OBJECTIVE To predict the possible impact of pembrolizumab（PEM） as a first-line drug after being included in the national medical insurance system in the treatment of advanced or metastatic non-small cell lung cancer based on real-world data from the perspective of the national medical insurance payer， to provide a basis for the decision-making of the medical insurance department. METHODS A budget impact analysis model was constructed to compare the impact of pembrolizumab not included in medical insurance and included in medical insurance on medical insurance fund expenditure in the next five years（ 2024- 2028） with 2023 as the baseline year. The target population was the patients with EGFR gene mutation-negative and anaplastic lymphoma kinase （ALK）-negative locally advanced or metastatic non-small cell lung cancer； estimated cost mainly included the cost of drugs， the cost of adverse reaction treatment， the cost of examination， the cost of admission and monitoring， etc； equipment ratio of PEM in 183 hospitals of Guangdong province from 2020 to 2022 was used as the market share. Univariate sensitivity analysis was used to test the robustness of the basic analysis results. RESULTS When PEM was not included in the medical insurance， the medical insurance reimbursement amount of the target population from 2024 to 2028 was 4 933 623.5 thousand yuan-5 151 198.3 thousand yuan， respectively. If PEM was included in the medical insurance， the above data were 11 871 972.2 thousand yuan-14 540 571.0 thousand yuan， respectively； the increase in medical insurance reimbursement under the two scenarios was 6 720 773.9 thousand yuan-9 606 947.5 thousand yuan， respectively. The proportion of medical insurance reimbursement to the medical insurance expenditure of the year after PEM was included in medical insurance was 0.298 0%， 0.262 1%， 0.228 8%， 0.208 2%， and 0.185 7%， respectively. The increase in medical insurance reimbursement accounted for 1.084 0%， 0.995 7%， 0.888 6%， 0.886 3%， and 0.861 6% of the increase in the expenditure of the medical insurance fund in the current year， all of which showed a decreasing trend year by year. CONCLUSIONS If PEM is included in medical insurance， due to its high unit price， the medical expenditure will increase accordingly， which will have a great impact on the medical insurance fund expenditure. However， when the drug is used in patients with EGFR mutation-negative and ALK-negative locally advanced or metastatic non-small cell lung cancer， the proportion of the medical insurance reimbursement amount in the current year’s medical insurance fund expenditure and the proportion of the increase in medical insurance reimbursement in the current year’s increase in medical insurance fund expenditure are decreasing year by year.

Objective To study the effect of reactive oxygen species(ROS)/p38 MAPK cascade reaction on the formation of kidney stones(KS)in rats and explore the mechanism.Methods Fifty SD rats were randomly divided into control group(normal feeding),N-acetylcysteine(NAC)group(intraperitoneally injected 200 mg/kg NAC),KS group(constructed calcium oxalate KS model),KS+NAC group(constructed calcium oxalate KS model,intraperitoneally injected 200 mg/kg NAC),KS+NAC+tunicamycin(TM)group(constructed calcium oxalate KS model,intraperitoneally injected 200 mg/kg NAC and 1 mg/kg TM),with 10 rats in each group.After 4 weeks of administration,24 hours urine volume and oxalic acid(Ox)of each group were measured,serum creatinine(Cr),urea nitrogen(BUN)and uric acid(UA)levels were detected by automatic biochemical analyzer.HE staining and Von Kossa staining were used to observe the histopathological changes and crystal deposition of the kidney.TUN EL staining was used to detect apoptosis of renal tissue cells.The activity of superoxide dismutase(SOD)and the content of malondialdehyde(MDA)in renal tissue were measured by the kit.DHE fluorescent probes detected the levels of reactive oxygen species(ROS)in kidney tissue.Immunohistochemical staining was used to detect the expressions of microtubule-associated protein 1 light chain 3 B(LC3B)and glucose regulatory protein 78(GRP78)in renal tissue,and the protein expression of LC3 Ⅱ/LC3 Ⅰ,Beclin1,GRP78,CCAAT/enhancer binding protein homologous protein(CHOP)in renal tissue was determined by Western blotting.Results Compared with the KS group,Ox in KS+NAC group decreased(P＜0.05),BUN,Cr and UA levels decreased(P＜0.05),renal tubule dilatation and calcium oxalate crystallization decreased,TUNEL positive cell rate decreased(P＜0.05),SOD activity increased and MDA content decreased(P＜0.05),ROS levels decreased(P＜0.05),LC3B and GRP78 positive staining levels decreased(P＜0.05),the relative protein expressions of p-p38 MAPK/p38 MAPK,LC3 Ⅱ/LC3 Ⅰ,Beclin1,GRP78 and CHOP were down-regulated(P＜0.05).Compared with the KS+NAC group,Ox in KS+NAC+TM group increased(P＜0.05),BUN,Cr and UA levels also increased(P＜0.05),renal tubule dilated significantly,calcium oxalate crystals increased,TUNEL positive cell rate increased(P＜0.05),SOD activity decreased and MDA content increased(P＜0.05),ROS levels increased(P＜0.05),LC3B and GRP78 positive staining levels increased(P＜0.05),the relative protein expressions of p-p38 MAPK/p38 MAPK,LC3 Ⅱ/LC3 Ⅰ,Beclin1,GRP78 and CHOP were also up-regulated(P＜0.05).Conclusion ROS/p38 MAPK cascade is involved in promoting KS formation in rats,which is related to the activation of endoplasmic reticulum stress-mediated autophagy pathway.

