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Background Intradialytic hypotension (IDH) is the primary complication of haemodialysis (HD); however, its diverse pathophysiology and inconsistent definitions complicate its prediction. Despite attempts using the heart rate variability (HRV) test for IDH prediction, studies on its usefulness for predicting IDH diagnosed per the nadir 90 criterion are lacking. We aimed to evaluate HRV test efficacy and reproducibility in predicting IDH based on the nadir 90 criterion. Methods Seventy patients undergoing HD participated in this multicentre prospective observational study. The HRV test was performed during non-HD periods and IDH was monitored during 12 HD sessions. IDH was diagnosed according to the nadir 90 criterion, defined as a decrease in systolic blood pressure of ≤90 mmHg during HD. After monitoring, the HRV test was repeated. An HRV–IDH index was developed using multivariate logistic regression analysis employing HRV test parameters. The predictive power of the HRV–IDH index was analysed using the area under the receiver operating characteristics curve (AUROC). Reproducibility was evaluated using correlation analysis of two HRV tests on the same patient. Results There were 37 and 33 patients in the IDH and non-IDH groups, respectively. The HRV–IDH index predicted IDH occurrence with AUROCs of 0.776 and 0.803 for patients who had experienced at least one or repeated IDH episodes, respectively. Spearman's correlation coefficient for HRV–IDH indices was 0.859 for the first and second HRV tests. Conclusions The HRV test holds promise for predicting IDH, particularly for patients with recurring IDH diagnosed based on the nadir 90 criterion. [ABSTRACT FROM AUTHOR]

Background: Small dense low-density lipoprotein cholesterol (sdLDL-C) is the lipoprotein marker among the various lipoproteins that is most strongly related to atherosclerosis. Insulin resistance (IR) can alter lipid metabolism, and sdLDL-C is characteristic of diabetic dyslipidemia. Therefore, this study sought to inspect the relationship between the triglyceride-glucose (TyG) index and mean low-density lipoprotein (LDL) particle size. Methods: In this study, a total of 128 adults participated. The correlation coefficients between various lipoproteins and the TyG index were compared using Steiger's Z test and the Spearman correlation. The independent link between the TyG index and mean LDL particle size was demonstrated by multiple linear regression analysis. To identify the TyG index cutoff value for the predominance of sdLDL particles, receiver operating characteristic curves were plotted. Results: Mean LDL particle size correlated more strongly with the TyG index than did very low-density lipoprotein, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Regression analysis demonstrated that mean LDL particle size had a strong association with the TyG index (β coefficient = -0.038, P-value < 0.001). The TyG index optimal cutoff value for sdLDL particle predominance and the corresponding area under the curve (standard error: 0.028, 95% confidence interval: 0.842–0.952) were 8.72 and 0.897, respectively, which were close to the cutoff value of diabetes risk in Koreans. Conclusions: Mean LDL particle size is more strongly correlated with the TyG index than do other lipid parameters. After correcting for confounding variables, mean LDL particle size is independently linked with the TyG index. The study indicates that the TyG index is strongly related to atherogenic sdLDL particles predominance. [ABSTRACT FROM AUTHOR]

Background: Paclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy. Methods: This study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative. The data included clinical (age [young-onset vs. others], sex, histology [intestinal vs. diffuse type], prior trastuzumab use, duration of first-line chemotherapy), and genomic factors (pathogenic or likely pathogenic variants). Data were randomly divided into training and validation sets (0.8:0.2). Four machine learning (ML) methods, namely random forest (RF), logistic regression (LR), artificial neural network (ANN), and ANN with genetic embedding (ANN with GE), were used to develop the prediction model and validated in the validation sets. Results: The median patient age was 64 years (range 25–91), and 65.6% of those were male. A total of 288 patients were divided into the training (n = 230) and validation (n = 58) sets. No significant differences existed in baseline characteristics between the training and validation sets. In the training set, the areas under the ROC curves (AUROC) for predicting better progression-free survival (PFS) with paclitaxel-based chemotherapy were 0.499, 0.679, 0.618, and 0.732 in the RF, LR, ANN, and ANN with GE models, respectively. The ANN with the GE model that achieved the highest AUROC recorded accuracy, sensitivity, specificity, and F1-score performance of 0.458, 0.912, 0.724, and 0.579, respectively. In the validation set, the ANN with GE model predicted that paclitaxel-sensitive patients had significantly longer PFS (median PFS 7.59 vs. 2.07 months, P = 0.020) and overall survival (OS) (median OS 14.70 vs. 7.50 months, P = 0.008). The LR model predicted that paclitaxel-sensitive patients showed a trend for longer PFS (median PFS 6.48 vs. 2.33 months, P = 0.078) and OS (median OS 12.20 vs. 8.61 months, P = 0.099). Conclusions: These ML models, integrated with clinical and genomic factors, offer the possibility to help identify patients with AGC who may benefit from paclitaxel chemotherapy. [ABSTRACT FROM AUTHOR]

