Objective To investigate the expression profile, prognostic value, gene co-expression network, and immunomodulatory role of BRF1 in a pan-cancer context, and to explore its biological functions and molecular regulatory mechanisms in esophageal squamous cell carcinoma (ESCC). Methods The pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized to analyze the differential expression of BRF1 in tumor versus normal tissues, its association with patient survival, pathway enrichment for co-expressed genes, and immune features (including immune checkpoints, cytokines, and immune cell infiltration). The expression profile of BRF1 in ESCC was validated using the Gene Expression Omnibus (GEO) database. In vitro, BRF1 was knocked down in ESCC cells using siRNA. Cell proliferation and migration were assessed by MTT and Transwell assays, respectively. The expression levels of proliferation- and migration-related proteins were detected by Western blotting. The correlation between BRF1 and ferroptosis was analyzed using TCGA data. Results BRF1 was significantly upregulated in over 20 types of cancer, and its high expression was associated with poor prognosis in patients with adrenocortical carcinoma and prostate adenocarcinoma. BRF1 was found to positively regulate the T-cell-mediated cell death pathway in esophageal adenocarcinoma and was associated with the circadian rhythm regulation pathway in pancreatic adenocarcinoma. The correlation of BRF1 with immune checkpoints, cytokine networks, and immune cell infiltration was found to be cancer type-specific. In vitro experiments demonstrated that knocking down BRF1 significantly inhibited the proliferation of ESCC cells, accompanied by the downregulation of the proliferation marker PCNA. Cell migration was also significantly impaired, with decreased expression of Vimentin and MMPs and increased expression of E-cadherin. Furthermore, the expression of BRF1 was positively correlated with that of ferroptosis-antagonizing genes, such as GPX4, HSPA5, and SLC7A11. Conclusion BRF1 plays complex roles in pan-cancer, participating in the regulation of tumorigenesis, progression, and immune infiltration. BRF1 promotes the proliferation and migration of ESCC cells, a mechanism potentially associated with the regulation of ferroptosis resistance. These findings suggest that BRF1 could be a potential therapeutic target for ESCC.

OBJECTIVE: To delineate the clinical and genetic features of a Chinese girl harboring a rare de novo variant of SYNGAP1 associated with Mental retardation, autosomal dominant 5 (MRD5), and to conduct a comprehensive genotype-phenotype correlation analysis within the Chinese population through an extensive literature review. METHODS: A 5-year-old girl presenting with seizures without an obvious cause was enrolled in September 2020. Genomic DNA was extracted from the patient and her parents. Whole exome sequencing (WES) was performed on the proband to identify suspected pathogenic variants based on her clinical phenotype. Sanger sequencing was used for validation, followed by bioinformatic analysis of the variant. Additionally, data from 54 previously reported Chinese cases with SYNGAP1 variants were integrated to summarize the distribution of variant types and clinical characteristics. Ethical approval was obtained from the Ethics Committee of Kunming Children's Hospital (Ethics No.: 2021-03-055-K01). RESULTS: WES identified a heterozygous nonsense variant, SYNGAP1 c.725G>A (p.Trp242*), in the proband. Sanger sequencing confirmed it was a de novo variant. According to the ACMG guidelines, this variant was classified as pathogenic (PVS1+PS2). Based on the clinical manifestations, the patient was diagnosed with MRD5. Bioinformatic analysis suggested that this variant introduces a premature stop codon at tryptophan 242, disrupting the PH domain and leading to the loss of the C2, Ras-GAP, and C-terminal domains. The pooled analysis of Chinese cases revealed that nonsense (38.2%) and frameshift (36.4%) variants were the predominant types. Intellectual disability/developmental delay was present in 100.0% of patients, epilepsy in 83.6%, and autism spectrum disorder in 41.3%. The incidence of epilepsy differed significantly among variant types (P = 0.045). Exons 8 and 15 were identified as mutation hotspots. CONCLUSION This study has identified a SYNGAP1 c.725G>A variant in the Chinese population and confirmed it as a potential cause of MRD5, which expanded the mutational spectrum of this disorder.
Humans ; Female ; Child, Preschool ; Intellectual Disability/genetics* ; ras GTPase-Activating Proteins/genetics* ; Phenotype ; Exome Sequencing ; Genetic Association Studies

