Single-Cell RNA Sequencing Reveals a Unique Fibroblastic Subset and Immune Disorder in Lichen Sclerosus Urethral Stricture.

Purpose: Lichen sclerosus urethral stricture disease (LS USD) is a refractory and progressive disease primarily affecting the anterior urethra in males. Various potential etiological factors, such as genetics, autoimmunity, infection, and exposure to infectious urine, have been suggested. However, the accurate etiology of LS in the male urethra remains unclear. Patients and Methods: In this study, we conducted single-cell RNA sequencing to identify the transcriptional profiles of three patients with LS USD and three patients with non-LS USD. Immunofluorescence was used to confirm the single-cell sequence results. Results: Our study revealed distinct subsets of vein endothelial cells (ECs), smooth muscle cells (SMCs), and fibroblasts (FBs) with high proportions in LS USD, contributing to the tissue microenvironment primarily involved in proinflammatory and immune responses. In particular, FBs displayed a unique subset, Fib7, which is exclusively present in LS USD, and exhibited high expression levels of SAA1 and SAA2. The accumulation of macrophages, along with the dysregulated ratios of M1/M2-like phenotype macrophages, may be engaged in the pathogenesis of LS USD. Through cell-cell communication analysis, we identified significant interactions involving CXCL8/ACKR1 and CCR7/CCL19 in LS USD. Remarkably, Fib7 exhibited exclusive communication with IL-1B macrophages through the SAA1/FPR2 receptor-ligand pair. Conclusion: Our study provides a profound understanding of the tissue microenvironment in LS USD, which may be valuable for understanding the pathogenesis of LS USD.

Assessing the importance of risk factors for diabetic retinopathy in patients with type 2 diabetes mellitus: Results from the classification and regression tree models.

BACKGROUND: Diabetic retinopathy (DR) is one of the serious complications of diabetes mellitus (DM). Many studies have identified the risk factors associated with DR, but there is not much evidence on the importance of these factors for DR. This study aimed to investigate the associated factors for patients with type 2 DM (T2DM) and calculate the importance of the identified factors. MATERIALS AND METHODS: Using probability proportionate to size sampling method in this community-based cross-sectional study, 22 community health service centers were selected from 10 administrative districts in Shenzhen, China. Approximately 60 T2DM patients were recruited from each center. The participants completed a structural questionnaire, had their venous blood collected, and underwent medical examinations and fundus photography. Logistic regression models were used to identify the risk factors of DR. The classification and regression tree (CART) model was used to calculate the importance of the identified risk factors. RESULTS: This study recruited 1097 T2DM patients, 266 of whom were identified as having DR, yielding a prevalence rate of 24.3% (95% confidence interval [CI]: 21.7%-26.9%). Results showed that a longer duration of DM, indoor-type lifestyle, and higher levels of hemoglobin A1c (HbA1c) or urea increased the risk of DR. Patients with HbA1c values ≥7% were about 2.45 times (odds ratio: 2.45; 95% CI: 1.83-3.29) more likely to have DR than their counterparts. The CART model found that the values of variable importance for HbA1c, DM duration, lifestyle (i.e., indoor type), and urea were 48%, 37%, 10%, and 4%, respectively. CONCLUSION: The prevalence of DR is high for T2DM patients who receive DM health management services from the primary healthcare system. HbA1c is the most important risk factor for DR. Integration of DR screening and HbA1c testing into the healthcare services for T2DM to reduce vision impairment and blindness is urgently warranted.

NL13, a novel curcumin analogue and polo like kinase 4 inhibitor, induces cell cycle arrest and apoptosis in prostate cancer models.

