Thymol improves the growth performance of blue foxes by regulating the gut microbiota.

Introduction: The drawbacks of using antibiotics as feed additives for blue foxes have gradually become apparent; moreover, thymol has wide-spectrum antimicrobial activity and has the potential to replace antibiotics in various animals. However, there are few reports on the effects of thymol on blue foxes. Methods: This study aimed to investigate the effects of different concentrations of thymol on the growth performance, apparent nutrient digestibility, serum biochemical indicators, intestinal morphology, and gut microbiota of blue foxes. Twenty-four male blue foxes (120 ± 5 d) of similar weight (6.05 ± 0.16 kg) were randomly divided into 4 groups. 0, 100, 200, and 300 mg/kg thymol were added to the basal diets of groups C, L, M, and H, respectively. Results: Compared with those in the C group, the addition of 100 mg/kg thymol to the diet significantly increased organic matter (OM) digestibility, crude protein (CP) digestibility, immunoglobulin (Ig) A, IgM, the VH of the duodenum, the CD of the jejunum, the VH of the ileum, and the VH/CD of the ileum (P < 0.05) and strongly significantly increased IgG (P < 0.01). The addition of 200 mg/kg thymol to the diet increased the VH/CD of the duodenum (P < 0.05). The addition of 300 mg/kg thymol to the diet significantly increased the VH and CD of the jejunum (P < 0.05). The addition of 200 mg/kg and 300 mg/kg thymol to the diets increased the final weight (FW) (P < 0.05). Adding 100 mg/kg thymol significantly increased the levels of interleukin-4 (IL-4) and catalase (CAT) compared with those in the other groups (P < 0.05). 16S rRNA gene detection revealed that thymol can change the abundances of Bifidobacterium, Fusobacterium, Allobaculum, Streptococcus, Megasphaera, and Lactobacillus in the gut. Conclusion: The addition of thymol to diets can increase the abundance of Bifidobacterium, Fusobacterium, and Allobaculum, which may contribute to improving the growth performance of blue foxes.

A tabular data generation framework guided by downstream tasks optimization.

Recently, generative models have been gradually emerging into the extended dataset field, showcasing their advantages. However, when it comes to generating tabular data, these models often fail to satisfy the constraints of numerical columns, which cannot generate high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. Responding to the challenge, we propose a tabular data generation framework guided by downstream task optimization (TDGGD). It incorporates three indicators into each time step of diffusion generation, using gradient optimization to align the generated fake data. Unlike the traditional strategy of separating the downstream task model from the upstream data synthesis model, TDGGD ensures that the generated data has highly focused columns feasibility in upstream real tabular data. For downstream task, TDGGD strikes the utility of tabular data over solely pursuing statistical fidelity. Through extensive experiments conducted on real-world tables with explicit column constraints and tables without explicit column constraints, we have demonstrated that TDGGD ensures increasing data volume while enhancing prediction accuracy. To the best of our knowledge, this is the first instance of deploying downstream information into a diffusion model framework.

Multi-scale signaling and tumor evolution in high-grade gliomas.

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

Amphiphilic Janus cotton gauze with enhanced moisture management and blood coagulation for rapid hemostasis and wound healing.

Cotton gauze is commonly used in initial emergency care. However, its high hydrophilicity and limited clotting capacity can lead to the excessive absorption of blood, resulting in unnecessary blood loss. Herein, an amphiphilic Janus cotton gauze with excellent moisture management and enhanced blood coagulation has been developed via in situ generating bioactive glass (BG) onto the cotton gauze (CG), and then attaching cardanol (CA) onto one side of the BG-loaded CG (CG@BG) via click reaction. The Janus gauze (CA-CG@BG) has asymmetric wetting properties with a hydrophilic side (CA-CG@BGHL) and a hydrophobic side (HBCA-CG@BG). When applied to hemostatic, the porous and active BG on CA-CG@BGHL can rapidly initiate coagulation cascade to form a robust thrombus. CA on HBCA-CG@BG can entangled with each other, creating a hydrophobic barrier that prevents blood from flowing out. The hemostatic performance of CA-CG@BG is superior to that of CG in both rats and pigs. Interestingly, CA-CG@BG possesses unidirectional exudate removal. When applied to wound healing, the exudate can penetrate the hydrophobic HBCA-CG@BG to the hydrophilic CA-CG@BGHL, resulting in faster wound healing than CG. CA-CG@BG exhibits excellent cytocompatibility and hemocompatibility. This unique Janus dressing shows promise as a potential material for clinical applications in the future.

