Background Injury poses a serious threat to human health. As global warming continues to intensify, there is an urgent need to explore the impact of temperature changes on injury deaths. However limited research has focused on this issue. Objective To investigate the relationship between daily mean temperature change (Tm) and injury death, as well as to estimate the associated future death burden in Hunan Province. Methods We employed an individual-level, time-stratified case-crossing design to establish a conditional logistic regression model to analyze the exposure-response relationship between daily mean temperature change and injury death in Hunan Province from 2013 to 2018. Consequently, we conducted subgroup analysis of gender, age group, and injury type. Finally, we estimated the excess burden of injury death attributable to temperature changes under a sustainable development path [low emission scenario (SSP1-2.6)], regional competition path [high emission scenario (SSP3-7.0)], or fossil fuel development path [very high emission scenario (SSP5-8.5)]. Results The study collected 155577 injury deaths in Hunan Province. The results indicated that for each 1 ℃ increment in daily mean temperature, the cumulative excess risk (CER) of injury mortality among Hunan residents increased by 0.71% (95%CI: 0.53%, 0.90%). Notably, the CER for intentional injuries (1.06%, 95%CI: 0.51%, 1.61%) was higher than that for unintentional injuries (0.66%, 95%CI: 0.46%, 0.87%). Warmer temperatures were positively associated with CER of mortality due to drowning, falls, transport injuries, assaults, and suicides, while they were negatively associated with mortality due to poisoning. Compared with 2010–2019, for 2090–2099, the excess mortality rate (EMR) due to ambient temperature increase in Hunan Province under the SSP5-8.5 scenario (14.62/100000, 95%CI: 10.81/100000, 18.40/100000) would be higher than that of the SSP3-7.0 scenario (10.28/100000, 95%CI: 7.58/100000, 12.97/100000) and the SSP1-2.6 scenario (3.35/100000, 95%CI: 2.46/100000, 4.23/100000); the EMR would be around 2.5 times among men (20.64/100000, 95%CI: 14.44/100000, 26.8/100000) than that among women (8.33/100000, 95%CI: 3.98/100000, 12.6/100000). Among unintentional injuries, the leading contributors to EMR would be drowning (4.46/100000), falls (3.96/100000), and transport injuries (2.96/100000). And among intentional injuries, the EMR for suicide (2.08/100000) would be higher than that for assault (0.47/100000). Conclusion Climate warming will increase the total burden of injury-related deaths among residents in Hunan Province, especially in the absence of effective mitigation strategies and measures. It is necessary to strengthen the prevention and control of severe injuries closely related to temperature changes, such as drowning, falls, traffic, and suicides injuries, in order to reduce injury deaths associated with climate change.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Objective To evaluate the application effect of joint inventory management method in drug procurement and control management in multiple hospital areas.Methods Based on the joint inventory management model,four pilot drugs were selected from a certain group hospital by reasonable data processing methods for research.The effects of the model application were compared and analyzed from aspects such as inventory cost,turnover situation,and supply situation.Results After applying the joint inventory management model led by the central hospital,the inventory and amount of drugs in the three pilot hospitals were significantly reduced,with the inventory reduced by 31.93%and the average inventory amount decreased by 16.23%;The inventory turnover days had significantly decreased,with the turnover days of all three branches decreasing by more than one day;The drug shortage rate had significantly decreased,with the most significant change among the pilot drugs being the doxorubicin liposome injection,which had a 6.7%decrease in the shortage rate;The comparison results of each group of data showed statistical significance(P＜0.05).Conclusion Adopting a central hospital led joint inventory management model in multiple hospital areas could significantly improve the effectiveness of drug procurement management and inventory management,which enhanced the efficiency of hospital fund utilization.

Objective:To explore the clinical and molecular basis for a Chinese pedigree affected with Complete androgen insensitivity syndrome (CAIS).Methods:A CAIS pedigree presented at Tianjin Medical University General Hospital between 2019 and 2021 was selected as the study subject. Clinical data of the proband was collected, along with peripheral blood samples from the proband and her family members. Chromosomal karyotyping, sex-determining region of the Y chromosome ( SRY) testing, and next-generation sequencing (NGS) were carried out for the proband, and candidate variant was verified by Sanger sequencing of her family members. Prenatal diagnosis was provided for the sister of the proband. This study was approved by Medical Ethics Committee of the Tianjin Medical University General Hospital (Ethics No. IRB2023-WZ-070). Results:The 18-year-old proband, who has a social gender of female, underwent laparoscopic examination, which showed no presence of uterus and ovaries. The karyotype of peripheral blood sample was 46, XY, with SRY gene detected. NGS indicated that the proband has harbored a heterozygous c. 1988C>G (p.Ser663Ter) variant of the AR gene. Sanger sequencing confirmed that her mother and sister had both harbored the same variant, whilst her father and younger sister were of the wild-type. Prenatal diagnosis revealed that her sister′s first fetus had harbored carried the same variant, which had led to termination of pregnancy. Her second fetus did not carry the variant, and a healthy boy was born. Based on guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was classified as likely pathogenic (PM2_Supporting+ PM4+ PP3_Moderate+ PP4). Conclusion:The c. 1988C>G (p.Ser663Ter) variant of the AR gene probably underlay the CAIS in the proband. The accurate diagnosis of sex development disorders will rely on the physicians′ thorough understanding of the clinical symptoms and pathogenic genes. Genetic testing and counseling can enable precise diagnosis, prenatal diagnosis, and guidance for reproduction

