Targeting 7-Dehydrocholesterol Reductase Integrates Cholesterol Metabolism and IRF3 Activation to Eliminate Infection.

Recent work suggests that cholesterol metabolism impacts innate immune responses against infection. However, the key enzymes or the natural products and mechanisms involved are not well elucidated. Here, we have shown that upon DNA and RNA viral infection, macrophages reduced 7-dehydrocholesterol reductase (DHCR7) expression. DHCR7 deficiency or treatment with the natural product 7-dehydrocholesterol (7-DHC) could specifically promote phosphorylation of IRF3 (not TBK1) and enhance type I interferon (IFN-I) production in macrophages. We further elucidated that viral infection or 7-DHC treatment enhanced AKT3 expression and activation. AKT3 directly bound and phosphorylated IRF3 at Ser385, together with TBK1-induced phosphorylation of IRF3 Ser386, to achieve IRF3 dimerization. Deletion of DHCR7 and the DHCR7 inhibitors including AY9944 and the chemotherapy drug tamoxifen promoted clearance of Zika virus and multiple viruses in vitro or in vivo. Taken together, we propose that the DHCR7 inhibitors and 7-DHC are potential therapeutics against emerging or highly pathogenic viruses.

Gut microbial fatty acid isomerization modulates intraepithelial T cells.

The human gut microbiome constantly converts natural products derived from the host and diet into numerous bioactive metabolites1-3. Dietary fats are essential micronutrients that undergo lipolysis to release free fatty acids (FAs) for absorption in the small intestine4. Gut commensal bacteria modify some unsaturated FAs-for example, linoleic acid (LA)-into various intestinal FA isomers that regulate host metabolism and have anticarcinogenic properties5. However, little is known about how this diet-microorganism FA isomerization network affects the mucosal immune system of the host. Here we report that both dietary factors and microbial factors influence the level of gut LA isomers (conjugated LAs (CLAs)) and that CLAs in turn modulate a distinct population of CD4+ intraepithelial lymphocytes (IELs) that express CD8αα in the small intestine. Genetic abolition of FA isomerization pathways in individual gut symbionts significantly decreases the number of CD4+CD8αα+ IELs in gnotobiotic mice. Restoration of CLAs increases CD4+CD8αα+ IEL levels in the presence of the transcription factor hepatocyte nuclear factor 4γ (HNF4γ). Mechanistically, HNF4γ facilitates CD4+CD8αα+ IEL development by modulating interleukin-18 signalling. In mice, specific deletion of HNF4γ in T cells leads to early mortality from infection by intestinal pathogens. Our data reveal a new role for bacterial FA metabolic pathways in the control of host intraepithelial immunological homeostasis by modulating the relative number of CD4+ T cells that were CD4+CD8αα+.

Microbial bile acid metabolites modulate gut RORγ+ regulatory T cell homeostasis.

The metabolic pathways encoded by the human gut microbiome constantly interact with host gene products through numerous bioactive molecules1. Primary bile acids (BAs) are synthesized within hepatocytes and released into the duodenum to facilitate absorption of lipids or fat-soluble vitamins2. Some BAs (approximately 5%) escape into the colon, where gut commensal bacteria convert them into various intestinal BAs2 that are important hormones that regulate host cholesterol metabolism and energy balance via several nuclear receptors and/or G-protein-coupled receptors3,4. These receptors have pivotal roles in shaping host innate immune responses1,5. However, the effect of this host-microorganism biliary network on the adaptive immune system remains poorly characterized. Here we report that both dietary and microbial factors influence the composition of the gut BA pool and modulate an important population of colonic FOXP3+ regulatory T (Treg) cells expressing the transcription factor RORγ. Genetic abolition of BA metabolic pathways in individual gut symbionts significantly decreases this Treg cell population. Restoration of the intestinal BA pool increases colonic RORγ+ Treg cell counts and ameliorates host susceptibility to inflammatory colitis via BA nuclear receptors. Thus, a pan-genomic biliary network interaction between hosts and their bacterial symbionts can control host immunological homeostasis via the resulting metabolites.

DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.

