Effects of the selective AMPA modulator NBI-1065845 on the pharmacokinetics of midazolam or ethinyl estradiol-levonorgestrel in healthy adults.

This parallel-arm, phase I study investigated the potential cytochrome P450 (CYP)3A induction effect of NBI-1065845 (TAK-653), an investigational α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor potentiator in phase II development for major depressive disorder. The midazolam treatment arm received the sensitive CYP3A substrate midazolam on Day 1, followed by NBI-1065845 alone on Days 5-13; on Day 14, NBI-1065845 was administered with midazolam, then NBI-1065845 alone on Day 15. The oral contraceptive treatment arm received ethinyl estradiol-levonorgestrel on Day 1, then NBI-1065845 alone on Days 5-13; on Day 14, NBI-1065845 was administered with ethinyl estradiol-levonorgestrel, then NBI-1065845 alone on Days 15-17. Blood samples were collected for pharmacokinetic analyses. The midazolam treatment arm comprised 14 men and 4 women, of whom 16 completed the study. Sixteen of the 17 healthy women completed the oral contraceptive treatment arm. After multiple daily doses of NBI-1065845, the geometric mean ratios (GMRs) (90% confidence interval) for maximum observed concentration were: midazolam, 0.94 (0.79-1.13); ethinyl estradiol, 1.00 (0.87-1.15); and levonorgestrel, 0.99 (0.87-1.13). For area under the plasma concentration-time curve (AUC) from time 0 to infinity, the GMRs were as follows: midazolam, 0.88 (0.78-0.98); and ethinyl estradiol, 1.01 (0.88-1.15). For levonorgestrel, the GMR for AUC from time 0 to the last quantifiable concentration was 0.87 (0.78-0.96). These findings indicate that NBI-1065845 is not a CYP3A inducer and support its administration with CYP3A substrates. NBI-1065845 was generally well tolerated, with no new safety signals observed after coadministration of midazolam, ethinyl estradiol, or levonorgestrel.

Pharmacokinetic interactions between fexuprazan, a potassium-competitive acid blocker, and nonsteroidal anti-inflammatory drugs in healthy males.

Fexuprazan, a novel potassium-competitive acid blocker, is expected to be used for the prevention of nonsteroidal anti-inflammatory drugs (NSAIDs) induced ulcer. This study aimed to evaluate pharmacokinetic (PK) interactions between fexuprazan and NSAIDs in healthy subjects. A randomized, open-label, multicenter, six-sequence, one-way crossover study was conducted in healthy male subjects. Subjects randomly received one of the study drugs (fexuprazan 40 mg BID, celecoxib 200 mg BID, naproxen 500 mg BID, or meloxicam 15 mg QD) for 5 or 7 days in the first period followed by the combination of fexuprazan and one of NSAIDs for the same days and the perpetrator additionally administered for 1-2 days in the second period. Serial blood samples for PK analysis were collected until 48- or 72-h post-dose at steady state. PK parameters including maximum plasma concentration at steady state (Cmax,ss) and area under plasma concentration-time curve over dosing interval at steady state (AUCτ,ss) were compared between monotherapy and combination therapy. The PKs of NSAIDs were not significantly altered by fexuprazan. For fexuprazan, differences in PK parameters (22% in Cmax, 19% in AUCτ,ss) were observed when co-administered with naproxen, but not clinically significant. The geometric mean ratio (90% confidence interval) of combination therapy to monotherapy for Cmax,ss and AUCτ,ss was 1.22 (1.02-1.46) and 1.19 (1.00-1.43), respectively. There were no significant changes in the systemic exposure of fexuprazan by celecoxib and meloxicam. Fexuprazan and NSAIDs did not show clinically meaningful PK interactions.

Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events.

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846-0.957) (clinical variables only) to 0.909 (95% CI: 0.856-0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002-0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations.

Commonly used software tools produce conflicting and overly-optimistic AUPRC values.