Artificial intelligence technology encompasses a broad intersection of disciplines including informatics,biolo-gy,and logic,and its application in the healthcare field has garnered significant attention in recent years.Electronic medical re-cords(EMRs)contain vast amounts of medical data with potentially immeasurable medical value.Utilizing artificial intelligence technology to mine this potential value can assist clinicians in decision-making,improve diagnostic and treatment efficiency,and enhance service quality,which is of great significance for the development of the healthcare sector.This paper introduces the ap-plication of artificial intelligence technology in data mining of electronic medical records and quality control of medical record con-tent.It also discusses the existing limitations and challenges,while providing insights into future developments.

ObjectiveTo observe the clinical effect of Jieyu Qingxin formula granules combined with remote interactive cognitive behavioral therapy for insomnia （CBT-I） on chronic insomnia of liver depression and fire-turning type. MethodThis study was a prospective randomized controlled trial. 120 patients with chronic insomnia of liver depression and fire-turning type in Lu'an traditional Chinese medicine Hospital from January 2022 to June 2023 were selected as objects. They were randomly divided into two groups，with 60 cases in each group. The control group received remote interactive CBT-I. The observation group was treated with Jieyu Qingxin formula granules on the basis of the control group. Intervention treatment lasted for four weeks，and observation lasted for six weeks. Comparison of data of each group：clinical efficacy，changes in traditional Chinese medicine （TCM） syndrome score before and after treatment，changes in insomnia severity index （ISI） score，self-rating depression scale （SDS） and self-rating anxiety scale （SAS） score changes，total sleep time，wake time，sleep latency，sleep efficiency， Actigraphy sleep parameter value changes，serum neuron specific enolase （NSE） ，adenosine，dopamine （DA）， 5-hydroxytryptamine （5-HT） level changes，and adverse reactions. ResultThe total effective rate in the observation group （92.45%，49/53） was higher than that in the control group（76.92%，40/52）， and the difference was statistically significant（χ²=4.711 1，P<0.05）. After treatment，TCM syndrome score，ISI score，SAS score， and SDS score were decreased in all groups. The total sleep time was extended，and wake time and sleep latency were shortened. The sleep efficiency was increased，but the NSE and DA levels were decreased. Adenosine and 5-HT levels were increased in all groups（P<0.05）. After treatment，compared with the control group，the observation group had lower TCM syndrome score，ISI score，SAS score， and SDS score，longer total sleep time，higher sleep efficiency，shorter wake time and sleep latency，lower NSE and DA levels， and higher adenosine and 5-HT level （P<0.05）. There was one case of nausea in the observation group and no adverse reaction in the control group during treatment. There was no significant difference between the two groups. ConclusionBy reducing NSE and DA and increasing the levels of 5-HT and adenosine，the anxiety （SAS score） and depression （SDS score） of patients can be improved， so as to improve their sleep and effectively treat chronic insomnia of liver depression and fire-turning type.

Objective: To examine the mediating effects of mindfulness level on resilience and symptoms of anxiety and depression among healthcare workers, so as to provide the reference for developing effective psychological intervention. Methods: The clinical doctors, nurses and public health professionals were selected using the stratified random cluster sampling method from hospitals, disease prevention and control centers, and health departments in five cities in Shandong Province, including Qingdao, Jinan, Rizhao, Jining and Liaocheng in January 2023. Basic information, mindfulness level, resilience and symptoms of anxiety and depression among healthcare workers were collected using general demographic questionnaires, the 5-item Mindful Attention Awareness Scale, 10-item Connor-Davidson Resilience Scale, Generalized Anxiety Disorder Scale and Patient Health Questionnaire, respectively. The Process program was used to analyze the mediating effects of mindfulness level on resilience and symptoms of anxiety and depression. Results: A total of 1 836 healthcare workers were investigated, including 472 males (25.71%) and 1 364 females (74.29%), and the median age was 39 (interquartile range, 12) years. There were 629 clinical doctors (34.26%), 963 nurses (52.45%) and 244 public health professionals (13.29%). The median scores of mindfulness level and resilience were 22 (interquartile range, 7) and 20 (interquartile range, 4) points, respectively. The detection rates of anxiety and depression symptoms were 49.78% and 72.28%, respectively. The mediation analysis showed that mindfulness level exerted a partial mediating effect between resilience and anxiety symptoms (β=-0.510, P<0.001), with a direct effect value of -0.130 and a mediating effect value of -0.046, and the mediating effect accounted for 26.14% of the total effect; mindfulness level also exerted a partial mediating effect between resilience and depression symptoms (β=-0.575, P<0.001), with a direct effect value of -0.120 and a mediating effect value of -0.052, and the mediating effect accounted for 30.23% of the total effect. Conclusion Mindfulness level plays a mediating effect between resilience and symptoms of anxiety and depression among healthcare workers.

