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.

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.

Objective To investigate the expression and prognostic significance of serum protease C1 inhib-itor(SERPING1)and plasminogen activator inhibitor type 1(SERPINE1)in patients with sepsis.Methods A total of 132 patients with sepsis treated in the hospital from March 2018 to March 2020 were se-lected as the sepsis group.According to whether they died within 28 days of admission,they were divided into a death group(n=34)and a survival group(n=98).Enzyme linked immunosorbent assay was used to detect the expression of serum SERPING1 and SERPINE1.Multivariate Logistic regression model and receiver oper-ating characteristic curve were used to study the value of serum SERPING1 and serpine1 in evaluating the prognosis of patients'death.Results[Compared with the control group,serum SERPING1(331.12±51.80 ng/L vs.639.04±91.12 ng/L)was lower and serum serpine1(412.67±64.84 ng/L vs.42.33±10.32 ng/L)was higher in the sepsis group,and the differences were statistically significant(P＜0.05).[Compared to the survival group,the levels of serum SERPINE1,procalcitonin,C-reactive protein,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score and Sequential Organ Failure Assessment(SOFA)score in the death group were higher,while serum SERPING1 was lower,and the differences were statistically significant(all P＜0.05).Serum SERPING1 showed negative correlation with APACHE Ⅱ and SOFA scores(r=-0.779,-0.653,P＜0.05),while serum SERPINE1 showed positive correlation with APACHE Ⅱ and SO-FA scores(r=0.740,0.685,P＜0.05).APACHE Ⅱ score,SOFA score,and serum SERPINE1 were risk fac-tors affecting the prognosis of sepsis patients,while serum SERPING1 was a protective factor.The area under the curve of serum SERPING1 and SERPINE1 combined for the evaluation of the death in sepsis patients was 0.938(95％CI:0.893-0.968),which was significantly higher than 0.860(95％CI:0.812-0.899)and 0.838(95％CI:0.781-0.868)of the single detection,and the differences were statistically significant(Z=3.861,4.015,P＜0.001).Conclusion The elevated levels of serum SERPING1 and SERPINE1 in patients with sepsis are related to the severity of the patient's condition.The combination of the two has high prognos-tic value for sepsis patients.

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.

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.

Objective To improve the CT image quality of the anorectal junction in venous phase using a new deep learning image reconstruction(DLIR)algorithm.Methods A retrospective analysis was conducted on 71 patients undergoing pelvic computed tomography(CT)scans.All CT images were reconstructed at a thin slice thickness of 0.625 mm using 50%ASiR-V,low-,medium-and high-intensity DLIR(DLIR-L,DLIR-M and DLIR-H).The CT attenuations and standard deviation values of anal canal and hip fat were measured for each reconstruction group.With the standard deviation of hip fat as background noise,the contrast-to-noise ratio(CNR)and signal-to-noise ratio(SNR)of anal canal were calculated.Two radiologists independently assessed image quality and diagnostic confidence for local invasion of rectal cancer using the 5-point Likert scale.The objective measurement indicators and subjective scores were analyzed and compared,and Kappa test was used to evaluate the consistency.Results The differences in CT value of anal canal and hip fat among the groups were trivial(P>0.05),but fat SD,anal canal SNR and CNR(P<0.05)differed significantly,with lowest fat SD,highest anal canal SNR and CNR in DLIR-H group,while highest fat SD,lowest anal canal SNR and CNR in 50%ASiR-V group.Compared with 50%ASiR-V group,DLIR-H group decreased fat SD by 44.3%,but increased anal canal SNR and CNR by 89.5%and 92.1%,respectively(P<0.05).The subjective score of 4 groups were significantly different(P<0.05),decreasing from DLIR-H to 50%ASiR-V,and the inter-group differences were significant(P<0.05),except the difference between 50%ASiR-V group and DLIR-L group(P>0.05).There was a statistically significant difference in the diagnostic confidence for local invasion of rectal cancer among different groups(P<0.05),and the scores were significantly higher in DLIR-M and DLIR-H groups than in 50%ASiR-V and DLIR-L groups(P<0.05).Conclusion Compared with the standard 50%ASiR-V image,DLIR-M and DLIR-H reconstruction algorithms can effectively improve the image quality for the anorectal junction in CT imaging.The higher-intensity DLIR results in better image quality and stronger ability to display fine structures,which can provide more evidences for clinical precision evaluation and personalized precision treatment.

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.