A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia.

Introduction: Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation. Materials and methods: We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups. Results: By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729. Conclusion: In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.

Multi-Dimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer (CRC) is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage CRC detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 CRC patients and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish CRC patients from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 CRC patients and 119 controls) and a prospective cohort of 242 participants (129 CRC patients and 113 controls). The ensemble stacked model showed remarkable discriminatory power between CRC patients and controls, outperforming all base models and achieving a high area under the ROC curve (AUC) of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting CRC in the validation cohort, with sensitivity increasing as cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early CRC detection and broad patient benefit.

Emerging Role of ERBB2 in Targeted Therapy for Metastatic Colorectal Cancer: Signaling Pathways to Therapeutic Strategies.

Despite recent improvements in the comprehensive therapy of malignancy, metastatic colorectal cancer (mCRC) continues to have a poor prognosis. Notably, 5% of mCRC cases harbor Erb-B2 receptor tyrosine kinase 2 (ERBB2) alterations. ERBB2, commonly referred to as human epidermal growth factor receptor 2, is a member of the human epidermal growth factor receptor family of protein tyrosine kinases. In addition to being a recognized therapeutic target in the treatment of gastric and breast malignancies, it is considered crucial in the management of CRC. In this review, we describe the molecular biology of ERBB2 from the perspective of biomarkers for mCRC-targeted therapy, including receptor structures, signaling pathways, gene alterations, and their detection methods. We also discuss the relationship between ERBB2 aberrations and the underlying mechanisms of resistance to anti-EGFR therapy and immunotherapy tolerance in these patients with a focus on novel targeted therapeutics and ongoing clinical trials. This may aid the development of a new standard of care in patients with ERBB2-positive mCRC.

Effective inhibition of coronavirus replication by Polygonum cuspidatum.

Background: The coronavirus disease 2019 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected more than 210 million individuals globally and resulted in over 4 million deaths since the first report in December 2019. The early use of traditional Chinese medicine (TCM) for light and ordinary patients, can rapidly improve symptoms, shorten hospitalization days and reduce severe cases transformed from light and normal. Many TCM formulas and products have a wide application in treating infectious and non-infectious diseases. Polygonum cuspidatum Sieb. et Zucc. (P. cuspidatum), is an important Traditional Chinese Medicine with actions of clearing away heat and eliminating dampness, draining the gallbladder to relieve jaundice, removing blood stasis to alleviate pain, resolving phlegm and arrest cough. In the search for anti-SARS-CoV-2, P. cuspidatum was recommended as as a therapeutic drug of COVID-19 pneumonia.In this study, we aimed to identifies P. cuspidatum is the potential broad-spectrum inhibitor for the treatment of coronaviruses infections. Methods: In the present study , we infected human malignant embryonal rhabdomyoma (RD) cells with the OC43 strain of the coronavirus, which represent an alternative model for SARS-CoV-2 and then employed the cell viability assay kit for the antiviral activity. We combined computer aided virtual screening to predicte the binding site and employed Surface plasmon resonance analysis (SPR) to comfirm the interaction between drugs and coronavirus. We employed fluorescence resonance energy transfer technology to identify drug's inhibition in the proteolytic activity of 3CLpro and Plpro. Results: Based on our results, polydatin and resveratrol derived from P. cuspidatum significantly suppressed HCoV-OC43 replication. 50% inhibitory concentration (IC50) values of polydatin inhibited SARS-CoV-2 Mpro and Plpro, MERS Mpro and Plpro were 18.66, 125, 14.6 and 25.42 µm, respectively. IC50 values of resveratrol inhibited SARS-CoV-2 Mpro and Plpro, MERS Mpro and Plpro were 29.81 ,60.86, 16.35 and19.04 µM, respectively. Finally, SPR assay confirmed that polydatin and resveratrol had high affinity to SARS-CoV-2, SARS-CoV 3Clpro, MERS-CoV 3Clpro and PLpro protein. Conclusions: we identified the antiviral activity of flavonoids polydatin and resveratrol on RD cells. Polydatin and resveratrol were found to be specific and selective inhibitors for SARS-CoV-2, 3CLpro and PLpro, viral cysteine proteases. In summary, this study identifies P. cuspidatum as the potential broad-spectrum inhibitor for the treatment of coronaviruses infections.