Prevalence and factors associated with antenatal depressive symptoms across trimesters: a study of 110,584 pregnant women covered by a mobile app-based screening programme in Shenzhen, China.

BACKGROUND: Antenatal depression is a significant public health issue affecting pregnant women both globally and in China. Using data from a mobile app-based screening programme, this study explored the prevalence and factors associated with antenatal depressive symptoms across different trimesters in Shenzhen. METHODS: A retrospective cross-sectional study was conducted on pregnant women who gave birth in any hospital in Shenzhen between July 2021 and May 2022 and underwent depression screening using an official maternal and infant health mobile app at least once during pregnancy. Depressive symptoms were evaluated using the 9-item Patient Health Questionnaire (PHQ-9), with cut-off scores of 5 and 10 for mild and high level of symptoms, respectively. The prevalence for each trimester was determined by calculating the proportion of women scoring 5 or higher. A variety of sociodemographic, obstetric, psychological, and lifestyle factors were assessed for their association with depressive symptoms. Chi-square test and multivariate logistic regression were performed to identify significant predictors. RESULTS: A total of 110,584 pregnant women were included in the study, with an overall prevalence of depressive symptoms of 18.0% and a prevalence of high-level symptoms of 4.2%. Depressive symptoms were most prevalent in the first trimester (10.9%) and decreased in the second (6.2%) and third trimesters (6.3%). Only a small proportion (0.4%) of women showed persistent depressive symptoms across all trimesters. Anxiety symptoms in early pregnancy emerged as the most significant predictor of depressive symptoms. Other factors linked to an increased risk throughout pregnancy include lower marital satisfaction, living with parents-in-law, experience of negative life events, as well as drinking before and during pregnancy. Factors associated with a reduced risk throughout pregnancy include multiparity and daily physical activity. CONCLUSIONS: This large-scale study provides valuable insights into the prevalence and factors associated with antenatal depressive symptoms in Shenzhen. The findings underscore the need for targeted interventions for high-risk groups and the integration of mental health care into routine antenatal services. Continuous, dynamic monitoring of depressive symptoms for pregnant women and ensuring at-risk women receive comprehensive follow-up and appropriate psychological or psychiatric care are crucial for effectively addressing antenatal depression and improving maternal and infant health outcomes.

Arctigenin from Fructus arctii Exhibits Antiaging Effects via Autophagy Induction, Antioxidative Stress, and Increase in Telomerase Activity in Yeast.

Aging is often accompanied by irreversible decline in body function, which causes a large number of age-related diseases and brings a huge economic burden to society and families. Many traditional Chinese medicines have been known to extend lifespan, but it has still been a challenge to isolate a single active molecule from them and verify the mechanism of anti-aging action. Drugs that inhibit senescence-associated secretory phenotypes (SASPs) are called "senomorphics". In this study, arctigenin (ATG), a senomorphic, was screened from the Chinese medicine Fructus arctii using K6001 yeast replicative lifespan. Autophagy, oxidative stress, and telomerase activity are key mechanisms related to aging. We found that ATG may act through multiple mechanisms to become an effective anti-aging molecule. In exploring the effect of ATG on autophagy, it was clearly observed that ATG significantly enhanced autophagy in yeast. We further verified that ATG can enhance autophagy by targeting protein phosphatase 2A (PP2A), leading to an increased lifespan. Meanwhile, we evaluated the antioxidant capacity of ATG and found that ATG increased the activities of the antioxidant enzymes, thereby reducing reactive oxygen species (ROS) and malondialdehyde (MDA) levels to improve the survival of yeast under oxidative stress. In addition, ATG was able to increase telomerase activity by enhancing the expression of EST1, EST2, and EST3 genes in yeast. In conclusion, ATG exerts anti-aging effects through induction of autophagy, antioxidative stress, and enhancement of telomerase activity in yeast, which is recognized as a potential molecule with promising anti-aging effects, deserving in-depth research in the future.

