Idiopathic pulmonary fibrosis （IPF） is a chronic and fibrotic lethal interstitial lung disease with poor prognosis. It is mainly treated by organ transplantation and administration of chemical drugs， which have poor efficacy and induce side effects， failing to meet the clinical needs. Therefore， it is urgent to develop more safe and effective drugs to treat IPF. Traditional Chinese medicine （TCM） has garnered increasing attention in recent years in the treatment of IPF due to its unique advantages. Increasing studies have shown that TCM has remarkable therapeutic effects on IPF and thus demonstrate broad application prospects. Modern medical research shows that the pathogenesis of IPF can be discussed from inflammation （macrophage polarization）， oxidative stress， epithelial-mesenchymal transition （EMT）， autophagy inhibition and other related signaling pathways， while few studies systematically explain the relationship between the signaling pathways and TCM theory. According to the theory of TCM， lung collateral obstruction is the basic pathogenesis of IPF. Therefore， according to the principle of dredging and replenishing lung collaterals， IPF can be treated with the methods of reinforcing healthy qi and eliminating pathogen， replenishing qi and activating blood， and detoxifying and dredging collaterals， which demonstrate definite curative effect and can effectively relieve clinical symptoms， restore the lung function and blood oxygen partial pressure， improve the quality of life of patients， and reduce adverse reactions. Experimental studies have found that dredging and replenishing lung collaterals have significant effects on IPF inflammation （macrophage polarization）， oxidative stress， EMT， autophagy inhibition and other signaling pathways. Therefore， from the perspective of impediment， this article reviews pathogenesis of IPF， the research progress in TCM treatment of IPF， and the treatment of IPF from active components， single herbs， and compound prescriptions of TCM， with the aim of revealing the scientific connotation of the treatment of IPF from impediment and providing a new theoretical basis for enriching the TCM methods of treating IPF.

Recent studies have indicated that the expression of ubiquitin-specific protease 51 (USP51), a novel deubiquitinating enzyme (DUB) that mediates protein degradation as part of the ubiquitin‒proteasome system (UPS), is associated with tumor progression and therapeutic resistance in multiple malignancies. However, the underlying mechanisms and signaling networks involved in USP51-mediated regulation of malignant phenotypes remain largely unknown. The present study provides evidence of USP51's functions as the prominent DUB in chemoresistant triple-negative breast cancer (TNBC) cells. At the molecular level, ectopic expression of USP51 stabilized the 78 kDa Glucose-Regulated Protein (GRP78) protein through deubiquitination, thereby increasing its expression and localization on the cell surface. Furthermore, the upregulation of cell surface GRP78 increased the activity of ATP binding cassette subfamily B member 1 (ABCB1), the main efflux pump of doxorubicin (DOX), ultimately decreasing its accumulation in TNBC cells and promoting the development of drug resistance both in vitro and in vivo. Clinically, we found significant correlations among USP51, GRP78, and ABCB1 expression in TNBC patients with chemoresistance. Elevated USP51, GRP78, and ABCB1 levels were also strongly associated with a poor patient prognosis. Importantly, we revealed an alternative intervention for specific pharmacological targeting of USP51 for TNBC cell chemosensitization. In conclusion, these findings collectively indicate that the USP51/GRP78/ABCB1 network is a key contributor to the malignant progression and chemotherapeutic resistance of TNBC cells, underscoring the pivotal role of USP51 as a novel therapeutic target for cancer management.

Glioma represents the most prevalent malignant tumor of the central nervous system, with chemotherapy serving as an essential adjunctive treatment. However, most chemotherapeutic agents exhibit limited ability to penetrate the blood-brain barrier (BBB). This study introduced a novel dual-targeting strategy for glioma therapy by modulating the formation of nanobody-driven protein coronas to enhance the brain and tumor-targeting efficiency of hydrophobic cisplatin prodrug-loaded lipid nanoparticles (C8Pt-Ls). Specifically, nanobodies (Nbs) with fibrinogen-binding capabilities were conjugated to the surface of C8Pt-Ls, resulting in the generation of Nb-C8Pt-Ls. Within the bloodstream, Nb-C8Pt-Ls could bound more fibrinogen, forming the protein corona that specifically interacted with LRP-1, a receptor highly expressed on the BBB. This interaction enabled a "Hitchhiking Effect" mechanism, facilitating efficient trans-BBB transport and promoting effective brain targeting. Additionally, the protein corona interacted with LRP-1, which is also overexpressed in glioma cells, achieving precise tumor targeting. Computational simulations and SPR detection clarified the molecular interaction mechanism of the Nb-fibrinogen-(LRP-1) complex, confirming its binding specificity and stability. Our results demonstrated that this strategy significantly enhanced C8Pt accumulation in brain tissues and tumors, induced apoptosis in glioma cells, and improved therapeutic efficacy. This study provides a novel framework for glioma therapy and underscores the potential of protein corona modulation-based dual-targeting strategies in advancing treatments for brain tumors.