BACKGROUND AND PURPOSE: Prostate cancer remains a major public health burden worldwide. Polo like kinase 4 (PLK4) has emerged as a promising therapeutic target in prostate cancer due to its key roles in cell cycle regulation and tumour progression. This study aims to develop and characterize the novel curcumin analogue NL13 as a potential therapeutic agent and PLK4 inhibitor against prostate cancer. EXPERIMENTAL APPROACH: NL13 was synthesized and its effects were evaluated in prostate cancer cells and mouse xenograft models. Kinome screening and molecular modelling identified PLK4 as the primary target. Antiproliferative and proapoptotic mechanisms were explored via cell cycle, apoptosis, gene and protein analyses. KEY RESULTS: Compared with curcumin, NL13 exhibited much greater potency in inhibiting PC3 (IC50, 3.51 µM vs. 35.45 µM) and DU145 (IC50, 2.53 µM vs. 29.35 µM) prostate cancer cells viability and PLK4 kinase activity (2.32 µM vs. 246.88 µM). NL13 induced G2/M cell cycle arrest through CCNB1/CDK1 down-regulation and triggered apoptosis via caspase-9/caspase-3 cleavage. These effects were mediated by PLK4 inhibition, which led to the inactivation of the AKT signalling pathway. In mice, NL13 significantly inhibited tumour growth and modulated molecular markers consistent with in vitro findings, including decreased p-AKT and increased cleaved caspase-9/3. CONCLUSION AND IMPLICATIONS: NL13, a novel PLK4-targeted curcumin analogue, exerts promising anticancer properties against prostate cancer by disrupting the PLK4-AKT-CCNB1/CDK1 and apoptosis pathways. NL13 represents a promising new agent for prostate cancer therapy.

Vacuolar H+-ATPase determines daughter cell fates through asymmetric segregation of the nucleosome remodeling and deacetylase complex.

Asymmetric cell divisions (ACDs) generate two daughter cells with identical genetic information but distinct cell fates through epigenetic mechanisms. However, the process of partitioning different epigenetic information into daughter cells remains unclear. Here, we demonstrate that the nucleosome remodeling and deacetylase (NuRD) complex is asymmetrically segregated into the surviving daughter cell rather than the apoptotic one during ACDs in Caenorhabditis elegans. The absence of NuRD triggers apoptosis via the EGL-1-CED-9-CED-4-CED-3 pathway, while an ectopic gain of NuRD enables apoptotic daughter cells to survive. We identify the vacuolar H+-adenosine triphosphatase (V-ATPase) complex as a crucial regulator of NuRD's asymmetric segregation. V-ATPase interacts with NuRD and is asymmetrically segregated into the surviving daughter cell. Inhibition of V-ATPase disrupts cytosolic pH asymmetry and NuRD asymmetry. We suggest that asymmetric segregation of V-ATPase may cause distinct acidification levels in the two daughter cells, enabling asymmetric epigenetic inheritance that specifies their respective life-versus-death fates.

Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data.

BACKGROUND AND OBJECTIVE: Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance. METHODS: We integrated multi-task and cost-sensitive learning, developing an enhanced deep neural network architecture that extends multi-task learning to predict mortality risk across multiple chronic diseases. Each patient cohort with a chronic disease was assigned to a separate task, with shared lower-level parameters capturing inter-disease complexities through distinct top-level networks. Cost-sensitive functions were incorporated to ensure learning of positive class characteristics for each task and achieve accurate prediction of the risk of death from multiple chronic diseases. RESULTS: Our study covers 15 prevalent chronic diseases and is experimented with real-world data from 482,145 patients (including 9,516 deaths) in Shenzhen, China. The proposed model is compared with six models including three machine learning models: logistic regression, XGBoost, and CatBoost, and three state-of-the-art deep learning models: 1D-CNN, TabNet, and Saint. The experimental results show that, compared with the other compared algorithms, MTL-CSDNN has better prediction results on the test set (ACC=0.99, REC=0.99, PRAUC=0.97, MCC=0.98, G-means = 0.98). CONCLUSIONS: Our method provides valuable insights into leveraging real-world data for precise multi-disease mortality risk prediction, offering potential applications in optimizing chronic disease management, enhancing well-being, and reducing healthcare costs for the elderly population.

Enhancing identification performance of cognitive impairment high-risk based on a semi-supervised learning method.

BACKGROUND: Cognitive assessment plays a pivotal role in the early detection of cognitive impairment, particularly in the prevention and management of cognitive diseases such as Alzheimer's and Lewy body dementia. Large-scale screening relies heavily on cognitive assessment scales as primary tools, with some low sensitivity and others expensive. Despite significant progress in machine learning for cognitive function assessment, its application in this particular screening domain remains underexplored, often requiring labor-intensive expert annotations. AIMS: This paper introduces a semi-supervised learning algorithm based on pseudo-label with putback (SS-PP), aiming to enhance model efficiency in predicting the high risk of cognitive impairment (HR-CI) by utilizing the distribution of unlabeled samples. DATA: The study involved 189 labeled samples and 215,078 unlabeled samples from real world. A semi-supervised classification algorithm was designed and evaluated by comparison with supervised methods composed by 14 traditional machine-learning methods and other advanced semi-supervised algorithms. RESULTS: The optimal SS-PP model, based on GBDT, achieved an AUC of 0.947. Comparative analysis with supervised learning models and semi-supervised methods demonstrated an average AUC improvement of 8% and state-of-art performance, repectively. CONCLUSION: This study pioneers the exploration of utilizing limited labeled data for HR-CI predictions and evaluates the benefits of incorporating physical examination data, holding significant implications for the development of cost-effective strategies in relevant healthcare domains.

Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis.

BACKGROUND: Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level. OBJECTIVE: The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control. METHODS: Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level. RESULTS: A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012. CONCLUSIONS: This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control.

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors.

Background: Preeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors. Methods: We retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24-45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher's exact test and Mann-Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors. Results: By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively. Conclusion: Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.

Community screening for dementia among older adults in China: a machine learning-based strategy.

BACKGROUND: Dementia is a leading cause of disability in people older than 65 years worldwide. However, diagnosing dementia in its earliest symptomatic stages remains challenging. This study combined specific questions from the AD8 scale with comprehensive health-related characteristics, and used machine learning (ML) to construct diagnostic models of cognitive impairment (CI). METHODS: The study was based on the Shenzhen Healthy Ageing Research (SHARE) project, and we recruited 823 participants aged 65 years and older, who completed a comprehensive health assessment and cognitive function assessments. Permutation importance was used to select features. Five ML models using BalanceCascade were applied to predict CI: a support vector machine (SVM), multilayer perceptron (MLP), AdaBoost, gradient boosting decision tree (GBDT), and logistic regression (LR). An AD8 score ≥ 2 was used to define CI as a baseline. SHapley Additive exPlanations (SHAP) values were used to interpret the results of ML models. RESULTS: The first and sixth items of AD8, platelets, waist circumference, body mass index, carcinoembryonic antigens, age, serum uric acid, white blood cells, abnormal electrocardiogram, heart rate, and sex were selected as predictive features. Compared to the baseline (AUC = 0.65), the MLP showed the highest performance (AUC: 0.83 ± 0.04), followed by AdaBoost (AUC: 0.80 ± 0.04), SVM (AUC: 0.78 ± 0.04), GBDT (0.76 ± 0.04). Furthermore, the accuracy, sensitivity and specificity of four ML models were higher than the baseline. SHAP summary plots based on MLP showed the most influential feature on model decision for positive CI prediction was female sex, followed by older age and lower waist circumference. CONCLUSIONS: The diagnostic models of CI applying ML, especially the MLP, were substantially more effective than the traditional AD8 scale with a score of ≥ 2 points. Our findings may provide new ideas for community dementia screening and to promote such screening while minimizing medical and health resources.

Themes and trends in marathon performance research: a comprehensive bibliometric analysis from 2009 to 2023.

Background: When marathon runners break the 2-h barrier at the finishing line, it attracts global attention. This study is aimed to conduct a bibliometric analysis of publications in the field of marathon running, analyze relevant research contributors, and visualize the historical trends of marathon performance research over the past 15 years. Methods: On 8 December 2023, we extracted high-quality publication data from the Web of Science Core Collection spanning from 1 January 2009 to 30 November 2023. We conducted bibliometric analysis and research history visualization using the R language packages biblioshiny, VOSviewer, and CiteSpace. Results: A total of 1,057 studies were published by 3,947 authors from 1,566 institutions across 63 countries/regions. USA has the highest publication and citation volume, while, the University of Zurich being the most prolific research institution. Keywords analysis revealed several hotspots in marathon research over the past 3 years: (1) physiology of the elite marathon runners, (2) elite marathon training intensity and pacing strategies, (3) nutritional strategies for elite marathon runners, (4) age and sex differences in marathon performance, (5) recovery of inflammatory response and muscle damage. Conclusion: This study presents the first comprehensive bibliometric analysis of marathon performance research over the past 15 years. It unveils the key contributors to marathon performance research, visually represents the historical developments in the field, and highlights the recent topical frontiers. The findings of this study will guide future research by identifying potential hotspots and frontiers.

An improved RIME optimization algorithm for lung cancer image segmentation.

Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.

PAX2 mediated upregulation of ESPL1 contributes to cisplatin resistance in bladder cancer through activating the JAK2/STAT3 pathway.

