Maximum Area Under The Curve Research Articles

Predictive diagnostic value of mean platelet volume to platelet count and neutrophil to lymphocyte ratios in the gray zone of prostate cancer with tPSA between 4 to 10 ng/mL.

Exploration of the Predictive Diagnostic Value of Mean Platelet Volume to Platelet Count Ratio (MPV/PLT,PVI) and Neutrophil-to-Lymphocyte Ratio (NLR) in the tPSA Gray Zone of Prostate Cancer. A retrospective study was conducted on 65 prostate cancer (Pca) patients and 52 benign prostatic hyperplasia (BPH) patients who underwent transperineal prostate biopsy at Xiangtan Central Hospital from December 2021 to December 2023. Descriptive statistics and logistic regression models were used to investigate the predictive diagnostic value of PVI and NLR in the tPSA gray zone of prostate cancer. Receiver operating characteristic (ROC) curves were constructed based on PVI and NLR values to determine the classification thresholds. A total of 117 patients were enrolled, including 65 cases of prostate cancer (PCa) and 52 cases of benign prostatic hyperplasia (BPH). There were no statistically significant differences in age, BMI, history of hypertension, history of diabetes, history of coronary heart disease, pre-biopsy white blood cell count, history of drinking, history of smoking, and tPSA between the PCa and BPH patients. The results of logistic regression analysis showed that PVI (OR=2.03, 95%CI: 1.34~3.07, P<0.00) and NLR (OR=0.32, 95%CI: 0.18~0.58, P<0.00) were independent predictors for diagnosing prostate cancer in the tPSA gray zone (VIF=1.04).The maximum area under the curve (AUC) for PVI was 0.70, with an optimal cut-off value of 0.05 (P ≤ 0.01). The maximum AUC for NLR was 0.76, with an optimal cut-off value of 2.86 (P ≤ 0.01).The calibration curve showed good consistency between the predicted and actual outcomes in both the PCa and BPH groups, indicating that the nomogram model had good predictive performance.When using PVI and NLR to plot the receiver operating characteristic (ROC) curves to predict the assessment of PCa in the tpsa gray zone, the area under the curve (AUC) for PVI was the largest at 0.70, with an optimal cutoff value of 0.05 (P ≤ 0.01). The AUC for NLR was the largest at 0.76, with an optimal cutoff value of 2.86 (P ≤ 0.01). PVI and NLR have certain predictive diagnostic value for Pca in the tPSA gray zone, and appropriate use of PVI and NLR can improve the positive rate of early screening for Pca in the gray zone.

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70% of the data for training and 30% for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.

Predictive value of thrombin-antithrombin III complex and tissue plasminogen activator-inhibitor complex biomarkers in assessing the severity of early-stage acute pancreatitis.

The development of acute pancreatitis (AP) is strongly linked to blood clotting and fibrinolysis issues. Modern clinical practices now utilize advanced blood markers like thrombin-antithrombin III complex (TAT), plasmin-α2-plasmin inhibitor complex, thrombomodulin (TM), and tissue plasminogen activator-inhibitor complex (t-PAIC) to assess thrombosis risk. Our study used a highly sensitive chemiluminescence technique to measure these markers in AP patients, aiming to determine their early predictive value for AP severity. There were 173 patients with AP, all of whom developed symptoms within 72h; 102 individuals had onset symptoms within 48h. The biomarkers were measured upon admission before determining the severity of AP. The levels of TAT, plasmin-α2-plasmin inhibitor complex, TM, and t-PAIC were significantly higher in the severe acute pancreatitis (SAP) group compared with the mild acute pancreatitis and moderate severe acute pancreatitis groups. For the patients within 72h of onset, TAT, TM, and t-PAIC predicted the occurrence of SAP. For the patients within 48h of onset, TAT and t-PAIC predicted the occurrence of SAP. The area under the curve (AUC) of prediction models is similar to Bedside Index for Severity in Acute Pancreatitis (BISAP) but significantly higher than C-reactive protein (P<0.05). Notably, t-PAIC had a larger AUC than TAT, BISAP, and C-reactive protein. In the initial 48h, plasma TAT and t-PAIC levels may predict the development of SAP. Within 72h, plasma levels of TAT, TM, and t-PAIC may predict the development of SAP, and the TAT+TM+t-PAIC prediction model achieved a maximum AUC of 0.915, comparable to BISAP.

