Disease computable phenotype is a data model designed to identify specific clinical conditions or characteristics, which automatically extracts information from clinical databases such as electronic health records through algorithms. Phenotypic data for rare diseases often reside in unstructured text. Due to the scarcity of rare disease cases, atypical symptoms, and insufficient physician experience, misdiagnosis and underdiagnosis rates remain high. In this context, the application of computable phenotype technology holds promise for improving the accuracy and efficiency of rare disease diagnosis. This article reviews the current research status, challenges, and opportunities of computable phenotype technology in biomedicine, particularly in the field of rare diseases, and proposes a development and validation framework for rare disease computable phenotypes, aiming to provide research and development insights for computable phenotypes to empower the diagnosis and treatment of rare diseases.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.

To summarize the applications of data-intelligence technology in diagnosing lysosomal storage disease(LSD), analyze their opportunities and challenges in clinical practice as well as their development trends, and provide insights and recommendations for advancing digitally driven auxiliary diagnostic technologies.

Objective:To analyze the incidence and risk factors of ocular surface disease among power grid construction workers in plateau.Methods:A total of 11 132 construction personnel from the Ngari prefecture-central Tibet power grid interconnection project were included from 2019 to 2020.Baseline characteristics including age, gender, body mass index, developmental and nutritional status, relevant clinical indicators, etc.and follow-up data regarding incidence of ocular surface diseases were obtained from the medical records of Ali interconnection project staff medical station.The altitude of workplace and residence of the study population were obtained from the website (https: //zh-cn.topographic-map.com/legal/).The mean age of the subjects was (36.17±10.48) years, of which 95.33%(10, 612 subjects) were male.The median follow-up time was 1.53 years.The altitude of the residence and workplace were (1 954.77±940.64) and (4 535.09±232.71) meters, respectively.The incidence of ocular surface diseases in groups with different characteristics was calculated.Differential variables for the incidence of ocular surface diseases were screened by univariate Cox proportional hazards regression model.Influencing factors of ocular surface diseases multivariate were explored by Cox proportional hazards model.This study was approved by the Ethics Committee of Peking University Health Science Center (No.IRB00001052-21066).Results:During the follow-up period, the incidence of ocular surface disease was 9.27% (1 032 cases), and the incidence of conjunctivitis and keratitis was 6.58% (733 cases) and 1.80% (200 cases), respectively.Multivariate Cox proportional hazards regression analysis showed that for every 1 000 meters increase in altitude of residence, the risk of ocular surface disease decreased by 15% ( HR[95% CI]: 0.85[0.80~0.91], P<0.001).For every 100 meters increase in altitude of workplace, the risk of ocular surface disease increased by 5% ( HR[95% CI]: 1.04[1.01~1.07], P=0.006).Decreased blood oxygen saturation ( HR[95% CI]: 1.09[1.02~1.16], P=0.007), hearing pulmonary dry rales (hazard ratio ( HR)[95% CI]: 1.53[1.12~2.09], P=0.007) and heart murmurs ( HR[95% CI]: 4.44[1.43~13.83], P=0.010) were associated with ocular surface disease. Conclusions:The incidence of ocular surface disease in personnel engaged in electric grid construction at high altitudes should not be ignored.High working altitude, low residence altitude, pulmonary dry rales, heart murmurs and low blood oxygen saturation are factors associated with the incidence of ocular surface disease.

Objective The aim is to understand the common scenarios of non-medical use of prescription opioids(NMUPO)and analyze the potential facilitating and hindering factors in the regulatory process of NMUPO from the perspective of healthcare professionals.Methods Healthcare professionals in local hospitals were surveyed through a two-stage purposive sampling from June to August 2022 in Ningbo,China.The survey was conducted using a semi-structured questionnaire on topics,and thematic analysis were used to identify and summarise key themes and patterns.Results A total of 75 participants were included,the average age was(43.9±7.2)years,and 54(72.0%)were male.The most common NMUPO scenarios involved middle-aged males pretending acute severe pain to obtain injectable opioids.The facilitating and hindering factors related to the regulation of NMUPO can be categorized into three types:institutional governance,technical support,and individual behaviors.At the institutional level,facilitating factors included strict national prescribing policies and local"narcotic drug card"systems,while barriers comprised incomplete lists of controlled substances.At the technological support level,facilitating factors included the establishment of regional health information platforms,while barriers included the lack of standardized prescription guidelines and diagnostic decision-support tools.At the individual level,facilitating factors included the public's cautious attitude toward drug misuse,while barriers included strained doctor-patient relationships.Conclusion China still faces significant challenges in addressing NMUPO and urgently needs to improve the existing regulatory system.It is recommended that reforms be carried out in areas such as pharmaceutical control mechanisms,drug treatment and rehabilitation services,preventive health education activities,and the optimized use of health information systems.

