Stroke is the leading cause of death and disability among adults in China， and its common complications include digestive system abnormalities， cognitive impairment， depression， stroke-associated pneumonia， and hemiplegia. The combination of traditional Chinese and Western medicine has great potential in treating post-stroke complications. Xinglou Chengqitang （XLCQT） is a representative prescription of alleviating the disease in the upper part by treating the lower part. It has definite therapeutic effect and high safety. Clinically， XLCQT is often used to treat stroke and its complications. However， the quantity and quality of clinical trials of XLCQT in treating post-stroke complications need to be improved. Additionally， since the basic research is weak， the material basis and multi-target mechanism for the efficacy of this prescription are unknown. This article reviews XLCQT in terms of the pharmacodynamic basis， medicinal properties， safety evaluation， and progress in clinical research and mechanisms in treating post-stroke complications. This article summarizes 22 key active ingredients of XLCQT in treating acute stroke complicated with syndrome of phlegm heat and fu-organ excess. Among these key active ingredients， resveratrol， kaempferol， luteolin， chrysoeriol， apigenin， （+）-catechin， and adenosine have good pharmacokinetic properties and high bioavailability. The mechanisms of XLCQT in treating post-stroke complications are complex， including inflammatory response， brain-gut axis， hypothalamic-pituitary-adrenal （HPA） axis， intestinal flora， neurotrophic factors， autophagy， oxidative stress， and free radical damage. This review helps to deeply understand the pharmacodynamic basis and mechanisms of XLCQT in treating post-stroke complications and provides a theoretical basis for the clinical application of XLCQT against post-stroke complications and the development of drugs.

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.

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.

OBJECTIVES: To develop a heterogeneous graph prediction method based on the fusion of multi-layer semantics and topological information for addressing the challenges in drug-target interaction prediction, including insufficient modeling of high-order semantic dependencies, lack of adaptive fusion of semantic paths, and over-smoothing of node features. METHODS: A heterogeneous graph network with multiple types of entities such as drugs, proteins, side effects, and diseases was constructed, and graph embedding techniques were used to obtain low-dimensional feature representations. An adaptive metapath search module was introduced to automatically discover semantic path combinations for guiding the propagation of high-order semantic information. A semantic aggregation mechanism integrating multi-head attention was designed to automatically learn the importance of each semantic path based on contextual information and achieve differentiated aggregation and dynamic fusion among paths. A structure-aware gated graph convolutional module was then incorporated to regulate the feature propagation intensity for suppressing redundant information and redcuing over-smoothing. Finally, the potential interactions between drugs and targets were predicted through an inner product operation. RESULTS: Compared with existing drug-target interaction prediction methods, the proposed method achieved an average improvement of 3.4% and 2.4%, 3.0% and 3.8% in terms of the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC) on public datasets, respectively. CONCLUSIONS The drug-target interaction prediction method developed in this study can effectively extract complex high-order semantic and topological information from heterogeneous biological networks, thereby improving the accuracy and stability of drug-target interaction prediction. This method provides technical support and theoretical foundation for precise drug target discovery and targeted treatment of complex diseases.
Semantics ; Humans ; Drug Interactions ; Neural Networks, Computer ; Algorithms

Zebrafish larvae are useful for identifying chemicals against lateral line (LL) hair cell (HC) damage and this type of chemical screen mainly focuses on searching for protectors against cell death. To expand the candidate pool of HC protectors, a self-built acoustic escape response (AER)-detecting system was developed to apply both low-frequency near-field sound transmission and AER image acquisition/processing modules. The device quickly confirmed the changed LL HC functions caused by most known ototoxins, protectors, and neural transmission modifiers, or knockdown of LL HC-expressing genes. With ten devices wired in tandem, five 'hit' chemicals were identified from 124 cyclin-dependent kinase inhibitors to partially restore cisplatin-damaged AER in less than a day. AS2863619, ribociclib, and SU9516 among the hits, protected the HCs in the mouse cochlea. Therefore, using free-swimming larval zebrafish, the self-made AER-detecting device can efficiently identify compounds that are protective against HC damage, including cell death and loss-of-function.
Animals ; Zebrafish ; Hair Cells, Auditory/physiology* ; Lateral Line System/cytology* ; Escape Reaction/physiology* ; Larva ; Mice ; Cisplatin/toxicity* ; Drug Evaluation, Preclinical/methods*