Objective To explore the relevant risk factors of H.pylori infection,and provide reference for prevention and treatment of H.pylori in this area,and further provide theoretical basis for the prevention and treatment of gastric cancer.Methods A total of 1200 residents in four districts of Haikou city were investigated by questionnaire and urea 14 C breath test by holistic stratified random sampling to calculate the population infection rate and analyze the risk factors of infection.Results The total infection rate was 32.5％,which was lower than the national H.pylori infection rate.No consumption of fruits and vegetables,no habit of washing hands before meals,and people with gastrointestinal symptoms,are independent risk factors of H.pylori infection.No consumption of pickled products is of great significance to prevent H.pylori infection.Conclusion The prevalence of H.pylori infection in the population of Haikou is lower than the national average,and H.pylori infection is closely related to the poor living habits of residents.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Inositol requiring enzyme 1 alpha (IRE1α), a widespread transmembrane protein in mammals, is an endoplasmic reticulum stress (ER stress) receptor. Among the three signaling pathways of the unfolded protein response (UPR), the IRE1α pathway is the most conservative. And there is a growing body of evidence that the occurrence and development of tumors is closely related to the over-expression of IRE1α. Therefore, the study of the IRE1α inhibitors is of great significance to the discovery of new anti-tumor drugs and has been attracting more and more attention. In the hope of providing ideas for the research of targeting IRE1α for cancer therapy, this paper reviewed the data of representative IRE1α inhibitors, including inhibitory activity, the mechanism of action, structural characteristics, and so on.

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/diagnostic imaging*

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.
Humans ; Male ; Female ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Deep Learning ; Reproducibility of Results ; Radiography ; Algorithms ; Cone-Beam Computed Tomography

Humans ; Consensus ; Hypersensitivity ; Desensitization, Immunologic/adverse effects* ; Immunotherapy/adverse effects* ; Injections, Subcutaneous ; Allergens

This study aims to characterize the cell atlas of the epididymis derived from a 46,XY disorders of sex development (DSD) patient with a novel heterozygous mutation of the nuclear receptor subfamily 5 group A member 1 (NR5A1) gene. Next-generation sequencing found a heterozygous c.124C>G mutation in NR5A1 that resulted in a p.Q42E missense mutation in the conserved DNA-binding domain of NR5A1. The patient demonstrated feminization of external genitalia and Tanner stage 1 breast development. The surgical procedure revealed a morphologically normal epididymis and vas deferens but a dysplastic testis. Microfluidic-based single-cell RNA sequencing (scRNA-seq) analysis found that the fibroblast cells were significantly increased (approximately 46.5%), whereas the number of main epididymal epithelial cells (approximately 9.2%), such as principal cells and basal cells, was dramatically decreased. Bioinformatics analysis of cell-cell communications and gene regulatory networks at the single-cell level inferred that epididymal epithelial cell loss and fibroblast occupation are associated with the epithelial-to-mesenchymal transition (EMT) process. The present study provides a cell atlas of the epididymis of a patient with 46,XY DSD and serves as an important resource for understanding the pathophysiology of DSD.
Male ; Humans ; Epididymis ; Disorder of Sex Development, 46,XY/genetics* ; Disorders of Sex Development ; Mutation ; Mutation, Missense ; Steroidogenic Factor 1/genetics*

This study collected epidemic data of COVID-19 in Zhengzhou from January 1 to January 20 in 2022. The epidemiological characteristics of the local epidemic in Zhengzhou High-tech Zone caused by the SARS-CoV-2 Delta variant were analyzed through epidemiological survey and big data analysis, which could provide a scientific basis for the prevention and control of the Delta variant. In detail, a total of 276 close contacts and 599 secondary close contacts were found in this study. The attack rate of close contacts and secondary close contacts was 5.43% (15/276) and 0.17% (1/599), respectively. There were 10 confirmed cases associated with the chain of transmission. Among them, the attack rates in close contacts of the first, second, third, fourth and fifth generation cases were 20.00% (5/25), 17.86% (5/28), 0.72% (1/139) and 14.81% (4/27), 0 (0/57), respectively. The attack rates in close contacts after sharing rooms/beds, having meals, having neighbor contacts, sharing vehicles with the patients, having same space contacts, and having work contacts were 26.67%, 9.10%, 8.33%, 4.55%, 1.43%, and 0 respectively. Collectively, the local epidemic situation in Zhengzhou High-tech Zone has an obvious family cluster. Prevention and control work should focus on decreasing family clusters of cases and community transmission.
Humans ; SARS-CoV-2 ; COVID-19 ; Epidemics ; Incidence