BACKGROUND: MicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease associations. RESULTS: In the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity matrices of miRNAs and diseases, performing a logistic function transformation, balancing positive and negative samples with k-means clustering, and constructing training samples. Then, deep autoencoder was used to extract low-dimensional feature from two kinds of feature representations for miRNAs and diseases, namely, original association information-based and similarity information-based. Next, we combined the resulting features for each miRNA-disease pair and used a logistic regression (LR) classifier to infer all unknown miRNA-disease interactions. Under five and tenfold cross-validation (CV) frameworks, DAE-CFR not only outperformed six popular algorithms and nine classifiers, but also demonstrated superior performance on an additional dataset. Furthermore, case studies on three diseases (myocardial infarction, hypertension and stroke) confirmed the validity of DAE-CFR in practice. CONCLUSIONS: DAE-CFR achieved outstanding performance in predicting miRNA-disease associations and can provide evidence to inform biological experiments and clinical therapy.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

BACKGROUND: Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping. METHODS: We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision. RESULTS: wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes. CONCLUSIONS: The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Doping and strain modulation of the electronic, optical and photocatalytic properties of the GaN/C2N heterostructure.

The electronic, optical and photocatalytic properties of GaN/C2N van der Waals heterostructures are investigated using the first-principles theory, and effective regulation through element doping or strain is achieved further. The results show that the GaN/C2N heterostructure exhibits a type-II band alignment with an indirect band gap of 2.25 eV, which benefits photocatalytic water splitting. In this study, both type-I and type-II band alignments can be obtained through doping or strain modulation. Doping with P or As atoms reduces the band gap of the GaN/C2N heterostructure and transforms it to a type-I direct bandgap semiconductor, which makes the doped GaN/C2N heterostructure more suitable for optoelectronic devices. In addition, the GaN/C2N heterostructure retains type-II band alignment and has a decreased band gap under tensile strain (0 to +4%), which is more favorable for photocatalytic water splitting. Compressive strain (0 to -4%) converts the type-II band alignment to type-I, resulting in a wider light absorption range, making the GaN/C2N heterostructure more suitable for optoelectronic devices. These theoretical results are helpful for the design of GaN/C2N vdW heterostructures in the fields of optoelectronic devices and photocatalysts.

Association of cardiovascular health metrics with all-cause and cardiovascular disease mortality in chronic kidney disease: A cohort study.

BACKGROUND AND AIMS: Since the global burden of chronic kidney disease (CKD) is rising rapidly, the study aimed to assess the association of cardiovascular health (CVH) metrics with all-cause and cardiovascular disease (CVD) mortality among individuals with CKD. METHODS AND RESULTS: The cohort study included 5834 participants with CKD from the National Health and Nutrition Examination Survey 1999-2018. A composite CVH score was calculated based on smoking status, physical activity, body mass index, blood pressure, total cholesterol, diet quality, and glucose control. Primary outcomes were all-cause and CVD mortality as of December 31, 2019. Multivariable-adjusted Cox proportional hazards models were used to estimate the association between CVH metrics and deaths in CKD patients. During a median follow-up of 7.2 years, 2178 all-cause deaths and 779 CVD deaths were documented. Compared to participants with ideal CVH, individuals with intermediate CVH exhibited a 46.0% increase in all-cause mortality (hazard ratio, 1.46; 95% confidence interval: 1.17, 1.83), while those with poor CVH demonstrated a 101.0% increase (2.01; 1.54, 2.62). For CVD mortality, individuals with intermediate CVH experienced a 56.0% increase (1.56; 1.02, 2.39), and those with poor CVH demonstrated a 143.0% increase (2.43; 1.51, 3.91). Linear trends were noted for the associations of CVH with both all-cause mortality (P for trend <0.001) and CVD mortality (P for trend = 0.02). CONCLUSIONS: Lower CVH levels were associated with higher all-cause and CVD mortality in individuals with CKD, which highlights the importance of maintaining good CVH in CKD patients.

Cognitive-behavioral therapy for patients with somatoform disorders: A pilot preliminary randomized controlled trial.

