Objective To investigate the current distribution and predict the future suitable habitats of Haemaphysalis longicornis in Nanjing City, so as to provide insights into control and early warning of ticks and management of tick-borne diseases in Nanjing City. Methods The electronic map of Nanjing City was obtained from the National Platform for Common GeoSpatial Information Services. The distribution of H. longicornis and the longitude and latitude of distribution points from 2022 to 2024 were obtained from centers for disease control and prevention across each district in Nanjing City. Climatic and environmental variable data in Nanjing City were captured from the Worldclim database. Initially, 19 bioclimatic variables in this database were selected, including annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the warmest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The elevation and normalized difference vegetation index were obtained from Data Sharing Platform of the Center for Resources and Environmental Sciences, Chinese Academy of Sciences. Then, the distribution points of H. longicornis, elevation, vegetation index and 19 bioclimatic variables were loaded into the software MaxEnt 3.4.4 to evaluate and screen out the variables with a contribution rate of 1% and higher. ArcGIS 10.8.1 software was used to extract the elevation, vegetation index and 19 bioclimatic variables of the distribution points of H. longicornis for a correlation analysis. If the absolute value of the correlation coefficient was 0.8 and higher, the variable with the higher contribution was retained. The 2050 dataset of the BCCCSM2-MR atmospheric circulation model in the coupled model intercomparison project phase 6 (CMIP6) were obtained from the Worldclim database as climate data for 2050. Screened H. longicornis species data and environmental and climate data were loaded into the maximum entropy (MaxEnt) model with the software MaxEnt 3.4.4 for training and validation, and then, all data generated from the model were imported into the software ArcGIS 10.8.1 to generate raster data and yield the map pertaining to the distribution of H. longicornis risk in Nanjing City. The accuracy of the model was evaluated with a receiver operating characteristic (ROC) curve, and the predictive effect of the model was assessed with area under the ROC curve (AUC). The suitable habitats of H. longicornis were classified in Nanjing City with the software ArcGIS 10.8.1, and the areas of distribution of suitable habitats in various categories were recorded to create the map of current H. longicornis suitable habitats classification in Nanjing City. The climatic and geographic information data in 2050 were employed as future environmental and climatic factors, and current environmental and climatic factors and current H. longicornis distribution data were additionally used to predict the future suitable habitats of H. longicornis in Nanjing City. In addition, the contributions of environmental and climatic factors to distribution of suitable habitats of H. longicornis was evaluated with the Jackknife method in Nanjing City. Results A total of 10 environmental and climatic variables were screened for analysis of the suitability of H. longicornis in Nanjing City based on correlation analyses and contributions of the MaxEnt model, including annual mean temperature, precipitation of the warmest quarter, vegetation index, precipitation of the wettest month, temperature annual range, annual precipitation, mean temperature of the warmest quarter, elevation, mean temperature of the wettest quarter, and maximum temperature of the warmest month, and annual mean temperature (34.8%), precipitation of the warmest quarter (17.3%), vegetation index (13.1%), and precipitation of the wettest month (10.8%) contributed relatively highly to the distribution of suitable habitats of H. longicornis in Nanjing City. The mean AUC of the ROC curve was 0.810 ± 0.055 for 10 repeated modeling results of the MaxEnt model, indicating high predictive performance of the model. The potential distribution areas of H. longicornis were predicted to be mainly located in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District in Nanjing City with the MaxEnt model. Under current climatic conditions, the area of potential suitable habitats of H. longicornis was 4 182.42 km² in Nanjing City, including 1 252.94 km² highly suitable habitats, which accounted for 19.00% of the total area of Nanjing City. Under the climate scenario in 2050, the area of potential suitable habitats of H. longicornis was projected to increase to 5 467.58 km² in Nanjing City, accounting for 82.95% of the total area of the city, and these habitats were mainly concentrated in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District. The areas of suitable habitats of H. longicornis at various categories were predicted to vary greatly in 2050, and the area of highly suitable habitats of H. longicornis was projected to increase to 2 378.82 km², accounting for 36.08% of the total area of Nanjing City. Based on jackknife tests and contributions of environmental and climatic variables, 6 dominant environmental and climatic factors were screened, including annual mean temperature (34.8% contribution), precipitation of the warmest quarter (17.3% contribution), vegetation index (13.1% contribution), precipitation of the wettest month (10.8% contribution), temperature annual range (5.4% contribution), and mean temperature of the warmest quarter (5.0% contribution), with cumulative contributions of 86.4%. Conclusion The distribution of H. longicornis is strongly associated with vegetation, temperature and precipitation in Nanjing City. Future climate change may lead to an expansion of the distribution area of H. longicornis in Nanjing City.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

