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PurposeWe sought to develop and validate a nomogram for predicting extra-urinary recurrence (EUR) following radical nephroureterectomy (RNU) in patients with upper urinary tract urothelial carcinoma (UTUC).MethodsData from 556 UTUC patients post-RNU at the First Hospital of Jilin University were retrospectively analyzed. These patients were categorized into a training group (n=389) and a validation group (n=167). Variables significantly associated with prognosis were identified using univariate Cox regression and most minor absolute shrinkage and selection operator (LASSO) analysis. These independent predictors were incorporated into the nomogram to estimate extra-urinary recurrence-free survival (EURFS). Validation of the nomogram involved ROC curves, calibration plots, and decision curve analysis (DCA). Patients were stratified into two risk categories based on their nomogram scores to compare EURFS using the Kaplan-Meier method.ResultsEight predictors were identified: T-stage, N-stage, tumor grade, local and nerve invasion, preoperative hemoglobin level, neutrophil-to-lymphocyte ratio (NLR), and creatinine, all proving to be independent predictors of EUR. A nomogram was created based on these eight factors, and using the ROC, calibration curves, and DCA, good prediction results were shown in both the training and validation groups. The training and validation groups also showed reliable predictive performance. In particular, there was a significant difference in survival between the high-risk and low-risk groups (P

IntroductionSpread through air spaces (STAS) represents a novel invasive pattern in lung adenocarcinoma (LUAD) and is a risk factor for poor prognosis in stage T1 LUAD. This study aims to develop and validate a CT habitat imaging analysis model for predicting STAS in stage T1 invasive LUAD.MethodsWe retrospectively analyzed 217 patients with preoperative stage T1 invasive LUAD (115 STAS-positive and 102 STAS-negative cases, including 151 in the train set and 66 in the test set). Semi-automatic segmentation was performed on the regions of interest (ROIs) in all CT images, with an automatic 3mm expansion around the tumor, considering the intratumoral and peritumoral 3mm area. This area was divided into three sub-regions via K-means clustering, and 1197 radiomic features were extracted from each sub-region and the overall combined region. After dimension reduction through the Mann-Whitney U test, Pearson correlation analysis, and least absolute shrinkage and selection operator(LASSO), the best features for each sub-region and overall were selected. Models were then built using the selected radiomic features through the Adaptive Boosting (AdaBoost) and Multilayer Perceptron (MLP) classifiers. Four different models were established based on different sub-regions and the overall features. The performance of these models was evaluated through receiver operating characteristic curves (AUC) under the DeLong test, calibration curves via the Hosmer-Lemeshow test, and decision curve analysis to assess the performance of these features.ResultsIn this study, we evaluated the predictive performance of AdaBoost and MLP classifiers on rad feature models across various subregions and the overall dataset. In the test set, the AdaBoost classifier achieved a maximum AUC of 0.871 in Habitat 3, whereas the MLP classifier demonstrated slightly superior performance with an AUC of 0.879. Both classifiers exhibited high efficiency in habitat 3, with the MLP algorithm showing enhanced model performance.ConclusionsCT habitat imaging analysis for the preoperative prediction of STAS in stage T1 invasive LUAD shows satisfactory diagnostic performance, with the habitat3 model exhibiting the highest efficacy, reflecting tumor heterogeneity.

Monitoring the status of grafts and the occurrence of postoperative complications, such as rejection, is crucial for ensuring the success and long-term survival of organ transplants. Traditional histopathological examination, though effective, is an invasive procedure and poses risks of complications, making frequent use impractical. In recent years, graft-derived cell-free DNA (gd-cfDNA) has emerged as a promising non-invasive biomarker. It not only provides early warnings of rejection and other types of graft injury but also offers important information about the effectiveness of immunosuppressive therapy and prognosis. gd-cfDNA shows potential in the monitoring of organ transplants. The early, real-time information on graft injury provided by gd-cfDNA facilitates timely individualized treatment and improves patient outcomes. However, the progress of research on gd-cfDNA varies across different organs. Therefore, this article will comprehensively review the application and findings of gd-cfDNA in monitoring various solid organs, discussing the advantages, limitations, and some future research directions to aid in its clinical application.

IntroductionThe development of advanced sewage technologies empowers the industry to produce high-quality recycled water, which greatly influences human’s life and health. Thus, this study investigates the mechanism of individuals’ adoption of recycled water from the technology adoption perspective.MethodsEmploying the mixed method of structural equation modeling and artificial neural network analysis, we examined a research model developed from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework. To examine the research model, this study employs a leading web-survey company (Sojump) to collect 308 valid samples from the residents in mainland China.ResultsThe structural equation modeling results verified the associations between the six predictors (performance expectancy, effort expectancy, social influence, facilitating conditions, environmental motivation, and price value), individuals’ cognitive and emotional attitudes, and acceptance intention. The artificial neural network analysis validates and complements the structural equation modeling results by unveiling the importance rank of the significant determinants of the acceptance decisions.DiscussionThe study provides theoretical implications for recycled water research and useful insights for practitioners and policymakers to reduce the environmental hazards of water scarcity.

