For rare real-world data involving off-label drug use or comorbidity-associated polypharmacy,researchers have increasingly adopted target trial emulation to investigate drug repurposing for target indications.The success of such studies hinges on rigorous trial design and strict adherence to predefined protocols and standardized pipelines.Key elements in the trial design include the precise definition of inclusion and exclusion criteria,the selection of trial and control drugs and determination of treatment allocation time,the determination of appropriate efficacy endpoints for the target indication,the identification of causal estimands,and the development of robust strategies for confounding adjustment.The execution of the trial follows a structured process:screening eligible subjects,extracting relevant drug exposure data,constructing treatment and control groups,emulating the target trial,and ultimately generating hypotheses for drug repurposing through statistical inference.Propensity score methods,including stratification,matching and weighting techniques,are critical tools for addressing confounding bias and ensuring accurate estimation of causal effects.In recent years,creative progress has been made in target trial emulation,particularly in the calculation of propensity scores.Researchers have adopted advanced machine learning techniques,to enhance variable selection and have actively explored the use of innovative methods of digital intelligence technology like classification and regression trees,support vector machines,and deep learning for the application of propensity score calculation.Target trial emulation based on real-world data has achieved remarkable advancements in drug repurposing,demonstrating broad application prospects,particularly in cardiovascular diseases,metabolic disorders,Alzheimer's disease,and cancer.

In recent decades, the global community has encountered several significant viral outbreaks, including the Ebola epidemic in West Africa, the Zika virus epidemic in South America, and the recent worldwide COVID-19 pandemic. In these instances, the deployment of effective vaccines has been instrumental in protecting public health. Nevertheless, as new challenges emerge in the prevention and management of infectious diseases, the traditional model of global vaccine development confronts both unprecedented opportunities and challenges. These circumstances underscore the limitations inherent in conventional vaccine development, particularly the protracted timelines and substantial costs involved. This article examines innovative approaches in contemporary vaccine clinical trials, investigates randomization techniques specific to vaccine studies, and delineates essential statistical considerations pertinent to vaccine trial design. The objective is to provide scientific support for vaccine development and to foster ongoing innovation and optimization within the realm of vaccine research and development.

In recent decades, the global community has encountered several significant viral outbreaks, including the Ebola epidemic in West Africa, the Zika virus epidemic in South America, and the recent worldwide COVID-19 pandemic. In these instances, the deployment of effective vaccines has been instrumental in protecting public health. Nevertheless, as new challenges emerge in the prevention and management of infectious diseases, the traditional model of global vaccine development confronts both unprecedented opportunities and challenges. These circumstances underscore the limitations inherent in conventional vaccine development, particularly the protracted timelines and substantial costs involved. This article examines innovative approaches in contemporary vaccine clinical trials, investigates randomization techniques specific to vaccine studies, and delineates essential statistical considerations pertinent to vaccine trial design. The objective is to provide scientific support for vaccine development and to foster ongoing innovation and optimization within the realm of vaccine research and development.

For rare real-world data involving off-label drug use or comorbidity-associated polypharmacy,researchers have increasingly adopted target trial emulation to investigate drug repurposing for target indications.The success of such studies hinges on rigorous trial design and strict adherence to predefined protocols and standardized pipelines.Key elements in the trial design include the precise definition of inclusion and exclusion criteria,the selection of trial and control drugs and determination of treatment allocation time,the determination of appropriate efficacy endpoints for the target indication,the identification of causal estimands,and the development of robust strategies for confounding adjustment.The execution of the trial follows a structured process:screening eligible subjects,extracting relevant drug exposure data,constructing treatment and control groups,emulating the target trial,and ultimately generating hypotheses for drug repurposing through statistical inference.Propensity score methods,including stratification,matching and weighting techniques,are critical tools for addressing confounding bias and ensuring accurate estimation of causal effects.In recent years,creative progress has been made in target trial emulation,particularly in the calculation of propensity scores.Researchers have adopted advanced machine learning techniques,to enhance variable selection and have actively explored the use of innovative methods of digital intelligence technology like classification and regression trees,support vector machines,and deep learning for the application of propensity score calculation.Target trial emulation based on real-world data has achieved remarkable advancements in drug repurposing,demonstrating broad application prospects,particularly in cardiovascular diseases,metabolic disorders,Alzheimer's disease,and cancer.

Objective:To investigate the allocation of nursing human resources in burn centers in China.Methods:A cross-sectional survey was conducted. Using a self-designed questionnaire, a survey was carried out from January to March 2022 to investigate the January to December 2021 status of 39 burn centers in China that met the inclusion criteria based on six strategic regions and other regions, including the hospital grade and the region, the number of nurses and opening beds in the burn centers and burn intensive care units (BICUs), the age, working seniority in burn specialty, educational background, professional title, personnel employment, and turnover of nurses and training of newly recruited nurses in the burn centers.Results:This survey covered 30 provinces, municipalities, and autonomous regions in China (excluding Hong Kong Special Administrative Region, Macao Special Administrative Region, and Taiwan region of China). A total of 39 questionnaires were collected, all of which were valid. The 39 burn centers were located in 38 tertiary A hospitals and 1 tertiary B hospital, with 26 burn centers in strategic areas. The nurse/bed ratio of burn centers in the Greater Bay Area of Guangdong, Hong Kong, and Macao was the highest, while the nurse/bed ratio of burn centers in border ethnic minority area was the lowest. Except for the Chengdu-Chongqing Economic Circle, BICUs had been set up in burn centers in other regions. Among the 39 burn centers, the percentage of nurses aged 25 to 34 years was 51.21% (738/1 441), the percentage of nurses worked in burn specialty for less than 5 years was 31.16% (449/1 441), the percentage of nurses with bachelor's degree was 69.74% (1 005/1 441), and the percentage of nurses with nursing professional title was 44.14% (636/1 441), which were the highest. There were significant differences in the employment of nurses, the percentage of permanent nurses in burn centers in the collaborative development zone of Beijing-Tianjin-Hebei was 82.48% (113/137), while the percentage of permanent nurses in burn centers in important military strategic area was only 9.42% (34/361); the turnover rate of nurses was 9.03% (143/1 584), among which the turnover rate of nurses was 18.14% (80/441) in burn centers in important military strategic area. The training for newly recruited nurses in 39 burn centers was mainly based on the guidance of senior nurses and the pre-job education+specialist training.Conclusions:The burn nursing human resources in strategic areas in China are seriously insufficient and unevenly distributed, with unstable nurse team and lack of standardized specialist training. In particular, the nursing human resources in BICUs need to be equipped and supplemented urgently.