OBJECTIVE This study aims to investigate the epidemiological characteristics and influencing factors of skin diseases among soldiers performing duties and working in tunnel environments,and to propose targeted pre-ventive and therapeutic measures.METHODS A cross-sectional survey was conducted on 537 soldiers from multi-ple sites within a military unit from Nov.2022 to Oct.2023.The survey collected data on general information,liv-ing habits,and details of current skin diseases,including types,symptoms,duration and treatment status.Logis-tic regression analysis was applied to identify risk factors for the skin diseases observed.RESULTS A total of 21 types of skin diseases were identified,with dermatophyte infections being the most prevalent(59.96％,322/537),followed by acne(26.82％,144/537).Further analysis revealed that the incidence of dermatophyte infec-tions was closely associated with the region where the soldiers were stationed[OR(95％CI)=1.694(1.062,2.693),P=0.032]and the frequency of sock washing[OR(95％CI)=1.734(1.023,2.988),P=0.043],but no significant correlation was found between the prevalence of dermatophyte infections and the frequency of washing feet[OR(95％CI)=1.520(0.836,2.824),P=0.175].CONCLUSIONS This study highlights the epidemiological char-acteristics and key risk factors for skin diseases among soldiers in tunnel environments.Targeted prevention strategies are proposed,providing valuable scientific evidence for the prevention and control of skin diseases in similar environments.

With the full implementation of the Diagnosis Related Groups(DRG)payment model,its institutional advantages in optimizing resource allocation and controlling medical costs through fixed disease payment standards have gradually emerged.However,it has also triggered structural value conflicts in the doctor-patient relationship.Based on the four principles of medical ethics,this paper constructed an analytical framework for the value conflicts in doctor-patient relationships under the DRG payment model.Starting from the manifestations of value conflicts,the inducements creating them were analyzed in depth.On these foundations,multi-dimensional optimization paths were proposed,including repairing respect-related conflicts through information transparency and decision-making co-governance;constructing a refined cost management system and embedding an ethical review mechanism to resolve non-harm conflicts;implementing a phased payment mechanism for innovative technologies and an ethical review exemption mechanism to alleviate benefit conflicts;as well as designing dynamic payment rules,unifying payment standards for insurance participation types,and strengthening dynamic monitoring to address justice conflicts.Under this framework,this paper aimed to promote the gradual transformation of DRG from a cost-control tool to a governance tool.While ensuring the security of the fund,it was necessary to maintain the bottom line of quality,stimulate technological innovation,and return to the patient-centered concept,thereby promoting the doctor-patient relationship to shift from a zero-sum game to a symbiotic and win-win situation.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Objective:To analyze the correlation between thymus dose-volume parameters and lymphopenia in patients with early-stage breast cancer (BC) receiving adjuvant radiotherapy (RT).Methods:Medical records of 54 patients with early-stage BC who received postoperative adjuvant RT in the Second Affiliated Hospital of Soochow University from January to December 2019 were retrospectively analyzed. Absolute lymphocyte counts (ALC) were collected at 1 month before (baseline) and weekly during RT. Lymphopenia was graded based according to the common terminology criteria for adverse events version 5.0 and nadir/baseline ALC was calculated. The thymus was delineated according to anatomical boundaries in the original RT planning system. Dosimetric parameters were obtained from the dose volume histograms. Stepwise multiple linear regression analysis was used to explore the factors associated with nadir/baseline ALC. The cutoff values of dosimetric parameters for predicting ≥grade 3 lymphopenia were obtained using the receiver operating characteristic (ROC) curve.Results:The proportion of 54 patients experiencing ≥ grade 3 lymphopenia was 38.9%. The median value of thymus volume, mean dose, V 5 Gy, V 10 Gy were 14.02 cm 3, 4.95 Gy, 36.18%, and 6.61%, respectively. Stepwise multiple linear regression analysis revealed that baseline ALC ( P=0.005), quadrant location ( P=0.005) and mean thymus dose ( P<0.001) were significantly associated with nadir/baseline ALC. ROC curve analysis indicated that the cutoff values of thymus mean dose, V 5 Gy and V 10 Gy for predicting ≥ grade 3 lymphopenia were 6.12 Gy, 35.2%, and 7.4%, respectively. Conclusions:Lymphopenia in early-stage BC patients is significantly correlated with high dosimetric parameters of the thymus during postoperative adjuvant RT. Thymus may be considered as an organ at risk during RT.

