OBJECTIVE: Acoustic lie detection, prized for its covert nature and capability for remote processing, has spurred growing interest in acoustic features that can reliably aid in lie detection. In this study, the aim was to construct an acoustic polygraph based on a variety of phonetic and acoustic features rather than on electrodermal, cardiovascular, and respiratory values.
METHODS: Sixty-two participants from the University of Science and Technology of China, aged 18-30 years old, were involved in the mock crime experiment and were randomly assigned to the innocent and guilty groups. We collected 31 deceptive and truthful audios to analyze the performance of voice onset time (VOT) in lie detection.
RESULTS: Our findings revealed that VOT performed well in lie detection. Both the average sensitivity and specificity of the area under the curve are 0.888, and its lower and upper confidence limit are up to 0.803 and 0.973 respectively at the 95% confidence level. Although the other acoustic features had a lower reference value, they also provided a general trend in the judgment of lie detection.
CONCLUSIONS: Our results suggested that some acoustic features can be effectively used as aids to lie detection. Through a similar approach, we will explore more acoustic and phonetic features that contribute to detecting lies in the future.

UNASSIGNED: Obstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes.
UNASSIGNED: Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve.
UNASSIGNED: The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases).
UNASSIGNED: In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD.

OBJECTIVE: This study aims to investigate the association of Chinese visceral adiposity index (CVAI) with incident hyperuricemia (HUA).
RESULTS: We included 5186 adults aged ≥45 years from China Health and Retirement Longitudinal Study. Modified Poisson regression model was used to estimate the relative risks (RRs) of incident HUA associated with baseline CVAI, and logistic model was used to estimate the odds ratios (ORs) of HUA for CVAI change. Restricted cubic splines analysis was adopted to model the dose-response associations. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the predictive value of CVAI. During 4-year follow-up, a total of 510 (9.8%) HUA cases were identified. The RRs (95%CIs) of incident HUA were 3.75 (2.85-4.93) for quartile 4 versus quartile 1 and 1.56 (1.45-1.69) for per-standard deviation increase in baseline CVAI. For the analyses of CVAI change, compared with stable group, participants in decreased group had 34% lower risk (OR 0.66, 95%CI 0.49-0.87) and those in increased group had 35% (1.35, 1.03-1.78) higher risk of HUA. Linear associations of baseline CVAI and its change with HUA were observed (Pnonlinear >0.05). Besides, the AUC value for HUA was 0.654 (0.629-0.679), which was higher than other five obesity indices.
CONCLUSIONS: Our study found linear associations between baseline CVAI and its change and risk of HUA. CVAI had the best predictive performance in predicting incident HUA. These findings suggest CVAI as a reliable obesity index to identify individuals with higher HUA risk.

OBJECTIVE: In the cognitive process of establishing a diagnosis, the performance of a diagnostician can be characterized in terms of sensitivity and specificity. The aims of the present study are to analyze in quantitative terms how cognitive bias affects the performance of a diagnostician, and how a diagnostician\'s biased decision making is further influenced by personal cost-benefit considerations.
METHODS: The test matrices of two sequential diagnostic tests are manipulated according to the rules of linear algebra, using multiplication of the second with the first test matrix to calculate their joint test characteristics. The decision tree and receiver operating characteristic (ROC) of a biased and unbiased diagnostician are used to calculate which combination of test characteristics maximizes the expected utility value.
RESULTS: Biased diagnosticians cannot establish a diagnosis beyond their own limited or distorted level of understanding. An unbiased and a biased diagnostician alike adjust their choice of test characteristics according to their different cost-benefit estimation of the various test outcomes. From the perspective of an unbiased diagnostician, the choices made by a biased diagnostician appear to invert reality. However, the same appearance of inverted reality is perceived by the biased diagnostician, judging the choices made by the unbiased diagnostician.
CONCLUSIONS: As a general principle, human testers cannot test beyond their own level of understanding. They only see what they know. As they base their judgment on preconceived notions about the utilities associated with different test outcomes, human testers also tend to only know what they want to know.

OBJECTIVE: To identify which non-invasive infection indicators could better predict post-cervical cerclage (CC) infections, and on which days after CC infection indicators should be closely monitored.
METHODS: The retrospective, single-center study included 619 single-pregnancy patients from January 2021 to December 2022. Patients were categorized into infected and uninfected groups based on physicians\' judgments of post-CC infections. Registered information included patient characteristics, cervical insufficiency history, gestational age at CC, surgical method (McDonald/Shirodkar), purpose of CC, mid-pregnancy miscarriage/preterm birth, infection history or risk factors, and infection indices on days 1, 3, 5, and 7 after CC. Propensity score matching (PSM) was applied to reduce patient characteristic bias. Statistical analysis of C-reactive protein (CRP), white blood cell (WBC), neutrophil count (NEU), percentage of neutrophil count (NEU_P), interleukin-6 (IL-6), and procalcitonin (PCT) in the infected group compared with the uninfected group was performed using chi-square tests and t-tests. Receiver operating characteristic (ROC) curves were used to further assess the diagnostic value of CRP, PCT, and CRP-PCT in combination.
RESULTS: Among the 619 included patients, 206 patients were matched using PSM and subsequently assessed. PCT values on day 1 and day 3 after CC exhibited significant differences between the two groups in two statistical ways (P < 0.01, P < 0.05). The CRP levels on day 1 were significantly higher in the infected group compared to the uninfected group in two statistical ways (P < 0.05). On day 3, the mean CRP value was significantly elevated in the infected group compared to the uninfected group (P < 0.05). Analyses of IL-6, WBC, NEU, and NEU_P did not yield clinically significant results. The area under the ROC curves for CRP, PCT, and CRP-PCT on day 1 and day 3 were all below 0.7. In the preventive CC group, the AUC values of CRP and CRP-PCT obtained on d1 were found to be higher than 0.7, indicating moderate diagnostic accuracy.
CONCLUSIONS: For women after CC surgery, especially of preventive aim, increased serum CRP and PCT levels from post-CC day 1 to day 3 may signal a potential postoperative infection, warranting close monitoring.

