A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features.

OBJECTIVE: The aim of this study is to develop a nomogram model for predicting the occurrence of intramyocardial hemorrhage (IMH) in patients with Acute Myocardial Infarction (AMI) following Percutaneous Coronary Intervention (PCI). The model is constructed utilizing clinical data and the SYNTAX Score (SS), and its predictive value is thoroughly evaluated. METHODS: A retrospective study was conducted, including 216 patients with AMI who underwent Cardiac Magnetic Resonance (CMR) within a week post-PCI. Clinical data were collected for all patients, and their SS were calculated based on coronary angiography results. Based on the presence or absence of IMH as indicated by CMR, patients were categorized into two groups: the IMH group (109 patients) and the non-IMH group (107 patients). The patients were randomly divided in a 7:3 ratio into a training set (151 patients) and a validation set (65 patients). A nomogram model was constructed using univariate and multivariate logistic regression analyses. The predictive capability of the model was assessed using Receiver Operating Characteristic (ROC) curve analysis, comparing the predictive value based on the area under the ROC curve (AUC). RESULTS: In the training set, IMH post-PCI was observed in 78 AMI patients on CMR, while 73 did not show IMH. Variables with a significance level of P < 0.05 were screened using univariate logistic regression analysis. Twelve indicators were selected for multivariate logistic regression analysis: heart rate, diastolic blood pressure, ST segment elevation on electrocardiogram, culprit vessel, symptom onset to reperfusion time, C-reactive protein, aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatine kinase-MB, high-sensitivity troponin T (HS-TnT), and SYNTAX Score. Based on multivariate logistic regression results, two independent predictive factors were identified: HS-TnT (Odds Ratio [OR] = 1.61, 95% Confidence Interval [CI]: 1.21-2.25, P = 0.003) and SS (OR = 2.54, 95% CI: 1.42-4.90, P = 0.003). Consequently, a nomogram model was constructed based on these findings. The AUC of the nomogram model in the training set was 0.893 (95% CI: 0.840-0.946), and in the validation set, it was 0.910 (95% CI: 0.823-0.970). Good consistency and accuracy of the model were demonstrated by calibration and decision curve analysis. CONCLUSION: The nomogram model, constructed utilizing HS-TnT and SS, demonstrates accurate predictive capability for the risk of IMH post-PCI in patients with AMI. This model offers significant guidance and theoretical support for the clinical diagnosis and treatment of these patients.

A novel natural killer-related signature to effectively predict prognosis in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor that poses a significant threat to human health, with 80% of cases being primary HCC. At present, Early diagnosis and predict prognosis of HCC is challenging and the it is characterized by a high degree of invasiveness, both of which negatively impact patient prognosis. Natural killer cells (NK) play an important role in the development, diagnosis and prognosis of malignant tumors. The potential of NK cell-related genes for evaluating the prognosis of patients with hepatocellular carcinoma remains unexplored. This study aims to address this gap by investigating the association between NK cell-related genes and the prognosis of HCC patients, with the goal of developing a reliable model that can provide novel insights into evaluating the immunotherapy response and prognosis of these patients. This work has the potential to significantly advance our understanding of the complex interplay between immune cells and tumors, and may ultimately lead to improved clinical outcomes for HCC patients. METHODS: For this study, we employed transcriptome expression data from the hepatocellular carcinoma cancer genome map (TCGA-LIHC) to develop a model consisting of NK cell-related genes. To construct the NK cell-related signature (NKRLSig), we utilized a combination of univariate COX regression, Area Under Curve (AUC) LASSO COX regression, and multivariate COX regression. To validate the model, we conducted external validation using the GSE14520 cohort. RESULTS: We developed a prognostic model based on 5-NKRLSig (IL18RAP, CHP1, VAMP2, PIC3R1, PRKCD), which divided patients into high- and low-risk groups based on their risk score. The high-risk group was associated with a poor prognosis, and the risk score had good predictive ability across all clinical subgroups. The risk score and stage were found to be independent prognostic indicators for HCC patients when clinical factors were taken into account. We further created a nomogram incorporating the 5-NKRLSig and clinicopathological characteristics, which revealed that patients in the low-risk group had a better prognosis. Moreover, our analysis of immunotherapy and chemotherapy response indicated that patients in the low-risk group were more responsive to immunotherapy. CONCLUSION: The model that we developed not only sheds light on the regulatory mechanism of NK cell-related genes in HCC, but also has the potential to advance our understanding of immunotherapy for HCC. With its strong predictive capacity, our model may prove useful in evaluating the prognosis of patients and guiding clinical decision-making for HCC patients.

