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Background: Portal hypertension commonly occurs due to liver cirrhosis, and esophageal varices (EV) is one of the major complications associated with it. The most common cause of death in liver cirrhosis is EV bleeding. Hence, GE screening for EV is required, which is an invasive procedure. Regular use of endoscopy results in low compliance due to cost and discomfort for patients. Hence, identifying non-invasive markers that could grade EV provides a useful screening tool for family physicians and primary health centers (PHCs) by referring the patient to higher centers for definitive treatment, which could reduce mortality due to variceal bleeding in cirrhotic patients. Aims: To assess non-invasive predictors of grade EV in patients diagnosed with liver cirrhosis. Settings and Design: Cross-sectional study. Methods and Material: A total of 109 patients with liver cirrhosis underwent clinical and biochemical evaluation, USG abdomen with spleen bipolar diameter, ascitic fluid analysis, and upper GE with a grade of EV are recorded. Statistical Analysis Used: SPSS software with Student t-test, Chi-square t-test, analysis of variance, receiver operator characteristic (ROC) curves, and Spearman correlation with 95% CI is used. P 1.815, PC/SD ≤909, and SAAG >1.1g/dl showed EV in liver cirrhosis (P < 0.05). The order of prediction with ROC curves shows APRI score > PC/SD > SAAG. In grading EV, APRI scores of 1.9–2.5 and >2.5 showed small and large EV, respectively (P < 0.05). Conclusions: APRI score may be used in PHC as an early intervention to grade EV and refer the patient to higher centers for definitive treatment. This would prevent the progression of varices to rupture and reduce mortality due to variceal bleeds in liver cirrhosis patients.

Background: Acute respiratory distress syndrome (ARDS) is a severe complication that can lead to fatalities in multiple trauma patients. Nevertheless, the incidence rate and early prediction of ARDS among multiple trauma patients residing in high-altitude areas remain unknown. Methods: This study included a total of 168 multiple trauma patients who received treatment at Shigatse People's Hospital Intensive Care Unit (ICU) between January 1, 2019 and December 31, 2021. The clinical characteristics of the patients and the incidence rate of ARDS were assessed. Univariable and multivariable logistic regression models were employed to identify potential risk factors for ARDS, and the predictive effects of these risk factors were analyzed. Results: In the high-altitude area, the incidence of ARDS among multiple trauma patients was 37.5% (63/168), with a hospital mortality rate of 16.1% (27/168). Injury Severity Score (ISS) and thoracic injuries were identified as significant predictors for ARDS using the logistic regression model, with an area under the curve (AUC) of 0.75 and 0.75, respectively. Furthermore, a novel predictive risk score combining ISS and thoracic injuries demonstrated improved predictive ability, achieving an AUC of 0.82. Conclusions: This study presents the incidence of ARDS in multiple trauma patients residing in the Tibetan region, and identifies two critical predictive factors along with a risk score for early prediction of ARDS. These findings have the potential to enhance clinicians' ability to accurately assess the risk of ARDS and proactively prevent its onset. [ABSTRACT FROM AUTHOR]