BACKGROUND: Cardiopulmonary exercise testing (CPET) is a unique diagnostic tool that assesses the functional capacity of the heart, lungs, and peripheral oxidative system in an integrated manner. However, the clinical utility of CPET for evaluating interstitial lung disease (ILD) remains uncertain. The objective of this study was to determine the predictive value of CPET for mortality in subjects with ILD.
METHODS: We prospectively enrolled subjects with ILD who underwent CPET at a tertiary medical center in Taiwan and followed up their survival status for 12 months. Mortality prediction was based on comparing CPET parameters between subjects who survived and those who died. We further analyzed CPET parameters that showed significant differences using receiver operating characteristic curves to identify their optimal cutoff values.
RESULTS: A total of 106 newly diagnosed subjects with ILD underwent CPET, and the 1-y mortality rate was 7.5%. Six CPET variables were found to be significant predictors of mortality: peak oxygen consumption, oxygen pulse, end-tidal partial pressure of carbon dioxide, heart rate recovery 1 min after CPET, minute ventilation to carbon dioxide output slope, and functional aerobic impairment. We calculated a summed score by adding the number of CPET variables that exceeded their cutoff values. Subjects with a summed score of 6 had a 1-y survival rate of only 25%, whereas subjects with scores of 0-5 had a survival rate of 98%.
CONCLUSIONS: In conclusion, the summed score represents a useful tool for screening patients with ILD who can undergo a CPET to determine their prognosis.

OBJECTIVE: Few researchers have compared the effectiveness of traditional and novel obesity indicators in predicting stroke incidence. We aimed to evaluate the associations between six obesity indices and stroke risk, and to further identify the optimal indicator.
RESULTS: A total of 14,539 individuals from the Rural Chinese Cohort Study were included in the analyses. We used the Cox proportional hazards regression models to evaluate the association between six obesity indices (including body mass index [BMI], waist circumference [WC], conicity index [C-index], lipid accumulation product [LAP], visceral adiposity index [VAI], and Chinese visceral adiposity index [CVAI]) and stroke risk. Receiver operating characteristic curves were employed to compare their predictive ability on stroke risk. During a median follow-up period of 11.13 years, a total of 1257 cases of stroke occurred. In the multiple-adjusted Cox regression model, WC, BMI, C-index, and CVAI were positively associated with ischemic stroke (P < 0.01) rather than hemorrhagic stroke risk. Dose-response analyses showed a linear correlation of WC, BMI, C-index, and LAP (Poverall <0.05, and Pnonlinear >0.05), but a non-linear correlation of CVAI (Poverall <0.05, and Pnonlinear <0.05) with the risk of ischemic stroke. CVAI demonstrates the highest areas under the curves (AUC: 0.661, 95% CI: 0.653-0.668), indicating a superior predictive ability for ischemic stroke occurrence compared to other five indices (P < 0.001).
CONCLUSIONS: WC, BMI, C-index, LAP, and CVAI were all positively related to the risk of ischemic stroke, among which CVAI exhibited stronger predictive ability for ischemic stroke.

BACKGROUND: This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0.
METHODS: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis.
RESULTS: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis.
CONCLUSIONS: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

BACKGROUND: The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear.
METHODS: A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively.
RESULTS: PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP.
CONCLUSIONS: Our study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

BACKGROUND: Certain studies have indicated a link between obstructive sleep apnea and insulin resistance in specific populations. To gain more clarity, extensive research involving a broad sample of the overall population is essential. The primary objective of this study was to investigate this correlation by utilizing data from the National Health and Nutrition Examination Survey database.
METHODS: The analysis incorporated data from the National Health and Nutrition Examination Survey database spanning the time periods from 2005 to 2008 and from 2015 to 2018, with a focus on American adults aged 18 years and older after applying weight adjustments. Key variables such as obstructive sleep apnea, triglyceride glucose index, and various confounding factors were considered. A generalized linear logistic regression model was used to investigate the association between obstructive sleep apnea and the triglyceride glucose index, with additional exploration of the consistency of the results through hierarchical analysis and other techniques.
RESULTS: The study included participants aged between 18 and 90 years, with an average age of 46.75 years. Among the total sample, 50.76% were male. The triglyceride glucose index demonstrated a diagnostic capability for obstructive sleep apnea, with an AUC of 0.701 (95% CI: 0.6619-0.688). According to the fully adjusted model, individuals in the fourth quartile of the triglyceride glucose index showed an increased likelihood of having obstructive sleep apnea compared to those in the first quartile (OR: 1.45; 95% CI: 1.02-2.06; P < 0.05). Subgroup analysis indicated that male sex (OR: 2.09; 95% CI: 1.76-2.45; P < 0.05), younger age (OR: 2.83; 95% CI: 2.02-3.96; P < 0.05), white ethnicity (OR: 2.29; 95% CI: 1.93-2.73; P < 0.05), and obesity (OR: 1.54; 95% CI: 1.28-1.85; P < 0.05) were correlated with an elevated risk of OSA.
CONCLUSIONS: This study demonstrated a strong association between an elevated TG index and OSA. Additionally, the triglyceride glucose index could serve as an independent predictor of obstructive sleep apnea.