Jiangxi University of Traditional Chinese Medicine (Chinese: 江西中医药大学; pinyin: Jiangxi Zhong Yi Yao Da Xue), designated as a medical university in Nanchang, Jiangxi, is an institution of higher learning under the leadership of Jiangxi provincial government. According to the survey by CUCAS, Jiangxi is ranked 43rd in Chinese medical universities, 577th University in China by 4icu ranking.
Chronic pharyngitis (CP) is a prevalent but poorly defined inflammatory disorder of the upper aerodigestive tract. Despite increasing attention, research in this field remains fragmented, with heterogeneous diagnostic criteria and inconsistent therapeutic approaches. To clarify the intellectual landscape and inform research prioritization and hypothesis generation, we conducted the first comprehensive bibliometric analysis of CP research. Publications on CP from 2007 to 2025 were retrieved from the Web of Science Core Collection. After screening, 185 eligible articles were analyzed using VOSviewer, CiteSpace, and the bibliometrix R package. Trends in publication output, citation impact, collaboration networks, and thematic evolution were systematically evaluated. Global publication output increased steadily, with a peak in 2021. China contributed the largest number of articles, whereas the United States demonstrated greater citation impact and broader international collaboration. Major research hotspots included laryngopharyngeal reflux, proton pump inhibitors, and chronic cough. In contrast, microbial biofilms, neurogenic inflammation, epithelial barrier dysfunction, and patient-centered outcomes were underexplored. This study reveals critical gaps in CP research: lack of standardized diagnostic frameworks, insufficiently powered multicenter clinical trials, and limited international collaboration. Future priorities include harmonizing diagnostic criteria, validating biomarkers, and expanding investigations into microbial and neurogenic mechanisms. Strengthening methodological rigor and fostering global cooperation will be essential to translate bibliometric insights into a more coherent research framework that can guide future clinical and translational research.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a clinicopathological syndrome characterized by abnormal accumulation of fat within hepatocytes, and there is currently no standardized clinical treatment available. Consequently, there is an urgent need to discover new pharmacological interventions and to investigate novel therapeutic targets for MASLD. Melatonin, known for its multifaceted biological functions, has shown therapeutic potential for the treatment of MASLD. However, the underlying mechanisms remain unclear, particularly since the direct targets of melatonin remain poorly understood. In our study, we found that melatonin significantly improved various indicators in a mouse MASLD model and protected palmitic acid-induced mouse hepatocytes from lipid accumulation. We successfully identified and validated the mitochondrial trifunctional enzyme α-subunit HADHA as a binding target for melatonin using the cellular thermal shift assay (CETSA). This interaction enhanced the expression of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), which subsequently promotes mitochondrial biogenesis and accelerates lipid metabolism. In addition, melatonin reduced lipid accumulation and ameliorated MASLD through its regulatory effect on key proteins involved in fatty acid metabolism, including acyl coenzyme A oxidase 1 (ACOX1), cluster of differentiation 36 (CD36), and fatty acid synthase (FASN). Importantly, these beneficial effects were diminished when HADHA was knocked down. In conclusion, our study suggests that melatonin ameliorates MASLD through HADHA-mediated regulation of mitochondrial biogenesis and lipid oxidation, highlighting its potential as a promising therapeutic agent for MASLD. These results offer novel insights into the role of melatonin in the treatment of MASLD.
In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.
Carthamus tinctorius L. is widely used for cardiovascular and cerebrovascular diseases. Hydroxysafflor yellow A (HSYA), its major quinochalcone glycoside, is effective against myocardial ischaemia/reperfusion injury (MI/RI), while other compounds' roles remain unclear. This study established chemical fingerprints of 9 C. tinctorius populations via ultra-performance liquid chromatography, evaluated cardiomyocyte protective effects using Cell Counting Kit-8 assays and performed spectrum-effect analysis integrating grey correlation analysis and partial least-square (PLSR), combined with compound identification via ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Seven potential active components were screened: HSYA, kaempferol-3-O-β-sophorose, saffloquinoside A, roseoside, quercetin-3,7-di-O-β-D-glucoside, 6-hydroxykaempferol-3,6-di-O-β-D-glucoside, saffloquinoside C. These may be key anti-MI/RI substances in C. tinctorius, with specific pharmacological mechanisms to be further verified, providing a reference for exploring its representative active components.