BACKGROUND: High temperatures are known to be associated with an increased risk of self-harm, but the influence of demographic changes and country-level indicators on the burden of heat-related self-harm remains unclear. This study examined the key factors driving changes in self-harm mortality linked to high temperatures and explored their impact at the country level. METHODS: This is an ecological study that analyzes data from the 2021 Global Burden of Disease (GBD) study, the World Bank, and the Climate Research Unit (CRU) were analyzed. Decomposition analyses were used to identify key factors driving changes in high temperature-related self-harm mortality between 1990 and 2021. A panel data model assessed the impact of national indicators on heat-related self-harm mortality. RESULTS: In 2021, 14,885 deaths globally were attributed to heat-related self-harm, a 41.94% increase from 1990, with low-middle SDI regions accounting for 47.84% of these deaths. While the global death rate from heat-related self-harm declined slightly over this period, South Asia and low-middle SDI regions contributed most to the decline. However, population aging exacerbated mortality rates. Demographic and meteorological factors were also linked to heat-related self-harm. CONCLUSION The global decline in heat-related self-harm mortality is largely driven by reductions in females, low-middle SDI regions, and South Asia. However, population aging and growth in these regions have added to the mortality burden, slowing the overall decline. Factors such as population density are also associated with heat-related self-harm. Targeted measures are needed to mitigate heat-induced self-harm more effectively in future.
Humans ; Self-Injurious Behavior/etiology* ; Hot Temperature/adverse effects* ; Global Health/statistics & numerical data* ; Female ; Male ; Adult ; Middle Aged ; Aged ; Young Adult ; Adolescent

Objective:To investigate the effect of circular RNA (circRNA)_005987 on contrast-associated acute kidney injury (CA-AKI) and its mechanism, and provide new ideas for the prevention and treatment of CA-AKI.Methods:CA-AKI rat models and HK-2 cell injury models were established using iopromide, and CA-AKI-related circRNA_005987 was screened based on circRNA expression chip and real-time quantitative PCR (RT-qPCR). Knockdown and overexpression of circRNA_005987 were performed in HK-2 cell model, respectively. Cell counting kit-8 (CCK-8) and Edu staining assays were used to evaluate cell proliferation. Western blotting was used to detect the protein expression of autophagy-related protein microtubule-associated protein 1 light chain 3B (LC3B), P62, beclin-1 and autophagy-related gene 14 (ATG14). Immunofluorescence staining was used to detect protein expression of LC3B. Electron microscope was used to observe the autophagosome formation. Autophagy activator rapamycin and autophagy inhibitor 3-methyladenine were used for in vitro rescue experiments to observe the changes of the above indicators. Mechanistically, bioinformatics analysis was applied to analyze the binding site among circRNA_005987, miR-129-5p and ATG14, and dual luciferase reporter assay was used to verify their interactions. CircRNA_005987 was knocked down and overexpressed in HK-2 cell model, and RT-qPCR was used to detect the expression of miR-129-5p. HK-2 cells were treated with miR-129-5p inhibitor and mimic, Western blotting was used to detect the protein expression of ATG14, and CCK8 and Edu staining assays were used to evaluate cell proliferation. Results:CircRNA_005987 expression was up-regulated in vitro and vivo CA-AKI models (both P<0.05). Overexpression of circRNA_005987 inhibited cell proliferation and promoted cell autophagy, while knockdown of circRNA_005987 had opposite effects (all P<0.05). In vitro rescue experiments confirmed that circRNA_005987 inhibited cell proliferation by activating autophagy ( P<0.05). The dual luciferase reporter assay suggested that there was an interaction between circRNA_005987, miR-129-5p and ATG14. Knockdown of circRNA_005987 increased miR-129-5p expression, while overexpression of circRNA_005987 inhibited miR-129-5p expression (both P<0.05). Knockdown of miR-129-5p inhibited cell proliferation, while overexpression of miR-129-5p reversed the effect (both P<0.05). Conclusion:CircRNA_005987 promotes CA-AKI through activating autophagy via sponging miR-129-5p, suggesting that circRNA_005987 plays an important role in the pathological process of CA-AKI.