Background and objective: Cognitive-behavioral therapy (CBT) for somatoform disorders (SFDs) is understudied in China. Western findings may not be applicable to Chinese culture. This preliminary study evaluated the efficacy of CBT for patients in China, relative to treatment-as-usual (TAU). Methods: Seventy patients with SFDs randomly received either combined CBT and TAU (CBT + TAU), or TAU alone between January 2018 to May 2019. The CBT + TAU group received 12 weekly individual 50-minute CBT sessions. Participants were blindly assessed at 4 timepoints (baseline, week 6, end of treatment: week 12; 12 weeks post-treatment: week 24) using the following outcome measures: SQSS (Self-screening Questionnaire for Somatic Symptoms); PHQ-15 (Patient-Health-Questionnaire-15) and the WI (Whiteley Index); GAD-7 (General Anxiety Disorder-7); HAMD-17 (Hamilton Depression Rating Scale-17); Family Burden Interview Schedule (FBIS); Sheehan Disability Scale (SDS); and the Short Form of Quality-of-Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q-SF). The primary endpoint was the difference between the SQSS total score at week 24 and the baseline. A mixed model for repeated measures was used to analyze inter- and intra-group changes from the baseline. Results: At week 24, The least-squares mean (LSM) change of the total score on the SQSS was -18.87 points and -9.69 points, respectively in the CBT + TAU group and in the TAU group (LSM difference, -9.18 points; 95% confidence interval, -15.72 to -2.64; P = 0.0068). At week 24, the LSM changes from baseline in the WI, HAMD, PHQ15, FBIS and SDS total scores were significantly different between the two groups, however, there was no significant difference in the Q-LES-Q-SF. The SQSS of group effect sizes were 0.63 at 24 weeks. The dropout rates of the CBT + TAU and TAU groups were comparable (22.9% and 19.3%). Conclusions: These preliminary findings suggest that CBT may be helpful for improving the symptoms of patients with SFDs in China.

Association of Gut Microbiota With Objective Sleep Measures in Women With and Without Human Immunodeficiency Virus Infection: The IDOze Study.

BACKGROUND: Poor sleep health is an underrecognized health challenge, especially for people with human immunodeficiency virus (HIV). Gut microbiota related to sleep are underinvestigated. METHODS: The IDOze microbiota substudy included 190 women (114 with HIV and 76 without HIV). Wrist actigraphy measured total sleep duration, sleep efficiency, number of wake bouts, wake after sleep onset, fragmentation index, and sleep timing. 16S rRNA gene sequencing identified gut microbial genera. Analysis of compositions of microbiomes with bias correction was used to investigate cross-sectional associations between gut microbiota and sleep. Abundances of sleep-related gut microbial genera were compared between women with and without HIV. RESULTS: Enrichment of 7 short-chain fatty acid-producing genera (eg, Butyricimonas, Roseburia, and Blautia) was associated with lower fragmentation index. Enrichment of 9 genera (eg, Dorea) was associated with lower sleep efficiency and/or more wake after sleep onset. Enrichment of proinflammatory Acidaminococcus was associated with late sleep midpoint and offset time. These associations were largely consistent regardless of HIV status. The abundance of Butyricimonas was lower among women with HIV compared to those without HIV. CONCLUSIONS: Seventeen genera were identified to be associated with sleep continuity or timing. Butyricimonas, a potentially beneficial genus associated with sleep continuity, was less abundant among women with HIV.

Applying dimensional psychopathology: transdiagnostic prediction of executive cognition using brain connectivity and inflammatory biomarkers.