Artificial intelligence (AI) has developed rapidly in the twentieth century, and has substantialy changed the modern way of life.At the same time, AI has greatly contributed to the development of the pharmaceutical industry, playing a key role in precision medicine, intelligent diagnosis, computer-aided drug design, and clinical trial decision-making, and has also greatly developed itself through its integration with the pharmaceutical industry.This paper outlines the key issues in research, describes the key applications of AI in the health and pharmaceutical industries, and finally analyzes the opportunities and challenges of AI in the health pharmaceutical industry to provide reference for the development of AI in the health and pharmaceutical fields.

The intention to treat (ITT) principle is widely applied in the randomized controlled trials (RCTs), which is based on the intention to treat a subject (i.e. the planned treatment regimen) rather than the actual treatment given. With the development and progress of clinical trials, ITT principle needs to be reinterpreted. In the standard definition of ITT principle, the intentional grouping of subjects does not specifically refer to randomization, but rather to planned treatment regimen. In non-randomized clinical trials, that subjects agree to be included in a certain treatment regimen is also a type of intention. ITT principles can be applied to evaluate the effectiveness of treatment regimens under patient/doctor determined intentionality in single-arm clinical trials and non-randomized RWS (real world study, RWS). In the practical application, the grouping strategy based on ITT principle should be considered comprehensively according to the research purpose.

Objective To compare the perioperative outcomes and long-term therapeutic response of laparoscopic splenectomy versus open splenectomy for idiopathic thrombocytopenic purpura.Methods A retrospective analysis of 124 patients who under-went splenectomy(68 LS and 56 OS)for ITP between January 2011 and January 2015 was conducted.Results Patients undergoing LS were found to require a longer operative time(P <0.05 )but had reduced hospital stay,lower intra-operative blood loss(P <0.05),less postoperative pain,earlier drain removal,and decreased incidence of complications(P <0.05).Conversion to OS was re-quired in 4 patients for excessive loss of blood(5.8%).Deep venous thrombosis(DVT)was observed in 1 patients after OS.One pa-tient died from pneumonia after LS.Mean follow-up of (33±11.8)months was performed in LS group and of (32±12.9)months in OS group.50 patients(73.5%)in LS group and 43(76.7%)in OS group reached sustained complete haematological response(P >0.05).Kaplan-Meier analysis showed that there was no significant difference in the relapse-free survival rate between the groups (P =0.679).Conclusion Compared with open splenectomy,laparoscopic splenectomy for patients with ITP has similar long-term therapeutic response,but it has advantages of minimally invasiveness.

It is widely acknowledged that the liver is the most common organ for colorectal cancer metastasis, and radical resection is thought to be the only therapeutic method to cure colorectal cancer liver metastasis (CRLM). Unfortunately, only about 20% of all patients are eligible for surgical resection. In patients who cannot be treated with surgery, transcatheter arterial chemoembolization (TACE) has been widely used as the preferred therapeutic method by scholars at home and abroad. This article introduces the application basis, indications, contraindications, therapeutic effect, chemotherapeutic agents, and embolic agents of TACE in the treatment of CRLM, and points out that TACE is a palliative treatment regimen with a clear therapeutic effect, minimal invasion, and few side effects and can be used as the preferred therapeutic regimen for patients with unresectable CRLM.

Objective Admission hyperglycemia is thought to be related to poor neurological function and high mortality in patients with spontaneous intracerebral hemorrhage (sICH).However, it is not known whether preictal glycemic status affects functional outcome of sICH.The study is aimed to disclose the association between preictal glycemic status and neurological outcomes in patients with acute sICH.Methods Three hundred and thirty-two patients with sICH, admitted to our hospital from January 2011 to September 2013 (within 24 h of onset), were chosen in our study.Pre-stroke glycemic status, represented by hemoglobin A1c (HbA1c), was determined the next day after admission.Patients were categorized into four groups according to HbA1c values (＜6％, 6.1％-7％, 7.1％-8％ and ≥8％).The correlations of HbA1c in the four groups with different variables (NIHSS scores, hematoma volume, modified Rankin scale [mRS] scores) were analyzed using Spearman correlation test.Patients were also categorized into two groups according to hematoma volume (≤25 mL or ＞25 mL) or mRS scores (≤2 or ＞2).Logistic regression analyses were used to determine the relative independent risk factors for both hematoma volume and mRS scores.Results The hematoma volume, NIHSS scores, mRS scores, and number of patients with diabetes mellitus were significantly different among the four groups (P＜0.05).Blood glucose at admission and HbA1c level were significantly correlated with hematoma volume (r=0.085, P=0.027;r=0.164, P=0.014).Age and HbA1c level were significantly correlated with mRS scores (r=0.027, P=0.019;r=0.199, P=0.003).Conclusion HbA1C alone could serve as a better predictor of poor outcome after sICH than glucose at admission;HbA 1 C is the independent risk factor of poor prognosis for sICH.