Occupational noise and genetic variants in stress hormone biosynthesis-based genes and rates of blood lipid changes in China: A five-year longitudinal study.

Lipid profiles are influenced by both noise and genetic variants. However, little is known about the associations of occupational noise and genetic variants with age-related changes in blood lipids, a crucial event in the initiation and evolution of atherosclerotic cardiovascular diseases. We aimed to evaluate the associations of blood lipid change rates with occupational noise and genetic variants in stress hormone biosynthesis-based genes. This cohort was established in 2012 and 2013 and was followed up until 2017. A total of 952 participants were included in the final analysis and all of them were categorized to two groups, the exposed group and control group, according to the exposed noise levels in their working area. Single nucleotide polymorphisms (SNPs) in stress hormone biosynthesis-based genes were genotyped. Five physical examinations were conducted from 2012 to 2017 and lipid measurements were repeated five times. The estimated annual changes (EACs) of blood lipid were calculated as the difference in blood lipid levels between any 2 adjacent examinations divided by their time interval (year). The generalized estimating equations for repeated measures analyses with exchangeable correlation structures were used to evaluate the influence of exposing to noise (versus being a control) and the SNPs mentioned above on the EACs of blood lipids. We found that the participants experienced accelerated age-related decline in high-density lipoprotein cholesterol (HDL-C) levels as they were exposed to noise (ß = -0.38, 95% confidence interval (CI), -0.66 to -0.10, P = 0.007), after adjusting for work duration, gender, smoking, alcohol consumption, and pack-years. This trend was only found in participants with COMT-rs165815 TT genotype (ß = -1.19, 95% CI, -1.80 to -0.58, P < 0.001), but not in those with the CC or CT genotypes. The interaction of noise exposure and rs165815 was marginally significant (Pinteraction = 0.010) after multiple adjustments. Compared with DDC-rs11978267 AA genotype carriers, participants carrying rs11978267 GG genotype had decreased EAC of triglycerides (TG) (ß = -5.06, 95% CI, -9.07 to -1.05, P = 0.013). Participants carrying DBH-rs4740203 CC genotype had increased EAC of total cholesterol (TC) (ß = 1.19, 95% CI, 0.06 to 2.33, P = 0.039). However, these findings were not statistically significant after multiple adjustments. These results indicated that Occupational noise exposure was associated with accelerated age-related decreases in HDL-C levels, and the COMT-rs165815 genotype appeared to modify the effect of noise exposure on HDL-C changes among the occupational population.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

Exploring the anti-inflammatory and immune regulatory effects of Taohe Chengqi decoction in sepsis-induced lung injury.

ETHNOPHARMACOLOGICAL RELEVANCE: Sepsis presents complex pathophysiological challenges. Taohe Chengqi Decoction (THCQ), a traditional Chinese medicine, offers potential in managing sepsis-related complications, though its exact mechanisms are not fully understood. AIM OF THE STUDY: This research aimed to assess the therapeutic efficacy and underlying mechanisms of THCQ on sepsis-induced lung injury. MATERIALS AND METHODS: The study began with validating THCQ's anti-inflammatory effects through in vitro and in vivo experiments. Network pharmacology was employed for mechanistic exploration, incorporating GO, KEGG, and PPI analyses of targets. Hub gene-immune cell correlations were assessed using CIBERSORT, with further scrutiny at clinical and single-cell levels. Molecular docking explored THCQ's drug-gene interactions, culminating in qPCR and WB validations of hub gene expressions in sepsis and post-THCQ treatment scenarios. RESULTS: THCQ demonstrated efficacy in modulating inflammatory responses in sepsis, identified through network pharmacology. Key genes like MAPK14, MAPK3, MMP9, STAT3, LYN, AKT1, PTPN11, and HSP90AA1 emerged as central targets. Molecular docking revealed interactions between these genes and THCQ components. qPCR results showed significant modulation of these genes, indicating THCQ's potential in reducing inflammation and regulating immune responses in sepsis. CONCLUSION: This study sheds light on THCQ's anti-inflammatory and immune regulatory mechanisms in sepsis, providing a foundation for further research and potential clinical application.