Microglial functions are linked to Ca²⁺ signaling, with endoplasmic reticulum (ER) calcium stores playing a crucial role. Microglial abnormality is a hallmark of Alzheimer's disease (AD), but how ER Ca²⁺ receptors regulate microglial functions under physiological and AD conditions remains unclear. We found reduced ryanodine receptor 2 (Ryr2) expression in microglia from an AD mouse model. Modulation of RyR2 using S107, a RyR-Calstabin stabilizer, blunted spontaneous Ca²⁺ transients in controls and normalized Ca²⁺ transients in AD mice. S107 enhanced ATP-induced migration and phagocytosis while reducing ramification in control microglia; however, these effects were absent in AD microglia. Our findings indicate that RyR2 stabilization promotes an activation state shift in control microglia, a mechanism impaired in AD. These results highlight the role of ER Ca²⁺ receptors in both homeostatic and AD microglia, providing insights into microglial Ca²⁺ malfunctions in AD.
Animals ; Microglia/pathology* ; Alzheimer Disease/pathology* ; Phagocytosis/drug effects* ; Ryanodine Receptor Calcium Release Channel/metabolism* ; Disease Models, Animal ; Mice ; Cell Movement/drug effects* ; Mice, Transgenic ; Calcium Signaling/physiology* ; Calcium/metabolism* ; Mice, Inbred C57BL ; Male ; Endoplasmic Reticulum/metabolism*

Objective:To study the risk and pattern of deaths caused by various diseases in the population and in the case of long-term occupational X-ray exposure.Methods:Using a combination of retrospective and prospective cohort research methods, an exposure group of medical X-ray diagnostic workers who worked in the radiology department of Jiangsu Provincial Hospital between 1950 and 1980 and a control group of medical workers from other departments who did not work with radiolog during the same period in the same hospital were selected to form a study cohort. The cumulative number of person years of observation in the cohort during 1950-2011 was calculated, and the Cox regression model was used to calculate the risk of deaths of medical X-ray diagnostic workers for various diseases through adjusting sex, attained age, birth age, and starting working age.Results:A total of 6 953 follow-up visits was completed to the cohort, including 3 649 in the radiation group and 3 304 in the control group, with a total of 347 362 person years of observation. There were 2 099 deaths in the cohort as of December 31, 2021. Cox regression results showed that diseases with a significantly higher risk of death in the radiation group compared with the control group were: all-cancer ( RR=1.20, 95% CI: 1.04-1.39, Z=2.56, P<0.05, including lung cancer RR=1.52, 95% CI: 1.17-1.98, Z=3.10, P<0.01, and pancreatic cancer RR=1.96, 95% CI: 1.11-3.46, Z=2.31, P<0.05); diseases of the circulatory system ( RR=1.29, 95% CI: 1.06-1.57, Z=2.58, P<0.01); endocrine, nutritional and metabolic diseases ( RR=1.90, 95% CI: 1.06-3.42, Z=2.14, P<0.05). Stratified analyses showed that, of male workers, the radiation group had an increased risk of death caused by all-cancer and lung cancer ( Z=3.50, 2.92, P<0.01); of the age group at starting ages 26 to 30, the radiation group had an increased risk of death distributed to circulatory diseases ( Z=2.06, P<0.05); and in the age group at attained ages elder than 61 years, the radiation group had an increased risk of death distributed to all cancers, lung cancer, and circulatory diseases ( Z=2.90-4.31, P<0.01). Conclusions:As has been shown, threre is an increasing risk of death distributed to lung cancer, pancreatic cancer, and circulatory diseases in medical X-ray diagnostic workers in Jiangsu province. Further in-depth research in related areas may be conducted in the future.

