The role of EMG1 in lung adenocarcinoma progression: Implications for prognosis and immune cell infiltration.

Background And Aims: Lung adenocarcinoma (LUAD) is the most common and aggressive cancer with a high incidence. N1-specific pseudouridine methyltransferase (EMG1), a highly conserved nucleolus protein, plays an important role in the biological development of ribosomes. However, the role of EMG1 in the progression of LUAD is still unclear.

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.

It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies.

Analysis of mA-related lncRNAs for prognostic and immunotherapeutic response in hepatocellular carcinoma.

RNA methylation modifications are important post-translational modifications that are regulated in an epigenetic manner. Recently, N-methyladenosine (mA) RNA modifications have emerged as potential epigenetic markers in tumor biology. Gene expression and clinicopathological data of LIHC were obtained from the cancer genome atlas (TCGA) database.

Systematic analysis of the role of LDHs subtype in pan-cancer demonstrates the importance of LDHD in the prognosis of hepatocellular carcinoma patients.

Background: Lactate dehydrogenase (LDHs) is an enzyme involved in anaerobic glycolysis, including LDHA, LDHB, LDHC and LDHD. Given the regulatory role in the biological progression of certain tumors, we analyzed the role of LDHs in pan-cancers.

Methods: Cox regression, Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves, and correlation of clinical indicators in tumor patients were used to assess the prognostic significance of LDHs in pan-cancer.

Bioinformatics analysis identifies coagulation factor II receptor as a potential biomarker in stomach adenocarcinoma.

Coagulation factor 2 thrombin receptor (F2R), a member of the G protein-coupled receptor family, plays an important role in regulating blood clotting through protein hydrolytic cleavage mediated receptor activation. However, the underlying biological mechanisms by which F2R affects the development of gastric adenocarcinoma are not fully understood. This study aimed to systematically analyze the role of F2R in gastric adenocarcinoma.

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis.

mA-Mediated Upregulation of lncRNA CHASERR Promotes the Progression of Glioma by Modulating the miR-6893-3p/TRIM14 Axis.

Long noncoding RNAs (lncRNAs) play crucial roles in tumor progression and are dysregulated in glioma. However, the functional roles of lncRNAs in glioma remain largely unknown. In this study, we utilized the TCGA (the Cancer Genome Atlas database) and GEPIA2 (Gene Expression Profiling Interactive Analysis 2) databases and observed the overexpression of lncRNA CHASERR in glioma tissues.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning.

Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH.

Materials And Methods: We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People's Hospital from the renal disease treatment information system.

Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.

Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020.

Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning.

Background: Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms.

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes.

Glycosylated hemoglobin (HbA1c) is recommended for diagnosing and monitoring type 2 diabetes. However, the monitoring frequency in real-world applications has not yet reached the recommended frequency in the guidelines. Developing machine learning models to screen patients with poor glycemic control in patients with T2D could optimize management and decrease medical service costs.

CLEC5A regulates the proliferation and migration of colon cancer via the AKT/mTOR signaling pathway.

Background: The purpose of this study is to understand the CLEC5A mechanism in colon cancer's proliferation and migration.

Methods: The CLEC5A expression levels in colon cancer tissues were analyzed using bioinformatics method based on Oncomine and The Cancer Genome Atlas (TCGA) databases, which were further tested by immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR). The CLEC5A expression levels in 4 types of colon cancer cell lines (HCT116, SW620, HT29, and SW480) were also examined by qRT-PCR.

mA-mediated upregulation of LINC01003 regulates cell migration by targeting the CAV1/FAK signaling pathway in glioma.

Background: Long non-coding RNAs (lncRNAs) play important roles in the progression of glioma. Here, we examined the potential functions of a lncRNA, LINC01003, in glioma and characterized the underlying molecular mechanisms.

Methods: The GEIPA2 and Chinese Glioma Genome Atlas (CCGA) databases were employed to analyze gene expression and the overall survival curve in patients with glioma.

