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• In response to jasmonates (JAs), the JA receptor Coronatine Insensitive 1 (COI1) recruits JA-zinc-finger inflorescence meristem (ZIM)-domain (JAZ) family repressors for destruction to regulate plant growth, development, and defense. As Arabidopsis encodes 13 JAZ repressors, their functional specificity, diversity, and redundancy in JA/COI1-mediated responses remain unclear. • We generated a broad range of jaz mutants based on their phylogenetic relationship to investigate their roles in JA responses. The group I JAZ6 may play an inhibitory role in resistance to Botrytis cinerea, group II (JAZ10)/III (JAZ11/12) in JA-regulated root growth inhibition and susceptibility to Pseudomonas syringae pv tomato DC3000, and group IV JAZ3/4/9 in flowering time delay and defense against insects. JAZs exhibit high redundancy in apical hook curvature. • The undecuple jaz1/2/3/4/5/6/7/9/10/11/12 (jaz1-7,9-12) mutations enhance JA responses and suppress the phenotypes of coi1-1 in flowering time, rosette growth, and defense. The JA hypersensitivity of jaz1-7,9-12 in root growth, hook curvature, and leaf yellowing is blocked by coi1-1. jaz1-7,9-12 does not influence the stamen phenotypes of wild-type and coi1-1. jaz1-7,9-12 affects JA-regulated transcriptional profile and recovers a fraction of that in coi1-1. • This study contributes to elucidating the specificity, diversity, and redundancy of JAZ members in JA/COI1-regulated growth, development, and defense responses.

Under high-temperature and high-pressure conditions, understanding the competitive adsorption and mobilization mechanisms of gas and water in fractured tight sandstone gas reservoirs is crucial for optimizing the recovery factor. This study employs molecular dynamics simulation to investigate the adsorption behavior and mobilization characteristics of H2O and CH4 in 10 nm quartz nanopores under the conditions of the Keshen fractured tight sandstone gas reservoir. The results indicate that H2O exhibits strong adsorption on the quartz surface, forming two high-density adsorption layers with a thickness of approximately 0.6 nm, whereas CH4 forms three adsorption layers with a thickness of about 1.1 nm. Under gas–water coexistence conditions, the competitive adsorption effect of the water phase significantly influences the distribution of CH4. Due to the hydrophilicity of the quartz wall, H2O molecules preferentially adsorb onto the wall surface, forming a stable water film that significantly inhibits CH4 adsorption. When the water saturation reaches 35%, water molecules form liquid bridges within the pores, segmenting the gas phase into different regions. As water saturation further increases, more stable liquid bridge structures develop, and microscopic water lock effects emerge, further restricting gas flow. During depletion development, H2O remains difficult to mobilize due to strong wall adsorption, with a recovery factor of only 7%. In contrast, CH4 exhibits high mobility, with a recovery factor of up to 75%. However, as water saturation increases from 30% to 70%, the recovery factor of CH4 decreases significantly from 75% to 29%, indicating that the water phase not only occupies pore space, but also exerts a blocking effect that significantly inhibits CH4 percolation and production. This study provides important theoretical support for the development strategies of ultra-deep fractured tight sandstone gas reservoirs and offers key insights for improving the ultimate recovery factor under gas–water coexistence conditions.

Background and aims: Currently, there are no definitive therapies for coronavirus disease 2019 (COVID-19). Gut microbial dysbiosis has been proved to be associated with COVID-19 severity and probiotics is an adjunctive therapy for COIVD-19. However, the potential benefit of probiotics in COVID-19 has not been studied. We aimed to assess the relationship of probiotics use with clinical outcomes in patients with COVID-19. Methods: We conducted a propensity-score matched retrospective cohort study of adult patients with COVID-19. Eligible patients received either probiotics plus standard care (probiotics group) or standard care alone (non-probiotics group). The primary outcome was the clinical improvement rate, which was compared among propensity-score matched groups and in the unmatched cohort. Secondary outcomes included the duration of viral shedding, fever, and hospital stay. Results: Among the propensity-score matched groups, probiotics use was related to clinical improvement rates (log-rank p = 0.028). This relationship was driven primarily by a shorter (days) time to clinical improvement [difference, −3 (−4 to −1), p = 0.022], reduction in duration of fever [−1.0 (−2.0 to 0.0), p = 0.025], viral shedding [−3 (−6 to −1), p < 0.001], and hospital stay [−3 (−5 to −1), p = 0.009]. Using the Cox model with time-varying exposure, use of probiotics remained independently related to better clinical improvement rate in the unmatched cohort. Conclusion: Our study suggested that probiotics use was related to improved clinical outcomes in patients with COVID-19. Further studies are required to validate the effect of probiotics in combating the COVID-19 pandemic.