Diagnostic Study of Nodular Pulmonary Cryptococcosis Based on Radiomic Features Captured from CT Images.

BACKGROUND: Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC). OBJECTIVE: This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC. METHODS: This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC vs. non-PC), 2 (PC vs. TB), and 3 (PC vs. LC) were established using radiomic features. Models 4 (PC vs. TB) and 5 (PC vs. LC) were established based on radiomic and CT features. RESULTS: Five radiomic features were predictive of PC vs. non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC vs. TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC vs. LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90. CONCLUSION: The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.

Genetic association of the gut microbiota with epigenetic clocks mediated by inflammatory cytokines: a Mendelian randomization analysis.

Background: A new aging biomarker epigenetic clock has been developed. There exists a close link between aging and gut microbiota, which may be mediated by inflammatory cytokines. However, the relationship between the epigenetic clock, gut microbiota, and the mediating substances is unclear. Methods: Two large genome-wide association meta-analyses were analyzed by two-sample Mendelian randomization. The results between gut microbiota and epigenetic clock were investigated using the four methods (Inverse variance weighted, MR-Egger, weighted median, MR-PRESSO). Genetic correlation was measured by Linked disequilibrium score regression (LDSC). The correctness of the study direction was checked by the Steiger test. Cochran's Q statistic and MR-Egger intercept were used as sensitivity analyses of the study. The two-step method was used to examine the mediating role of inflammatory cytokines. We use the Benjamini-Hochberg correction method to correct the P value. Results: After FDR correction, multiple bacterial genera were significantly or suggestively associated with four epigenetic clocks (GrimAge, HannumAge, IEAA, PhenoAge). And we detected several inflammatory factors acting as mediators of gut microbiota and epigenetic clocks. Conclusion: This study provides genetic evidence for a positive and negative link between gut microbiota and aging risk. We hope that by elucidating the genetic relationship and potential mechanisms between aging and gut microbiota, we will provide new avenues for continuing aging-related research and treatment.

APOE2 protects against Aß pathology by improving neuronal mitochondrial function through ERRα signaling.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disease and apolipoprotein E (APOE) genotypes (APOE2, APOE3, and APOE4) show different AD susceptibility. Previous studies indicated that individuals carrying the APOE2 allele reduce the risk of developing AD, which may be attributed to the potential neuroprotective role of APOE2. However, the mechanisms underlying the protective effects of APOE2 is still unclear. METHODS: We analyzed single-nucleus RNA sequencing and bulk RNA sequencing data of APOE2 and APOE3 carriers from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort. We validated the findings in SH-SY5Y cells and AD model mice by evaluating mitochondrial functions and cognitive behaviors respectively. RESULTS: The pathway analysis of six major cell types revealed a strong association between APOE2 and cellular stress and energy metabolism, particularly in excitatory and inhibitory neurons, which was found to be more pronounced in the presence of beta-amyloid (Aß). Moreover, APOE2 overexpression alleviates Aß1-42-induced mitochondrial dysfunction and reduces the generation of reactive oxygen species in SH-SY5Y cells. These protective effects may be due to ApoE2 interacting with estrogen-related receptor alpha (ERRα). ERRα overexpression by plasmids or activation by agonist was also found to show similar mitochondrial protective effects in Aß1-42-stimulated SH-SY5Y cells. Additionally, ERRα agonist treatment improve the cognitive performance of Aß injected mice in both Y maze and novel object recognition tests. ERRα agonist treatment increased PSD95 expression in the cortex of agonist-treated-AD mice. CONCLUSIONS: APOE2 appears to enhance neural mitochondrial function via the activation of ERRα signaling, which may be the protective effect of APOE2 to treat AD.