Objective:To preliminarily establish a sample clot warning model for coagulation screening tests using 5 machine learning methods.Methods:This cross-sectional study collected 7 401 routine screening test samples from Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, from January 1st, 2015, to August 18th, 2024, including 4 786 clotted (positive) and 2 615 qualified (negative) samples for model development. The dataset was divided into Dataset 1 and Dataset 2 based on a reagent change for APTT in December 2018, with separate models developed for each. An additional 2 493 samples (October 31st to November 8th, 2024) were used to evaluate consistency between the model and manual assessment, while 23 200 samples (October 17th to December 31st, 2024) were used for assessing real-world predictive performance. Five machine learning algorithms were employed to develop the clot prediction model: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), naive bayes (NB), and artificial neural network (ANN), with the ANN model constructed using two different hidden layer and neuron parameter settings. Model selection was based on AUC, accuracy, sensitivity, specificity, F1-score, PPV, and NPV, with the optimal model integrated into the LIS for validation.Results:Among the six models using 5 machine learning algorithms, XGBoost demonstrated the highest performance (AUC=0.961, sensitivity=0.945, F1-score=0.934) and robustness to reagent changes ( Z=-1.333, P=0.113). When deployed, the differences between the model's predictions and manual pre-judgment were statistically significant ( Z=-5.289 to 8.933, all P<0.01). The predictive efficacy indices AUC (95% CI), sensitivity, specificity, and accuracy of the XGBoost model deployed in real-world operation of the LIS were 0.939 (0.918—0.960), 0.958, 0.921, and 0.921 respectively. Conclusion:In this study, a clot warning model for coagulation screening samples was established based on the XGBoost algorithm, and its prediction efficacy is good, providing a foundation for intelligent pre-analytical quality control for coagulation screening tests.

Objective To explore the time-varying pattern of potential symptoms in breast cancer patient before and after chemotherapy,and further analyze its influencing factors,aims to provide reference for clinical nursing practice.Methods 233 cases of breast cancer in a tertiary A hospital in Ningxia from June to October,2023 were selected as the research subjects.By the general information questionnaire and the Chinese version of Anderson Symptom Assessment Scale,the potential change of symptoms of breast cancer patients undergoing chemotherapy 1-3 days before the first chemotherapy(T1,233 cases)and 7-14 days after the third or fourth chemotherapy(T2,225 cases)was analyzed.Results 225 cases of breast cancer patients completed 2 surveys,and their symptom characteristics before and after chemotherapy can be divided into 2 categories:a high symptom troubled group and a low symptom troubled group.The high-symptom troubled group has strong stability,and the probability of maintaining the original group is 85.20％,while the low-symptom troubled group is more inclined to change to the high-symptom troubled group,and the transition probability is 26.60％.Logistic regression analysis showed that the mass was located on the left side,and the patients who obtained disease knowledge mainly through the Internet were more likely to change from the high symptom troubled group to the low symptom troubled group,while the patients who were employed,with unsatisfactory sleep at night,and never exercised were more likely to change from the low symptom troubled group to the high symptom troubled group.Conclusion The symptoms of breast cancer patients before and after chemotherapy are heterogeneous.Patients who are on the job,have unsatisfied sleep at night and never exercise are more likely to change from the low-symptom troubled group to the high-symptom troubled group,which suggests that medical staff should identify potential high-risk groups at an early stage,and make full use of the predictive function of symptom characteristics to carry out individualized management based on the dynamic monitoring of symptoms according to different types of population characteristics,so as to reduce the burden of symptoms and improve their quality of life.