The precision-recall curve (PRC) and the area under the precision-recall curve (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluate 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in more than 3000 published studies. We find the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

The stereoselective pharmacokinetics of the desmethyl-phencynonate hydrochloride in beagle dogs.

The aim of this study was to investigate the chiral inversion and the stereoselective pharmacokinetic profiles of desmethyl-phencynonate hydrochloride after administration of the single isomer and its racemate to beagle dogs. A liquid chromatography with tandem mass spectrometry (LC-MS/MS) method was established for determination of the stereoisomers on chiral columns in beagle dog plasma, which met all the requirements. The chiral inversion in dogs of the desmethyl-phencynonate hydrochloride were studied after administration of the single isomer or the racemic modification. The stereoselective pharmacokinetic profiles of the desmethyl-phencynonate hydrochloride were studied by assays for simultaneous isomers after administration of the racemic modification. The results showed that the absorption of the R-configuration dosed as the single isomer was higher than it dosed as the racemic modification. The AUC(0-t), AUC(0-∞), and Cmax of the S-configuration were much higher than those of R-configuration after oral administration of the racemic desmethyl-phencynonate hydrochloride. The chiral inversion of desmethyl-phencynonate isomers could not occur in dogs after administration of the R-configuration.

Racial Differences in Diagnostic Accuracy of Retinal Nerve Fiber Layer Thickness in Primary Open-Angle Glaucoma.

Purpose: To evaluate the diagnostic accuracy of retinal nerve fiber layer thickness (RNFLT) by spectral-domain optical coherence tomography (OCT) in primary open-angle glaucoma (POAG) in eyes of African (AD) and European descent (ED). Design: Comparative diagnostic accuracy analysis by race. Participants: 379 healthy eyes (125 AD and 254 ED) and 442 glaucomatous eyes (226 AD and 216 ED) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. Methods: Spectralis (Heidelberg Engineering GmbH) and Cirrus (Carl Zeiss Meditec) OCT scans were taken within one year from each other. Main Outcome Measures: Diagnostic accuracy of RNFLT measurements. Results: Diagnostic accuracy for Spectralis-RNFLT was significantly lower in eyes of AD compared to those of ED (area under the receiver operating curve [AUROC]: 0.85 and 0.91, respectively, P=0.04). Results for Cirrus-RNFLT were similar but did not reach statistical significance (AUROC: 0.86 and 0.90 in AD and ED, respectively, P =0.33). Adjustments for age, central corneal thickness, axial length, disc area, visual field mean deviation, and intraocular pressure yielded similar results. Conclusions: OCT-RNFLT has lower diagnostic accuracy in eyes of AD compared to those of ED. This finding was generally robust across two OCT instruments and remained after adjustment for many potential confounders. Further studies are needed to explore the potential sources of this difference.

Blood miRNAs as Potential Diagnostic Biomarkers for Chronic Obstructive Pulmonary Disease: A Meta-Analysis.

Purpose: Investigate the efficacy of blood microRNAs (miRNAs) as diagnostic biomarkers for Chronic Obstructive Pulmonary Disease (COPD). Patients and Methods: We conducted a comprehensive search in English and Chinese databases, selecting studies based on predetermined criteria. Diagnostic parameters like summarized sensitivity (SSEN), summarized specificity (SSPE), summarized positive likelihood ratio (SPLR), summarized negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) curves were analyzed using a bivariate model. Each parameter was accompanied by a 95% confidence interval (CI). Results: Eighteen high-quality studies were included. For diagnosing COPD with blood miRNAs, the SSEN was 0.83 (95% CI 0.76-0.89), SSPE 0.76 (95% CI 0.70-0.82), SPLR 3.50 (95% CI 2.66-4.60), SNLR 0.22 (95% CI 0.15-0.33), DOR 15.72 (95% CI 8.58-28.77), and AUC 0.86 (95% CI 0.82-0.88). In acute exacerbations, SSEN was 0.85 (95% CI 0.76-0.91), SSPE 0.80 (95% CI 0.73-0.86), SPLR 4.26 (95% CI 3.05-5.95), SNLR 0.19 (95% CI 0.12-0.30), DOR 22.29 (95% CI 11.47-43.33), and AUC 0.89 (95% CI 0.86-0.91). Conclusion: Blood miRNAs demonstrate significant accuracy in diagnosing COPD, both in general and during acute exacerbations, suggesting their potential as reliable biomarkers.