Extra spindle-polar body like 1 (ESPL1) is associated with the development of a variety of cancers, including bladder cancer, and is closely related to chemoresistance. In this study, we aimed to reveal the role of ESPL1 in bladder cancer progression and cisplatin (DDP) resistance. First, ESPL1 was found to be highly expressed in tumor tissues and cells of bladder cancer, and more highly expressed in cisplatin resistant tumor tissues or cells. The binding of PAX2 in ESPL1 promoter region was predicted by Jaspar database and verified by Ch-IP analysis and the luciferase reporter gene assay. Next, cisplatin-resistant T24 cells (T24/DDP) were established and transfected with ESPL1 siRNA (si-ESPL1) or overexpression vector (pcDNA-ESPL1) or co-transfected with PAX2 siRNA (si-PAX2) or overexpression vector (pcDNA-PAX2), and then treated with DDP or AG490, an inhibitor of JAK2. The results showed that silencing ESPL1 significantly reduced T24/DDP cell viability, colony formation and invasion, enhanced sensitivity to DDP, and induced cell apoptosis. Silencing PAX2 decreased ESPL1 expression, enhanced sensitivity to DDP, and induced apoptosis of T24/DDP cells, and inhibited activation of JAK2/STAT3 pathway. Overexpressing ESPL1 reversed the effect of PAX2 silencing on T24/DDP cells, while AG490 counteracted the reversal effect of overexpressing ESPL1. Finally, a xenograft tumor model was established and found that silencing ESPL1 or DDP treatment inhibited tumor growth, while silencing ESPL1 combined with DDP treatment had the best effect. In summary, this study suggested that PAX2-mediated ESPL1 transcriptional activation enhanced cisplatin resistance in bladder cancer by activating JAK2/STAT3 pathway.

A lineage-resolved cartography of microRNA promoter activity in C. elegans empowers multidimensional developmental analysis.

Elucidating the expression of microRNAs in developing single cells is critical for functional discovery. Here, we construct scCAMERA (single-cell cartography of microRNA expression based on reporter assay), utilizing promoter-driven fluorescent reporters in conjunction with imaging and lineage tracing. The cartography delineates the transcriptional activity of 54 conserved microRNAs in lineage-resolved single cells throughout C. elegans embryogenesis. The combinatorial expression of microRNAs partitions cells into fine clusters reflecting their function and anatomy. Notably, the expression of individual microRNAs exhibits high cell specificity and divergence among family members. Guided by cellular expression patterns, we identify developmental functions of specific microRNAs, including miR-1 in pharynx development and physiology, miR-232 in excretory canal morphogenesis by repressing NHR-25/NR5A, and a functional synergy between miR-232 and miR-234 in canal development, demonstrating the broad utility of scCAMERA. Furthermore, integrative analysis reveals that tissue-specific fate determinants activate microRNAs to repress protein production from leaky transcripts associated with alternative, especially neuronal, fates, thereby enhancing the fidelity of developmental fate differentiation. Collectively, our study offers rich opportunities for multidimensional expression-informed analysis of microRNA biology in metazoans.

Interferon-Gamma-Inducible Protein 16 Inhibits Hepatocellular Carcinoma via Interferon Regulatory Factor 3 on Chemosensitivity.

BACKGROUND AND AIM: Previous reports have suggested IFI16 as a tumor suppressor in hepatocellular carcinoma (HC). Nonetheless, the biological significance of IFI16 and its mechanism concerning resistance to cisplatin (DDP) in HC requires further exploration. METHODS: Samples of tumor and corresponding para-carcinoma tissues were acquired from patients with HC. Furthermore, DDP-resistant cell lines of HC, specifically HCC, Huh7 and Hepatoblastoma, HepG3, were generated by gradually increasing the concentration of DDP. Cell apoptosis and DNA damage were evaluated by utilizing flow cytometry assay and TUNEL staining. The interaction between IFI16 and interferon regulatory factor 3 (IRF3) proteins were analyzed using Co-Immunoprecipitation (Co-IP) assay. In vivo assays were conducted by establishing HC subcutaneous xenograft tumor models. RESULTS: The study found a reduction in IFI16 expression in both HC tissues and DDP-resistant HC cell lines. The binding of IFI16 to IRF3 regulated DNA damage-associated markers in vitro. Overexpression of IFI16 heightened the susceptibility of DDP-induced apoptosis and DNA damage, which was counteracted by IRF3 knockdown, while strengthened by IRF3 overexpression. Moreover, overexpression of IFI16 diminished in vivo DDP-resistant HC tumorigenicity. CONCLUSION: In summary, our findings suggest that IFI16 serves as a tumor suppressor in HC by promoting DNA damage via its interaction with IRF3, thereby reversing DDP resistance.

