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PURPOSE: The Blood First Assay Screening Trial (BFAST) is a prospective study using next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) in treatment-naïve advanced/metastatic non–small-cell lung cancer (NSCLC). We compared liquid biopsy to tissue testing and analyzed genomic alterations in Taiwanese patients with NSCLC using the BFAST database. MATERIALS AND METHODS: A total of 269 patients underwent FoundationOne Liquid Companion Diagnostic (F1LCDx) assay at the National Taiwan University Hospital, of whom 264 underwent tissue-based genetic testing also. We analyzed the actionable mutations and the concordance between tissue-based genetic testing, which was limited to EGFR , ALK , ROS1 , and BRAF , in a real-life clinical setting and blood-based NGS in the clinical trial. Additionally, we analyzed the co-occurring genomic alterations from the blood-based ctDNA assay. RESULTS: A total of 76.2% patients showed actionable mutations. Standard tissue testing did not detect known driver alterations in about 22.7% of the patients (sensitivity, 70.24%). Liquid NGS detected additional mutations (RET , KRAS , MET , and ErbB2) in 14% of the patients, which went undetected by the standard-of-care testing. The complementary use of ctDNA NGS increased the detection rate by 42%. The F1LCDx assay had a sensitivity of 83.41%. Lower tumor and metastasis stages predicted nondetected blood-based NGS ctDNA results. Common co-occurring mutations in the blood-based NGS ctDNA assay were TP53 , DNMT3A , TET2 , PIK3CA , CTNNB1 , and RB1. Among the patients with EGFR-mutated NSCLC, TET2 co-occurring alterations correlated with shorter progression-free survival of EGFR tyrosine kinase inhibitor treatment. CONCLUSION: NGS ctDNA analysis in comprehensive genetic testing improves actionable mutation identification, vital for treating Asian NSCLC cases with high actionable mutation rates. Lower stages correlated with undetected blood-based NGS ctDNA assay results. Genomic analysis using a blood-based ctDNA assay enhances actionable mutation detection. [ABSTRACT FROM AUTHOR]

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This study aimed to develop the Taiwan HRV normative database covering individuals aged 20 to 70 years and to assess its diagnosing validity in patients with major depressive disorder (MDD). A total of 311 healthy participants were in the HRV normative database and divided into five groups in 10-year age groups, and then the means and standard deviations of the HRV indices were calculated. We recruited 272 patients with MDD for cross-validation, compared their HRV indices with the normative database, and then converted them to Z-scores to explore the deviation of HRV in MDD patients from healthy groups. The results found a gradual decline in HRV indices with advancing age in the HC group, and females in the HC group exhibit higher cardiac vagal control and parasympathetic activity than males. Conversely, patients in the MDD group demonstrate lower HRV indices than those in the HC group, with their symptoms of depression and anxiety showing a negative correlation with HRV indices. The Taiwan HRV normative database has good psychometric characteristics of cross-validation. [ABSTRACT FROM AUTHOR]

This study referred to the standard of electroencephalography (EEG) collection of normative databases and collected the Taiwan normative database to examine the reliability and validation of the Taiwan EEG normative database. We included 260 healthy participants and divided them into five groups in 10-year age-group segments and calculated the EEG means, standard deviation, and z-scores. Internal consistency reliability was verified at different frequencies between the three electrode locations in the Taiwan normative database. We recruited 221 major depressive disorder (MDD) patients for cross-validation between the Taiwan and NeuroGuide normative databases. There were high internal consistency reliabilities for delta, theta, alpha, beta, and high-beta at C3, Cz, and C4 in the HC group. There were high correlations between the two z-scores of the Taiwan and NeuroGuide normative databases in the frontal, central, parietal, temporal, and occipital lobes from MDD patients. The beta z-scores in the frontal lobe and central area, and the high-beta z-scores in the frontal, central, parietal, temporal, and occipital lobes were greater than one for MDD patients; in addition, the beta and high-beta absolute value z-scores in the whole brain were greater than the ones of MDD patients. The Taiwan EEG normative database has good psychometric characteristics of internal consistency reliability and cross-validation. [ABSTRACT FROM AUTHOR]