The Effect of 0.1 Hz Blood Flow Oscillations on Microvascular Blood Flow Responses Following Severe Ischemia

Background: We have shown that inducing 10 second (0.1 Hz) oscillations in arterial pressure and blood flow protects against reductions in tissue oxygenation during ischemia, independent of changes in macrovascular blood flow. However, it is unknown whether 0.1 Hz hemodynamic oscillations impacts microvascular function and vasodilatory capacity following severe ischemia. To examine this question, we assessed the reactive hyperemic response following a prolonged peripheral limb ischemia protocol with and without induced 0.1 Hz hemodynamic oscillations. Hypothesis: 0.1 Hz oscillations in blood pressure and blood flow will increase microvascular blood flow, assessed via reactive hyperemia following a 10-min period of ischemia. Methods: Thirteen healthy human participants (6M, 7F; 27.3 ± 4.2 y) completed two experimental protocols separated by ≥48 h. In both conditions, ischemia of the forearm was induced with a pneumatic cuff on the upper arm to decrease brachial artery blood velocity by ~70-80% from baseline. In the oscillation condition (OSC), 0.1 Hz oscillations in mean arterial pressure (MAP) and brachial artery blood flow were induced by inflating and deflating bilateral thigh cuffs every 5-s (10-s cycles; 0.1 Hz) throughout the forearm ischemia period. In the control condition (CON), the thigh cuffs were in place, but were inactive throughout the forearm ischemia period. Beat to beat arterial pressure was measured via finger photo plethysmography, and brachial artery diameter and blood velocity were measured via duplex Doppler ultrasound during baseline, ischemia, and the reperfusion period. The maximum mean brachial artery blood velocity, and 3-min area under the curve (AUC) of mean brachial artery blood velocity were used to determine the reactive hyperemia response. Results: The magnitude of forearm ischemia, indexed by the reduction in brachial artery conductance, was matched between conditions (CON: -74.8 ± 10.4% vs. OSC: -75.6 ± 6.7%, p=0.39). Reactive hyperemia was not different between conditions as indexed by maximum mean brachial artery blood velocity (CON: 36.4 ± 12.4 cm/s vs. OSC: 39.3 ± 11.2 cm/s, p=0.53) or 3-min brachial artery blood velocity AUC (CON: 1495 ± 744 (cm/s)2 vs. OSC: 1596 ± 804 (cm/s)2, p=0.74). Conclusion: Inducing 0.1 Hz hemodynamic oscillations during severe ischemia does not affect microvascular function, indexed by reactive hyperemia following release of the ischemic stimulus. A more direct measure of microvascular blood flow is needed to examine whether 0.1 Hz hemodynamic oscillations improves microvascular perfusion during ischemia. NIH Neurobiology of Aging &amp; Alzheimer’s Disease Training Grant (T32 AG02049; KAD); American Heart Association Transformational Project Award (19TPA34910743; CAR); American Heart Association Predoctoral Fellowship (23PRE1018469; KAD); UNTHSC Physiology and Anatomy Seed Grant 2021 (CAR). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study

ObjectiveTo develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL). MethodsThe AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms. Finally, we evaluated the image level and patient level results. Additionally, 50 videos of VFL were prospectively gathered to assess the system's real-time diagnostic capabilities. A human-machine comparison database was also constructed to compare the diagnostic performance of otolaryngologists with and without AI assistance. ResultsIn internal and external validation sets, the maximum area under the curve (AUC) for image level segmentation models was 0.775 (95 % CI 0.740–0.811) and 0.720 (95 % CI 0.684–0.756), respectively. Utilizing a MIL-based fusion strategy, the AUC at the patient level increased to 0.869 (95 % CI 0.798–0.940) and 0.851 (95 % CI 0.756–0.945). For real-time video diagnosis, the maximum AUC at the patient level reached 0.850 (95 % CI, 0.743–0.957). With AI assistance, the AUC improved from 0.720 (95 % CI 0.682–0.755) to 0.808 (95 % CI 0.775–0.839) for senior otolaryngologists and from 0.647 (95 % CI 0.608–0.686) to 0.807 (95 % CI 0.773–0.837) for junior otolaryngologists. ConclusionsThe MIL based AI-assisted diagnosis system can significantly improve the diagnostic performance of otolaryngologists for VFL and help to make proper clinical decisions.

