Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Medical artificial intelligence （AI） is a new type of application formed by the combination of machine learning， computer vision， natural language processing， and other technologies with clinical medical treatment. With the continuous iteration and development of relevant technologies， medical AI has shown great potential in improving the efficiency of diagnosis and treatment， and service quality， but it also increases the possibility of triggering ethical issues. Ethical issues resulting from the clinical application of medical AI were analyzed， including the lack of algorithmic interpretability and transparency of medical AI， leading to information asymmetry and cognitive discrepancies； the concerning status of security and privacy protection of medical data； and the complex and unclear division of responsibilities due to the collaborative participation of multiple subjects in the clinical application of medical AI， resulting in increased difficulty in the identification of medical accidents and clarification of responsibilities. The paper proposed the principles of not harming patients’ interests， physician’s subjectivity， fairness and inclusiveness， and rapid response. It also explored the strategies and implementation paths for responding to the ethical issues of medical AI from multiple perspectives， including standardizing the environment and processes， clarifying responsibility attribution， continuously assessing the impact of data protection， guaranteeing data security， ensuring model transparency and interpretability， carrying out multi-subject collaboration， as well as the principles of being driven by ethical values and adhering to the “human health-centeredness.” It aimed to provide guidance for the healthy development of medical AI， ensuring technological progress while effectively managing and mitigating accompanying ethical risks， thereby promoting the benign development of medical AI technology and better serving the healthcare industry and patients.

Microplastics are widely present in various environments such as water bodies,land,and atmosphere,which pose threats to the ecological environment and human health through transmission and accumulation in the food chain.The existing detection techniques for microplastics face challenges such as complex preparation procedure of samples,low efficiency in processing large batches of samples,and difficulties in handling complex samples.Therefore,there is an urgent need for rapid and efficient detection techniques suitable for complex microplastics samples in the field of environmental monitoring.Raman spectroscopy,known for its advantages such as rapidity,accuracy,high sensitivity,non-destructiveness,and non-contact,demonstrates great application potential in detection of microplastics.Deep learning,an artificial intelligence method known for its large-scale data processing,nonlinear modeling and automatic feature extraction capabilities,is receiving increasing attention in the analysis of Raman spectroscopy signals.The application of deep learning-based Raman spectroscopy has significantly improved performance indicators such as detection efficiency and accuracy.This article introduced the existing Raman enhancement techniques,summarized the deep learning methods applied in Raman spectroscopy signal analysis,reviewed the recent research and application progress of deep learning-based Raman spectroscopy in detection of microplastics,and finally discussed the challenges and future prospects of deep learning-based Raman spectroscopy in detection of microplastics.

The conversion of abundant and low-cost biomass waste into highly efficient two-electron oxygen reduction(ORR)electrocatalyst is an important link in the degradation of pollutants in industrial wastewater through the electro-Fenton process.In this work,porous biocarbon materials doped with manganese and nitrogen(MnNBC)were prepared from corn stalk.The H2O2 selectivity of MnNBC in acidic media was up to 81% @0.6 Vvs RHE,also MnNBC exhibited a long-term stability in a 10-h uninterrupted lifetime test.The ORR activity of MnNBC could be attributed to the synergistic effect of the hierarchical porous structure,improved defect level and heteroatom doping.Moreover,MnNBC as a cathode material for the electro-Fenton system could completely degrade four kinds of common organic dye pollutants,e.g.,Rhodamine B,methyl orange,methylene blue and crystal violet(25 mg/L),respectively,within 40?60 min.The present study provided valuable insights into the transformation of corn stalk waste into efficient cathode materials for the electro-Fenton process.

Nuclear receptor co-activator 4 (NCOA4) acts as a selective cargo receptor that binds to ferritin, a cytoplasmic iron storage complex. By mediating ferritinophagy, NCOA4 regulates iron metabolism and releases free iron in the body, thus playing a crucial role in a variety of biological processes, including growth, development, and metabolism. Recent studies have shown that NCOA4-mediated ferritinophagy is closely associated with the occurrence and development of iron metabolism-related diseases, such as liver fibrosis, renal cell carcinoma, and neurodegenerative diseases. In addition, a number of clinical drugs have been identified to modulate NCOA4-mediated ferritinophagy, significantly affecting disease progression and treatment efficacy. This paper aims to review the current research progress on the role of NCOA4-mediated ferritinophagy in related diseases, in order to provide new ideas for targeted clinical therapy.
Humans ; Nuclear Receptor Coactivators/physiology* ; Ferritins/metabolism* ; Animals ; Neurodegenerative Diseases/metabolism* ; Iron/metabolism* ; Autophagy/physiology* ; Liver Cirrhosis/metabolism* ; Carcinoma, Renal Cell/metabolism* ; Kidney Neoplasms/physiopathology*