Associations between Chinese visceral adiposity index and risks of all-cause and cause-specific mortality: A population-based cohort study.

AIM: To determine the associations between the Chinese visceral adiposity index (CVAI) and the risks of all-cause and cause-specific mortality. MATERIALS AND METHODS: A total of 3 916 214 Chinese adults were enrolled in a nationwide population cohort covering all 31 provinces of mainland China. The CVAI was calculated based on age, body mass index, waist circumference, and triglyceride and high-density lipoprotein cholesterol concentrations. We used a Cox proportional hazards regression model to determine the hazard ratios and 95% confidence intervals (CIs) for risk of mortality associated with different CVAI levels. RESULTS: The median follow-up duration was 3.8 years. A total of 86 158 deaths (34 867 cardiovascular disease [CVD] deaths, 29 884 cancer deaths, and 21 407 deaths due to other causes) were identified. In general, after adjusting for potential confounding factors, a U-shaped relationship between CVAI and all-cause mortality was observed by restricted cubic spline (RCS). Compared with participants in CVAI quartile 1, those in CVAI quartile 4 had a 23.0% (95% CI 20.0%-25.0%) lower risk of cancer death, but a 23.0% (95% CI 19.0-27.0) higher risk of CVD death. In subgroup analysis, a J-shaped and inverted U-shaped relationship for all-cause mortality and cancer mortality was observed in the group aged < 60 years. CONCLUSIONS: The CVAI, an accessible indicator reflecting visceral obesity among Chinese adults, has predictive value for all-cause, CVD, and cancer mortality risks. Moreover, the CVAI carries significance in the field of health economics and secondary prevention. In the future, it could be used for early screening purposes.

Fenofibrate inhibits MOXD1 and PDZK1IP1 expression and improves lipid deposition and inflammation in mice with alcoholic fatty liver.

AIMS: Alcoholic liver disease (ALD) can develop into cirrhosis and hepatocellular carcinoma but no specific drugs are available. Fenofibrate is therapeutically effective in ALD, however, the exact mechanism remains unknown. We explored the hub genes of ALD and the role of fenofibrate in ALD. MAIN METHODS: The hub genes of ALD were screened by bioinformatics method, and their functional enrichment, signalling pathways, target genes and their correlation with immune microenvironment and pathogenic genes were analysed. We also analysed the binding affinity of fenofibrate to proteins of hub genes using molecular docking techniques, and the effects on hub gene expression, lipid deposition, oxidative stress and inflammation in the liver of National Institute on Alcohol Abuse and Alcoholism (NIAAA) model mice. The regulatory effects of fenofibrate on MOXD1 and PDZK1P1 were investigated after gene silencing of peroxisome proliferator-activated receptor-α (Ppar-α). KEY FINDINGS: Hub genes identified, including monooxygenase DBH-like 1 (MOXD1), PDZK1-interacting protein 1 (PDZK1IP1) and solute carrier 51 ß (SLC51B), are highly predictive for ALD. Hepatic MOXD1 and PDZK1IP1 expression was elevated in patients with ALD and NIAAA model mice, with no significant difference in SLC51B expression between the groups. Fenofibrate binds tightly to MOXD1 and PDZK1IP1, inhibits their hepatic expression independently of PPAR-α signalling, and ameliorates lipid deposition, oxidative stress and inflammatory responses in NIAAA model mice. SIGNIFICANCE: MOXD1 and PDZK1IP1 are key genes in ALD progression; fenofibrate improves liver damage in NIAAA model mice by downregulating their expression. Our findings provide insight for improving diagnostic and therapeutic strategies for ALD.

Arbutin inhibits androgen biosynthesis by rat immature Leydig cells in vitro.