Prediction of pathological activity in Crohn’s disease based on dual-energy CT enterography

PurposeTo explore the feasibility of predicting the pathological activity of Crohn’s disease (CD) based on dual-energy CT enterography (DECTE).MethodsThe clinical, endoscopic, imaging and pathological data of 55 patients with CD scanned by DECTE were retrospectively analyzed; the pathological results were used as a reference standard to classify the diseased bowel segments into active and inactive phases. The normalized iodine concentration (NIC), energy-spectrum curve slope K, dual energy index (DEI), fat fraction (FF) of the arterial phases and venous phases were compared. To assess the parameters’ predictive ability, receiver-operating characteristic curves were used. The Delong test was used to compare the differences between the diagnostic efficiency of each parameter.ResultsA total of 84 intestinal segments were included in the study, including 54 active intestinal segments and 30 inactive intestinal segments. The NIC, energy-spectrum curve slope K and DEI were significantly different between active and inactive bowel segments in the arterial and venous phases (P < 0.05), while FF were not significantly different (P > 0.05). The largest area under the curve (AUC) of NIC, energy-spectrum curve slope K and DEI were higher in arterial phase than in venous phase. For identifying the intestinal activity of CD, the maximum AUC of NIC in arterial phase was 0.908, with a sensitivity of 0.833 and a specificity of 0.800, and the DEI in arterial phase had the highest sensitivity (0.944).ConclusionThe NIC, energy-spectrum curve slope K and DEI can effectively distinguish the active and inactive phases of the intestinal segments of CD patients and provide good assistance for determining further treatment.Graphical abstract

Procalcitonin and C-reactive protein as early markers of anastomotic leakage in intestinal resections for advanced ovarian cancer (EDMOCS).

Serum levels of procalcitonin and C-reactive protein (CRP) have been used to predict anastomotic leakage after colorectal surgery, but information is scarce in advanced ovarian cancer (AOC) surgery with bowel resection. This study aimed to assess the predictive value of procalcitonin and CRP in detecting anastomotic leakage after AOC surgery with bowel resection. The study also aimed to determine the optimal postoperative reference values and the best day for evaluating these markers. This prospective, observational and multicentric trial included 92 patients with AOC undergoing debulking surgery with bowel resection between 2017 and 2020 in 10 reference hospitals in Spain. Procalcitonin and CRP levels were measured at baseline and on postoperative days 1-6. Receiver operating characteristic analysis was performed to evaluate the predictive value of procalcitonin and CRP at each postoperative day. Sensitivity, specificity, positive and negative predictive values were calculated. Anastomotic leakage was detected in six patients (6.5%). Procalcitonin and CRP values were consistently higher in patients with anastomotic leakage at all postoperative days. The maximum area under the curve (AUC) for procalcitonin was observed at postoperative day 1 (AUC = 0.823) with a cutoff value of 3.8 ng/mL (83.3% sensitivity, 81.3% specificity). For CRP, the maximum AUC was found at postoperative day 3 (AUC = 0.833) with a cutoff level of 30.5 mg/dL (100% sensitivity, 80.4% specificity). Procalcitonin and C-reactive protein are potential biomarkers for early detection of anastomotic leakage after ovarian cancer surgery with bowel resection. Further prospective studies with a larger sample size are needed to confirm these findings.

Cardiac computed tomography angiography-derived pulmonary vein volumetry as a predictor for atrial fibrillation recurrence after catheter ablation.

