Using Bayesian networks with tabu algorithm to explore factors related to chronic kidney disease with mental illness: A cross-sectional study.

While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.

Recent advances in Zn2+ imaging: From organelles to in vivo applications.

Zn2+ is involved in various physiological and pathological processes in living systems. Monitoring the dynamic spatiotemporal changes of Zn2+ levels in organelles, cells, and in vivo is of great importance for the investigation of the physiological and pathological functions of Zn2+. However, this task is quite challenging since Zn2+ in living systems is present at low concentrations and undergoes rapid dynamic changes. In this review, we summarize the design and application of fluorescent probes for Zn2+ imaging in organelles, cells, and live organisms reported over the past two years. We aim to provide inspiration for the design of novel Zn2+ probes for multi-level monitoring and deepen the understanding of Zn2+ biology.

A Near-Infrared Fluorescent and Photoacoustic Probe for Visualizing Biothiols Dynamics in Tumor and Liver.

Biothiols, including glutathione (GSH), homocysteine (Hcy) and cysteine (Cys), play crucial roles in various physiological processes. Though an array of fluorescent probes have been designed to visualize biothiols in living organisms, few one-for-all imaging agents for sensing biothiols with fluorescence and photoacoustic imaging capabilities have been reported, since instructions for synchronously enabling and balancing every optical imaging efficacy are deficient. Herein, a new near-infrared thioxanthene-hemicyanine dye (Cy-DNBS) has been constructed for fluorescence and photoacoustic imaging of biothiols in vitro and in vivo. Upon treatment with biothiols, the absorption peak of Cy-DNBS shifted from 592 nm to 726 nm, resulting in a strong NIR absorption as well as a subsequent turn-on PA signal. Meanwhile, the fluorescence intensity increased instantaneously at 762 nm. Then, Cy-DNBS was successfully utilized for imaging endogenous and exogenous biothiols in HepG2 cells and mice. In particular, Cy-DNBS was employed for tracking biothiols upregulation in the liver of mice triggered by S-adenosyl methionine by means of fluorescent and photoacoustic imaging methods. We expect that Cy-DNBS serves as an appealing candidate for deciphering biothiols-related physiological and pathological processes.

Single-atom engineering of hemicyanine and its amphiphilic derivative for optimized near infrared phototheranostics.

Near-infrared (NIR) dyes are widely used in the field of in vivo phototheranostics. Hemicyanine dyes (HDs) have recently received tremendous attention due to their easy synthesis and excellent NIR features. However, HDs can easily form non-fluorescent aggregates and their potential for phototherapy still needs further exploration due to their poor ability to generate reactive oxygen species (ROS). Herein, a series of hemicyanine dyes with different chalcogen atom (O, S, Se) substitutions were constructed to achieve optimized potential for phototheranostics. By replacing O with the heavy atom Se in the xanthene skeleton, CySe-NEt2 showed much more favourable features such as extended NIR absorption/emission wavelength, boosted 1O2 generation rate and higher photothermal effect. In addition, a poly(ethylene glycol) (PEG) group was introduced into the scaffold and yielded a nanotheranostic agent CySe-mPEG5K, which easily formed nanoparticles with appealing features such as excellent photostability, effective prevention of unpleasant H-aggregation, fast/selective tumor accumulation and minimum dark toxicity. Solid tumor growth was significantly suppressed through combined photodynamic therapy (PDT) and photothermal therapy (PTT) guided by NIR fluorescence (NIRF) and photoacoustic (PA) imaging. This study not only presents the first example of selenium-substituted hemicyanine dyes, but also offers a reliable design strategy for the development of potent NIR phototheranostic agents with multi-mode imaging-guided combination therapeutic ability.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning.

Objectives: Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females. Methods: Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females. Results: Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value. Conclusions: This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

VHL syndrome without clear family history: A rare case report and literature review of Chinese patients.

Objective: To analyze the clinical manifestations and imaging features of a hospitalized patient with intermittent headache who was finally diagnosed with von Hippel-Lindau (VHL) syndrome and to perform whole-exon gene detection to improve the understanding of the diagnosis and treatment strategies of the disease. Methods: A case of suspected VHL syndrome in Shanxi Provincial People's Hospital was analyzed. Proband DNA was also extracted for whole exome sequencing and screened for causative mutation sites, which were validated by Sanger sequencing. The literature about VHL gene mutations in Chinese patients in the past 10 years were also reviewed. Results: There is a heterozygous mutation site c.499C > G on the VHL gene on the short arm of chromosome 3 of the patient, which is a missense mutation. The mutation results in the substitution of arginine with glycine at amino acid 167 of the encoded protein, which may be primarily responsible for the disease in the patient with VHL syndrome. However, the mutation did not occur in other family members. Conclusion: Early recognition and treatment of VHL syndrome can be available with genetic testing technology. Strengthening the understanding of this complex genetic disease and improving the diagnostic rate of VHL syndrome are helpful for the precise treatment of patients with this disease, which may help prolong the survival time of patients to a certain extent and improve their quality of life.

Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.

Objectives: Multimorbidity (MMD) is a medical condition that is linked with high prevalence and closely related to many adverse health outcomes and expensive medical costs. The present study aimed to construct Bayesian networks (BNs) with Max-Min Hill-Climbing algorithm (MMHC) algorithm to explore the network relationship between MMD and its related factors. We also aimed to compare the performance of BNs with traditional multivariate logistic regression model. Methods: The data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. Missing value imputation was first performed using Random Forest. Afterward, the variables were included into logistic regression model construction and BNs model construction. The structural learning of BNs was achieved using MMHC algorithm and the parameter learning was conducted using maximum likelihood estimation. Results: Among 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, there are 9,129 ones without MMD (46.2%) and 10,623 ones with MMD (53.8%). Logistic regression model suggests that physical activity, sex, age, sleep duration, nap, smoking, and alcohol consumption are associated with MMD (P < 0.05). BNs, by establishing a complicated network relationship, reveals that age, sleep duration, and physical activity have a direct connection with MMD. It also shows that education levels are indirectly connected to MMD through sleep duration and residence is indirectly linked to MMD through sleep duration. Conclusion: BNs could graphically reveal the complex network relationship between MMD and its related factors, outperforming traditional logistic regression model. Besides, BNs allows for risk reasoning for MMD through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package "glmnet", and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.

Using random forest algorithm for glomerular and tubular injury diagnosis.

Objectives: Chronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD. Methods: Demographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively. Results: A total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively. Conclusion: RF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice.

An Endoplasmic Reticulum-Targeted Ratiometric Fluorescent Molecule Reveals Zn2+ Micro-Dynamics During Drug-Induced Organelle Ionic Disorder.

The endoplasmic reticulum (ER) is the main storage site of Zn2+, and Zn2+ plays an important role in regulating ER homeostasis. Therefore, we designed and synthesized a ratiometric fluorescent Zn2+ probe ER-Zn targeting ER stress. The probe displayed a specific Zn2+ induced blue shift at the spectral maximum values of excitation (80 nm) and emission (30 nm). The ratio imaging capability of Zn2+ under dual excitation mode can be applied not only to quantitative and reversible detection of exogenous Zn2+, but also the observation of the Zn2+ level change under ER stress, elucidating the different behaviors of Zn2+ release in ER stimulated by tunicamycin and thapsigargin. Additionally, the NIR imaging capability of ER-Zn provides an important basis for further research on animal models and is expected to realize the visualization and treatment of ER stress-related diseases through the regulation of ER stress by Zn2+. We envision that this probe can be applied to screen drugs for diseases related to ER stress regulation.

Landscape of intestinal microbiota in patients with IgA nephropathy, IgA vasculitis and Kawasaki disease.

Objective: To explore the common differential flora of IgAN, Kawasaki disease and IgA vasculitis by screening and analyzing the differential intestinal flora between the three disease groups of IgAN, Kawasaki disease and IgA vasculitis and their healthy controls. Methods: Papers on 16srRNA sequencing-related intestinal flora of IgAN, Kawasaki disease and IgA vasculitis were searched in databases, the literature was systematically collated and analysed, the original data was download from the relevant databases, and then the operational taxonomic unit and species classification analysis were performed. Besides, Alpha diversity analysis and Beta diversity analysis were performed to screen for IgAN, Kawasaki disease and I1gA vasculitis groups and finally compare the common intestinal differential flora among the three groups. Results: Among the common differential flora screened, Lachnospiracea_incertae_sedis was lower in both the IgAN and Kawasaki disease groups than in the respective healthy controls; Coprococcus was low in the IgAN group but high in the IgA vasculitis group. Fusicatenibacter was lower in both the Kawasaki disease and IgA vasculitis groups than in their respective healthy controls, and Intestinibacter was low in the Kawasaki disease group, but its expression was high in the IgA vasculitis group. Conclusion: The dysbiosis of the intestinal flora in the three groups of patients with IgAN, Kawasaki disease and IgA vasculitis, its effect on the immunity of the organism and its role in the development of each disease group remain unclear, and the presence of their common differential flora may further provide new ideas for the association of the pathogenesis of the three diseases.

The associations and roles of microRNA single-nucleotide polymorphisms in cervical cancer.