Architecting O3/P2 layered oxides by gradient Mn doping for sodium-ion batteries.

O3 phase layered oxides are highly attractive cathode materials for sodium-ion batteries because of their high capacity and decent initial Coulombic efficiency. However, their rate capability and long cycling life are unsatisfactory due to the narrow Na+ transfer channel and irreversible phase transitions of O3 phase during sodiation/desodiation process. Constructing O3/P2 multiphase structures has been proven to be an effective strategy to overcome these challenges. In this study, we synthesized bi-phasic structured O3/P2 Na(Ni2/9Fe1/3Cu1/9Mn1/3)1-xMnxO2 (x = 0.01, 0.02, 0.03, 0.04, 0.05) materials through Mn doping during sodiation process. Benefiting from surface P2 phase layer with the enhanced Na+ transfer dynamics and high structural stability, the Na(Ni2/9Fe1/3Cu1/9Mn1/3)0.98Mn0.02O2 (NFCM-M2) cathode delivers a reversible capacity of 139.1 mA h g-1 at 0.1 C, and retains 71.4 % of its original capacity after 300 cycles at 1 C. Our work provides useful guidance for designing multiphase cathodes and offers new insights into the structure-performance correlation for sodium-ion cathode materials.

Metabolomic discoveries for early diagnosis and traditional Chinese medicine efficacy in ischemic stroke.

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

Identification and characterization of a novel intronic splicing mutation in CSF1R-related leukoencephalopathy.

AIMS: Colony stimulating factor 1 receptor (CSF1R)-related leukoencephalopathy is a rapidly progressing neurodegenerative disease caused by CSF1R gene mutations. This study aimed to identify and investigate the effect of a novel intronic mutation (c.1754-3C>G) of CSF1R on splicing. METHODS: A novel intronic mutation was identified using whole-exome sequencing. To investigate the impact of this mutation, we employed various bioinformatics tools to analyze the transcription of the CSF1R gene and the three-dimensional structure of its encoded protein. Furthermore, reverse transcription polymerase chain reaction (RT-PCR) was performed to validate the findings. RESULTS: A novel mutation (c.1754-3C>G) in CSF1R was identified, which results in exon 13 skipping due to the disruption of the 3' splice site consensus sequence NYAG/G. This exon skipping event was further validated in the peripheral blood of the mutation carrier through RT-PCR and Sanger sequencing. Protein structure prediction indicated a disruption in the tyrosine kinase domain, with the truncated protein showing significant structural alterations. CONCLUSIONS: Our findings underscore the importance of intronic mis-splicing mutations in the diagnosis and management of CSF1R-related leukoencephalopathy.

Dynamic changes of peripheral inflammatory markers link with disease severity and predict short-term poor outcome of myasthenia gravis.

The relationship between peripheral inflammatory markers, their dynamic changes, and the disease severity of myasthenia gravis (MG) is still not fully understood. Besides, the possibility of using it to predict the short-term poor outcome of MG patients have not been demonstrated. This study aims to investigate the relationship between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification (primary outcome) and predict the short-term poor outcome (secondary outcome) in MG patients. The study retrospectively enrolled 154 MG patients from June 2016 to December 2021. The logistic regression was used to investigate the relationship of inflammatory markers with MGFA classification and determine the factors for model construction presented in a nomogram. Finally, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were utilized to evaluate the incremental capacity. Logistic regression revealed significant associations between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI) and MGFA classification (p = 0.013, p = 0.032, p = 0.017, respectively). Incorporating dynamic changes of inflammatory markers into multivariable models improved their discriminatory capacity of disease severity, with significant improvements observed for NLR, systemic immune-inflammation index (SII) and AISI in NRI and IDI. Additionally, AISI was statistically associated with short-term poor outcome and a prediction model incorporating dynamic changes of inflammatory markers was constructed with the area under curve (AUC) of 0.953, presented in a nomograph. The inflammatory markers demonstrate significant associations with disease severity and AISI could be regarded as a possible and easily available predictive biomarker for short-term poor outcome in MG patients.