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Associations Between Gait Speed and Fat Mass in Older Adults.

Purpose: Although both gait speed and fat mass are crucial for healthy aging, evidence suggests that the associations between these components remain unclear. Therefore, the main purpose of the study was to examine the associations between gait speed and fat mass. Patients and Methods: In this cross-sectional study, we recruited 643 older men and women aged >60 years. Fat mass was assessed using bioelectrical impedance analysis, while gait speed was determined by calculating the time an individual has taken to walk across a 4.6-m distance. Receiver operating characteristic (ROC) curves and odds ratios (OR) were performed to determine cut-off points and mutual associations. Results: In older men, the optimal threshold of gait speed to detect high level of fat mass was 1.40 m/s with the area under the curve (AUC) being 0.82 (95% CI 0.76-0.89, p < 0.001). In older women, the optimal cut-off point was 1.37 m/s (AUC = 0.85, 95% CI 0.81-0.90, p < 0.001). Older men and women who walked below the newly developed threshold were approximately 12 times more likely to have high level of fat. Conclusion: In summary, newly developed cut-off points of gait speed have adequate discriminatory ability to detect older men and women with high level of fat mass. Although gait speed may be considered as a satisfactory screening tool for fat mass, its utility in clinical practice needs to be further investigated.

Construction and evaluation of short-term and long-term mortality risk prediction model for patients with sepsis based on MIMIC-IV database. / 基于MIMIC-IVæž„å»ºåŠè¯„ä¼°è„“æ¯’ç—‡æ‚£è€ è¿‘æœŸå’Œè¿œæœŸæ­»äº¡é£Žé™©é¢„æµ‹æ¨¡åž‹.

OBJECTIVES: Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. METHODS: Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. RESULTS: A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. CONCLUSIONS: The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

Bioequivalence and the food effect of macitentan/tadalafil 10/20 fixed-dose combination tablets versus the use of single-component tablets in healthy subjects.

The primary aim was to demonstrate bioequivalence between the 10/20 mg fixed-dose combination (FDC) of macitentan/tadalafil in a single tablet and the free combination of both drugs, and to evaluate the food effect on the 10/20 mg FDC in healthy participants. In this single-center, randomized, open-label, 3-way crossover, single-dose Phase 1 study in healthy adult participants, macitentan/tadalafil was administered as a 10/20 mg FDC formulation and compared with the free combination of macitentan and tadalafil. The food effect on the FDC was also evaluated. Pharmacokinetic sampling (216 h) was conducted. The 90% confidence intervals (CIs) for the geometric mean ratios of maximum observed plasma analyte concentration (Cmax) and area under the plasma analyte concentration-time curves (AUCs) for Treatment A (FDC, fasted) versus C (free combination, fasted) were within bioequivalence limits demonstrating that the FDC formulation can be considered bioequivalent to the free combination. The 90% CIs for the geometric mean ratios of Cmax and AUC for Treatment B (FDC, fed) versus A (FDC, fasted) were contained within bioequivalence limits demonstrating that there was no food effect. The administration of the 10/20 mg FDC was generally safe and well tolerated in healthy participants. This study demonstrated bioequivalence between the FDC of macitentan/tadalafil (10/20 mg) in a single tablet and the free combination of both drugs in healthy participants, and that the FDC can be taken without regard to food, similarly to the individual components. The FDC was generally safe and well tolerated.

Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.

