Moossavi, Maryam

BACKGROUND: Anthracyclines are a widely used and effective class of chemotherapy. However, a major limitation for their use is cardiotoxicity, manifesting as systolic dysfunction, congestive heart failure, and arrhythmias. The objective of this study was to identify genes and genetic risk variants for doxorubicin-induced arrhythmia. METHODS: We performed genome-wide association studies and meta-analysis across 2 independent nonoverlapping, genetically homogenous data sets from the Molecular Epidemiology Resource (cases N=77, controls N=1184) and Tapestry (cases N=13, controls N=172). Doxorubicin-related arrhythmia was the primary cardiac outcome, followed by investigation of a broader phenotype of cardiac events. Effects of doxorubicin on top-associated genes were assessed by quantitative polymerase chain reaction on RNA extracted from human cardiomyocytes following treatment with doxorubicin and by echocardiography in a zebrafish model of doxorubicin-induced cardiomyopathy. RESULTS: In the meta-analysis, rs12151014 at the KCTD15 locus was associated with doxorubicin-related arrhythmia at the genome-wide significance (minor allele frequency in Molecular Epidemiology Resource cases 0.195 versus 0.069 in controls; Tapestry cases 0.192 versus 0.079 in controls) P =9.01×10 −9 ; odds ratio, 3.61. MMP 16 rs1094199 was associated with the broader phenotype of arrhythmia and heart failure (minor allele frequency in Molecular Epidemiology Resource cases 0.079 versus 0.019 in controls; Tapestry cases 0.038 versus 0.015) P =1.98×10 − 8 ; odds ratio=6.15. In human cardiomyocytes, MMP16 and KCTD15 gene expression was significantly downregulated by doxorubicin, and in a zebrafish MMP16 knockout was protective of doxorubicin-induced decline in left ventricular ejection fraction. CONCLUSIONS: Genetic variants at KCTD15 and MMP16 are associated with doxorubicin-related arrhythmia and cardiac events. MMP16 is a potential cardioprotective therapeutic target.

Abstract Objective: This study aimed to develop a machine learning model for the diagnosis of chronic renal failure (CRF) with spleen–kidney qi deficiency (SKQD) syndrome by investigating facial chromatic biomarkers based on traditional Chinese medicine (TCM) theory. Materials and Methods: This cross-sectional study analyzed TCM-defined facial regions in 303 patients with CRF (166 SKQD and 137 non-SKQD) and 25 healthy controls using active appearance models and RGB/Lab color spaces. Key chromatic features were selected using Chi-square automatic interaction detection (CHAID) decision trees to construct a multibranch deep convolutional neural network (CNN), nomogram, and hybrid model. The model performances were compared using the DeLong test. Results: The SKQD group showed a higher glomerular filtration rate, albumin level, and facial R-value with a lower serum creatinine level (all P < 0.05), accompanied by a higher prevalence of shortness of breath with reluctance to speak, and cold extremities ( P < 0.05) compared with the non-SKQD group. Chromatic analysis revealed elevated R/a values in the kidney region using the natural facial color region method ( P < 0.05), and distinctively higher R -values in the bladder-uterus region and a -values in the right small intestine region using the Mingtang facial color region method ( P < 0.05). The CHAID algorithm selected four diagnostic determinants, including the overall facial R -values. The CNN achieved superior discriminative performance (test area under the receiver operating characteristic curve = 0.74 vs. nomogram 0.68, DeLong test P < 0.001) with no added benefit from model integration ( P > 0.05). Conclusions: Facial chromatic biomarkers effectively differentiated SKQD in patients with CRF when analyzed using TCM-defined regions. The superior performance of the CNN validates AI-driven TCM diagnostics as a clinically actionable tool for precise syndrome classification.

Context: Traditional Chinese medicine (TCM) techniques, grounded in TCM theory, offer flexible and personalized therapeutic strategies. Increasing evidence indicates that gut microbiota dysbiosis is closely associated with the onset and progression of various diseases, highlighting a complex mutual relationship. Investigating the interaction between TCM techniques and gut microbiota may provide both a theoretical and practical foundation for clinical application. However, comprehensive systematic analyses remain limited. Objective: This study aimed to identify core themes and emerging trends in research on the interactions between TCM techniques and gut microbiota. In addition, it sought to explore underlying correlations using data mining approaches to provide insights supporting clinical applications and mechanistic studies. Materials and Methods: A systematic literature review was conducted using the Web of Science Core Collection and China National Knowledge Infrastructure for bibliometric analysis. Co-occurrence analysis was performed using CiteSpace software to identify research characteristics and development trends. Key data were extracted and visualized through an interactive network diagram generated with Cytoscape. Results: Current research predominantly focuses on acupoint application, moxibustion, and TCM enema, all of which exhibit significant effects in promoting beneficial microbiota and suppressing pathogenic strains. The regulatory impact of these TCM techniques on gut microbiota is closely associated with inflammatory response pathways and immunity. Conclusions: TCM techniques are extensively applied in disease management. Among them, moxibustion, acupoint application, TCM enema, and Tuina remain prominent and evolving areas of interest. Further empirical research is warranted to elucidate the biological mechanism underpinning these effects within the guidance of TCM pattern theory.