Comprehensive proteogenomic characterization of rare kidney tumors.

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Hsa_circ_0009096/miR-370-3p modulates hepatic stellate cell proliferation and fibrosis during biliary atresia pathogenesis.

Background: Hepatic stellate cell (HSC) activation and hepatic fibrosis mediated biliary atresia (BA) development, but the underlying molecular mechanisms are poorly understood. This study aimed to investigate the roles of circRNA hsa_circ_0009096 in the regulation of HSC proliferation and hepatic fibrosis. Methods: A cellular hepatic fibrosis model was established by treating LX-2 cells with transforming growth factor ß (TGF-ß1). RNaseR and actinomycin D assays were performed to detect hsa_circ_0009096 stability. Expression of hsa_circ_0009096, miR-370-3p, and target genes was detected using reverse transcription-qPCR. Direct binding of hsa_circ_0009096 to miR-370-3p was validated using dual luciferase reporter assay. Cell cycle progression and apoptosis of LX-2 cells were assessed using flow cytometry. The alpha-smooth muscle actin (α-SMA), collagen 1A1 (COL1A1), and TGF beta receptor 2 (TGFBR2) protein levels in LX-2 cells were analyzed using immunocytochemistry and western blotting. Results: Hsa_circ_0009096 exhibited more resistance to RNase R and actinomycinD digestion than UTRN mRNA. Hsa_circ_0009096 expression increased significantly in LX-2 cells treated with TGF-ß1, accompanied by elevated α-SMA and COL1A1 expression. Hsa_circ_0009096 siRNAs effectively promoted miR-370-3p and suppressed TGFBR2 expression in LX-2 cells, mediated by direct association of hsa_circ_0009096 with miR-370-3p. Hsa_circ_0009096 siRNA interfered with the cell cycle progression, promoted apoptosis, and reduced α-SMA and COL1A1 expression in LX-2 cells treated with TGF-ß1. MiR-370-3p inhibitors mitigated the alterations in cell cycle progression, apoptosis, and α-SMA, COL1A1, and TGFBR2 expression in LX-2 cells caused by hsa_circ_0009096 siRNA. In conclusion, hsa_circ_0009096 promoted HSC proliferation and hepatic fibrosis during BA pathogenesis by accelerating TGFBR2 expression by sponging miR-370-3p.

Sex differences in the correlation between white matter hyperintensity and 3-month outcome in acute stroke patients.

Background: The severity of white matter hyperintensities (WMH) has been shown to be an independent predictor of poor stroke outcome, but the effect of sex on this correlation has not been investigated further. Therefore, the purpose of our study was to assess whether there was a sex difference between the severity of WMH and poor stroke outcome. Methods: This retrospective study included 449 patients with acute ischemic stroke (AIS) who received intravenous thrombolysis. WMH severity was graded based on the Fazekas scale. The association between WMH severity and stroke outcome was explored through multivariable regression analyses in men and women. Results: Among women, when dividing WMH severity into tertiles, T3 (Fazekas scale >3) had a 5.334 times higher risk for unfavorable outcomes than T1 (Fazekas scale <2) (p-trend = 0.026) in the adjusted model. In addition, moderate-severe WMH (Fazekas scale 3-6) had a 3.391 (1.151-9.991) times higher risk than none-mild WMH (Fazekas scale 0-2) (p = 0.027). Conclusions: The risk of unfavorable outcomes increased proportionally with the enlargement of the WMH severity in females, suggesting the sex-specific value of the WMH severity in optimizing the risk stratification of stroke.