To examine the diagnostic test accuracy (DTA) of the FRAIL-NH and four frailty screening instruments among institutionalized older adults. Cross-sectional study. Institutionalized setting, Jinan, China. A total of 305 older adults (mean age 79.3 ± 8.4 years, 57.0% female) were enrolled from nursing homes. Frailty was assessed by the FRAIL-NH, Physical Frailty Phenotype (PFP), FRAIL, Tilburg Frailty Indicator (TFI), and Groningen Frailty Indicator (GFI), respectively. The Comprehensive Geriatric Assessment (CGA) was used as a reference standard of frailty. The receiver operating characteristic (ROC) curve was plotted to examine the DTA of five frailty screening instruments against the CGA. The optimal cut-point was determined by the maximum value of the Youden index (YI, calculated as sensitivity + specificity − 1). The prevalence of frailty ranged from 25.9% (FRAIL) to 56.4% (GFI). Areas under the curve (AUCs) against the CGA ranged from 0.80 [95% confidence interval (CI) 0.74–0.85: FRAIL] to 0.83 (95% CI 0.78–0.88: PFP). At their original cut-points, all five frailty screening instruments presented low sensitivity (32.9%–69.3%) and high specificity (80.0%–93.8%), as well as high positive predictive values (90.7%–94.9%) and low negative predictive values (33.2%–48.1%). At their optimal cut-points, the sensitivity and specificity of the FRAIL-NH, PFP, and FRAIL tended to be balanced, and their correctly classified rates (76.1%–81.3%) and kappa values (0.465–0.523) increased a lot. ROC contrasts showed that all five frailty screening instruments had similarly good diagnostic accuracy (χ2: 0.0003–1.38, P >.05). In the institutionalized setting, the specific FRAIL-NH, self-report FRAIL, TFI, and GFI as well as hybrid PFP, show similarly good diagnostic properties in identifying frailty against the CGA.

Objectives The purpose of this study was to investigate the effect of COVID-19 infection on antipsychotic drug concentrations. Methods The drug concentration data of schizophrenia patients taking antipsychotic drugs before and after infection with the novel coronavirus were collected from medical records. At the same time, blood and serological test results before and after COVID-19 infection were collected. A total of 139 valid data were collected and analysed statistically. Results After COVID-19 infection, the serum concentrations of olanzapine, quetiapine, clozapine and risperidone were significantly increased, and the C/D ratio of each drug was significantly increased. After COVID-19 infection, the CRP level, liver function index, kidney function index, blood glucose, blood lipid, electrolyte, myocardial enzyme spectrum, routine blood test results and other indicators changed to varying degrees. An increasing olanzapine concentration was positively correlated with increasing HCY, GLO, SA and MCV. The increasing amplitude of the quetiapine concentration was negatively correlated with the increasing amplitudes of NEFAs and Ca and positively correlated with the increasing amplitude of MON%. An increase in the clozapine concentration was positively correlated with an increase in the CRP level and negatively correlated with an increase in the WBC and NEU%. An increase in the risperidone concentration was positively correlated with increases in the ADA, GGT and RBC levels and negatively correlated with increases in the TBIL, DBIL and BUN levels. Conclusions COVID-19 infection resulted in a significant increase in the concentration of antipsychotic drugs and a significant decrease in the metabolic rate of antipsychotic drugs. This may be related to CRP, HCY, ADA, TBIL, DBIL, GGT, GLO, SA, BUN, NEFA, Ca, WBC, NEU#, RBC, and MON%. The monitoring of antipsychotic drug concentrations should be strengthened for patients with COVID-19 infection or other types of inflammation to avoid the occurrence of adverse reactions.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD) worldwide, and the importance of tubular injury has been highlighted in recent years. However, the underlying mechanisms and effective therapeutic targets are still unclear. In this study, we investigated mtDNA, mitochondrial dynamics, function and metabolic pathways to determine if mitochondrial damage plays a critical role in the development of tubular injury in DKD patients. A cross-sectional study was carried out among healthy controls (HCs, n = 65), diabetes patients without kidney disease (DCs, n = 48) and DKD patients (n = 60). Serum, peripheral blood mononuclear cells (PBMCs) and kidney biopsy specimens were obtained from participants. Metabolomics was employed to investigate cellular metabolism. DKD patients had decreased mtDNA copy numbers and increased mtDNA damage compared to DCs. Mitochondrial fragmentation was specifically presented in tubules, but not in podocytes of DKD patients. The accumulation of damaged mtDNA and fragmented mitochondria resulted in increased reactive oxygen species (ROS) generation, activation of apoptosis and loss of mitochondrial membrane potential (ΔΨm) in tubules and PBMCs. Furthermore, glycolysis and tricarboxylic acid (TCA) cycle was perturbed, and increased dihydroxyacetone phosphate (DHAP) and decreased succinyl-CoA synthetase (SCS) respectively in these two metabolic pathways were identified as potential biomarkers for tubular injury in DKD. Our study indicates that mitochondrial damage could be the hallmark of tubular injury in DKD patients, and this would provide a novel and attractive therapeutic target to improve this disease.

Background Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions. Methods RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses. Results ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients. Conclusions The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Background Clear cell renal cell carcinoma (ccRCC) is characterized as one of the most common types of urological cancer with high degrees of malignancy and mortality. Due to the limited effectiveness of existing traditional therapeutic methods and poor prognosis, the treatment and therapy of advanced ccRCC patients remain challenging. Tryptophan metabolism has been widely investigated because it significantly participates in the malignant traits of multiple cancers. The functions and prognostic values of tryptophan metabolism-related genes (TMR) in ccRCC remain virtually obscure. Methods We employed the expression levels of 40 TMR genes to identify the subtypes of ccRCC and explored the clinical characteristics, prognosis, immune features, and immunotherapy response in the subtypes. Then, a model was constructed for the prediction of prognosis based on the differentially expressed genes (DEGs) in the subtypes from the TCGA database and verified using the ICGC database. The prediction performance of this model was confirmed by the receiver operating characteristic (ROC) curves. The relationship of Risk Score with the infiltration of distinct tumor microenvironment cells, the expression profiles of immune checkpoint genes, and the treatment benefits of immunotherapy and chemotherapy drugs were also investigated. Results The two subtypes revealed dramatic differences in terms of clinical characteristics, prognosis, immune features, and immunotherapy response. The constructed 6-gene-based model showed that the high Risk Score was significantly connected to poor overall survival (OS) and advanced tumor stages. Furthermore, increased expression of CYP1B1, KMO, and TDO2 was observed in ccRCC tissues at the translation levels, and an unfavorable prognosis for these patients was also found. Conclusion We identified 2 molecular subtypes of ccRCC based on the expression of TMR genes and constructed a prognosis-related model that may be used as a powerful tool to guide the prediction of ccRCC prognosis and personalized therapy. In addition, CYP1B1, KMO, and TDO2 can be regarded as the risk prognostic genes for ccRCC.

Organ dysfunction caused by sepsis is life-threatening and results in high mortality. Therapeutic options for sepsis are limited. Pathogenic factors are considered as components of environmental pressure that modify DNA methylation patterns thereby enhancing disease progression. Here, we found that sepsis patients exhibited higher levels of genomic DNA methylation patterns and hypermethylated genes associated with the NF-kB signaling pathway. Therefore, we hypothesized that a DNA methyl transferase inhibitor, Decitabine, may mitigate inflammation and improve survival by inhibiting the NF-κB signaling pathway. To test the hypothesis, mice challenged with caecal ligation and puncture (CLP) were subcutaneously injected with Decitabine solution (0.5, 1, and 1.5 mg/kg) 2 h following operation. Our results indicated that Decitabine reduces DNA methyltransferases (DNMTs), attenuates NF-κB activation, downregulates inflammatory cytokine levels, and inhibits the progression of sepsis. Thus, DNA methylation may be indispensable for sepsis and serve as a predicting factor. The use of Decitabine could represent a novel strategy in the treatment of sepsis.

Acute rejection (AR) in kidney transplantation is an established risk factor that reduces the survival rate of allografts. Despite standard immunosuppression, molecules with regulatory control in the immune pathway of AR can be used as important targets for therapeutic operations to prevent rejection. We downloaded the microarray data of 15 AR patients and 37 non-acute rejection (NAR) patients from Gene Expression Omnibus (GEO). Gene network was constructed, and genes were classified into different modules using weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cytoscape were applied for the hub genes in the most related module to AR. Different cell types were explored by xCell online database and single-cell RNA sequencing. We also validated the SLAMF8 and TLR4 levels in Raw264.7 and human kidney tissues of TCMR. A total of 1,561 differentially expressed genes were filtered. WGCNA was constructed, and genes were classified into 12 modules. Among them, the green module was most closely associated with AR. These genes were significantly enriched in 20 pathway terms, such as cytokine-cytokine receptor interaction, chemokine signaling pathway, and other important regulatory processes. Intersection with GS > 0.4, MM > 0.9, the top 10 MCC values and DEGs in the green module, and six hub genes (DOCK2, NCKAP1L, IL2RG, SLAMF8, CD180, and PTPRE) were identified. Their expression levels were all confirmed to be significantly elevated in AR patients in GEO, Nephroseq, and quantitative real-time PCR (qRT-PCR). Single-cell RNA sequencing showed that AR patient had a higher percentage of native T, CD1C+_B DC, NKT, NK, and monocytes in peripheral blood mononuclear cells (PBMCs). Xcell enrichment scores of 20 cell types were significantly different (p<0.01), mostly immune cells, such as B cells, CD4+ Tem, CD8+ T cells, CD8+ Tcm, macrophages, M1, and monocytes. GSEA suggests that highly expressed six hub genes are correlated with allograft rejection, interferon γ response, interferon α response, and inflammatory response. In addition, SLAMF8 is highly expressed in human kidney tissues of TCMR and in M1 phenotype macrophages of Raw264.7 cell line WGCNA accompanied by high expression of TLR4. This study demonstrates six hub genes and functionally enriched pathways related to AR. SLAMF8 is involved in the M1 macrophages TLR4, which contributed to AR process.