Objective:To understand the basic situation of occupational internal exposure to 131I among nuclear medicine staff in Jiangsu province, and to analyze the relevant factors that contribute to the formation of internal exposure. Methods:A four-year external direct measurement of thyroid organ counting was conducted on nuclear medicine staff who underwent 131I treatment in Jiangsu province. The result were statistically analyzed, and the effective dose of accumulated internal exposure was estimated. The relationship between the detection rate of internal exposure and 10 possible factors was analyzed. Results:A total of 847 person-times were monitored during the monitoring period, of which 345 person-times were detected for 131I, with a detection rate of 40.7%. The highest individual monitoring result for internal exposure was 24.83 kBq, with an average value of 77.3 Bq and a median of 28.3 Bq among the detected individuals. The average committed effective dose of internal exposure for staff was 0.201 mSv, with a median of 0.074 mSv among the detected individuals. Conclusions:There is statistically significant difference in the detection rate of individual doses of internal exposure among nuclear medicine staff in each year. It has been observed that the main factors related to the detection rate are age, hospital grade, job position, location, whether there is an automatic dispensing operation, operating method, exposure level and length of exposure. Sufficient attention should be given to the situation of internal exposure, and further effective monitoring should be conducted on all such staff members.

Objective:To investigate the basic situation of occupational internal exposure to 131I among staff in the nuclear medicine department. Methods:Direct in- vitro measurements of thyroid doses from internal exposure were conducted for six months on 36 staff members of a hospital′s nuclear medicine department in Nanjing using the ORTEC Detective-100 portable high-purity germanium (HPGe) spectrometer. The cumulative effective doses were estimated, and the distribution of internal doses and their relationship with job type and external doses were analyzed. Results:During the monitoring period, a total of 203 monitorings were made. Of the monitoring result, 85.7% were below the recording level. Of the result exceeding the recording level, 12.8% were below the investigation level, while 1.5% exceeded the investigation level. The highest personal internal dose during the monitoring period was 2.54 mSv, the lowest was 0.015 mSv, and the median was 0.094 mSv.Conclusions:During the monitoring period, 14.3% of the monitored staff had result above the recorded levels. In the working environment of radionuclide treatment, there is no significant difference in the internal dose received between doctors, nurses, technicians, or other relevant personnel.

Objective:To explore the relationship between life events, anxiety, depression, and behavior problems in adolescents.Methods:From September to October 2022, the cluster sampling method was used to select 5 341 adolescents from 4 middle schools in Xinxiang urban area.The subjects and their parents were investigated by the adolescent self-rating life events check list (ASLEC), generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and child behavior checklist (CBCL). SPSS 27.0 software was used for Spearman correlation analysis, and AMOS 28.0 software was used to construct the structural equation model.Results:The scores of anxiety, depression, and behavioral problems were 1 (0, 4), 1 (0, 4), and 3 (0, 10). The total score of life events was 5 (1, 13), and the dimensions scored as follows: interpersonal conflict 1 (0, 4), academic pressure 2 (0, 5), punishment 0 (0, 2), loss 0 (0, 0), health and adaptation problem 0 (0, 1), and others 0 (0, 2). There were positive correlations between life events and its dimensions, depression, anxiety and behavioral problems ( r=0.28-0.69, all P<0.01). In the overall population, anxiety and depression played parallel mediating roles in the impact of life events on behavior problems. Life events could positively predict anxiety ( β=0.68, P<0.01), and anxiety could positively predict behavior problems ( β=0.04, P=0.02). Life events could positively predict depression ( β=0.77, P<0.01), and depression could positively predict behavior problems ( β=0.18, P<0.01). The standardized total effect size of the impact of life events on behavioral problems was 0.622 (95% CI=0.564-0.675). The standardized direct effect size and indirect effect size were 0.460 (95% CI=0.374-0.539) and 0.162 (95% CI=0.108-0.218), accounting for 74.0% and 26.0%of the total effect, respectively. After stratification by gender, the results for male adolescents were consistent with the overall population, while the mediating effect of anxiety was not significant in the female adolescents. Conclusion:Life events can lead to anxiety and depression in adolescents, thereby increasing the risk of behavior problems.