Objective: This study aimed to develop and validate a predictive model for assessing the risk of new-onset liver injury following cardiac surgery under cardiopulmonary bypass (CPB), using non-redundant and informative features extracted from electronic health records. Materials and Methods: We employed machine learning algorithms including Generalized Additive Model (GAM), Random Forest, XGBoost, LightGBM, and Fully Convolutional Network (FCN) to construct the models using data from 5,364 patients at a large medical center in China, and validated these models with an independent dataset of 1,207 patients from another center. A three-stage feature selection process was used to refine the input variables. Results: The GAM model displayed the best performance with good predictive accuracy in both internal and external validations, despite a noticeable performance decline in the external dataset potentially due to differences in feature distributions. The most impactful factors included CPB time, cryo time, and preoperative bilirubin levels. Conclusion: The predictive model developed provides a valuable tool for early identification of patients at risk of postoperative liver injury, potentially aiding in preventive treatment planning. However, the model requires further prospective validation and optimization for broader application across different medical centers. The model's robustness against clinical practice variations highlights its potential utility in improving patient safety and reducing healthcare costs.

Background: Obesity is a serious public health problem. According to statistics, there are millions obese people worldwide. The intestinal microbiota is the microbiota that lives in the gastrointestinal tract of the human body and can interact with the external environment through diet. Probing and summarizing the relationship between intestinal microbes and obesity has important guiding significance for the accurate control of the research direction and expanding the choice of obesity treatment methods. We aimed to use bibliometrics to qualitatively and quantitatively analyze the published literature and to reveal the research hotspots and development trends in the effects of intestinal microbes on obesity research. Methods: We obtained documents from the core collection of Web of Science (WoSCC) from 2013-2022 on 2nd February 2023. Microsoft Excel, Biblioshiny R language software packages and CiteSpace were employed to collect publication data, analyze publication trends, and visualize relevant results. Results: We identified 8888 original research articles on the effects of intestinal microbes on obesity published between 2013 and 2022. China made the highest number of publications on the topic, with 3036 publications (34.16%), and the United States with 87065 citations made the greatest contribution. University of Copenhagen was the most prolific institution (209 publications). Patrice D. Cani published the most related articles (97 publications), whereas Peter J. Turnbaugh was cited the most frequently (3307 citations). Nutrients was the journal with the most studies (399 publications, 4.49%), and Nature was the most commonly co-cited journal (6446 citations). The burst keywords "obese patient" and "serum" recently appeared as research frontiers. Conclusion: Although China has the largest number of publications, the United States, depending on its largest citations, has become the leader in the effects of intestinal microbes on obesity research. Current research hotspots focus on related mechanisms of the effects of intestinal microbes on obesity and therapeutic methods for obesity with intestinal microbes. Through diet, probiotic preparations and regulation of the intestinal flora, fecal microbe transplantation may have a significant impact on reducing the obesity epidemic. Considering the great potential and application prospects, intestinal microbe applications in obesity therapy will remain a research hotspot in the future.

Obesity is a serious public health problem. According to statistics, there are millions of obese people worldwide. Research studies have discovered a complex and intricate relationship between the gut microbiota and obesity. Probing and summarizing the relationship between intestinal microbes and obesity has important guiding significance for the accurate control of the research direction and expanding the choice of obesity treatment methods. We used bibliometric analysis to analyze the published literature with the intention to reveal the research hotspots and development trends on the effects of intestinal microbes on obesity from a visualization perspective, both qualitatively and quantitatively. The results showed that current research is focusing on related mechanisms of the effects of intestinal microbes on obesity and therapeutic methods for obesity. Several noteworthy hotspots within this field have garnered considerable attention and are expected to remain the focal points of future research. Of particular interest are the mechanisms by which intestinal microbes potentially regulate obesity through metabolite interactions, as well as the role of microbiomes as metabolic markers of obesity. These findings strongly suggest that gut microbes continue to be a key target in the quest for effective obesity treatments. Co-operation and communication between countries and institutions should be strengthened to promote development in this field to benefit more patients with obesity.