Arbutin, a widely used skin lightening agent, has raised concerns regarding its potential side effects. In this study, we investigated the impact of arbutin on Leydig cell function using an in vitro model. We measured medium androgen levels, as well as the gene and protein expression related to Leydig cell steroidogenesis. Rat immature Leydig cells from age of 35 days were exposed to arbutin at concentrations ranging from 0.5 to 50 µM for a duration of 3 hrs. Following treatment, we observed a significant inhibition of androgen secretion by Leydig cells at both the 5 and 50 µM concentrations of arbutin. Furthermore, at a concentration of 50 µM, arbutin effectively blocked the stimulatory effects of luteinizing hormone (LH) and 8Br-cAMP on androgen secretion. Subsequent analysis revealed that arbutin downregulated the expression of crucial genes involved in androgen production, including Lhcgr, Hsd3b1, Cyp17a1, and Srd5a1. In silico computer program analysis predicted that arbutin exhibits good absorption, possesses a long elimination half-life, and may have other potential toxicity such as hepatoxicity. Taken together, our results demonstrate that arbutin negatively influences Leydig cell function and androgen production, potentially impacting male reproductive health.

Over-activation of cold tolerance in arabidopsis causes carbohydrate shortage compared with Chorispora bungeana.

Many plants cope with cold stress by developing acquired freezing tolerance (AFT) through cold acclimation (CA), and some species have strong basal freezing tolerance (BFT) independent of CA. Although CA has been extensively studied, its potential in agricultural applications is still unclear. Here, carbohydrate metabolism and transcriptome in AFT plant Arabidopsis and BFT plant Chorispora bungeana were compared with each other. The results showed that, although both species were able to accumulate soluble sugars during CA, leaf starch accumulation in the daytime was almost blocked in Arabidopsis while it was greatly enhanced in C. bungeana, revealing that Arabidopsis experienced carbohydrate shortage during CA. Transcriptome and pathway enrichment analysis found that genes for photosynthesis antenna proteins were generally repressed by cold stress in both species. However, cold-up-regulated genes were enriched in protein translation in Arabidopsis, whilst they were enriched in carotenoid biosynthesis, flavonoid biosynthesis, and beta-amylases in C. bungeana. Furthermore, weighted gene co-expression network analysis (WGCNA) showed that the inhibition of starch accumulation was associated with down-regulation of genes for photosynthesis antenna proteins and up-regulation of genes for protein translation, DNA repair, and proteasome in Arabidopsis but not in C. bungeana. Taken together, our results revealed that over-activation of common tolerant mechanisms resulted in insufficient carbohydrate supplies in Arabidopsis during CA, and photoprotective mechanisms played important roles in cold adaptation of C. bungeana. These findings uncovered the drawback of CA in improving freezing tolerance and highlighted photoprotection as a possible solution for agricultural applications.

Effect of a two-phase tobacco control regulation on incidence from ischemic stroke and hemorrhagic stroke, Shenzhen, China, 2007-2016.

INTRODUCTION: The Shenzhen government is widely considered to be most efficiently implementing smoke-free legislation in China. We evaluated and compared the impact of Shenzhen's two-phase smoke-free regulation on the incidence rates for ischemic and hemorrhagic stroke. METHODS: An interrupted time series design was used to capture immediate and annual incidence changes from 2007 to 2016 for both ischemic and hemorrhagic stroke due to two-phase smoke-free regulation in Shenzhen, China, by using a generalized additive model. The first phase, implemented on 9 March 2010, required five main public places to be smoke-free. In the second phase, the comprehensive law was expanded to the whole city on 1 March 2014. RESULTS: The regulation implementation during phase I was associated with a strong immediate decline in the incidence rate of ischemic stroke (-14.2%, 95% CI: -19.6 - -8.4) and hemorrhagic stroke (-10.1%, 95% CI: -18.2 - -1.2), but without showing the annual changes (p>0.05). Following the implementation of the comprehensive law, the gradual annual effect showed a significant change in ischemic stroke, with a 6.3% (95% CI: 8.9 - -3.6) reduction. Neither the immediate nor gradual decreases in hemorrhagic stroke incidences associated with the comprehensive regulation were statistically significant during phase II (p>0.05). Subgroup analyses indicate that a much larger health effect of the regulation during phase I was greater among those aged ≥65 years than among those aged 35-64 years. CONCLUSIONS: Shenzhen's two-phase smoke-free regulation was well implemented. Even though the regulation did not extend to the whole city, the immediate health benefits on the incidence rates of ischemic stroke and hemorrhagic stroke could be seen. However, the health benefits brought by the implementation of comprehensive smoke-free legislation were attenuated by previous smoke-free regulations in five main public places, which were more evident in hemorrhagic stroke.