Cervical cancer is one of the fourth most common gynecological malignancies and has been identified as the fourth leading cause of cancer death in women worldwide. MicroRNAs (miRNAs) are single-stranded sequences of noncoding RNAs that are approximately 22-24 nucleotides in length. They modulate posttranscriptional mRNA expression and play critical roles in cervical cancer. Single nucleotide polymorphisms (SNPs) in miRNA genes may alter miRNA expression and maturation and have been associated with various cancers. This review mainly focuses on the roles of SNPs in miRNA genes in the development of cervical cancer and summarizes the research progress of miRNA SNPs in cervical cancer and their molecular regulation mechanisms.

Polymorphisms in endoplasmic reticulum aminopeptidase genes are associated with cervical cancer risk in a Chinese Han population.

BACKGROUND: Antigen-processing machinery molecules play crucial roles in infectious diseases and cancers. Studies have shown that polymorphisms in endoplasmic reticulum aminopeptidase (ERAP) genes can influence the enzymatic activity of ERAP proteins and are associated with the risk of diseases. In the current study, we evaluated the influence of ERAP gene (ERAP1 and ERAP2) polymorphisms on susceptibility to cervical intraepithelial neoplasia (CIN) and cervical cancer. METHODS: Six single nucleotide polymorphisms (SNPs) in ERAP1 and 5 SNPs in ERAP2 were selected and genotyped in 556 CIN patients, 1072 cervical cancer patients, and 1262 healthy control individuals. Candidate SNPs were genotyped using SNaPshot assay. And the association of these SNPs with CIN and cervical cancer was analysed. RESULTS: The results showed that allelic and genotypic frequencies of rs26653 in ERAP1 were significantly different between cervical cancer and control groups (P = 0.001 and 0.004). The allelic frequencies of rs27044 in ERAP1 and rs2287988 in ERAP2 were significantly different between control and cervical cancer groups (P = 0.003 and 0.004). Inheritance model analysis showed that genotypes of rs27044, rs26618, rs26653 and rs2287988 SNPs may be associated with the risk of cervical cancer (P = 0.003, 0.004, 0.001 and 0.002). Additionally, haplotype analysis results showed that the ERAP1 haplotype, rs27044C-rs30187T-rs26618T-rs26653G-rs3734016C, was associated with a lower risk of cervical cancer (P = 0.001). The ERAP2 haplotypes rs2549782G- rs2548538A-rs2248374A-rs2287988G-rs1056893T (P = 0.009 and 0.006) and rs2549782T-rs2548538T-rs2248374G-rs2287988A-rs1056893T (P = 0.003 and 0.009) might be associated with cervical cancer and the development from CIN to cervical cancer. CONCLUSION: Our results indicated that rs27044, rs26618 and rs26653 in ERAP1 and rs2287988 in ERAP2 influenced susceptibility to cervical cancer.

Study of the association of seventeen single nucleotide polymorphisms and their haplotypes in the TNF-α, IL-2, IL-4 and IL-10 genes with the antibody response to inactivated Japanese encephalitis vaccine.

To investigate whether the TNF-α, IL-2, IL-4 and IL-10 genes contribute to variations in vaccine-induced immune responses after immunization with the inactivated Japanese encephalitis vaccine (IJEV), a total of 369 individuals who received the IJEV were enrolled. Based on Japanese encephalitis virus (JEV) neutralization antibodies (NAbs), the individuals were divided into seropositive (SP) and seronegative (SN) groups. Then, 17 SNPs in the TNF-α, IL-2, IL-4 and IL-10 genes were genotyped using the TaqMan method. Although there was no association of the TNF-α, IL-2, IL-4 and IL-10 genes with JEV seropositivity triggered by JEV vaccination when all the individuals in the SP and SN groups were compared, differences were observed in a subgroup analysis. In the male group, rs2243291 in the IL-4 gene showed a difference between the JEV SP and SN groups with the overdominant model (P = .045), and the C/G genotypes conferred more JEV seropositivity (OR = 1.87; 95% CI: 1.01-3.49); the CT genotype of rs3093726 in the TNF-α gene showed higher JEV NAbs geometric mean titer (GMT) than the TT genotype (P = .018, CT: 1.677 ± 0.144 vs TT: 1.271 ± 0.039). Furthermore, the rs1800629 genotype in the TNF-α gene and the rs1800896 genotype in the IL-10 gene exhibited a trend of association with JEV seropositivity in the female group, but the difference was not significant. The present study suggested that the polymorphisms in the cytokine genes could be associated with sex-specific JEV NAbs seroconversion. However, more samples should be studied, and further functional verification should be performed.