Few histological prognostic indicators for end-stage renal disease (ESRD) have been validated in diabetic patients. This biopsy-based study aimed to identify nephropathological risk factors for ESRD in Chinese patients with type 2 diabetes. Histological features of 322 Chinese type 2 diabetic patients with biopsy-confirmed diabetic nephropathy (DN) were retrospectively analysed. Cox proportional hazards analysis was used to estimate the hazard ratio (HR) for ESRD. Single glomerular proteomics and immunohistochemistry were used to identify differentially expressed proteins and enriched pathways in glomeruli. During the median follow-up period of 24 months, 144 (45%) patients progressed to ESRD. In multivariable models, the Renal Pathology Society classification failed to predict ESRD, although the solidified glomerulosclerosis (score 1: HR 1.65, 95% confidence interval [CI] 1.04–2.60; score 2: HR 2.48, 95% CI 1.40–4.37) and extracapillary hypercellularity (HR 2.68, 95% CI 1.55–4.62) were identified as independent risk factors. Additionally, single glomerular proteomics, combined with immunohistochemistry, revealed that complement C9 and apolipoprotein E were highly expressed in solidified glomerulosclerosis. Therefore, solidified glomerulosclerosis and extracapillary hypercellularity predict diabetic ESRD in Chinese patients. Single glomerular proteomics identified solidified glomerulosclerosis as a unique pathological change that may be associated with complement overactivation and abnormal lipid metabolism.

Objective. In order to find the quantitative relationship between timing of surgical intervention and risk of death in necrotizing pancreatitis. Methods. The generalized additive model was applied to quantitate the relationship between surgical time (from the onset of acute pancreatitis to first surgical intervention) and risk of death adjusted for demographic characteristics, infection, organ failure, and important lab indicators extracted from the Electronic Medical Record of West China Hospital of Sichuan University. Results. We analyzed 1,176 inpatients who had pancreatic drainage, pancreatic debridement, or pancreatectomy experience of 15,813 acute pancreatitis retrospectively. It showed that when surgical time was either modelled alone or adjusted for infection or organ failure, an L-shaped relationship between surgical time and risk of death was presented. When surgical time was within 32.60 days, the risk of death was greater than 50%. Conclusion. There is an L-shaped relationship between timing of surgical intervention and risk of death in necrotizing pancreatitis.

Background: The timing of surgery for necrotizing pancreatitis remains a matter of controversial debate, which has not been resolved by randomized controlled trial (RCT). This study aims to classify surgical timing within or beyond 4 weeks for patients with infected necrotizing pancreatitis by using machine learning methods. Methods: This study analyzed 223 patients who underwent surgery for infected pancreatic necrosis at West China Hospital of Sichuan University. We used logistic regression, support vector machine, and random forest with/without the simulation of generative adversarial networks to classify the surgical intervention within or beyond 4 weeks in the patients with infected necrotizing pancreatitis. Results: Our analyses showed that interleukin 6, infected necrosis, the onset of fever and C-reactive protein were important factors in determining the timing of surgical intervention (< 4 or ≥ 4 weeks) for the patients with infected necrotizing pancreatitis. The main factors associated with postoperative mortality in patients who underwent early surgery (< 4 weeks) included modified Marshall score on admission and preoperational modified Marshall score. Preoperational modified Marshall score, time of surgery, duration of organ failure and onset of renal failure were important predictive factors for the postoperative mortality of patients who underwent delayed surgery (≥ 4 weeks). Conclusions: Machine learning models can be used to predict timing of surgical intervention effectively and key factors associated with surgical timing and postoperative survival are identified for infected necrotizing pancreatitis.

Previous research focused on the qualitative discussion of the correlation between surgical time and mortality in acute pancreatitis (AP). Recommendations for surgical timing of necrotizing pancreatitis are delayed as far as possible, without recommendations for individuals. The aim of this article is to predict timing of surgical intervention in necrotizing pancreatitis with recurrent neural network (RNN). Time series data in AP were retrospectively extracted from a hospital in China (n = 15,813) to develop model, and the Cerner Health Facts database in the United States (n = 142,650) was used to externally validate. The developed model, time-aware Phased-Decay long short-term memory (LSTM), was used to predict timing of surgical intervention and critical clinical features in necrotizing pancreatitis based on laboratory tests, compared to other machine learning models. Area under the ROC curve (AUC) of RNN-based models was more than 0.70. The AUC of time-aware Phased-Decay LSTM (0.75) with more explanatory was similar to that (0.76) of Phased-Decay LSTM to predict surgical timing. The developed model visualized the specific surgical process and laboratory indicators changes for the patients with AP from the onset to discharge. Different clinical features had different contribution modes with time to the specific surgical event in AP. Heart function contributed the most to the prediction of surgical intervention at the onset and in the first week. Our developed model could monitor the specific surgical process from the onset of AP to discharge and extract the contribution modes of clinical features with time in necrotizing pancreatitis.