BACKGROUND: The association between executive dysfunction, brain dysconnectivity, and inflammation is a prominent feature across major psychiatric disorders (MPDs), schizophrenia, bipolar disorder, and major depressive disorder. A dimensional approach is warranted to delineate their mechanistic interplay across MPDs. METHODS: This single site study included a total of 1543 participants (1058 patients and 485 controls). In total, 1169 participants underwent diffusion tensor and resting-state functional magnetic resonance imaging (745 patients and 379 controls completed the Wisconsin Card Sorting Test). Fractional anisotropy (FA) and regional homogeneity (ReHo) assessed structural and functional connectivity, respectively. Pro-inflammatory cytokine levels [interleukin (IL)-1ß, IL-6, and tumor necrosis factor-α] were obtained in 325 participants using blood samples collected with 24 h of scanning. Group differences were determined for main measures, and correlation and mediation analyses and machine learning prediction modeling were performed. RESULTS: Executive deficits were associated with decreased FA, increased ReHo, and elevated IL-1ß and IL-6 levels across MPDs, compared to controls. FA and ReHo alterations in fronto-limbic-striatal regions contributed to executive deficits. IL-1ß mediated the association between FA and cognition, and IL-6 mediated the relationship between ReHo and cognition. Executive cognition was better predicted by both brain connectivity and cytokine measures than either one alone for FA-IL-1ß and ReHo-IL-6. CONCLUSIONS: Transdiagnostic associations among brain connectivity, inflammation, and executive cognition exist across MPDs, implicating common neurobiological substrates and mechanisms for executive deficits in MPDs. Further, inflammation-related brain dysconnectivity within fronto-limbic-striatal regions may represent a transdiagnostic dimension underlying executive dysfunction that could be leveraged to advance treatment.

Activation of vascular endothelial growth factor receptor-3 in macrophages restrains TLR4-NF-κB signaling and protects against endotoxin shock.

Toll-like receptors (TLRs) are critical in mediating innate immune responses against infections. However, uncontrolled TLR-triggered inflammation is associated with endotoxin shock. To better understand the homeostatic mechanisms induced by TLR4 signaling, we screened a group of key cytokines, chemokines, growth factors, and their receptors for bacteria- or LPS-induced expression. The surface vascular endothelial growth factor receptor-3 (VEGFR-3) and its ligand VEGF-C were upregulated in macrophages. VEGFR-3 ligation by VEGF-C significantly attenuated proinflammatory cytokine production. Notably, ablation of the ligand-binding domain or tyrosine kinase activity of VEGFR-3 rendered mice more sensitive to septic shock. VEGFR-3 restrained TLR4-NF-κB activation by regulating the PI3-kinase-Akt signaling pathway and SOCS1 expression. Aside from targeting lymphatic vessels, we suggest a key role of VEGFR-3 on macrophages to prevent infections that is complicated with lymphoedema. Thus, VEGFR-3-VEGF-C signaling represents a "self-control" mechanism during antibacterial innate immunity.

Acute Glycemic Variability and Early Outcomes After Cardiac Surgery: A Meta-Analysis.

The influence of acute glycemic variability (GV) on early outcomes of patients after cardiac surgery remains not fully determined. We performed a systematic review and meta-analysis to evaluate the association between acute GV and in-hospital outcomes of patients after cardiac surgery. Relevant observational studies were obtained by search of electronic databases including Medline, Embase, Cochrane Library, and Web of Science. A randomized-effects model was selected to pool the data by incorporating the influence of potential heterogeneity. Nine cohort studies involving 16 411 patients after cardiac surgery were included in this meta-analysis. Pooled results showed that a high acute GV was associated with an increased risk of major adverse events (MAE) during hospitalization for patients after cardiac surgery [odds ratio [OR]: 1.29, 95% CI: 1.15 to 1.45, p<0.001, I22=38%]. Sensitivity analysis limited to studies of on-pump surgery and GV evaluated by coefficient of variation of blood glucose showed similar results. Subgroup analysis suggested that a high acute GV was related to an increased incidence of MAE in patients after coronary artery bypass graft, but not for those after isolated valvular surgery (p=0.04), and the association was weakened after adjustment of glycosylated hemoglobin (p=0.01). Moreover, a high acute GV was also related to an increased risk of in-hospital mortality (OR: 1.55, 95% CI: 1.15 to 2.09, p=0.004; I22=0%). A high acute GV may be associated with poor in-hospital outcomes in patients after cardiac surgery.

The prevalence and clinical features of fibromyalgia in Chinese hospital patients with primary headache: The survey of fibromyalgia comorbid with headache.