Objective:To evaluate the effectiveness of fosaprepitant in preventing postoperative nausea and vomiting (PONV) following gynecological laparoscopic surgery.Methods:In this randomized parallel-controlled trial, 100 American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ patients, aged 18-64 yr, undergoing elective gynecological laparoscopic surgery under general anesthesia at the First Affiliated Hospital of Zhengzhou University, were selected and divided into 2 groups ( n=50 each) in a ratio of 1∶1 using blocked randomization: fosaprepitant group (group F) and tropisetron group (group T). At 30 min before anesthesia induction, fosaprepitant 150 mg was intravenously infused in group F, and tropisetron 5 mg was intravenously infused in group T, both diluted in 150 ml of normal saline. Anesthesia was induced by intravenous injection of midazolam, etomidate, sufentanil and cisatracurium. Anesthesia was maintained by intravenous infusion of remifentanil and propofol. Patient-controlled intravenous analgesia was performed with hydromorphone at the end of operation until 48 h after operation. Metoclopramide was given as rescue antiemetic. The PONV, requirement for antiemetic drugs and related adverse reactions were recorded within 24 h after surgery. Results:The incidence of PONV (10% vs 30%), the incidence of vomiting(2% vs 16%) and the rescue rate of antiemetic drugs(2% vs 12%)were significantly lower in group F than in group T ( P<0.05). There was no significant difference in the incidence of related adverse reactions between the two groups ( P>0.05). Conclusions:Intravenous infusion of fosaprepitant 150 mg at 30 min before anesthesia induction effectively prevents PONV in patients undergoing gynecological laparoscopic surgery, and the efficacy is superior to that of the conventional use of tropisetron.

Objective:To evaluate the effectiveness of fosaprepitant in preventing postoperative nausea and vomiting (PONV) following gynecological laparoscopic surgery.Methods:In this randomized parallel-controlled trial, 100 American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ patients, aged 18-64 yr, undergoing elective gynecological laparoscopic surgery under general anesthesia at the First Affiliated Hospital of Zhengzhou University, were selected and divided into 2 groups ( n=50 each) in a ratio of 1∶1 using blocked randomization: fosaprepitant group (group F) and tropisetron group (group T). At 30 min before anesthesia induction, fosaprepitant 150 mg was intravenously infused in group F, and tropisetron 5 mg was intravenously infused in group T, both diluted in 150 ml of normal saline. Anesthesia was induced by intravenous injection of midazolam, etomidate, sufentanil and cisatracurium. Anesthesia was maintained by intravenous infusion of remifentanil and propofol. Patient-controlled intravenous analgesia was performed with hydromorphone at the end of operation until 48 h after operation. Metoclopramide was given as rescue antiemetic. The PONV, requirement for antiemetic drugs and related adverse reactions were recorded within 24 h after surgery. Results:The incidence of PONV (10% vs 30%), the incidence of vomiting(2% vs 16%) and the rescue rate of antiemetic drugs(2% vs 12%)were significantly lower in group F than in group T ( P<0.05). There was no significant difference in the incidence of related adverse reactions between the two groups ( P>0.05). Conclusions:Intravenous infusion of fosaprepitant 150 mg at 30 min before anesthesia induction effectively prevents PONV in patients undergoing gynecological laparoscopic surgery, and the efficacy is superior to that of the conventional use of tropisetron.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.