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

The morphological characteristics and pathological progression of vulnerable plaques in atherosclerosis （AS） exhibit a high degree of similarity to the concept of "abscesses and ulcers" in traditional Chinese medicine （TCM）. Therefore， vulnerable atherosclerotic plaques can be analogized as "abscesses and ulcers within the vessels"， for which deficiency， stasis and toxin constitute the core pathogenesis. The pathological evolution progresses through three sequential stages， deficiency leading to pathological substances， with phlegm and stasis accumulating into a mass； the mass transforming into putridity， with internal decay and external thinning； putridity brewing toxin， and toxin accumulation threatening rupture， ultimately resulting in plaque instability. Accordingly， a stage-specific treatment strategy is established. In the early stage， it is suggested to supplement deficiency， resolve the mass， dissipate stasis， and prevent putridity， using Liujunzi Decoction （六君子汤） combined with Danshen Decoction （丹参饮） with modifications. In the middle stage， the treatment should focus on transforming putridity to generate new tissue， and rectifying healthy qi to consolidate the body， with modified Tuoli Xiaodu Powder （托里消毒散）. In the late stage， the treatment principle is clearing and resolving putridity toxin， cooling the blood， and preventing ulcerative rupture， using modified Simiao Yong'an Decoction （四妙勇安汤） combined with Xijiao Dihuang Decoction （犀角地黄汤）.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