In vivo bioimaging and detection of endogenous hypochlorous acid in lysosome using a near-infrared fluorescent probe.

The phagocyte's lysosome is the primary site of hypochlorous acid (HOCl) synthesis, and HOCl can be used as a biomarker for osteoarthritis diagnosis and treatment evaluation. Accurate detection of HOCl with high sensitivity and selectivity is required to understand its activities in healthy bio-systems and diseases. By integrating acceptable design principles and dye screening methodologies, we proposed and developed a novel near-infrared fluorescent HOCl sensing probe (FNIR-HOCl). The FNIR-HOCl probe has a quick reaction rate, high sensitivity (LOD = 70 nM), and excellent selectivity toward HOCl over other metal ions and reactive oxygen species. It has been successfully implemented to detect endogenous HOCl produced by RAW264.7 cells, as well as in vivo imaging towards mice with osteoarthritis. As a result, the probe FNIR-HOCl is extremely promising as a biological tool for revealing the roles of HOCl in various physiological and pathological contexts.

The effects of high-pressure treatment on the structure, physicochemical properties and digestive property of starch - A review.

High hydrostatic pressure (HHP) is a novel technology used in the food-processing industry. Starch is an important renewable natural resource. The applications of starch are determined by its properties, which in turn are determined by its structure. In this study, the effects of HHP treatment on starch structure (granular structure, crystalline structure, molecular structure, and molecular conformation) and properties (pasting, retrogradation, thermal, digestive, rheological, swelling, solubility, water absorption, and oil absorption properties) are summarised. Additionally, the mechanism of HHP-induced gelatinisation is discussed. First, the strong hydration ability of starch molecules under high pressure facilitates the binding of water molecules to starch molecules via hydrogen bonding. These bound water molecules may block the channels inside the starch granules, leading to the formation of a sealed space. Finally, the granules disintegrate because of the intra/extra pressure difference. This study provides a reference for the application of HHP to starch processing and modification.

A novel near-infrared fluorescent probe with hemicyanine scaffold for sensitive mitochondrial pH detection and mitophagy study.

Mitochondria, as an energy-producing powerhouse in live cells, is considered to be directly linked to cellular health. However, dysfunctional mitochondria and abnormal mitochondria pH would possibly activate mitophagy, cell apoptosis and intercellular acidification process. In this work, we synthesized a novel near infrared fluorescent probe (FNIR-pH) for measurement of mitochondrial pH based on the hemicyanine skeleton as a fluorophore. The FNIR-pH probe functioned as a mitochondrial pH substrate and exhibited quick and sensitive turn-on fluorescence responses to mitochondrial pH in basic solution due to the deprotonation of hydroxy group in the structure. From pH 3.0 to 10.0, the FNIR-pH exhibited almost 100-fold increase in fluorescence intensity at 766 nm wavelength. The FNIR-pH also displayed superior selectivity to various metal ions, excellent photostability, and low cytotoxicity, which facilitated further biological application. Owing to the proper pKa value of 7.2, the FNIR-pH paved the way for real-time monitoring of mitochondria pH changes in live cells and sensitive sensing of mitophagy. Moreover, the FNIR-pH probe was also implemented for fluorescent imaging of tumor-bearing mice to validate its potential application for in vivo imaging of bioanalytes and biomarkers.

Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches.

BACKGROUND: The wide adoption of electronic health record systems (EHRs) in hospitals in China has made large amounts of data available for clinical research including breast cancer. Unfortunately, much of detailed clinical information is embedded in clinical narratives e.g., breast radiology reports. The American College of Radiology (ACR) has developed a Breast Imaging Reporting and Data System (BI-RADS) to standardize the clinical findings from breast radiology reports. OBJECTIVES: This study aims to develop natural language processing (NLP) methods to extract BI-RADS findings from breast ultrasound reports in Chinese, thus to support clinical operation and breast cancer research in China. METHODS: We developed and compared three different types of NLP approaches, including a rule-based method, a traditional machine learning-based method using the Conditional Random Fields (CRF) algorithm, and deep learning-based approaches, to extract all BI-RADS finding categories from breast ultrasound reports in Chinese. RESULTS: Using a manually annotated dataset containing 540 reports, our evaluation shows that the deep learning-based method achieved the best F1-score of 0.904, when compared with rule-based and CRF-based approaches (0.848 and 0.881 respectively). CONCLUSIONS: This is the first study that applies deep learning technologies to BI-RADS findings extraction in Chinese breast ultrasound reports, demonstrating its potential on enabling international collaborations on breast cancer research.