Background: Portal hypertension commonly occurs due to liver cirrhosis, and esophageal varices (EV) is one of the major complications associated with it. The most common cause of death in liver cirrhosis is EV bleeding. Hence, GE screening for EV is required, which is an invasive procedure. Regular use of endoscopy results in low compliance due to cost and discomfort for patients. Hence, identifying non-invasive markers that could grade EV provides a useful screening tool for family physicians and primary health centers (PHCs) by referring the patient to higher centers for definitive treatment, which could reduce mortality due to variceal bleeding in cirrhotic patients. Aims: To assess non-invasive predictors of grade EV in patients diagnosed with liver cirrhosis. Settings and Design: Cross-sectional study. Methods and Material: A total of 109 patients with liver cirrhosis underwent clinical and biochemical evaluation, USG abdomen with spleen bipolar diameter, ascitic fluid analysis, and upper GE with a grade of EV are recorded. Statistical Analysis Used: SPSS software with Student t-test, Chi-square t-test, analysis of variance, receiver operator characteristic (ROC) curves, and Spearman correlation with 95% CI is used. P <0.05 is considered significant. Results: Aminotransferase to Platelet count Ratio Index (APRI) score >1.815, PC/SD ≤909, and SAAG >1.1g/dl showed EV in liver cirrhosis (P < 0.05). The order of prediction with ROC curves shows APRI score > PC/SD > SAAG. In grading EV, APRI scores of 1.9-2.5 and >2.5 showed small and large EV, respectively (P < 0.05). Conclusions: APRI score may be used in PHC as an early intervention to grade EV and refer the patient to higher centers for definitive treatment. This would prevent the progression of varices to rupture and reduce mortality due to variceal bleeds in liver cirrhosis patients. [ABSTRACT FROM AUTHOR]

Background Acute respiratory distress syndrome (ARDS) is a severe complication that can lead to fatalities in multiple trauma patients. Nevertheless, the incidence rate and early prediction of ARDS among multiple trauma patients residing in high-altitude areas remain unknown. Methods This study included a total of 168 multiple trauma patients who received treatment at Shigatse People’s Hospital Intensive Care Unit (ICU) between January 1, 2019 and December 31, 2021. The clinical characteristics of the patients and the incidence rate of ARDS were assessed. Univariable and multivariable logistic regression models were employed to identify potential risk factors for ARDS, and the predictive effects of these risk factors were analyzed. Results In the high-altitude area, the incidence of ARDS among multiple trauma patients was 37.5% (63/168), with a hospital mortality rate of 16.1% (27/168). Injury Severity Score (ISS) and thoracic injuries were identified as significant predictors for ARDS using the logistic regression model, with an area under the curve (AUC) of 0.75 and 0.75, respectively. Furthermore, a novel predictive risk score combining ISS and thoracic injuries demonstrated improved predictive ability, achieving an AUC of 0.82. Conclusions This study presents the incidence of ARDS in multiple trauma patients residing in the Tibetan region, and identifies two critical predictive factors along with a risk score for early prediction of ARDS. These findings have the potential to enhance clinicians’ ability to accurately assess the risk of ARDS and proactively prevent its onset.

Background: Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown. Aims: The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant. Methods: A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation. Results: The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction. Conclusions: A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice. [ABSTRACT FROM AUTHOR]

The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models. [ABSTRACT FROM AUTHOR]

Abstract Background The incidence rates of obesity and gestational diabetes mellitus (GDM) are increasing in parallel. This study aimed to evaluate the relationship between different obesity indices, including prepregnancy body mass index (preBMI), the first-trimester abdominal circumference (AC), and first-trimester abdominal circumference/height ratio (ACHtR), and GDM, and the efficacy of these three indices in predicting GDM was assessed. Methods A total of 15,472 pregnant women gave birth to a singleton at the Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China. Prepregnancy weight was self-reported by study participants, body height and AC were measured by nurses at the first prenatal visit during weeks 11 to 13+6 of pregnancy. GDM was diagnosed through a 75-g oral glucose tolerance test at 24–28 gestational weeks. Using receiver operator characteristic (ROC) curve analysis, we evaluated the association between obesity indices and GDM. Results A total of 1912 women (12.4%) were diagnosed with GDM. Logistic regression analysis showed that AC, ACHtR, and preBMI (P