Cold has been a long-term survival challenge in the evolutionary process of mammals. In response to cold stress, in addition to brown adipose tissue (BAT) dissipating energy as heat through glucose and lipid oxidation to maintain body temperature, cold stimulation can strongly activate thermogenesis and energy expenditure in beige fat cells, which are widely distributed in the subcutaneous layer. However, the effects of cold stimulation on other tissues and systemic lipid metabolism remain unclear. Our previous research indicated that, under cold stress, BAT not only produces heat but also secretes numerous exosomes to mediate BAT-liver crosstalk. Whether subcutaneous fat has a similar mechanism is still unknown. Therefore, this study aimed to investigate the alterations in lipid metabolism across various tissues under cold exposure and to explore whether subcutaneous fat regulates systemic glucose and lipid metabolism via exosomes, thereby elucidating the regulatory mechanisms of lipid metabolism homeostasis under physiological stress. RT-qPCR, Western blot, and H&E staining methods were used to investigate the physiological changes in lipid metabolism in the serum, liver, epididymal white adipose tissue, and subcutaneous fat of mice under cold stimulation. The results revealed that cold exposure significantly enhanced the thermogenic activity of subcutaneous adipose tissue and markedly increased exosome secretion. These exosomes were efficiently taken up by hepatocytes, where they profoundly influenced hepatic lipid metabolism, as evidenced by alterations in the expression levels of key genes involved in lipid synthesis and catabolism pathways. This study has unveiled a novel mechanism by which subcutaneous fat regulates lipid metabolism through exosome secretion under cold stimulation, providing new insights into the systemic regulatory role of beige adipocytes under cold stress and offering a theoretical basis for the development of new therapeutic strategies for obesity and metabolic diseases.
Animals ; Lipid Metabolism/physiology* ; Mice ; Exosomes/metabolism* ; Cold Temperature ; Subcutaneous Fat/physiology* ; Thermogenesis/physiology* ; Adipose Tissue, Brown/metabolism* ; Male

Signal detection is a critical task in drug safety regulation. However, it inevitably generates irrelevant or false signals, posing challenges for resource allocation by marketing authorization holders. To reasonably assess these signals, different countries have established various principles for prioritizing the evaluation of risk signals. This study systematically compares these principles and finds that the U.S. Food and Drug Administration(FDA) focuses on practical issues, such as identifying drug confusion or drug interactions. However, China's Good Pharmacovigilance Practices and the European Medicines Agency(EMA) emphasize a comprehensive evaluation framework. The Council for International Organizations of Medical Sciences(CIOMS) emphasizes the consistency of multiple data sources, highlighting the reliability of signal evaluation. China practices a multidisciplinary approach combining traditional Chinese and western medicine, and the risk signals related to traditional Chinese medicine(TCM) have unique characteristics, including complex components, cumulative toxicity, specific theoretical foundations, and drug interactions. The different priorities in risk signal evaluation principles across countries suggest that China should strengthen clinical trial research, emphasize corroboration with evidence of multiple sources, and pay particular attention to the risks of drug interactions in the TCM regulatory science. Establishing the risk signal prioritization principles that align with the characteristics of TCM enables more precise and efficient scientific regulation of TCM.
Humans ; Medicine, Chinese Traditional/standards* ; China ; Drugs, Chinese Herbal/adverse effects* ; United States ; United States Food and Drug Administration

To investigate the effects of phosphorus fertilizer on the morphological traits, active ingredients and rhizosphere soil microbial community of Polygala tenuifolia. The phosphorus fertilizer was calculated in terms of P_2O_5. Five treatments were set up: 0(CK), 17(P1), 34(P2), 51(P3), and 68(P4) kg per Mu(1 Mu≈667 m~2). A randomized block design was adopted. Samples of P. tenuifolia and its rhizosphere soil were collected under different superphosphate fertilizer treatments. Illumina high-throughput sequencing was used to analyze the rhizosphere soil microbial community, 9 morphological traits were measured and the content of 11 active ingredients were determined. The results showed that the whole plant weight, shoot fresh weight, root weight, and root peel thickness were the highest under P1 treatment, increasing by 34.41%, 38.80%, 39.21%, and 3.17% respectively compared to CK. Under P2 treatment, the plant height, stem diameter, root thickness, and core thickness were significantly higher than CK. Phosphorus fertilizer had a significant impact on the content of tenuifolin, sibiricose A5, sibiricose A6, arillanin A, 3,6'-disinapoyl sucrose, and polygalaxanthone Ⅲ. Correlation analysis results showed that the relative abundance of Arthrobacter, Bacillus, norank＿f＿Vicinamibacteraceae, norank＿o＿Vicinamibacterales, MND1 and other bacteria, as well as the relative abundance of Neocosmospora, Paraphoma and other fungi were positively correlated with root diameter, wood core diameter, the whole plant weight, root weight, shoot fresh weight of P. tenuifolia. Bacillus, Neocosmospora, Subulicystidium were significantly positively correlated with oligosaccharides such as 3,6'-disinapoyl sucrose, sibiricose A5、sibiricose A6、glomeratose A、arillanin A and tenuifoliside C. Arthrobacter, Humicola, Aspergillus, Paraphoma were positively correlated with tenuifolin and norank＿f＿Vicinamibacteraceae, norank＿o＿Vicinamibacterales, Fusarium were positively correlated with polygalaxanthone Ⅲ. Evidently, appropriate phosphorus application is conducive to the growth and quality improvement of P. tenuifolia, and can increase the abundance of beneficial microorganisms in the soil.
Rhizosphere ; Phosphorus/pharmacology* ; Soil Microbiology ; Polygala/anatomy & histology* ; Fertilizers/analysis* ; Bacteria/metabolism* ; Soil/chemistry* ; Microbiota/drug effects* ; Plant Roots/metabolism*

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.