OBJECTIVE: The aims were to explore the prevalence and clinical features of fibromyalgia in Chinese hospital patients with primary headache. BACKGROUND: Studies done in non-Chinese populations suggest that around one-third of patients with primary headache have fibromyalgia, but data from mainland China are limited. Investigations into the prevalence and clinical features of fibromyalgia in Chinese patients with primary headache would improve our understanding of these two complex disease areas and help guide future clinical practice. METHODS: This cross-sectional study included adults with primary headache treated at 23 Chinese hospitals from September 2020 to May 2021. Fibromyalgia was diagnosed using the modified 2010 American College of Rheumatology criteria. Mood and insomnia were evaluated employing the Hospital Anxiety and Depression Scale and the Insomnia Severity Index. RESULTS: A total of 2782 participants were analyzed. The fibromyalgia prevalence was 6.0% (166/2782; 95% confidence interval: 5.1%, 6.8%). Compared to primary headache patients without combined fibromyalgia, patients with primary headache combined with fibromyalgia were more likely to be older (47.8 vs. 41.7 years), women (83.7% [139/166] vs. 72.8% [1904/2616]), less educated (65.1% [108/166] vs. 45.2% [1183/2616]), and with longer-duration headache (10.0 vs. 8.0 years). Such patients were more likely to exhibit comorbid depression (34.3% [57/166] vs. 9.9% [260/2616]), anxiety (16.3% [27/166] vs. 2.7% [70/2612]), and insomnia (58.4% [97/166] vs. 17.1% [447/2616]). Fibromyalgia was more prevalent in those with chronic (rather than episodic) migraine (11.1% [46/414] vs. 4.4% [72/1653], p < 0.001) and chronic (rather than episodic) tension-type headache (11.5% [27/235] vs. 4.6% [19/409], p = 0.001). Most fibromyalgia pain was in the shoulders, neck, and upper back. CONCLUSIONS: The prevalence of fibromyalgia in mainland Chinese patients with primary headache was 6.0%. Fibromyalgia was more common in those with chronic rather than episodic headache. The most common sites of fibromyalgia pain were the neck, shoulders, and back.

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge.

BACKGROUND: Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients. METHODS: CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application. RESULTS: CHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes. CONCLUSION: CHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient's general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge. CLINICAL TRIAL REGISTRATION: URL: http://www.chictr.org.cn/index.aspx ; Unique identifier: ChiCTR2100043337.

Associations between Differential Aging and Lifestyle, Environment, Current, and Future Health Conditions: Findings from Canadian Longitudinal Study on Aging.

INTRODUCTION: An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions. METHODS: We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions. RESULTS: BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, and kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking are associated with positive BioAge gap ("older" BioAge than expected). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" BioAge than expected). CONCLUSIONS: Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the differential biological aging process that can be associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for future research and guidelines to promote healthy longevity.

Individualized Prospective Prediction of Opioid Use Disorder.

OBJECTIVE: Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data. METHOD: We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (n = 699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 (n = 174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 (n = 316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes. RESULTS: With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders. CONCLUSION: Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.

Association Between Daily Internet Use and Incidence of Chronic Diseases Among Older Adults: Prospective Cohort Study.