ObjectiveTo investigate the impact of a cold environment on the pathophysiological changes in dogs after abdominal gastrointestinal blast injury. MethodsEight healthy common-grade adult male Beagles were selected and randomly divided into a cold group and a normal temperature group, with 4 dogs in each group. Prior to surgery, Beagles dogs were fasted and deprived of water for 12 hours; after establishing a vein at the edge of the ear, propofol （2 mg·kg^-1·min^-1） was continuously infused using a microinfusion pump to maintain anesthesia. Tracheal intubation was performed while preserving spontaneous breathing, and the electroencephalogram bifrequency index was maintained at 40-60 to ensure stable anesthesia. A model of abdominal gastrointestinal blast injury in Beagle dogs was established using a multifunctional combat trauma modeling platform. After blast injury, the cold group was exposed to a cold chamber at -20 ℃ combined with 10% body surface area ice water immersion for 40 min, while the normal temperature group was kept in an environment at (25±2) ℃ for 40 min after blast injury. Vital signs such as heart rate, blood pressure, and blood oxygen saturation were monitored in both groups before blast injury, immediately after blast injury, and 40 min after blast injury. And arterial blood gas parameters, including pH, partial pressure of oxygen in arterial blood (PaO₂), partial pressure of carbon dioxide in arterial blood (PaCO₂), actual bicarbonate radical (HCO₃^-), lactic acid (Lac), and blood base excess (BEb), were measured. After the experiment, gastrointestinal tissues (gastric antrum and upper jejunum 5 cm distal to the suspensory ligament of the duodenum) were harvested for pathological analysis via hematoxylin-eosin (HE) staining combined with semi-quantitative scoring. ResultsPhysiological indicators showed that the body temperature of the cold group was significantly lower than that of the normal temperature group (P<0.01), and the heart rate and systolic blood pressure of the cold group were significantly higher than those of the normal temperature group (P<0.000 1). PaCO₂ was markedly elevated and BEb was markedly decreased in the cold group relative to the normal temperature group (P<0.000 1), suggesting aggravated respiratory acidosis and metabolic acidosis. Pathological analysis showed that gastro-intestinal tissue damage was more severe in the cold group: the gastric mucosal epithelial shedding rate was > 60% (vs. <25% in the normal temperature group), the necrosis rate of the epithelial cells at the tips of intestinal villi reached more than 70% (vs. < 30% in the normal temperature group), and inflammatory cell infiltration involved the submucosa (vs. limited to the superficial mucosa in the normal temperature group). The semi-quantitative scoring results were consistent with the pathological analysis results, further confirming that the cold environment exacerbated gastrointestinal tissue damage caused by abdominal gastrointestinal blast injury. ConclusionA cold environment can exacerbate circulatory dysfunction and gastrointestinal tissue damage in Beagle dogs after blast injury, providing experimental evidence for early rewarming intervention for combat trauma in cold regions.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Objective To understand and grasp the status quo of resistance of Culex pipiens pallens to four commonly used insecticides in Hefei City, and to provide a scientific basis for the chemical control of mosquito larvae. Methods From June to July 2023, Cx. pipiens pallens larvae were collected from 9 counties (cities and districts) in Hefei City. The LC50 of late third-instar to early fourth-instar larvae of Cx. pipiens pallens to commonly used insecticides was determined by larval immersion method (sensitive baseline method). Results Cx.pipiens pallens larvae in Hefei City exhibited different degrees of resistance to four insecticides: permethrin, beta-cypermethrin, temephos, and propoxur. The relative resistance coefficients to permethrin and beta-cypermethrin were 26.96 and 21.17, respectively, indicating the moderate resistance level. The relative resistance coefficients to propoxur were 6.70, indicating a low resistance level. The relative resistance coefficient to temephos was 2.43, indicating a sensitivity level. Culex pipiens pallens against pyrethroids such as 0.25% permethrin, 0.025% deltamethrin and 0.025% cypermethrin in 1 h knockout rate and 24 h mortality rates were 3.25% (4/123) and 46.34% (57/123), 3.60% (5/139) and 35.97% (50/139), 3.85% (6/156) and 40.38% (63/156), respectively. For 5% malathion and 0.1% propoxur, the 1 h knockdown rate and 24 h mortality rate were 97.69% (127/130) and 99.23% (129/130), 94.48% (137/145) and 100.00% (145/145), respectively. It showed resistance to 0.25% permethrin, 0.025% deltamethrin and 0.025% cypermethrin, and sensitivity to 5% malathion and 0.1% propoxur. Conclusions Culex pipiens pallens in Hefei City have developed varying degrees of resistance to parathyroid and carbamate insecticides. In the control of mosquito vectors, it is essential to strengthen the scientific and rational use of chemical control in combination with environmental and physical control measures to form an integrated control strategy. This approach will improve the control efficiency while delaying the occurrence and development of insecticide resistance.

OBJECTIVE: To develop an artificial intelligence-assisted system for the automatic detection of the features of common 21 auricular points based on the YOLOv8 neural network. METHODS: A total of 660 human auricular images from three research centers were collected from June 2019 to February 2024. The rectangle boxes and features of images were annotated using the LabelMe5.3.1 tool and converted them into a format compatible with the YOLO model. Using these data, transfer learning and fine-tuning training were conducted on different scales of pretrained YOLO neural network models. The model's performance was evaluated on validation and test sets, including the mean average precision (mAP) at various thresholds, recall rate (recall), frames per second (FPS) and confusion matrices. Finally, the model was deployed on a local computer, and the real-time detection of human auricular images was conducted using a camera. RESULTS: Five different versions of the YOLOv8 key-point detection model were developed, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. On the validation set, YOLOv8n showed the best performance in terms of speed (225.736 frames per second) and precision (0.998). On the external test set, YOLOv8n achieved the accuracy of 0.991, the sensitivity of 1.0, and the F1 score of 0.995. The localization performance of auricular point features showed the average accuracy of 0.990, the precision of 0.995, and the recall of 0.997 under 50% intersection ration (mAP⁵⁰). CONCLUSION The key-point detection model of 21 common auricular points based on YOLOv8n exhibits the excellent predictive performance, which is capable of rapidly and automatically locating and classifying auricular points.
Humans ; Neural Networks, Computer ; Artificial Intelligence ; Acupuncture Points