INTRODUCTIONThere is no single method of measuring insulin resistance that is both accurate and can be easily performed by general researchers. We validate the accuracy of oral glucose insulin sensitivity (OGIS) in the Chinese by comparing the OGIS120 and OGIS180, homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (OUICKI) with steady-state plasma glucose (SSPG) in different glucose tolerance subjects.

MATERIALS AND METHODSWe enrolled 515 subjects, aged between 20 and 75 years old, during routine health evaluations. All subjects were divided into normal, obese, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and type 2 diabetes (T2D) groups. Participants had a 3-hour oral glucose tolerance test (OGTT) and SSPG with an insulin suppression test. The relationships between SSPG and OGIS120, OGIS180, HOMA-IR, and QUICKI were evaluated.

RESULTSThe normal group had the highest OGIS120, OGIS180 and lowest SSPG as compared with the other 4 groups. OGIS180, HOMA-IR and QUICKI in all 5 groups were significantly related to SSPG (r = 0.397-0.621, all P <0.05). OGIS120 in all 5 groups was not significantly related to SSPG (r = 0.003-0.226). Additionally, the r value of OGIS180 against SSPG was not higher than the other 2 insulin sensitivity surrogates from OGTT.

CONCLUSIONSAlthough OGIS180 was more accurate in estimating insulin sensitivity than OGIS120 in the Chinese, it was not superior to the traditional surrogates such as HOMA-IR or QUICKI.

Adult ; Aged ; Case-Control Studies ; China ; Female ; Glucose Tolerance Test ; methods ; Humans ; Insulin Resistance ; Male ; Middle Aged ; Prediabetic State ; diagnosis ; Young Adult

Objective: Hypoactivity in the reward system among patients with attention deficit hyperactivity disorder (ADHD) is a well-known phenomenon. Whether the activity in the reward pathway is related to harm avoidance, such as in sensitivity to punishment, is unclear. Evidence regarding the potential difference between ADHD patients and controls in terms of this association is scarce. Methods: Event-related functional magnetic resonance imaging was conducted on subjects performing the Iowa gambling test. Fourteen adults with ADHD and 14 controls were enrolled in the study. Results: Harm avoidance was found to be positively correlated with the activities of the bilateral orbitofrontal cortex and right insula in individuals with ADHD. A group difference was also confirmed. Conclusion Understanding the roles of harm avoidance and brain activation during risk tasks is important.

Natriuretic peptides (NPs) have been found to be useful markers in differentiating acute dyspneic patients presenting to the emergency department (ED) and emerged as potent prognostic markers for patients with congestive heart failure (CHF). The best-established and widely used clinical application of BNP and NT-proBNP testing is for the emergent diagnosis of CHF in patients presenting with acute dyspnea. Nevertheless, elevated NPs levels can be found in many circumstances involving left ventricular (LV) dysfunction or hypertrophy; right ventricular (RV) dysfunction secondary to pulmonary diseases; cardiac inflammatory or infectious diseases; endocrinology diseases and high output status without decreased LV ejection fraction. Even in the absence of significant clinical evidence of volume overload or LV dysfunction, markedly elevated NP levels can be found in patients with multiple comorbidities with a certain degree of prognostic value. Potential clinical applications of NPs are expanded accompanied by emerging reports regarding screening the presence of secondary cardiac dysfunction; monitoring the therapeutic responses, risk stratifications and providing prognostic values in many settings. Clinicians need to have expanded knowledge regarding the interpretation of elevated NPs levels and potential clinical applications of NPs. Clinicians should recognize that currently the only reasonable application for routine practice is limited to differentiation of acute dyspnea, rule-out-diagnostic-tests, monitoring of therapeutic responses and prognosis of acute or decompensated CHF. The rationales as well the potential applications of NPs in these settings are discussed in this review article.
Acute Coronary Syndrome/metabolism ; Arrhythmias, Cardiac/metabolism ; Heart Failure/*metabolism ; Humans ; Hypertension, Pulmonary/metabolism ; Natriuretic Peptides/*metabolism ; Sepsis/metabolism