BACKGROUND: Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. METHODS: Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. RESULTS: A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. CONCLUSIONS: The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

Usefulness of the qSOFA score for predicting hospital mortality in cancer patients.

BACKGROUND: The quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) is a score that has been proposed to quickly identify patients at higher risk of death. OBJECTIVE: To describe the usefulness of the qSOFA score to predict in-hospital mortality in cancer patients. MATERIAL AND METHODS: Cross-sectional study carried out between January 2021 and December 2022. Hospital mortality was the dependent variable. The area under the ROC curve (AUC) was calculated to determine the discriminative ability of qSOFA to predict in-hospital mortality. RESULTS: A total of 587 cancer patients were included. A qSOFA score higher than 1 obtained a sensitivity of 57.2%, specificity of 78.5%, a positive predictive value of 55.4% and negative predictive value of 79.7%. The AUC of qSOFA for predicting in-hospital mortality was 0.70. In-hospital mortality of patients with qSOFA scores of 2 and 3 points was 52.7 and 64.4%, respectively. In-hospital mortality was 31.9% (187/587). CONCLUSION: qSOFA showed acceptable discriminative ability for predicting in-hospital mortality in cancer patients.

ANTECEDENTES: El quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) es una puntuación propuesta para identificar de forma rápida a pacientes con mayor probabilidad de morir. OBJETIVO: Describir la utilidad de la puntuación qSOFA para predecir mortalidad hospitalaria en pacientes con cáncer. MATERIAL Y MÉTODOS: Estudio transversal realizado entre enero de 2021 y diciembre de 2022. La mortalidad hospitalaria fue la variable dependiente. Se calculó el área bajo la curva ROC (ABC) para determinar la capacidad discriminativa de qSOFA para predecir mortalidad hospitalaria. RESULTADOS: Se incluyeron 587 pacientes con cáncer. La puntuación qSOFA < 1 obtuvo una sensibilidad de 57.2 %, una especificidad de 78.5 %, un valor predictivo positivo de 55.4 % y un valor predictivo negativo de 79.7 %. El ABC de qSOFA para predecir mortalidad hospitalaria fue de 0.70. La mortalidad hospitalaria de los pacientes con qSOFA de 2 y 3 puntos fue de 52.7 y 64.4 %, respectivamente. La mortalidad hospitalaria fue de 31.9 % (187/587). CONCLUSIÓN: qSOFA mostró capacidad discriminativa aceptable para predecir mortalidad hospitalaria en pacientes con cáncer.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

BACKGROUND: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. METHODS: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software. RESULTS: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models. CONCLUSION: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns.

OBJECTIVE: To compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns. METHODS: For this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: Of the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns. CONCLUSIONS: The XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.

Correlation between triglyceride glucose-body mass index and hypertension risk: evidence from a cross-sectional study with 60,283 adults in eastern China.

BACKGROUND: Insulin resistance (IR) and obesity are established risk factors for hypertension, with triglyceride-glucose (TyG) serving as a recognized surrogate marker for IR. The aim of this study was to investigate the association between TyG-BMI and hypertension in the general population. METHODS: A total of 60,283 adults aged ≥18 years who underwent face-to-face questionnaires, anthropometric measurements, and laboratory examination were included in this study. Multivariable logistic regression models and receiver operating characteristic curve (ROC) were used to determine the association between TyG-BMI and hypertension. The restricted cubic spline model was used for the dose-response analysis. RESULTS: After fully adjusting for confounding variables, multivariate logistic regression model showed a stable positive association between TyG-BMI and hypertension (OR: 1.61 per SD increase; 95% CI: 1.55-1.67; P-trend < 0.001). The multivariate adjusted OR and 95% CI for the highest TyG-BMI quartile compared with the lowest quartile were 2.52 (95% CI 2.28-2.78). Dose-response analysis using restricted cubic spline confirmed that the association between TyG-BMI index and hypertension was linear. Subgroup analyses showed that stronger associations between TyG-BMI index and hypertension were detected in young and middle-aged individuals (P for interaction < 0.05). ROC analysis showed that TyG-BMI index could better predict the risk of hypertension than other parameters (TyG-BMI cut-off value: 207.105, AUC: 0.719, sensitivity 65.5%, specificity 66.8%), particularly among young and middle-aged people. CONCLUSION: The TyG-BMI index was independently associated with hypertension in the study population. Further studies are required to confirm this relationship.