There is an increasing evidence that pulmonary vein (PV) enlargement is associated with atrial fibrillation (AF); however, the predictive value of PV enlargement in AF recurrence remains unclear. This study sought to evaluate whether PV volume quantification derived from cardiac computed tomographic angiography (CCTA) could serve as a predictive indicator of the success of the catheter ablation (CA) procedure. The data of 160 patients diagnosed with AF who underwent both CCTA and CA treatments from January to June 2020 were retrospectively examined; the CCTA was conducted before the CA surgery. The study focused on documenting the PV structure, and the volume of the PV and left atrium (LA). The clinical, CCTA, and echocardiographic predictors of the recurrence and no-recurrence groups were compared. A multivariable logistic regression analysis was performed to adjust for confounders. Receiver operating characteristic (ROC) curves were analyzed to assess the predictive performance of the predictors of AF recurrence. Of the 160 patients [55.6% male, 62.00 (55.25-68.00) years, 23.1% with persistent AF], 45 (28.1%) experienced AF recurrence within a one-year period. Notably, patients with AF recurrence had elevated CHADS2 scores (P=0.020) and increased LA and PV volumes (P<0.05). Patients with persistent AF (n=37) had significantly larger LA volume indexes (P<0.001) than those with paroxysmal AF, but there was no difference between the two groups in terms of the PV maximum volume index (P=0.200). Moreover, the PV maximum volume index [odds ratio (OR): 1.244, 95% confidence interval (CI): 1.008-1.536, P=0.042] and the LA minimum volume index (OR: 1.026, 95% CI: 1.001-1.052, P=0.038) were found to be significant predictors of AF recurrence. The ROC curves revealed that the PV maximum volume index threshold for predicting AF recurrence was 7.13 mL/m2, with a sensitivity of 84.4% and a specificity of 34.8% [area under the curve (AUC): 0.635, 95% CI: 0.540-0.730, P=0.008], and the LA minimum volume index threshold for predicting AF recurrence was 46.16 mL/m2, with a sensitivity of 88.9% and a specificity of 31.3% (AUC: 0.629, 95% CI: 0.534-0.723, P=0.012). A sub-analysis of patients with a lower left atrial dimension (LAD ≤38 mm in females, LAD ≤40 mm in males, n=120) demonstrated that the PV maximum volume index was a noteworthy indicator of AF recurrence (OR: 1.443: 95% CI: 1.145-1.820, P=0.002). Conversely, no significant correlation between AF recurrence and the LA volume index was found. The AUC value for the PV maximum volume index predictive of recurrent AF was 0.680 (95% CI: 0.577-0.781, P=0.003), with a sensitivity of 75.8%, specificity of 54%, and the cut-off value of the maximum AUC was 7.89 mL/m2. PV volume, derived from CCTA, may help to predict the recurrence of AF after CA, and is superior to the LA size in patients with less pronounced LA enlargement.

Comparison of Different Doses of ACTH Used in ACTH Stimulation Test to Determine the Subtypes of Primary Aldosteronism

To compare the diagnostic value of adrenocorticotropic hormone (ACTH) stimulation test (AST) with different doses of ACTH combined with midnight administration of 1 mg dexamethasone for the determination of the subtypes of primary hyperaldosteronism (PA). This is a prospective observational study. Patients diagnosed with PA in the Department of Endocrinology, the First Medical Center of of Chinese PLA General Hospital from January 1, 2020 to September 30, 2022 underwent AST with different doses of ACTH. All patients received 1 mg dexamethasone at midnight for inhibition. Then, the patients were randomly assigned to 25-unit and 50-unit ACTH treatment groups by a ratio of 1:2. Subtype classification and diagnosis of aldosterone-producing adenoma (APA) and idiopathic hyperaldosteronism (IHA) was made on the basis of adrenal venous blood samples and/or postoperative pathology and clinical follow-up findings. Receiver operating characteristics (ROC) curves were plotted to examine the diagnostic efficacy and the difference of AST by varying doses of ACTH in distinguishing APA and IHA. A total of 82 patients, including 49 patients with APA (59.8%) and 33 patients with IHA (40.2%), were enrolled. There were 29 patients in the 25-unit ACTH group (35.4%) and 53 patients in the 50-unit ACTH group (64.6%). There were no significant differences in age, sex, blood pressure, minimum serum potassium, and biochemical parameters between the 25-unit and 50-unit groups. After ACTH stimulation, plasma aldosterone concentration (PAC), cortisol (F), and PAC/F at different points of time showed no statistical difference between the two groups (P>0.05). The area under the curve (AUC) of PAC in the 25-unit group was higher than that of PAC/F. The AUC of PAC reached the maximum at 90 minutes (0.948, 95% confidence interval [CI]: 0870-1.000) and the optimal cutoff was 38.0 ng/dL, which had a sensitivity of 92.9% and a specificity of 86.7% for differentiating APA and IHA. Similar to the 25-unit group, the maximum AUC of PAC in the 50-unit group was greater than that of PAC/F. The AUC of PAC reached the maximum 90 minutes (0.930, 95% CI: 0.840-0.994) and the optimal cutoff was 39.6 ng/dL, which had a sensitivity of 91.2% and a specificity of 83.3%. The AUC of PAC at different points of time in the 25-unit ACTH group (0.862-0.948) was greater than that of 50-unit ACTH group (0.823-0.930), but the difference was not statistical significance. AST with 25-unit or 50-unit ACTH combined with small-dose dexamethasone can be used in PA subtype determination, ie, differentiation between APA and IHA. The optimal PAC cut-off values for 25-unit or 50-unit ACTH are similar, being 38.0 ng/dL and 39.6 ng/dL, respectively, and both cutoff values show higher sensitivity and specificity at 90 min. The AST with 25-unit ACTH has the smaller dose and the better safety. Therefore, it is recommended for the diagnosis of PA subtypes.