The liquid biopsy approach, a less-invasive diagnostic tool, enables the detection of disease-specific genetic and epigenetic aberrations. Approximately 66–69% of the human genome may be composed of transposable repetitive elements, including Alu and LINE-1. This study aimed to investigate whether Alu -derived cell-free DNA (cfDNA) concentrations, Alu index, and LINE-1 methylation could be used to distinguish patients with cancers from healthy individuals. Two sets of primers, shorter and longer Alu fragments, were used to amplify Alu elements, followed by the quantitation of Alu DNA concentration and its integrity index. LINE-1 methylation status was then analyzed with quantitative PCR using methylation- and unmethylation-specific TaqMan probes. Both Alu index and LINE-1 methylation level were significantly different in comparison between patients with lung or breast cancer and the healthy controls. The area under the ROC curve of the Alu index and LINE-1 hypomethylation was 0.742 and 0.848 for lung cancer, respectively, and 0.724 and 0.890 for breast cancer, respectively. However, Alu longer fragment DNA concentration was significantly correlated with Alu index in comparison to LINE-1 hypomethylation. Regression analysis suggested that the LINE-1 methylation level, rather than the Alu index, was a good discriminator for lung and breast cancers. This study investigated the genome-wide Alu index and LINE-1 methylation status; their associations with cancers suggested that these combinatory panels could be implemented as a triage test to discriminate cancer patients from healthy individuals. [ABSTRACT FROM AUTHOR]

To determine whether shear-wave elastography (SWE)-measured tumor stiffness is associated with disease-free survival in females with early-stage invasive breast cancer. This retrospective study included 202 consecutive females (mean age, 52.9 years; range, 25–84 years) with newly diagnosed T1–two breast cancer who underwent preoperative SWE between April 2015 and January 2016. Tumor stiffness was assessed and quantitative SWE features of each breast lesion were obtained by a breast radiologist. Cox proportional hazard models were used to identify associations between SWE features and disease-free survival after adjusting for clinicopathologic factors. Fifteen (7.4%) patients exhibited recurrence after a median follow-up of 56 months. Mean (Emean), minimum, and maximum elasticity values were higher in females with recurrence than in those without recurrence (184.4, 138.3, and 210.5 kPa vs 134.9, 101.7, and 159.8 kPa, respectively; p = 0.005, p = 0.005, and p = 0.012, respectively). Receiver operating characteristics curve analysis for prediction of recurrence showed that Emean yielded the largest area under the curve (0.717) among the quantitative SWE parameters, and the optimal cut-off value was 121.7 kPa. Multivariable Cox proportional hazards analysis revealed that higher Emean (>121.7 kPa) [adjusted hazard ratio (HR), 10.01; 95% CI: 1.31–76.33; p = 0.026] and lymphovascular invasion (adjusted HR, 7.72; 95% CI: 1.74–34.26; p = 0.007) were associated with worse disease-free survival outcomes. Higher SWE-measured Emean was associated with worse disease-free survival in females with early-stage invasive breast cancer. Tumor stiffness assessed with shear-wave elastography might serve as a quantitative imaging biomarker of disease-free survival in females with T1–two breast cancer. [ABSTRACT FROM AUTHOR]