Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis. This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree. Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor. Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis. 

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants

Abstract The advent of radiomics has revolutionized medical image analysis, affording the extraction of high dimensional quantitative data for the detailed examination of normal and abnormal tissues. Artificial intelligence (AI) can be used for the enhancement of a series of steps in the radiomics pipeline, from image acquisition and preprocessing, to segmentation, feature extraction, feature selection and model development. The aim of this review is to present the most used AI methods for radiomics analysis, explaining the advantages and limitations of the methods. Some of the most prominent AI architectures mentioned in this review include Boruta, random forests, gradient boosting, generative adversarial networks, convolutional neural networks, and transformers. Employing these models in the process of radiomics analysis can significantly enhance the quality and effectiveness of the analysis, while addressing several limitations that can reduce the quality of predictions. Addressing these limitations can enable high quality clinical decisions and wider clinical adoption. Importantly, this review will aim to highlight how AI can assist radiomics in overcoming major bottlenecks in clinical implementation, ultimately improving the translation potential of the method. 

AI-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence

An efficient and intelligent forewarning model for financial risk is essential to assist company managers, investors, and market regulators in risk management. This research aims to establish an accurate and understandable financial distress forewarning model based on advanced ensemble learning techniques, including Bootstrap Aggregation (Bagging) and Light Gradient Boosting Machine (LightGBM). Meanwhile, the Borderline-Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is employed to address the imbalance problem between financial distress and non-distressed samples. The Multi-Objective Particle Swarm Optimization (MOPSO) is applied to perform multi-objective optimization of the hyperparameters of the proposed model. In addition, the SHapley Additive exPlanations (SHAP) is adopted to conduct an interpretability analysis of the forewarning results. The Chinese listed companies from 2006 to 2022 are considered as the research samples, and the financial ratios of the sample companies in the first, second, and third years before the financial distress is utilized as forewarning features. The experimental findings reveal that the proposed model has the best forewarning performance by using the features from first year before the financial distress. The model proposed in this study also outperforms other comparative models in terms of Accuracy (88.45%), Negative Predictive Value (74.44%), Recall (91.10%), and Specificity (80.26%). Finally, this research has discovered that the Return on Total Assets is the most influential indicator in forewarning financial distress. Therefore, the proposed model can provide listed company managers, investors, and market regulators with an intelligent and effective tool in decision-making for financial distress forewarning. 

Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies

Background Acute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for these transitions are not fully understood. Interpretable machine learning (ML) models offer insights into how clinical features influence long-term renal function outcomes after nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes. Objective This study aimed to (1) evaluate postnephrectomy rates of AKI, AKD, and CKD, analyzing long-term renal outcomes along different trajectories; (2) interpret AKD and CKD models using Shapley Additive Explanations values and Local Interpretable Model-Agnostic Explanations algorithm; and (3) develop a web-based tool for estimating AKD or CKD risk after nephrectomy. Methods We conducted a retrospective cohort study involving patients who underwent nephrectomy between July 2012 and June 2019. Patient data were randomly split into training, validation, and test sets, maintaining a ratio of 76.5:8.5:15. Eight ML algorithms were used to construct predictive models for postoperative AKD and CKD. The performance of the best-performing models was assessed using various metrics. We used various Shapley Additive Explanations plots and Local Interpretable Model-Agnostic Explanations bar plots to interpret the model and generated directed acyclic graphs to explore the potential causal relationships between features. Additionally, we developed a web-based prediction tool using the top 10 features for AKD prediction and the top 5 features for CKD prediction. Results The study cohort comprised 1559 patients. Incidence rates for AKI, AKD, and CKD were 21.7% (n=330), 15.3% (n=238), and 10.6% (n=165), respectively. Among the evaluated ML models, the Light Gradient-Boosting Machine (LightGBM) model demonstrated superior performance, with an area under the receiver operating characteristic curve of 0.97 for AKD prediction and 0.96 for CKD prediction. Performance metrics and plots highlighted the model’s competence in discrimination, calibration, and clinical applicability. Operative duration, hemoglobin, blood loss, urine protein, and hematocrit were identified as the top 5 features associated with predicted AKD. Baseline estimated glomerular filtration rate, pathology, trajectories of renal function, age, and total bilirubin were the top 5 features associated with predicted CKD. Additionally, we developed a web application using the LightGBM model to estimate AKD and CKD risks. Conclusions An interpretable ML model effectively elucidated its decision-making process in identifying patients at risk of AKD and CKD following nephrectomy by enumerating critical features. The web-based calculator, found on the LightGBM model, can assist in formulating more personalized and evidence-based clinical strategies. 

Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study

To investigate the physicochemical properties, osteogenic properties, and osteogenic ability in rabbit model of femoral condylar defect of acellular dermal matrix (ADM)/dicalcium phosphate (DCP) composite scaffold. 

