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.

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.

The exploration and pilot studies of applying blockchain to drug supply chain show great potential in promoting information sharing, collaboration competence among the actors, regulatory efficiency, and etc. In the future, with the help of blockchain, the optimization of the entire supply chain for orphan drugs is expected to be realized. However, there is no such exploration in China at present. This paper systematically sorts out the whole process of supply chain for orphan drugs and the existing problems of the chain. The article concludes that at present, blockchain is mainly used in the " circulation" and " use" of the drug supply chain. It helps to improve the traceability of drugs, to cope with the problem of counterfeit drugs, to enable actors of the drug supply chain to form a collaborative network in optimizing resource allocation, and to improve the operation and supervision efficiency of the supply chain. In the future, the application faces challenges such as high costs in system conversion, lack of personnel awareness, and incomplete supporting systems. Based on the three dimensions of technology, practice, and research, this paper also looks into the future and suggests for the future use of blockchain in the supply chain of orphan drugs by constructing a practice model, the so called DI-GIVE (Digital, Intelligence, Government′s supervision, Innovation, Views of variety, Evaluation-based) hoping to innovate the supply chain of orphan drugs and to ensure the drug use for the patients with rare diseases in China.

To summarize the current status in the application of digital and intelligent technologies in the field of pharmacovigilance and to provide reference to the selection and development of methods for pharmacovigilance of rare diseases.

Our study aims at systematically summarizing and evaluating the applications of digital intelligence technologies in the field of rare disease medical care insurance now and in the future and at constructing a conceptual framework for the digital powered mechanism for the medical care insurance for rare diseases. By using Chinese keywords of " rare disease" " medical insurance"" artificial intelligence"" prediction model"" machine learning"" big data"" algorithm" and their English equivalents, we searched the databases of PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, collected relevant literature, and decided the criteria of inclusion and exclusion. The finding of our study shows that medical care insurance mechanism of rare disease in China faces significant challenges in drug accessbility and the funding sustainability. Meanwhile, our study shows that the digital intelligence technologies have broad potential in applications-in financing, accessbility, payment, and supervision. Specifically, dynamic simulation models and big data analysis can make precise prediction of the demand for funding of medical care insurance. The machine learning algorithms improve the dynamic evaluation of drug safety and cost-effectiveness. The personalized payment models enhance the efficiency in identifying the cohort with high expenditure so as to alleviate fund expenditure pressures. The intelligent monitoring technologies can accurately detect the abnormal behaviors in funds of medical care insurance. These technologies provide systematic and scientific solutions for improving the medical care mechanism for rare diseases. Even though further investigation is needed, the digital intelligence technologies have shown remarkable potential in enhancing the flexibility, efficiency, and sustainability of the medical care insurance system and a promising future in meeting the needs of patients with rare diseases.

In recent years, the rapid development of digital intelligence has provided a new path for rare disease data sharing and injected new power into the progress of research of rare diseases. This research is aimed at summarizing and consolidating relevant literatures on data sharing driven by digital intelligence (DI) in China and abroad, and constructing a local theoretical framework of DI-driven data sharing for rare diseases based on the status of rare diseases in China. Searching PubMed, EMbase, Cochrane, CNKI, Wanfang, and VIP database, we obtain a total of 214 representative literatures. Through literature review, we find that DI technologies have played important roles in different aspects of rare disease data sharing. China, the United States, and Europe have formed their own DI-driven data sharing systems for rare disease. From the theory of " Information Commons", we analyze the gap between China′s current situation and the goal of a " Rare Disease Data Commons". Based on the analysis, we put forward the idea of framework of " DI-STARS". China should develop the Data Sharing system making DI as the core of the system. Meanwhile, China should strengthen the data standardization system, create an innovation-encouraging environment, and build a bridge between different platforms. Using the DI-STARS theory, China will be able to build the " Rare Disease Data Commons" so that the diagnosis and treatment of rare diseases will be enhanced in China to meet the patients′ needs.

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 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:To understand the characteristics of faculty in high-level public health schools in China, and analyze the differences in age, area and school level.Methods:Based on the internet information, the faculty information of 18 high-level public health schools was collected for a descriptive analysis on faculty characteristics.Results:There were 1 642 faculty members in the schools of public health in China, in whom 51.8% were women, 92.8% had doctorate, 32.4% had postdoctoral experience and 56.8% were former students staying to teach. The average age of the faculty members was (45.6±9.8) years. Meanwhile the top three study subjects were epidemiology and health statistics (31.0%), occupational health and environmental sanitation (16.5%), and health toxicology (16.3%). In the faculty members aged >40 years, 90.2% had doctorate, 62.6% were former students staying to teach, and 24.7% had no educational background of public health. The proportions of faculty members aged ≤40 years in the three groups mentioned above were 98.2%, 45.8% and 39.1% respectively. In terms of study subject, big data study were mainly conducted in the schools with top subject ranking and the schools in developed areas.Conclusions:The public health faculty was characterized by cross education background and high capability. The study subjects and sub-disciplines varied with schools and areas.

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic pulmonary infection that commonly occurs in immunocompromised children. We report a case of infantile leukemia complicated by PJP and review the relevant literature. A summary and analysis of 10 infantile leukemia patients with PJP infection (9 cases reported in the literature and 1 case from our center) showed that PJP mostly occurred in the early stages of chemotherapy (80%, 8/10). The main clinical manifestations were dyspnea (100%, 10/10) and hypoxemia (50%, 5/10), while pulmonary imaging findings lacked specificity. In most cases (50%, 5/10), diagnosis was established by identifying pathogens in bronchoalveolar lavage fluid under microscopy. In our case, diagnosis was confirmed using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid. Treatment with intravenous sulfamethoxazole complex was administered in 8 patients, all of whom eventually recovered. PJP may occur in the early stages of chemotherapy for infantile leukemia, thus early prevention is necessary. tNGS facilitates early diagnosis of PJP, and sulfamethoxazole complex remains an effective therapeutic option.
Humans ; Infant ; Bronchoalveolar Lavage Fluid/microbiology* ; Immunocompromised Host ; Leukemia/complications* ; Pneumocystis carinii/isolation & purification* ; Pneumonia, Pneumocystis/diagnosis* ; Trimethoprim, Sulfamethoxazole Drug Combination/therapeutic use*