Background: The incidence rates of obesity and gestational diabetes mellitus (GDM) are increasing in parallel. This study aimed to evaluate the relationship between different obesity indices, including prepregnancy body mass index (preBMI), the first-trimester abdominal circumference (AC), and first-trimester abdominal circumference/height ratio (ACHtR), and GDM, and the efficacy of these three indices in predicting GDM was assessed. Methods: A total of 15,472 pregnant women gave birth to a singleton at the Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China. Prepregnancy weight was self-reported by study participants, body height and AC were measured by nurses at the first prenatal visit during weeks 11 to 13+6 of pregnancy. GDM was diagnosed through a 75-g oral glucose tolerance test at 24–28 gestational weeks. Using receiver operator characteristic (ROC) curve analysis, we evaluated the association between obesity indices and GDM. Results: A total of 1912 women (12.4%) were diagnosed with GDM. Logistic regression analysis showed that AC, ACHtR, and preBMI (P < 0.001) were all independent risk factors for the development of GDM. In the normal BMI population, the higher the AC or ACHtR was, the more likely the pregnant woman was to develop GDM. The area under the ROC curve (AUC) was 0.63 (95% CI: 0.62–0.64) for the AC, 0.64 (95% CI: 0.62–0.65) for the ACHtR and 0.63 (95% CI: 0.62–0.64) for the preBMI. An AC ≥ 80.3 cm (sensitivity: 61.6%; specificity: 57.9%), an ACHtR of ≥ 0.49 (sensitivity: 67.3%; specificity: 54.0%), and a preBMI ≥ 22.7 (sensitivity: 48.4%; specificity: 71.8%) were determined to be the best cut-off levels for identifying subjects with GDM. Conclusions: An increase in ACHtR may be an independent risk factor for GDM in the first trimester of pregnancy. Even in the normal BMI population, the higher the AC and ACHtR are, the more likely a pregnant woman is to develop GDM. AC, ACHtR in the first trimester and preBMI might be anthropometric indices for predicting GDM, but a single obesity index had limited predictive value for GDM. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is one of the deadliest cancers, early diagnosis of which can efficiently enhance patient's survival. We aimed to screening out the serum miRNAs as diagnostic biomarkers for patients with lung cancer. Methods: A total of 416 remarkably differentially expressed miRNAs were acquired using the limma package, and next feature ranking was derived by the minimal-redundancy-maximal-relevance method. An incremental feature selection algorithm of a random forest (RF) classifier was utilized to choose the top 5 miRNA combination with the optimum predictive performance. The performance of the RF classifier of top 5 miRNAs was analyzed using the receiver operator characteristic (ROC) curve. Afterward, the classification effect of the 5-miRNA combination was validated through principal component analysis and hierarchical clustering analysis. Analysis of top 5 miRNA expressions between lung cancer patients and normal people was performed based on GSE137140 dataset, and their expression was validated by qPCR. The hierarchical clustering analysis was used to analyze the similarity of 5 miRNAs expression profiles. ROC analysis was undertaken on each miRNA. Results: We acquired top 5 miRNAs finally, with the Matthews correlation coefficient value as 0.988 and the area under the curve (AUC) value as 0.996. The 5 feature miRNAs were capable of distinguishing most cancer patients and normal people. Furthermore, except for the lowly expressed miR-6875-5p in lung cancer tissue, the other 4 miRNAs all expressed highly in cancer patients. Performance analysis revealed that their AUC values were 0.92, 0.96, 0.94, 0.95, and 0.93, respectively. Conclusion: By and large, the 5 feature miRNAs screened here were anticipated to be effective biomarkers for lung cancer. [ABSTRACT FROM AUTHOR]

The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models.

Abstract Background This paper presents quantitative analysis of blood flow shear stress by measuring the carotid arterial wall shear stress (WSS) and the intima-media thickness (IMT) of experimental rabbits fed with high-fat feedstuff on a weekly basis in order to cause atherosclerosis. Methods This study is based on establishing an atherosclerosis model of high-fat rabbits, and measuring the rabbits’ common carotid arterial WSS of the experimental group and control group on a weekly basis. Detailed analysis was performed by using WSS quantification. Results We have demonstrated small significant difference of rabbit carotid artery WSS between the experimental group and the control group (P