[Preparation and 

Development and validation of LightGBM algorithm for optimizing of Helicobacter pylori antibody during the minimum living guarantee crowd based gastric cancer screening program in Taizhou, China.

BACKGROUND
 Given the high incidence of esophageal cancer in China, an increasing number of patients there are undergoing endoscopic mucosal dissection (ESD). Although the 5-year survival rate after ESD can exceed 95%, esophageal stricture, the most common and serious postoperative complication, affects the long-term prognosis of patients and the quality of life. Autologous mucosal grafts have proven to be successful in preventing stricture after ESD for early esophageal cancer.
 AIM
 To examine the viability of acellular dermal matrix (ADM) as an alternative to autologous mucosa for the prevention of stricture after ESD.
 METHODS
 This is a prospective, single-center, controlled study. Consecutive patients who underwent ESD surgery and were willing to undergo autologous mucosal transplantation were recruited between January 1 and December 31, 2017. Consecutive patients who underwent ESD surgery and were willing to undergo ADM transplantation were recruited between January 1 to December 31, 2019. A final three-year follow-up of patients who received transplants was conducted.
 RESULTS
 Based on the current incidence of esophageal stricture, the sample size required for both the autologous mucosal graft group and the ADM group was calculated to be 160 cases. Due to various factors, a total of 20 patients with autologous mucosal grafts and 25 with ADM grafts were recruited. Based on the inclusion exclusion and withdrawal criteria, 9 patients ultimately received autologous mucosal grafts and completed the follow-up, while 11 patients received ADM grafts and completed the follow-up. Finally, there were 2 cases of stenosis in the autologous mucosal transplantation group with a stenosis rate of 22.22% and 2 cases of stenosis in the ADM transplantation group with a stenosis rate of 18.18%, with no significant difference noted between the groups (P = 0.94).
 CONCLUSION
 In this prospective, single-center, controlled trial, we compared the effectiveness of autologous mucosa transplantation and ADM for the prevention of esophageal stricture. Due to certain condition limitations, we were unable to recruit sufficient subjects meeting our target requirements. However, we implemented strict inclusion, exclusion, and withdrawal criteria and successfully completed three years of follow-up, resulting in valuable clinical insights. Based on our findings, we hypothesize that ADM may be similarly effective to autologous mucosal transplantation in the prevention of esophageal stricture, offering a comparable and alternative approach. This study provides a new therapeutic idea and direction for the prevention of esophageal stricture.

New hope for esophageal stricture prevention: A prospective single-center trial on acellular dermal matrix

Development and Validation of a Machine Learning Algorithm for the Detection of Helicobacter Pylori Antibody During the Minimum Living Guarantee Crowd Based Gastric Cancer Screening Program in Taizhou, China

Background: Primary biliary cholangitis (PBC) is a rare autoimmune liver disease with few effective treatments and a poor prognosis, and its incidence is on the rise. There is an urgent need for more targeted treatment strategies to accurately identify high-risk patients. The use of stochastic survival forest models in machine learning is an innovative approach to constructing a prognostic model for PBC that can improve the prognosis by identifying high-risk patients for targeted treatment. Method: Based on the inclusion and exclusion criteria, the clinical data and follow-up data of patients diagnosed with PBC-associated cirrhosis between January 2011 and December 2021 at Taizhou Hospital of Zhejiang Province were retrospectively collected and analyzed. Data analyses and random survival forest model construction were based on the R language. Result: Through a Cox univariate regression analysis of 90 included samples and 46 variables, 17 variables with p-values <0.1 were selected for initial model construction. The out-of-bag (OOB) performance error was 0.2094, and K-fold cross-validation yielded an internal validation C-index of 0.8182. Through model selection, cholinesterase, bile acid, the white blood cell count, total bilirubin, and albumin were chosen for the final predictive model, with a final OOB performance error of 0.2002 and C-index of 0.7805. Using the final model, patients were stratified into high-and low-risk groups, which showed significant differences with a P value <0.0001. The area under the curve was used to evaluate the predictive ability for patients in the first, third, and fifth years, with respective results of 0.9595, 0.8898, and 0.9088. Conclusion: The present study constructed a prognostic model for PBC-associated cirrhosis patients using a random survival forest model, which accurately stratified patients into low-and high-risk groups. Treatment strategies can thus be more targeted, leading to improved outcomes for high-risk patients.

Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiale Lin

Author Tools

Chat about Author

Papers

Development and validation of LightGBM algorithm for optimizing of Helicobacter pylori antibody during the minimum living guarantee crowd based gastric cancer screening program in Taizhou, China.

New hope for esophageal stricture prevention: A prospective single-center trial on acellular dermal matrix

Development and Validation of a Machine Learning Algorithm for the Detection of Helicobacter Pylori Antibody During the Minimum Living Guarantee Crowd Based Gastric Cancer Screening Program in Taizhou, China

Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model