Objective:In critical care medicine, extubation is a pivotal step in the management of mechanically ventilated patients. Accurately determining the optimal timing for extubation is essential for minimizing complications and improving patient survival rates. However, reliable indicators to predict clinical outcomes following extubation remain scarce. This study aims to identify a novel and robust predictor of extubation success in critically ill patients, thereby providing clinicians with more precise decision-making support.Methods:This retrospective study analyzed data from adult patients who underwent mechanical ventilation and were evaluated for extubation across six intensive care units (ICUs) at Xiangya Third Hospital of Central South University between January 2019 and December 2021. Patients with a history of difficult airway, upper airway obstruction, or neuromuscular disorders affecting respiratory function were excluded. The primary outcome was the reintubation rate within 24 hours post-extubation. Categorical variables were analyzed using the chi-square test or Fisher’s exact test, while between-group differences were assessed with the Mann-Whitney U test. Significant predictors identified in univariate analysis were further evaluated via multivariate logistic regression. The diagnostic accuracy of the maximum inspiratory pressure/body mass index (MIP/BMI) ratio was determined using receiver operating characteristic (ROC) curve analysis, with the Youden index employed to establish the optimal cutoff value. Kaplan-Meier analysis and log-rank tests were used to compare extubation success rates between groups. Statistical analyses were performed using SPSS V28.0 and Stata v.16.0. Results:Diabetes comorbidity ( OR: 8.181, 95% CI: 1.659–40.338) and MIP/BMI ( OR: 0.140, 95% CI: 0.042–0.469) were identified as independent predictors of reintubation. The area under the ROC curve (AUROC) for MIP/BMI was 0.753, demonstrating good predictive accuracy. The optimal cutoff value for MIP/BMI was 1.26 cmH 2O/(kg·m 2), with a sensitivity of 55.3% and specificity of 92.3%. Kaplan-Meier analysis revealed a significantly higher reintubation rate in the low MIP/BMI group compared to the high MIP/BMI group ( P = 0.009), further validating its predictive utility. Conclusions:This study establishes MIP/BMI as a novel and clinically valuable predictor of extubation outcomes in critically ill patients. A cutoff value of 1.26 cmH 2O/(kg·m 2) was found to best predict successful extubation.

Objective:To describe the distribution characteristics of alcohol consumption in adult twins in the Chinese National Twin Registry (CNTR), and further explore the influence of genetic factors on alcohol consumption in adult twins.Methods:The subjects of the study were twins registered by CNTR in 11 project areas across China from 2010 to 2018. A total of 56 966 twins (28 483 pairs) aged 18 years and above who answered questions about drinking behavior were included, and the random effect model was used to describe the population and regional distribution characteristics of alcohol consumption. Intra-pair analysis was performed to calculate the concordance rate and heritability of their alcohol consumption.Results:The age of all subjects was (36.6±12.0) years, and current drinkers accounted for 16.6% (9 461/56 966) of all subjects. In men, those aged 50-59 years, those in northern China, those living in rural area, those with low education level and those with high BMI, the proportions of current drinkers were higher. After excluding 468 pairs of twins who had stopped alcohol use and 21 764 pairs of twins who had no drink or had small amount drink, an intra-pair analysis was conducted in 4 929 pairs of same-sex twins, and found that the concordance rate of alcohol consumption was 64.0% (2 059/3 215) in monozygotic twins, and 52.6% (902/1 714) in dizygotic twins, the difference was significant ( P<0.001), and the heritability of alcohol consumption was 24.1% (95% CI: 18.9%- 29.3%). The further stratified analysis found that in southern men, the heritability was highest in those aged 40-49 years (36.1%, 95% CI: 21.6%-50.7%), while in northern men, the heritability was highest in those aged 50-59 years (34.2%, 95% CI: 18.1%-50.3%). Conclusions:In adult twins in China, there were population and regional differences in the distribution of alcohol consumption behavior, and alcohol consumption was influenced by genetic factors, and gender, age and region had potential modifying effects.

Tumor-associated fibroblasts (CAFs) play a crucial role in promoting the invasion, metastasis, angiogenesis, immune suppression, and drug resistance of pancreatic cancer. The diverse origins, and phenotypic and functional heterogeneity of CAFs poses a significant challenge for targeted anti-tumor therapies against CAFs. However, investigating the interactions between CAFs and pancreatic cancer cells can provide insights for innovative CAFs-targeted therapies for pancreatic cancer. This article reviews the current domestic and international researchs, focusing on the heterogeneity of CAFs and their mechanisms in the progression of pancreatic cancer, with the aim of providing a theoretical basis and research direction for the clinical diagnosis and treatment of pancreatic cancer.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Rare events are widely prevalent in various disciplines,including rare adverse reactions to vaccines and drugs,clinical rare diseases,and low-probability clinical outcomes.The reason for research interest on such events is that their occurrence often brings incalculable and serious consequences.In the context of big data,numerous methods have emerged for rare event data analysis,including sampling based,category weighting,ensemble learning,and deep learning.This article systematically summarizes the research progress of current rare event data analysis methods,and introduces their basic principles and applicable scenarios.By analyzing the advantages and disadvantages of existing methods,the challenges of rare event research are sorted out and summarized,and potential research directions in related fields are explored to provide references for researchers.