This study presents an innovative method for reducing the number of rating scale items without predictability loss. The "area under the receiver operator curve" method (AUC ROC) is used for the stepwise method of reducing items of a rating scale. RatingScaleReduction R package contains the presented implementation. Differential evolution (a metaheuristic for optimization) was applied to one of the analyzed datasets to illustrate that the presented stepwise method can be used with other classifiers to reduce the number of rating scale items (variables). The targeted areas of application are decision making, data mining, machine learning, and psychometrics. [ABSTRACT FROM AUTHOR]

Background: Increased brain volume (BV) and subsequent herniation are strongly associated with death in pediatric cerebral malaria (PCM), a leading killer of children in developing countries. Accurate noninvasive measures of BV are needed for optimal clinical trial design. Our objectives were to examine the performance of six different magnetic resonance imaging (MRI) BV quantification measures for predicting mortality in PCM and to review the advantages and disadvantages of each method. Methods: Receiver operator characteristics were generated from BV measures of MRIs of children admitted to an ongoing research project with PCM between 2009 and 2014. Fatal cases were matched to the next available survivor. A total of 78 MRIs of children aged 5 months to 13 years (mean 4.0 years), of which 45% were males, were included. Results: Areas under the curve (AUC) with 95% confidence interval on measures from the initial MRIs were: Radiologist-derived score = 0.69 (0.58-0.79; P = 0.0037); prepontine cistern anteroposterior (AP) dimension = 0.70 (0.56-0.78; P = 0.0133); SamKam ratio [Rt. parietal lobe height/(prepontine AP dimension + fourth ventricle AP dimension)] = 0.74 (0.63-0.83; P = 0.0002); and global cerebrospinal fluid (CSF) space ascertained by ClearCanvas = 0.67 (0.55-0.77; P = 0.0137). For patients with serial MRIs (n = 37), the day 2 global CSF space AUC was 0.87 (0.71-0.96; P < 0.001) and the recovery factor (CSF volume day 2/CSF volume day 1) was 0.91 (0.76-0.98; P < 0.0001). Poor prognosis is associated with radiologist score of =7; prepontine cistern dimension =3 mm; cisternal CSF volume =7.5 ml; SamKam ratio =6.5; and recovery factor =0.75. Conclusion: All noninvasive measures of BV performed well in predicting death and providing a proxy measure for brain volume. Initial MRI assessment may inform future clinical trials for subject selection, risk adjustment, or stratification. Measures of temporal change may be used to stage PCM. [ABSTRACT FROM AUTHOR]

Rapid explosion in data accumulation has yielded large scale data mining problems, many of which have intrinsically unbalanced or rare class distributions. Standard classification algorithms, which focus on overall classification accuracy, often perform poorly in these cases. Recently, Tayal et al. (IEEE Trans Knowl Data Eng 27(12):3347–3359, 2015) proposed a kernel method called RankRC for large-scale unbalanced learning. RankRC uses a ranking loss to overcome biases inherent in standard classification based loss functions, while achieving computational efficiency by enforcing a rare class hypothesis representation. In this paper we establish a theoretical bound for RankRC by establishing equivalence between instantiating a hypothesis using a subset of training points and instantiating a hypothesis using the full training set but with the feature mapping equal to the orthogonal projection of the original mapping. This bound suggests that it is optimal to select points from the rare class first when choosing the subset of data points for a hypothesis representation. In addition, we show that for an arbitrary loss function, the Nyström kernel matrix approximation is equivalent to instantiating a hypothesis using a subset of data points. Consequently, a theoretical bound for the Nyström kernel SVM can be established based on the perturbation analysis of the orthogonal projection in the feature mapping. This generally leads to a tighter bound in comparison to perturbation analysis based on kernel matrix approximation. To further illustrate computational effectiveness of RankRC, we apply a multi-level rare class kernel ranking method to the Heritage Health Provider Network’s health prize competition problem and compare the performance of RankRC to other existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we present the diagnostic capacity of the Worry and Anxiety Questionnaire (WAQ) in the Spanish version, which evaluates the criteria of generalized anxiety disorder (GAD) according to DSM-5 (APA, 2013). The ROC curves were performed to know the diagnostic accuracy of the WAQ, and logistic regression to analyze the classification of each of the items that comprise it. The questionnaire was administered to a sample of 94 people with no disorder and 33 individuals with generalized anxiety disorder, aged between 19 and 66 years, along with other GAD measurement tests. The results suggest a cut point above 56, which provide an adequate balance between sensitivity (86.7%) and specificity (89.4%). These results support the diagnostic utility of WAQ as a screening tool for the early detection of GAD in community, primary and secondary care contexts. The results are discussed taking into account the divergences of the work reviewed and the recommendations of the National Institute for Health and Care Excellence on the use of ultra-short tests as screening tools. [ABSTRACT FROM AUTHOR]