The inhibitory effect of growth hormone (GH) on adipose tissue growth is well known, but the underlying mechanism is not fully understood. In this study, we determined the possibility that GH inhibits adipose tissue growth by inhibiting adipogenesis, the process of formation of adipocytes from stem cells, in the lit/lit mice. The lit/lit mice are GH deficient because of a spontaneous mutation to the GH releasing hormone receptor (ghrhr) gene, and they have more subcutaneous fat despite being smaller than the lit/+ mice at the same age. We found that cells of the stromal vascular fraction (SVF) of subcutaneous fat from the lit/lit mice had greater adipogenic potential than those from the lit/+ mice, as evidenced by forming greater numbers of lipid droplets-containing adipocytes and having greater expression of adipocyte marker genes during induced adipocyte differentiation in culture. However, addition of GH to the culture did not reverse the superior adipogenic potential of subcutaneous SVF from the lit/lit mice. Through florescence-activated cell sorting and quantification of mRNAs of preadipocyte markers, including CD34, CD29, Sca-1, CD24, Pref-1, and PPARγ, we found that subcutaneous SVF from the lit/lit mice contained more preadipocytes than that from the lit/+ mice. These results support the notion that GH inhibits adipose tissue growth in mice at least in part by inhibiting adipogenesis. Furthermore, these results suggest that GH inhibits adipogenesis in mice not by inhibiting the terminal differentiation of preadipocytes into adipocytes, rather by inhibiting the formation of preadipocytes from stem cells or the recruitment of stem cells to the fat depot.

Background: The association between pretreatment skeletal muscle index (SMI) and long-term survival of gastric cancer patients remains unclear up to now. The aim of this meta-analysis was to identify the prognostic value of pretreatment SMI in gastric cancer.Methods: The PubMed, EMBASE and Web of Science electronic databases were searched up to 5 June 2022 for relevant studies. The primary outcome was overall survival (OS) and the second outcomes were disease-free survival (DFS) and cancer-specific survival (CSS). The hazard ratios (HRs) and 95% confidence intervals (CIs) were combined to assess the relationship between pretreatment SMI and survival of gastric cancer patients. All statistical analyses were conducted by STATA 15.0 software.Results: A total of 31 retrospective studies involving 12,434 patients were enrolled in this meta-analysis. The pooled results demonstrated that lower pretreatment was significantly associated with poorer OS (HR = 1.53, p < 0.001). Besides, lower pretreatment SMI was also related with worse DFS (HR = 1.39, p < 0.001) and CSS (HR = 1.96, p < 0.001).Conclusion: Pretreatment SMI was significantly associated with prognosis of gastric cancer patients and lower SMI predicted worse survival. However, more prospective high-quality studies are still needed to verify our findings.

Antibiotic resistance is an increasingly prevalent problem worldwide. Probiotics are live microorganisms that provide health benefits to human beings and animals and also antimicrobial activity against pathogens and might be an antibiotic alternative. The gastrointestinal tract of animals can be a suitable source of finding novel antimicrobial agents, where the vast majority of gut microbes inhabit and a plurality of antimicrobial producers exhibit either a wide or narrow spectrum. Animals that live in Northwest China might possess a special commensal community in the gut. Therefore, the purpose of this study was to assess the effects of three probiotic strains (including Lactobacillus salivarius ZLP-4b from swine, Lactobacillus plantarum FBL-3a from beef cattle, and Bacillus velezensis JT3-1 from yak), which were isolated from livestock in this area, on the overall growth performance, immune function, and gut microbiota of mice. The results showed that the L. salivarius ZLP-4b group not only improved the growth performance but also amended the intestinal mucosa morphology of mice. Furthermore, the supplementation of L. plantarum FBL-3a and L. salivarius ZLP-4b strains significantly increased the content of anti-inflammatory cytokines IL-4 and IL-10 but decreased the pro-inflammatory factor IL-17A. The levels of pro-inflammatory factors IL-6, IL-17A, and TNF-α were also decreased by the B. velezensis JT3-1 group pretreatment. The 16S rDNA sequence results showed that the probiotic administration could increase the proportion of Firmicutes/Bacteroidetes intestinal microbes in mice. Furthermore, the relative abundance of Lactobacillus was boosted in the JT3-1- and ZLP-4b-treated groups, and that of opportunistic pathogens (including Proteobacteria and Spirochaetes) was diminished in all treated groups compared with the control group. In conclusion, B. velezensis JT3-1 and L. salivarius ZLP-4b supplementation enhanced the overall performance, intestinal epithelial mucosal integrity, and immune-related cytokines and regulated the intestinal microbiota in mice.