Objective:To analyze the efficacy of allo-HSCT with total body irradiation (TBI) and chemotherapy alone in the treatment of adult ALL and to explore the factors affecting prognosis.Methods:The clinical data of 95 adult patients with ALL who underwent allo-HSCT from January 2015 to August 2022 were included. According to the conditioning regimen, the patients were divided into two groups: the TBI plus cyclophosphamide (TBI/Cy) group ( n=53) and the busulfan plus cyclophosphamide (Bu/Cy) group ( n=42). Hematopoietic reconstitution after transplantation, GVHD, transplantation-related complications, relapse rate (RR), non-relapse mortality (NRM), OS, and LFS were compared, and the factors related to prognosis were analyzed. Results:The median time of neutrophil engraftment was 14 (10-25) days in the TBI/Cy group and 14 (10-24) days in the Bu/Cy group ( P=0.106). The median time of megakaryocyte engraftment was 17 (10-42) days in the TBI/Cy group and 19 (11-42) days in the Bu/Cy group ( P=0.488). The incidence of grade Ⅱ-Ⅳ acute GVHD (aGVHD) in the TBI/Cy and Bu/Cy groups was 41.5% and 35.7%, respectively ( P=0.565). The incidence of grade Ⅲ-Ⅳ aGVHD in these two groups was 24.5% and 4.8%, respectively ( P=0.009). The incidence of severe chronic GVHD in the two groups was 16.7% and 13.5%, respectively ( P=0.689). The incidence of cytomegalovirus infection, Epstein-Barr virus infection, severe infection, and hemorrhagic cystitis in the two groups was 41.5% and 35.7% ( P=0.565), 34.0% and 35.7% ( P=0.859), 43.4% and 33.3% ( P=0.318), and 20.8% and 50.0% ( P=0.003), respectively. The median follow-up time was 37.1 months and 53.3 months in the TBI/Cy and Bu/Cy groups, respectively. The 2-year cumulative RR was 17.0% in the TBI/Cy group and 42.9% in the Bu/Cy group ( P=0.017). The 2-year cumulative NRM was 24.5% and 7.1%, respectively ( P=0.120). The 2-year LFS was 58.5% and 50.0%, respectively ( P=0.466). The 2-year OS rate was 69.8% and 64.3%, respectively ( P=0.697). In the multivariate analysis, the conditioning regimen containing TBI was a protective factor for relapse after transplantation ( HR=0.304, 95% CI 0.135-0.688, P=0.004), whereas the effect on NRM was not significant ( HR=1.393, 95% CI 0.355-5.462, P=0.634). Infection was an independent risk factor for OS after allo-HSCT in adult patients with ALL. Conclusion:allo-HSCT based on TBI conditioning regimen had lower relapse rate and lower incidence of hemorrhagic cystitis for adult ALL, compared with chemotherapy regimen. While the incidence o grade Ⅲ/Ⅳ aGVHD was hgher in TBI conditioning regimen than that in chemotherapy regimen.

Case 1: A 27-year-old female with ALK-positive anaplastic large cell lymphoma/leukemia; Case 2: A 27-year-old male with acute myeloid leukemia; Case 3: A 56-year-old male with myelodysplastic syndrome. These three patients underwent haploid hematopoietic stem cell transplantation and experienced severe oral mucosal inflammation, nausea, vomiting, diarrhea, and other symptoms over a long period, which significantly restricted eating. Neurological and psychiatric symptoms appeared at 50, 38, and 50 days following transplantation, respectively. The diagnosis of Wernicke encephalopathy was made by head magnetic resonance imaging, whereas the condition improved significantly after intravenous infusion of vitamin B 1.

Background::Growth retardation is a common complication of chronic kidney disease in children, which can be partially relieved after renal transplantation. This study aimed to develop and validate a predictive model for growth patterns of children with end-stage renal disease (ESRD) after kidney transplantation using machine learning algorithms based on genomic and clinical variables.Methods::A retrospective cohort of 110 children who received kidney transplants between May 2013 and September 2021 at the First Affiliated Hospital of Zhengzhou University were recruited for whole-exome sequencing (WES), and another 39 children who underwent transplant from October 2021 to March 2022 were enrolled for external validation. Based on previous studies, we comprehensively collected 729 height-related single-nucleotide polymorphisms (SNPs) in exon regions. Seven machine learning algorithms and 10-fold cross-validation analysis were employed for model construction.Results::The 110 children were divided into two groups according to change in height-for-age Z-score. After univariate analysis, age and 19 SNPs were incorporated into the model and validated. The random forest model showed the best prediction efficacy with an accuracy of 0.8125 and an area under curve (AUC) of 0.924, and also performed well in the external validation cohort (accuracy, 0.7949; AUC, 0.796). Conclusions::A model with good performance for predicting post-transplant growth patterns in children based on SNPs and clinical variables was constructed and validated using machine learning algorithms. The model is expected to guide clinicians in the management of children after renal transplantation, including the use of growth hormone, glucocorticoid withdrawal, and nutritional supplementation, to alleviate growth retardation in children with ESRD.