Metabolite levels measured using magnetic resonance spectroscopy (MRS) are often expressed as ratios rather than absolute concentrations. However, the inter-subject variability of the denominator metabolite can introduce uncertainty into a metabolite ratio. In a clinical setting, there are no guidelines on whether ratios or absolute quantification should be used for a more accurate classification of normal versus abnormal results based on their statistical properties. In a research setting, the choice of one over the other can have significant implications on sample size, which must be factored in at the study design stage. Herein, we derive the probability distribution function for the ratio of two normally distributed random variables, and present analytical expressions for the comparison of ratios with absolute quantification in terms of both sample size and area under the receiver operator characteristic curve. The two approaches are compared for typical metabolite values found in the literature, and their respective merits are illustrated using previously acquired clinical MRS data in two pathologies: mild traumatic brain injury and multiple sclerosis. Our analysis shows that the decision between ratios and absolute quantification is non-trivial: in some cases, ratios might offer a reduction in sample size, whereas, in others, absolute quantification might prove more desirable for individual (i.e. clinical) use. The decision is straightforward and exact guidelines are provided in the text, given that population means and standard deviations of numerator and denominator can be reliably estimated. [ABSTRACT FROM AUTHOR]

Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the number of rating scale items without the predictability loss. The 'area under the receiver operator curve method' (AUC ROC) is used. The presented method has reduced the number of rating scale items (variables) to 28.57% (from 21 to 6) making over 70% of collected data unnecessary. Results have been verified by two methods of analysis: Graded Response Model (GRM) and Confirmatory Factor Analysis (CFA). GRM revealed that the new method differentiates observations of high and middle scores. CFA proved that the reliability of the rating scale has not deteriorated by the scale item reduction. Both statistical analysis evidenced usefulness of the AUC ROC reduction method. [ABSTRACT FROM AUTHOR]

Background: In volumetric modulated arc therapy (VMAT), gantry angles, dose rate and the MLC positions vary with the radiation delivery. The quality assurance (QA) system should be able to catch the planning and machine errors. The aim of this study was to investigate the sensitivity of three VMAT QA systems to machine errors. Methods: Several types of potential linac machine errors unique to VMAT delivery were simulated in sinusoidal function of gantry angle, including gantry angle itself, MLC position and linac output. Two commercial QA systems, ArcCheck and Delta4, and an in-house developed EPID technique were compared in this study. Fifteen full arcs from head and neck plans were selected and modified to include five magnitudes of each type of error, resulting in measurements and γ analyses of 240 arcs on each system. Both qualitative and quantitative comparisons were performed using receiver operating characteristic (ROC), γ pass rate gradient, and overlap histogram methods. Results: In ROC analysis, the area under curve (AUC) represents the sensitivity and increases with the error magnitude. Using the criteria of 2%/2 mm/2° (angle to agreement, ATA, only for EPID) and keeping AUC > 0.95, the minimum error detectable of ArcCheck, Delta4 and EPID are (2, 3, 3)° in gantry angle and (4, 2, 3) mm in MLC positions for the head and neck plans. No system is sensitive to the simulated output error, the AUC values were all below 0.70 even with 5% output error. The γ gradient for gantry angle, MLC position and output errors are (-5.1, -2.6, -3.6)%/°, (-2.6, -7.1, -3.3)%/mm and (-0.2, -0.2, -0.3)%/% for ArcCheck, Delta4 and EPID, respectively. Therefore, these two analyses are consistent and support the same conclusion. The ATA parameter in EPID technique can be adjusted to tune its sensitivity. Conclusions: We found that ArcCheck is more sensitive to gantry angle error and Delta4 is more sensitive to MLC position error. All three systems are not sensitive to the simulated output error. With additional analysis parameter, the EPID technique can be tuned to have optimal sensitivity and is able to perform QA for full field size with highest resolution. In addition, ROC analysis avoids the choice of γ pass rate threshold and is more robust compared with other analysis methods. [ABSTRACT FROM AUTHOR]

