Haiyu Zhang

Background: Acute aortic dissection (AAD) is a cardiovascular catastrophe with substantial early mortality. Although ambient cold has been implicated as a trigger, existing evidence derives predominantly from temperate and subtropical settings. Populations enduring protracted severe winters–a hallmark of cold temperate zones–remain underrepresented, leaving the cardiovascular hazard of sustained extreme cold poorly defined. Methods: In this retrospective time-stratified case-crossover study, we analyzed 1 384 patients with AAD admitted to a tertiary cardiovascular center in Harbin, China–a city in the cold temperate zone of Northeast Asia with a harsh continental monsoon climate. Meteorological data (2014–2023) were interrogated using distributed lag non-linear models combined with conditional logistic regression to quantify exposure lag-response relationships between daily mean temperature and temperature change between neighboring days (TCN) with the AAD onset. Results: A near-linear increase in AAD risk was observed with decreasing daily mean temperature, with minimum morbidity at 25.9 °C. Extreme cold (−23.4 °C, 1st percentile) raised the risk significantly within 3 days. A pronounced risk elevation followed temperature drops between consecutive days–a decline of 9.2 °C was associated with a RR of 9.57 (95% CI 1.21–75.76). In contrast, temperature rises conferred protection. These effects were accentuated in winter and during the period of centralized indoor heating. Conclusion: In a cold temperate region marked by severe winters, both low ambient temperature and abrupt cooling act as potent environmental triggers for AAD. These findings call for enhanced clinical awareness and tailored preventive measures during cold exposure in such climates.

Background: Stem cells play an important role in acute myeloid leukemia (AML). However, their precise effect on AML tumorigenesis and progression remains unclear.Methods: The present study aimed to characterize stem cell-related gene expression and identify stemness biomarker genes in AML. We calculated the stemness index (mRNAsi) based on transcription data using the one-class logistic regression (OCLR) algorithm for patients in the training set. According to the mRNAsi score, we performed consensus clustering and identified two stemness subgroups. Eight stemness-related genes were identified as stemness biomarkers through gene selection by three machine learning methods.Results: We found that patients in stemness subgroup I had a poor prognosis and benefited from nilotinib, MK-2206 and axitinib treatment. In addition, the mutation profiles of these two stemness subgroups were different, which suggested that patients in different subgroups had different biological processes. There was a strong significant negative correlation between mRNAsi and the immune score (r= -0.43, p<0.001). Furthermore, we identified eight stemness-related genes that have potential to be biomarkers, including SLC43A2, CYBB, CFP, GRN, CST3, TIMP1, CFD and IGLL1. These genes, except IGLL1, had a negative correlation with mRNAsi. SLC43A2 is expected to be a potential stemness-related biomarker in AML.Conclusion: Overall, we established a novel stemness classification using the mRNAsi score and eight stemness-related genes that may be biomarkers. Clinical decision-making should be guided by this new signature in prospective studies.

Primitive neuroectodermal tumors (PNET) are rare malignant tumors, but the mortality rate of the patients is extremely high. The aim of this study was to identify the hub genes and pathways involved in the pathogenesis of PNET and to screen the potential small molecule drugs for PNET. We extracted gene expression profiles from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) through Limma package in R. Two expression profiles (GSE14295 and GSE74195) were downloaded, including 33 and 5 cases separately. Four hundred sixty-eight DEGs (161 upregulated; 307 downregulated) were identified. Functional annotation and KEGG pathway enrichment of the DEGs were performed using DAVID and Kobas. Gene Ontology analysis showed the significantly enriched Gene Ontology terms included but not limited to mitosis, nuclear division, cytoskeleton, synaptic vesicle, syntaxin binding, and GABA A receptor activity. Cancer-related signaling pathways, such as DNA replication, cell cycle, and synaptic vesicle cycle, were found to be associated with these genes. Subsequently, the STRING database and Cytoscape were utilized to construct a protein-protein interaction and screen the hub genes, and we identified 5 hub genes (including CCNB1, CDC20, KIF11, KIF2C, and MAD2L1) as the key biomarkers for PNET. Finally, we identified potential small molecule drugs through CMap. Seven small molecule compounds, including trichostatin A, luteolin, repaglinide, clomipramine, lorglumide, vorinostat, and resveratrol may become potential candidates for PNET drugs.