BACKGROUND/AIMS: Hematological abnormalities during hepatitis C virus (HCV) combination therapy with pegylated interferon alpha and ribavirin often necessitate dose reduction. Variants of the ITPA gene have been reported to protect against anemia during the early stages of HCV combination treatments but have also been associated with larger decreases in platelet counts. We aimed to identify the association between specific ITPA gene polymorphisms and hematological abnormalities in patients undergoing HCV combination therapy. METHODS: In this retrospective study, 175 patients treated with HCV combination therapy were enrolled at St. Martin De Porres Hospital in Taiwan between 2006 and 2012. Two single nucleotide polymorphisms (SNP) within or adjacent to the ITPA gene (rs1127354, rs6051702) were genotyped. We investigated the effect of ITPA gene variants on hematological abnormalities during the therapy. RESULTS: The ITPA rs1127354 minor variants were significantly associated with protection against anemia at week 4 (p=1.86 x 10(-6)) and with more severe decreases in platelet counts during HCV combination therapy. SNP rs6051702 was not associated with the hemoglobin decline to >3 g/dL at week 4 in our study (p=0.055). CONCLUSIONS: The ITPA SNP rs1127354 is a useful predictor of ribavirin-induced anemia in Taiwanese patients and may be related to more severe decreases in platelet counts during the early stage of HCV combination therapy.
Adult ; Aged ; Anemia/chemically induced/genetics ; Antiviral Agents/*adverse effects ; Cross-Sectional Studies ; Drug Therapy, Combination/adverse effects ; Female ; Hematologic Diseases/*chemically induced/genetics ; Hepacivirus ; Hepatitis C/*drug therapy ; Humans ; Interferon-alpha/adverse effects ; Male ; Middle Aged ; *Polymorphism, Single Nucleotide ; Pyrophosphatases/*genetics ; Retrospective Studies ; Ribavirin/adverse effects ; Taiwan ; Thrombocytopenia/chemically induced/genetics

BACKGROUND/AIMS: Hematological abnormalities during hepatitis C virus (HCV) combination therapy with pegylated interferon alpha and ribavirin often necessitate dose reduction. Variants of the ITPA gene have been reported to protect against anemia during the early stages of HCV combination treatments but have also been associated with larger decreases in platelet counts. We aimed to identify the association between specific ITPA gene polymorphisms and hematological abnormalities in patients undergoing HCV combination therapy. METHODS: In this retrospective study, 175 patients treated with HCV combination therapy were enrolled at St. Martin De Porres Hospital in Taiwan between 2006 and 2012. Two single nucleotide polymorphisms (SNP) within or adjacent to the ITPA gene (rs1127354, rs6051702) were genotyped. We investigated the effect of ITPA gene variants on hematological abnormalities during the therapy. RESULTS: The ITPA rs1127354 minor variants were significantly associated with protection against anemia at week 4 (p=1.86 x 10(-6)) and with more severe decreases in platelet counts during HCV combination therapy. SNP rs6051702 was not associated with the hemoglobin decline to >3 g/dL at week 4 in our study (p=0.055). CONCLUSIONS: The ITPA SNP rs1127354 is a useful predictor of ribavirin-induced anemia in Taiwanese patients and may be related to more severe decreases in platelet counts during the early stage of HCV combination therapy.
Adult ; Aged ; Anemia/chemically induced/genetics ; Antiviral Agents/*adverse effects ; Cross-Sectional Studies ; Drug Therapy, Combination/adverse effects ; Female ; Hematologic Diseases/*chemically induced/genetics ; Hepacivirus ; Hepatitis C/*drug therapy ; Humans ; Interferon-alpha/adverse effects ; Male ; Middle Aged ; *Polymorphism, Single Nucleotide ; Pyrophosphatases/*genetics ; Retrospective Studies ; Ribavirin/adverse effects ; Taiwan ; Thrombocytopenia/chemically induced/genetics

OBJECTIVE: Altered event-related potential (ERP) performances have been noted in attention deficit hyperactivity disorder (ADHD) patients and reflect neurocognitive dysfunction. Whether these ERP alterations and correlated dysfunctions exist in healthy parents with ADHD offspring is worth exploring. METHODS: Thirteen healthy parents with ADHD offspring and thirteen healthy controls matched for age, sex and years of education were recruited. The auditory oddball paradigm was used to evaluate the P300 wave complex of the ERP, and the Wechsler Adult Intelligence Scale-Revised, Wisconsin Card Sorting Test, and continuous performance test were used to measure neurocognitive performance. RESULTS: Healthy parents with ADHD offspring had significantly longer auditory P300 latency at Fz than control group. However, no significant differences were found in cognitive performance. CONCLUSION: The presence of a subtle alteration in electro-neurophysiological activity without explicit neurocognitive dysfunction suggests potential candidate of biological marker for parents with ADHD offspring.
Adult ; Attention Deficit Disorder with Hyperactivity ; Biomarkers ; Cognition ; Education ; Evoked Potentials ; Humans ; Intelligence ; Parents ; Wisconsin

There is growing evidence that the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with increased risks of psychiatric sequelae. Depression, anxiety, cognitive impairments, sleep disturbance, and fatigue during and after the acute phase of COVID-19 are prevalent, long-lasting, and exerting negative consequences on well-being and imposing a huge burden on healthcare systems and society. This current review presented timely updates of clinical research findings, particularly focusing on the pathogenetic mechanisms underlying the neuropsychiatric sequelae, and identified potential key targets for developing effective treatment strategies for long COVID. In addition, we introduced the Formosa Long COVID Multicenter Study (FOCuS), which aims to apply the inflammation theory to the pathogenesis and the psychosocial and nutrition treatments of post-COVID depression and anxiety.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.