Although the general consensus holds that emotional perception is impaired in patients with schizophrenia, the extent to which neural processing of emotional voices is altered in schizophrenia remains to be determined. This study enrolled 30 patients with chronic schizophrenia and 30 controls and measured their mismatch negativity (MMN), a component of auditory event-related potentials (ERP). In a passive oddball paradigm, happily or angrily spoken deviant syllables dada were randomly presented within a train of emotionally neutral standard syllables. Results showed that MMN in response to angry syllables and angry-derived nonvocal sounds was significantly decreased in individuals with schizophrenia. P3a to angry syllables showed stronger amplitudes but longer latencies. Weaker MMN amplitudes were associated with more positive symptoms of schizophrenia. Receiver operator characteristic analysis revealed that angry MMN, angry-derived MMN, and angry P3a could help predict whether someone had received a clinical diagnosis of schizophrenia. The findings suggested general impairments of voice perception and acoustic discrimination in patients with chronic schizophrenia. The emotional salience processing of voices showed an atypical fashion at the preattentive level, being associated with positive symptoms in schizophrenia.

Objective: To investigate clinical values of early-pregnancy (8-13 weeks) glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), and body mass index (BMI) in screening gestational diabetes mellitus (GDM)., Methods: A total of 1120 cases underwent a 75 g oral glucose tolerance test (OGTT), of which 216 cases with GDM were selected as the study group, and 278 cases without GDM were selected as the control group. FPG, HbA1c, and BMI in early pregnancy were measured. The correlation between FPG, HbA1c and BMI in early pregnancy and the incidence of GDM was analyzed by binary logistic regression, and the value of each index in predicting GDM alone or in combination was evaluated., Results: FPG, HbA1c, and BMI in early pregnancy in the GDM group were higher than those in the control group, and the differences were statistically significant (P < .05). Binary logistic regression analysis showed that FPG, HbA1c, and BMI were risk factors for GDM in early pregnancy (odds ratio [OR] values were 3.374 [P < .05], 4.644 [P < .001], and 1.077 [P < .001], respectively). The area under the receiver operating characteristic (ROC) curve of FPG, glycated hemoglobin, and BMI in screening GDM for early pregnancy were 0.647, 0.661, and 0.608, respectively, while the area under the ROC curve of the combination of these 3 indicators was 0.736., Conclusion: We found that FPG, HbA1c, and BMI in early pregnancy might be the potential risk factors for the occurrence of GDM, and the combination of them had certain clinical predictive value for GDM. However, it is still necessary for more studies, especially prospective studies, to validate our findings in the future., (© The Author(s) 2022. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction Alcohol use is an important contributor to injuries. Simple bedside tools to identify trauma patients with potentially harmful drinking may assist in brief intervention efforts in clinical practice. The objective of the study was to determine and compare the accuracy of alcohol use disorders identification test (AUDIT) and an abbreviated version of this test, in the detection of hazardous drinking. Methods A cross-sectional study of a convenience sample of admitted trauma patients at a single Australian major trauma centre. Eligible patients completed the AUDIT. AUDIT survey responses were scored in two ways, using the full form scale and secondly an abbreviated (AUDIT C) scale which uses only the first 3 questions. AUDIT and AUDIT-C scores were then evaluated with respect to the primary study measure; the detection of hazardous alcohol consumption based on a full alcohol consumption history. Sensitivities for each relevant score were calculated and receiver operator characteristic (ROC) curve analysis was used to determine test accuracy. Results During the study period, 523 trauma admissions were identified and of these 146 (28%) were screened. The optimum cut off scores for AUDIT and AUDIT-C were 8 and 5 respectively corresponding to sensitivities of 88% and 91% and both tests had excellent overall accuracy for the detection of hazardous alcohol consumption. There was no significant difference between AUDIT-C and AUDIT performance ( p = 0.395) (AUDIT-C AUROC 0.96 95%CI 0.93, 0.99). Conclusion AUDIT-C appears to be a potentially useful screening tool for use trauma centres, but that further research with larger samples is required. [ABSTRACT FROM AUTHOR]

