Post-traumatic growth, moral sensitivity and service behaviour among healthcare workers in the post-pandemic era of COVID-19 in mainland China: a cross-sectional study.

Objectives: To investigate how post-traumatic growth (PTG) and moral sensitivity influence service behaviour among healthcare workers (HCWs) in mainland China post-COVID-19, with a focus on the mediating role of moral sensitivity.

Design: Cross- sectional survey design.

Setting: This study was conducted in 27 provinces across mainland China, from 16 March to 2 April 2023.

Enhancing colorectal cancer screening in high-risk population through fecal immunochemical test surveillance: Results from a surveillance program.

Background: Current guidelines recommend colonoscopy-based surveillance to decrease the risk of colorectal cancer (CRC) among these participants with above-average risk. The fecal immunochemical test (FIT) holds promise as a viable alternative surveillance tool, but the existing evidence regarding the use of settings remains limited. Therefore, our aim is to evaluate the CRC incidence rates in individuals with above-average CRC risk and the relationship between FIT surveillance and CRC incidence.

Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.

Background: There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could help tailor treatments to each patient's needs.

Objective: We sought to discern the individual suitability of radiotherapy (RT) or chemoradiotherapy (CRT) in LGG patients using ML models.

Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning.

Background: The role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level.

Objective: The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required.

Individualized survival prediction and surgery recommendation for patients with glioblastoma.

Background: There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients.

Aim: This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection.

Methods: We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations.

Optimizing adjuvant treatment options for patients with glioblastoma.

Background: This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning.

Background: There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important.

Purpose: This study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients.

Methods: We introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB).

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer.

Purpose: The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL).

Methods: Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations.

Fecal Immunochemical Testing and the Risk of Advanced Colorectal Neoplasia: A Difference-In-Difference Analysis.

Purpose: To evaluate the effectiveness of fecal immunochemical testing (FIT) in colorectal cancer screening.

Methods: We conducted a prospective cohort study among 5,598 participants age 40-74 years between 2012 and 2020 in Tianjin, China. Inverse probability weighting was adopted to adjust for potential imbalanced factors between groups.

Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.

Background: Due to the heterogeneity of low-grade gliomas (LGGs), the lack of randomized control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is currently controversial.

Aim: To determine the best choice between subtotal resection (STR) and gross-total resection (GTR) for individual patients and to identify features that are potentially relevant to treatment heterogeneity.

Methods: Patients were enrolled from the SEER database.

Development, Reliability, and Structural Validity of the Scale for Knowledge, Attitude, and Practice in Ethics Implementation Among AI Researchers: Cross-Sectional Study.

Background: Medical artificial intelligence (AI) has significantly contributed to decision support for disease screening, diagnosis, and management. With the growing number of medical AI developments and applications, incorporating ethics is considered essential to avoiding harm and ensuring broad benefits in the lifecycle of medical AI. One of the premises for effectively implementing ethics in Medical AI research necessitates researchers' comprehensive knowledge, enthusiastic attitude, and practical experience.

A network-based study reveals multimorbidity patterns in people with type 2 diabetes.

Patients with type 2 diabetes mellitus (T2DM) are at a heightened risk of living with multiple comorbidities. However, the comprehension of the multimorbidity characteristics of T2DM is still scarce. This study aims to illuminate T2DM's prevalent comorbidities and their interrelationships using network analysis.

Insulin use and gout risk among patients with type 2 diabetes mellitus: a real-world cohort study in Shanghai, China.

Objectives: The effect of insulin use on gout risk remains unknown. This study aimed to investigate the association between insulin use and gout risk among patients with type 2 diabetes mellitus (T2DM).

Methods: Based on the Shanghai Link Healthcare Database, patients with newly diagnosed T2DM, with or without insulin exposure, were identified from January 1, 2014 to December 31, 2020, and followed until December 31, 2021.

Association between use of antihypertensive drugs and the risk of cancer: a population-based cohort study in Shanghai.

Background: Previously studies shown a potential risk of antihypertensive medicines in relation to cancer susceptibility, which creating significant debate in the scientific community and public concern. We sought to investigate the relationship between antihypertensive medicines and cancer risk, by drug type and class.

Methods: We conducted a population-based cohort study and enrolled patients diagnosed with hypertension from community healthcare centers in Changning District, Shanghai, China.

Integrated gene profiling of fine-needle aspiration sample improves lymph node metastasis risk stratification for thyroid cancer.

Background: Lymph node metastasis risk stratification is crucial for the surgical decision-making of thyroid cancer. This study investigated whether the integrated gene profiling (combining expression, SNV, fusion) of Fine-Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer.

Methods: In this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included.

[Hospitalization costs of pediatric community-acquired pneumonia in Shanghai].

To investigate the epidemiology and hospitalization costs of pediatric community-acquired pneumonia (CAP) in Shanghai. A retrospective case summary was conducted on 63 614 hospitalized children with CAP in 59 public hospitals in Shanghai from January 2018 to December 2020. These children's medical records, including their basic information, diagnosis, procedures, and costs, were extracted.

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China.

Background: All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap.

Methods: Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021.

D-Limonene inhibits the occurrence and progression of LUAD through suppressing lipid droplet accumulation induced by PM exposure in vivo and in vitro.

Background: PM exposure is associated with lung adenocarcinoma (LUAD), but the mechanism is unclear. The lack of understanding impedes our effort on prevention. This study examined a possible mechanism of lung cancer caused by PM exposure, and aimed to find a potential intervention for people living in PM polluted regions.

Attitudes and perspectives of 534 Chinese pediatricians toward internet hospitals.

Background: Internet hospitals introduced in China have effectively reduced service time and space, promoted high-quality pediatric medical resources to grassroots areas, solved the contradiction between supply and demand of pediatric medical resources, and met patients' increasing multi-level and diversified medical service needs. However, pediatricians' attitudes toward and satisfaction with the use of internet hospitals remain unknown.

Objective: This study aimed to investigate pediatricians' knowledge of, use of, and satisfaction with internet hospitals in order to identify major issues in internet hospital development, and to understand pediatricians' attitudes and opinions on the construction, development, and use of internet hospitals.

A Novel Signature of Lipid Metabolism-Related Gene Predicts Prognosis and Response to Immunotherapy in Lung Adenocarcinoma.

Lipid metabolism disorder, a new hallmark of cancer initiation, has been involved in lung adenocarcinoma (LUAD). However, few biomarkers about lipid metabolism-related genes (LMRGs) have been developed for prognosis prediction and clinical treatment of LUAD patients. In this study, we constructed and validated an effective prognostic prediction model for LUAD patients depending on LMRGs.

Insulin therapy and biliary tract cancer: insights from real-world data.

Objective: The association between insulin therapy and the risk of biliary tract cancer (BTC) is uncertain. We aimed to assess this risk in type 2 diabetic patients.

Methods: Using electronic medical data from the Shanghai Hospital Link database, 202,557 patients with type 2 diabetes (164,997 insulin never-users and 37,560 insulin ever-users) were identified in this study between January 1, 2013, and December 31, 2016, with follow-up until December 31, 2019.

Development and evaluation of cancer differentiation analysis technology: a novel biophysics-based cancer screening method.

Background: Routine health checkup is an essential strategy for monitoring population health and maintaining healthy workforces. However, there was a lack of cancer screening tests among routine health checkups due to high costs and unreliable methods.

Methods: We conducted a two-stage study to evaluate the value of a blood test, Cancer Differentiation Analysis (CDA), which is developed to differentiate the blood samples of healthy individuals from those of cancer patients through measuring and analyzing multiple biophysical properties.