Diagnostic value of BHI-V4 for heterogeneous and vancomycin-intermediate Staphylococcus aureus isolates: a systematic review and meta-analysis.

BACKGROUND: Brain-heart infusion agar supplemented with 4 µg/mL of vancomycin (BHI-V4) was commonly used for the detection of heterogeneous (hVISA) and vancomycin-intermediate Staphylococcus aureus (VISA). However, its diagnostic value remains unclear. This study aims to compare the diagnostic accuracy of BHI-V4 with population analysis profiling with area under the curve (PAP-AUC) in hVISA/VISA. METHODS: The protocol of this study was registered in INPLASY (INPLASY2023120069). The PubMed and Cochrane Library databases were searched from inception to October 2023. Review Manager 5.4 was used for data visualization in the quality assessment, and STATA17.0 (MP) was used for statistical analysis. RESULTS: In total, eight publications including 2153 strains were incorporated into the meta-analysis. Significant heterogeneity was evident although a threshold effect was not detected across the eight studies. The summary receiver operating characteristic (SROC) was 0.77 (95% confidence interval [CI], 0.74-0.81). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic score and diagnostic odds ratio were 0.59 (95% CI: 0.46-0.71), 0.96 (95%CI: 0.83-0.99), 14.0 (95% CI, 3.4-57.1), 0.43 (95%CI, 0.32-0.57), 3.48(95%CI, 2.12-4.85) and 32.62 (95%CI, 8.31-128.36), respectively. CONCLUSION: Our study showed that BHI-V4 had moderate diagnostic accuracy for diagnosing hVISA/VISA. However, more high-quality studies are needed to assess the clinical utility of BHI-V4.

Applying oversampling before cross-validation will lead to high bias in radiomics.

Class imbalance is often unavoidable for radiomic data collected from clinical routine. It can create problems during classifier training since the majority class could dominate the minority class. Consequently, resampling methods like oversampling or undersampling are applied to the data to class-balance the data. However, the resampling must not be applied upfront to all data because it would lead to data leakage and, therefore, to erroneous results. This study aims to measure the extent of this bias. Five-fold cross-validation with 30 repeats was performed using a set of 15 radiomic datasets to train predictive models. The training involved two scenarios: first, the models were trained correctly by applying the resampling methods during the cross-validation. Second, the models were trained incorrectly by performing the resampling on all the data before cross-validation. The bias was defined empirically as the difference between the best-performing models in both scenarios in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, balanced accuracy, and the Brier score. In addition, a simulation study was performed on a randomly generated dataset for verification. The results demonstrated that incorrectly applying the oversampling methods to all data resulted in a large positive bias (up to 0.34 in AUC, 0.33 in sensitivity, 0.31 in specificity, and 0.37 in balanced accuracy). The bias depended on the data balance, and approximately an increase of 0.10 in the AUC was observed for each increase in imbalance. The models also showed a bias in calibration measured using the Brier score, which differed by up to -0.18 between the correctly and incorrectly trained models. The undersampling methods were not affected significantly by bias. These results emphasize that any resampling method should be applied correctly only to the training data to avoid data leakage and, subsequently, biased model performance and calibration.

Evaluating the diagnostic accuracy of heat shock proteins and their combination with Alpha-Fetoprotein in the detection of hepatocellular carcinoma: a meta-analysis.