Leucine-rich repeat kinase 2 (LRRK2) is a complex, multidomain protein which is considered a valuable target for potential disease-modifying therapeutic strategies for Parkinson's disease (PD). In mammalian cells and brain, LRRK2 is phosphorylated and treatment of cells with inhibitors of LRRK2 kinase activity can induce LRRK2 dephosphorylation at a cluster of serines including Ser910/935/955/973. It has been suggested that phosphorylation levels at these sites reflect LRRK2 kinase activity, however kinase-dead variants of LRRK2 or kinase activating variants do not display altered Ser935 phosphorylation levels compared to wild type. Furthermore, Ser910/935/955/973 are not autophosphorylation sites, therefore, it is unclear if inhibitor induced dephosphorylation depends on the activity of compounds on LRRK2 or on yet to be identified upstream kinases. Here we used a panel of 160 ATP competitive and cell permeable kinase inhibitors directed against all branches of the kinome and tested their activity on LRRK2 in vitro using a peptide-substrate-based kinase assay. In neuronal SH-SY5Y cells overexpressing LRRK2 we used compound-induced dephosphorylation of Ser935 as readout. In silico docking of selected compounds was performed using a modeled LRRK2 kinase structure. Receiver operating characteristic plots demonstrated that the obtained docking scores to the LRRK2 ATP binding site correlated with in vitro and cellular compound activity. We also found that in vitro potency showed a high degree of correlation to cellular compound induced LRRK2 dephosphorylation activity across multiple compound classes. Therefore, acute LRRK2 dephosphorylation at Ser935 in inhibitor treated cells involves a strong component of inhibitor activity on LRRK2 itself, without excluding a role for upstream kinases. Understanding the regulation of LRRK2 phosphorylation by kinase inhibitors aids our understanding of LRRK2 signaling and may lead to development of new classes of LRRK2 kinase inhibitors. [ABSTRACT FROM AUTHOR]