Evaluation of ganglion cell-inner plexiform layer thickness in the diagnosis of pre-perimetric glaucoma and comparison to retinal nerve fiber layers.

Evaluation of ganglion cell-inner plexiform layer thickness in the diagnosis of pre-perimetric glaucoma (PPG) and comparison to retinal nerve fiber layers. This study was a prospective hospital-based study. A total of 30 PPG and control patients were studied for retinal nerve fiber layer thickness (RNFL) and ganglion cell-inner plexiform layer complex (GC-IPL) by spectral-domain optical coherence tomography. PPG was defined as eyes with a normal visual field and one or more localized RNFL defects that were associated with a typical glaucomatous disc appearance. Diagnostic abilities of GC-IPL, optic nerve head (ONH), and RNFL parameters were computed using area under receiver-operating curve (AUROC), sensitivity, and specificity. GC-IPL parameters showed significant changes in PPG cases as compared to normal subjects in each region ( P value < 0.001). RNFL parameters also differed significantly from normal subjects in all quadrants ( P value 0.003 to < 0.001). Within GC-IPL parameters, the superotemporal region had the maximum area under the curve (AUC), followed by inferior, superior, and inferotemporal regions. Within RNFL parameters, the inferior quadrant had the maximum AUC, followed by superior and nasal quadrants. the GC-IPL parameters in PPG showed that the AUC of the GC-IPL parameters was much higher than those of the ONH and RNFL values. Although both the parameters RNFL and GC-IPL showed significant changes in PPG patients compared to healthy subjects, a higher AUC of GC-IPL points toward the higher sensitivity of GC-IPL than RNFL for detecting glaucoma in early stages.

Whole-joint histogram analysis of different models of diffusion weighted imaging in evaluating disease activity of axial spondyloarthritis.

To assess whole-joint histogram analysis of mono-exponential and bi-exponential diffusion weighted and diffusion kurtosis imaging in evaluating disease activity of axial spondyloarthritis (axSpA). A total of 82 patients with axSpA who underwent both DKI and multi b diffusion weighted imaging of the sacroiliac joints were divided into active and inactive disease groups based on clinical activity indices. Another 17 patients with nonspecific low back pain were included as a control group. The SPARCC scoring system was used to assess the level of sacroiliac joint bone marrow edema. Histogram parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), pseudodiffusion coefficient (D*), mean kurtosis (MK), and mean diffusivity (MD) were calculated. Regions of interest were placed covering the entire sacroiliac joint. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of imaging parameters in distinguishing different groups. Regression analysis was applied to determine the correlation between imaging parameters and clinical activity indices. All of the histogram parameters distinguished the active from inactive groups with a low area under the curve (AUC) (max AUCs≤0.71), while the SPARCC score failed to differentiate the two groups (p = 0.08). MD, MK, D, D*, and ADC showed good performance in differentiating active and inactive from control groups (max AUC = 0. 81 ~ 0.98). f50 differentiated the active from control groups with an AUC of 0.72, significantly lower than the maximum AUC for MD, MK, D, ADC, and SPARCC score (all p < 0.05). The max AUC of MD in differentiating inactive from control groups was significantly higher than that of D* and the SPARCC score. MD, D, D*, f, and ADC were positively correlated with BASDAI, while MK was negatively correlated with BASDAI. Only MD was positively correlated with hsCRP. Whole-joint histogram analysis of mono-exponential, bi-exponential diffusion weighted, and diffusion kurtosis imaging showed good diagnostic performance in differentiating active and inactive axSpA from patients with non-specific back pain. All the imaging parameters were correlated with BASDAI except for SPARCC score. Only DKI-derived MD was correlated with an increase in hsCRP, suggesting its potential use as an imaging biomarker for disease activity in axSpA. 1. No significant difference was found between the three models of diffusion weighted imaging in evaluating disease activity of axial spondyloarthritis.2. Only DKI-derived MD was correlated with an increase in hsCRP, suggesting its potential use as an imaging biomarker for disease activity in axSpA.