BACKGROUND: A growing body of research suggests that heat shock proteins (HSPs) may serve as diagnostic biomarkers for hepatocellular carcinoma (HCC), but their results are still controversial. This meta-analysis endeavors to evaluate the diagnostic accuracy of HSPs both independently and in conjunction with alpha-fetoprotein (AFP) as novel biomarkers for HCC detection. METHODS: Pooled statistical indices, including sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with 95% confidence intervals (CI), were computed to assess the diagnostic accuracy of HSPs, AFP, and their combinations. Additionally, the area under the summary receiver operating characteristic (SROC) curve (AUC) was determined. RESULTS: A total of 2013 HCC patients and 1031 control subjects from nine studies were included in this meta-analysis. The summary estimates for HSPs and AFP are as follows: sensitivity of 0.78 (95% CI: 0.69-0.85) compared to 0.73 (95% CI: 0.65-0.80); specificity of 0.89 (95% CI: 0.81-0.95) compared to 0.86 (95% CI: 0.77-0.91); PLR of 7.4 (95% CI: 3.7-14.9) compared to 5.1 (95% CI: 3.3-8.1); NLR of 0.24 (95% CI: 0.16-0.37) compared to 0.31 (95% CI: 0.24-0.41); DOR of 30.19 (95% CI: 10.68-85.37) compared to 16.34 (95% CI: 9.69-27.56); and AUC of 0.90 (95% CI: 0.87-0.92) compared to 0.85 (95% CI: 0.82-0.88). The pooled sensitivity, specificity, PLR, NLR, DOR and AUC were 0.90 (95% CI: 0.82-0.95), 0.94 (95% CI: 0.82-0.98), 14.5 (95% CI: 4.6-45.4), 0.11 (95% CI: 0.06-0.20), 133.34 (95% CI: 29.65-599.61), and 0.96 (95% CI: 0.94-0.98) for the combination of HSPs and AFP. CONCLUSION: Our analysis suggests that HSPs have potential as a biomarker for clinical use in the diagnosis of HCC, and the concurrent utilization of HSPs and AFP shows notable diagnostic effectiveness for HCC.

Pharmacokinetics of bromoform in dairy heifers.

AIMS: To determine the pharmacokinetics in dairy heifers after oral and IV administration of bromoform, a potential antimethanogenic agent found in red seaweed, Asparagopsis spp. METHODS: Twenty-four dairy heifers with a mean weight of 319 (SD 36.9) kg were used. The study was conducted in two phases, and each cohort of 12 heifers received an escalating dose of bromoform. In the first phase, 12 heifers successively received doses of 200, 400, 800, and 1600 mg of bromoform orally, separated by a 72-hour washout period. In the second phase, a different cohort of 12 dairy heifers was used. Each heifer received a total of four doses of bromoform separated by a wash-out period of 72 hours. Sequentially the treatments were (for each of the 12 heifers) an oral dose of 50 mg, followed by an IV dose of 50 mg, followed by an oral dose of 100 mg and finally an IV dose of 100 mg.Blood samples were assayed by gas chromatography-mass spectrophotometry for bromoform and dibromomethane to estimate the pharmacokinetic parameters using a non-compartmental analysis. RESULTS: Bromoform was rapidly absorbed as indicated by a short time to the maximum observed concentration of 15 minutes. For the routes of administration and dose ranges investigated, the mean terminal half-life ranged from 0.32 (SE 0.03) hours to 5.73 (SE 1.64) hours when administered orally or IV. With values for the mean area under the curve (AUC) to dose ratio ranging from 0.25 (SE 0.04) to 0.82 (SE 0.19) for oral and 1.39 (SE 0.39) to 4.02 (SE 0.37) for IV administration, bromoform appeared to exhibit non-proportional pharmacokinetic behaviour. The mean absolute bioavailability was 39.13 (SE 10.4)% and 3.36 (SE 0.83)% for 50-mg and 100-mg doses, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: Bromoform is rapidly absorbed and exhibits dose dependent elimination kinetics.