We used aerial survey and corresponding digital land cover data to develop species-habitat models to describe breeding-ground distributions and landscape-level habitat associations of greater white-fronted geese ( Anser albifrons), Canada ( Branta canadensis) and cackling geese ( B. hutchinsii), tundra swans ( Cygnus columbianus), king eiders ( Somateria spectabilis), and long-tailed ducks ( Clangula hyemalis). We then used habitat associations in the Queen Maud Gulf Migratory Bird Sanctuary and the Rasmussen Lowlands, Nunavut, Canada, in models to predict distributions of focal species in each study area. We used the receiver operating characteristic (ROC) method and the area-under-the-curve (AUC) metric to evaluate predictive accuracy (hereafter, quality) of models. In the Queen Maud Gulf, AUC values suggested reasonable model discrimination for white-fronted geese, Canada geese, and tundra swans (i.e., AUC > 0.7). Quality of species-habitat models for king eiders and long-tailed ducks was less than other species considered, but these models still predicted encounters and non-encounters significantly better than the null model. For all species, quality of species-habitat models was lesser for the Rasmussen Lowlands than for the Queen Maud Gulf, although discrimination ability for Rasmussen Lowland distributions remained significantly better than corresponding null models for geese and swans, but not for seaducks. Our research suggested that species' distributions modeled with landscape-level habitat data is a tractable method to 1) identify habitat associations, 2) determine key habitats and regions, and 3) predict probable summer distributions of some waterfowl species over relatively large areas of the arctic from satellite imagery. © 2013 The Wildlife Society. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the risk factors associated with metastasis to the brain of primary breast cancer patients and evaluate a predictive model. The clinicopathological characteristics of 206 patients with primary breast cancer were analyzed retrospectively with a univariate and multivariate logistic regression model. A predictive model was generated, and its validity evaluated with a receiver operating characteristic (ROC) curve. Independent risk factors for brain metastasis in patients with primary breast cancer were: being younger than 35 years old at the time of diagnosis, having four or more metastatic axillary nodes, being estrogen receptor-negative, and with 24 months of metastasis-free survival. The predictive value of the brain metastasis risk model, measured as the area under the ROC curve, was 0.765 ± 0.040 (95 % CI 0.688-0.842). When 0.8 was considered the cutoff point of probability calculated by the model, the sensitivity and specificity for predicting the occurrence of brain metastases in these patients were 0.769 and 0.713, respectively. The predictive model constructed in this study can be used to forecast brain metastasis in breast cancer. Patients with a predictive level ≥0.8 could be treated preventively for brain metastases. [ABSTRACT FROM AUTHOR]

The article presents a study on predicting suitable habitat for King Rail (Rallus elegans) birds in agricultural area in Louisiana. It mentions that a study area was provided in which a 948,800 hectares of area converted to a rice agriculture in Louisiana was used and the predictive habitat modeling approach was conducted. It states that after three years of surveys in the study area, 44 King Rail placements were recorded, which came from 155 survey points.

Abstract: Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48h. Insulin sensitivity (S I ) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity S I values were calculated from glycemic control data of 36 patients with sepsis. The hourly S I is compared to the hourly sepsis score (ss) for these patients (ss=0–4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss≥2) are created for both S I and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S I cut-off value of 0.00013L/mU/min. Multivariate clinical biomarker combining S I , temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value. [Copyright &y& Elsevier]

Background: The Gail model has been commonly used to estimate a woman's risk of breast cancer within a certain time period. High bone mineral density (BMD) is also a significant risk factor for breast cancer, but it appears to play no role in the Gail model. The objective of the current study was to investigate whether hip BMD predicts postmenopausal breast cancer risk independently of the Gail score.Methods: In this prospective study, 9941 postmenopausal women who had a baseline hip BMD and Gail score from the Women's Health Initiative were included in the analysis. Their average age was 63.0 +/- 7.4 years at baseline.Results: After an average of 8.43 years of follow-up, 327 incident breast cancer cases were reported and adjudicated. In a multivariate Cox proportional hazards model, the hazards ratios (95% confidence interval [95% CI]) for incident breast cancer were 1.35 (95% CI, 1.05-1.73) for high Gail score (>or=1.67%) and 1.25 (95% CI, 1.11-1.40) for each unit of increase in the total hip BMD T-score. Restricting the analysis to women with both BMD and a Gail score above the median, a sharp increase in incident breast cancer for women with the highest BMD and Gail scores was found (P < .05).Conclusions: The contribution of BMD to the prediction of incident postmenopausal breast cancer across the entire population was found to be independent of the Gail score. However, among women with both high BMD and a high Gail score, there appears to be an interaction between these 2 factors. These findings suggest that BMD and Gail score may be used together to better quantify the risk of breast cancer. [ABSTRACT FROM AUTHOR]