Development and assessment of novel machine learning models to predict the probability of postoperative nausea and vomiting for patient-controlled analgesia.

Postoperative nausea and vomiting (PONV) can lead to various postoperative complications. The risk assessment model of PONV is helpful in guiding treatment and reducing the incidence of PONV, whereas the published models of PONV do not have a high accuracy rate. This study aimed to collect data from patients in Sichuan Provincial People's Hospital to develop models for predicting PONV based on machine learning algorithms, and to evaluate the predictive performance of the models using the area under the receiver characteristic curve (AUC), accuracy, precision, recall rate, F1 value and area under the precision-recall curve (AUPRC).

Develop an ADR prediction system of Chinese herbal injections containing Panax notoginseng saponin: a nested case-control study using machine learning.

Objective: This study aimed to develop an adverse drug reactions (ADR) antecedent prediction system using machine learning algorithms to provide the reference for security usage of Chinese herbal injections containing Panax notoginseng saponin in clinical practice.

Design: A nested case-control study.

Setting: National Center for ADR Monitoring and the Electronic Medical Record (EMR) system.

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.

Background: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods.

Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics.

Background: Medication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management.

Methods: This cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey.

A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs.

Lung adenocarcinoma (LUAD) is a malignant disease with an extremely poor prognosis, and there is currently a lack of clinical methods for early diagnosis and precise treatment and management. With the deepening of tumor research, more and more attention has been paid to the role of immune checkpoints (ICP) and long non-coding RNAs (lncRNAs) regulation in tumor development. Therefore, this study downloaded LUAD patient data from the TCGA database, and finally screened 14 key ICP-related lncRNAs based on ICP-related genes using univariate/multivariate COX regression analysis and LASSO regression analysis to construct a risk prediction model and corresponding nomogram.

A machine learning-based risk warning platform for potentially inappropriate prescriptions for elderly patients with cardiovascular disease.

Potentially inappropriate prescribing (PIP), including potentially inappropriate medications (PIMs) and potential prescribing omissions (PPOs), is a major risk factor for adverse drug reactions (ADRs). Establishing a risk warning model for PIP to screen high-risk patients and implementing targeted interventions would significantly reduce the occurrence of PIP and adverse drug events. Elderly patients with cardiovascular disease hospitalized at the Sichuan Provincial People's Hospital were included in the study.

Activation of LXRs Reduces Oxysterol Lipotoxicity in RPE Cells by Promoting Mitochondrial Function.

Effective treatments for age-related macular degeneration (AMD), the most prevalent neurodegenerative form of blindness in older adults, are lacking. Genome-wide association studies have identified lipid metabolism and inflammation as AMD-associated pathogenic changes. Liver X receptors (LXRs) play a critical role in intracellular homeostases, such as lipid metabolism, glucose homeostasis, inflammation, and mitochondrial function.

YTHDC1-mediated VPS25 regulates cell cycle by targeting JAK-STAT signaling in human glioma cells.

Background: Glioma is a common type of malignant brain tumor with a high mortality and relapse rate. The endosomal sorting complex required for transport (ESCRT) has been reported to be involved in tumorigenesis. However, the molecular mechanisms have not been clarified.

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes.

The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People's Hospital from January 2010 to December 2015 were collected.

RGD-modified multifunctional nanoparticles encapsulating salvianolic acid A for targeted treatment of choroidal neovascularization.

Background: The development of alternative anti-angiogenesis therapy for choroidal neovascularization (CNV) remains a great challenge. Nanoparticle systems have emerged as a new form of drug delivery in ocular diseases. Here, we report the construction and characterization of arginine-glycine-aspartic acid (RGD)-conjugated polyethyleneimine (PEI) as a vehicle to load antioxidant salvianolic acid A (SAA) for targeted anti-angiogenesis therapy of CNV.

A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study.

Objective: The prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat.

Methods: We identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts.