pressure at V2 and four weeks after discharge (V4), and percentage of patients experiencing bradycardia (heart rate < 50 beats/min), hypotension (blood pressure < 90/60 mmHg) and transient cardiac dysfunction at V2 and V4. RESULTS: Of the 998 patients, 29.46% of patients achieved the target dose (≥ 95 mg/d) at V2. The total population was divided into two groups: target group (patients achieving the target dose at V2) and non-target group (patients not achieving the target dose at V2). There was significant difference in the reduction of heart rate from baseline to discharge in the two groups (-4.97 ± 11.90 beats/min vs. -2.70 ± 9.47 beats/min, P = 0.034). There was no significant difference in the proportion of bradycardia that occurred in the two groups at V2 (0 vs. 0, P = 1.000) and V4 (0.81% vs. 0.33%, P = 0.715). There was no significant difference in the proportion of hypotension between the two groups at V2 (0.004% vs. 0.004%, P = 1.000) and V4 (0 vs. 0.005%, P = 0.560). No transient cardiac dysfunction occurred in two groups during the study. A total of five adverse events (1.70%) and one serious adverse event (0.34%) were related to the pathway in target group. CONCLUSIONS In Chinese ACS patients, the feasibility and tolerability of the MSDP have been proved to be acceptable.

Objective: To determine IgG antibody levels of measles, rubella, mumps in healthy population in Shanghai from 2010 to 2020 and analyze the trend of antibody changes in different age groups. Methods: 10 828 healthy people without measles, rubella and mumps in Shanghai were included in the study from 2010 to 2020. Serum samples were collected from 12 age groups, and the serum IgG antibody of measles, rubella and mumps were detected by ELISA. The difference of antibody positive rates and antibody levels were analyzed. Results: The median age M (Q₁, Q₃) of 10 828 objects were 8 years old (9 months old, 20 years old). Males accounted for 48.34% (5 234/10 828) and females accounted for 50.92% (5 514/10 828). Unknown gender information accounted for 0.74% (80/10 828), and 27.03% (2 927/10 828) of participants had unknown MMR immunization history. The total positive rates of measles, rubella and mumps IgG antibody were 76.78%, 64.46% and 64.29% and their GMCs were 541.45 mIU/ml, 31.76 IU/ml and 133.73 U/ml respectively. There were significant differences in serum IgG antibody GMC of measles, rubella and mumps in each year (F_measles=180.74, P<0.001; F_rubella=189.95, P<0.001; F_mumps=122.40, P<0.001). The positive rate of measles antibody was higher than that of rubella and mumps, and the difference was statistically significant (χ²=518.09, P<0.001). Conclusion: The level of measles IgG antibody in healthy people in Shanghai is higher, while the level of rubella and mumps IgG antibody is slightly lower.
Adult ; Antibodies, Viral ; Child ; China/epidemiology* ; Female ; Humans ; Immunoglobulin G ; Infant ; Male ; Measles/prevention & control* ; Measles-Mumps-Rubella Vaccine ; Mumps/prevention & control* ; Mumps virus ; Rubella/prevention & control* ; Young Adult

The detection method of characteristic spectrum for reference samples was established by preparing 15 batches of the reference samples of Huagan Decoction, and the peak attribution and the similarity range in the characteristic spectrum were clarified. The ranges of paste-forming rate, content, and transfer rate of the index components including geniposide, paeonol, and paeoniflorin were analyzed. The key quality attribute of the reference samples of Huagan Decoction was defined. The results showed that the 15 batches of the reference samples of Huagan Decoction had good similarities in the characteristic spectrum, which were all higher than 0.9. According to the information of characteristic peak, there were 18 characteristic peaks in the whole prescription, including seven common characteristic peaks from green tangerine peel and dried tangerine peel, four characteristic peaks from tree peony root bark(three of them were common characteristic peaks from tree peony root bark and red peony root), five characteristic peaks from cape jasmine fruit, one characteristic peak from paniculate bolbostemma, and one characteristic peak from oriental waterplantain rhizome.The paste-forming rate of the 15 batches of reference samples was 14.73%-18.83%. The content of geniposide was 1.68%-2.87%, with the average transfer rate of 70.05%±11.13%. The content of paeonol was 0.10%-0.16%, with the average transfer rate of 9.38%±1.78%. The content of paeoniflorin was 1.94%-2.74%, with the average transfer rate of 36.69%±4.63%. This study analyzed the quality value transfer of the reference samples of Huagan Decoction by the evaluation mode of combining the characteristic spectrum, the paste-forming rate, and the content of index components. The findings of this study initially established a stable and feasible standard decoction evaluation method and provided references for the quality control and the subsequent development of relevant preparations of Huagan Decoction.
Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal ; Paeonia ; Prescriptions ; Quality Control