BACKGROUND: Chronic disease incidence among the elderly is increasing, which is correlated with the acceleration of population aging. Evolving internet technologies may help prevent and provide interventions for chronic diseases in an accelerating aging process. However, the impact of daily internet use on the incidence of chronic diseases is not well understood. OBJECTIVE: This study aims to investigate whether daily internet use by middle-aged and older adults may inhibit or promote the occurrence of chronic diseases. METHODS: We included participants from the China Health and Retirement Longitudinal Study (CHARLS), a longitudinal survey of Chinese residents aged ≥45 years. We assessed 8-year data from wave 1 (June 2011-March 2012) to wave 4 (July-September 2018) in CHARLS. Data from wave 4 were used for a cross-sectional study, and data from all 4 waves were used for a longitudinal study. Self-reported data were used to track variables, including internet use, use frequency, and the incidence of different chronic diseases. Cox proportional hazards modeling was applied in the longitudinal study to examine the relationship between daily internet use and chronic diseases among middle-aged and older adults, while adjusting for sociodemographic characteristics and health behaviors. In addition, longitudinal data were used to analyze internet usage trends, and cross-sectional data were used to analyze the factors influencing internet use. RESULTS: Among the 20,113 participants included in the longitudinal analyses, internet use increased significantly, from 2% to 12.3%, between 2011 and 2018. The adjusted model found statistically significant relationships between daily internet use and a lower incidence of the following chronic diseases: hypertension (hazard ratio [HR] 0.78, 95% CI 0.65-0.95, P=.01), chronic lung disease (HR 0.74, 95% CI 0.57-0.97, P=.03), stroke (HR 0.69, 95% CI 0.50-0.94, P=.02), digestive disease (HR 0.73, 95% CI 0.58-0.91, P=.005), memory-related disorders (HR 0.58, 95% CI 0.37-0.91, P=.02), arthritis or rheumatism (HR 0.60, 95% CI 0.48-0.76, P<.001), asthma (HR 0.52, 95% CI 0.33-0.84, P=.007), depression (HR 0.80, 95% CI 0.71-0.89, P<.001), and vision impairment (HR 0.83, 95% CI 0.74-0.93, P=.004). Moreover, our study also showed that with increasing frequency of internet use, the risk of some chronic diseases decreases. CONCLUSIONS: This study found that middle-aged and older adults who use the internet have a reduced risk of developing chronic diseases versus those who do not use the internet. The increasing prevalence of daily internet use among middle-aged and older adults may stimulate contemplation of the potential role of internet platforms in future research on chronic disease prevention.

Identification and effective connections of core networks in patients with temporal lobe epilepsy and cognitive impairment: Granger causality analysis and multivariate pattern analysis.

OBJECTIVE: This study aimed to explore effective connectivity (EC) of the core networks in cognition impairment associated with temporal lobe epilepsy (CI-TLE) by applying resting state and Granger causality analysis (REST-GCA). The specific brain regions that played a critical role in classification were assessed using multivariate pattern analysis (MVPA). METHODS: Thirty-two patients with CI-TLE and 29 healthy controls who were matched based on age and gender underwent functional magnetic resonance imaging (fMRI). RESULTS: REST-GCA revealed that patients with CI-TLE displayed decreased GC values in the following brain areas: from the posterior cingulate cortex (PCC) to the left fusiform gyrus (lFFG) and the right parahippocampal gyrus (rPPG); from the right dorsal prefrontal cortex (rDPFC) to the left superior parietal lobule (lSPL); from the left amygdala (lAG) to the PCC. Inhibitory EC was observed from the rDPFC to the PCC compared to HCs. The GC values increased from the right dorsal prefrontal cingulate cortex (rdACC) to the PCC and from the right dorsal forebrain insula (rDAI) to the right middle temporal gyrus (rMTG) in the CI-TLE patients. MVPA showed that the classification yielded an accuracy of 81.91% (78.12%, specificity =85.71%). CONCLUSION: Our observations indicated that the abnormal EC between the frontal and parietal regions might be associated with the pathophysiological mechanism of CI-TLE. These results also indicated that EC might be play a role as a potential discriminative pattern to detect CI-TLE in patients.

A Review of NDT Methods for Wheel Burn Detection on Rails.

Wheel burn can affect the wheel-rail contact state and ride quality. With long-term operation, it can cause rail head spalling or transverse cracking, which will lead to rail breakage. By analyzing the relevant literature on wheel burn, this paper reviews the characteristics, mechanism of formation, crack extension, and NDT methods of wheel burn. The results are as follows: Thermal-induced, plastic-deformation-induced, and thermomechanical-induced mechanisms have been proposed by researchers; among them, the thermomechanical-induced wheel burn mechanism is more probable and convincing. Initially, the wheel burns appear as an elliptical or strip-shaped white etching layer with or without deformation on the running surface of the rails. In the latter stages of development, this may cause cracks, spalling, etc. Magnetic Flux Leakage Testing, Magnetic Barkhausen Noise Testing, Eddy Current Testing, Acoustic Emission Testing, and Infrared Thermography Testing can identify the white etching layer, and surface and near-surface cracks. Automatic Visual Testing can detect the white etching layer, surface cracks, spalling, and indentation, but cannot detect the depth of rail defects. Axle Box Acceleration Measurement can be used to detect severe wheel burn with deformation.

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning.

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.