Clinical application value of Abbott Alinity analyzer in syphilis-specific antibody testing

Abstract Background: We aimed to investigate the clinical application value of Abbott Alinity analyzer in syphilis-specific antibody testing. Methods: A total of 100 patients admitted from June 2021 to June 2022 for early syphilis diagnosis were selected and subjected to Treponema pallidum (TP) antibody testing by chemiluminescent microparticle immunoassay (CMIA) using Abbott Alinity analyzer. With TP particle agglutination (TPPA) retesting as the gold standard for syphilis diagnosis, the signal-to-cutoff (S/CO) ratio of the TP antibody testing was plotted into the receiver operating characteristic curve to determine the diagnostic value of CMIA and CLIA in detecting positive TP antibody and to identify the optimal cutoff point. Results: In the case of S/CO ratio ≥7.00, the patients with positive CMIA were diagnosed with positive findings after TPPA confirmation. With the S/CO ratio of 1.00-4.99, the coincidence rate of CLIA with positive TPPA was 81.82% (45/55), and all patients with positive CLIA had positive results confirmed by TPPA test when the S/CO ratio was &gt;5.00. When the optimal cutoff value of S/CO ratio for TP was determined as 6.98 by CMIA, the sensitivity, specificity, and maximum area under the curve (AUC) were 94%, 88% and 0.91, respectively. At the optimal cutoff value (S/CO ratio: 4.56) determined by CLIA, the sensitivity was 84%, the specificity was 80%, and the maximum AUC was 0.84. Conclusions: In the case of S/CO ratio ≥7.00, both methods have high sensitivity and specificity, which can directly give positive reports and shorten the sample turnaround time.

Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India

The number of rainfall-induced landslides and the resulting casualties are increasing worldwide. Efficient Landslide Early Warning Systems (LEWS) are the best way to reduce the risk due to such events, but the number of operational LEWS is still limited. A new data-driven approach for spatio-temporal landslide forecasting on a regional scale is proposed, integrating Landslide Susceptibility Maps (LSMs) using RF algorithm and probabilistic hydro-meteorological thresholds, considering both rainfall severity and antecedent soil wetness. The proposed method is also compared with two deterministic process-based approaches: Transient Rainfall Infiltration and Grid-based Regional Slope Stability (TRIGRS) and SHALSTAB, considering the spatial variability in soil thickness and properties, along with the rainfall data. The quantitative comparison is carried out for two test areas in the Western Ghats of India (Idukki and Wayanad), for two different spatial resolutions. The efficiency and area under the curve (AUC) values from a receiver operating characteristic curve (ROC) were used to evaluate the performance of different models. The results for Idukki indicate that the efficiency values of the data-driven approach were improved by 4.67 % by using fine resolution DEM (digital elevation model) of 12.5 m resolution, while in the case of TRIGRS and SHALSTAB models, the improvements were 3.39 % and 1.83 %, respectively. For Wayanad, the improvement in efficiencies was further lesser, 2.59 % in the case of data-driven model, and 0.95 % and 0.73 % in the cases of TRIGRS and SHALSTAB, respectively. The maximum efficiency and AUC values were obtained by the data-driven model for both regions, with a spatial resolution of 12.5 m. The maximum efficiency values were obtained as 81.21 % and 83.33 % for Idukki and Wayanad, respectively, and the corresponding AUC values were 0.92 and 0.93. The results indicate that the model proposed in this study, with data-driven approach performs better than the process-based approaches and can bypass the complexities involved in modeling.

Accuracy and modification of the STOP-bang questionnaire for screening patients with obstructive sleep apnea in China.

Despite the high sensitivity of the STOP-Bang questionnaire [snoring, tiredness, observed apneas, high blood pressure, body mass index, age, neck size, gender] questionnaire (SBQ), which is widely used to screen individuals at high risk of obstructive sleep apnea (OSA), few studies have evaluated the discrimination related to sex in the SBQ. Therefore, our study aimed to assess whether SBQ performance is gender-related and modified by changing body mass index (BMI) and neck circumference (NC) cut-off values. A total of 470 patients attended the sleep centres. They were divided into moderate-to-severe OSA and non-moderate-to-severe OSA groups based on the apnea-hypopnea index (AHI) and the results of the SBQ screening. The predictive performance of the SBQ screening instrument was evaluated by 2 × 2 contingency tables and discriminatory ability, which was estimated from the area under the curve (AUC) obtained from receiver operating characteristic curve. Our results suggest that when screening for moderate-to-severe OSA, the original SBQ has higher sensitivity and lower specificity for men; however, women have higher specificity and lower sensitivity. The SBQ achieved the maximum AUC of 0.783 for men and 0.634 for women when the BMI cut-off value was established at 30 kg/m2 and the NC cut-off value was established at 38 cm in men, and the BMI cut-off value was established at 25 kg/m2 and the NC cut-off value at 36 cm in women. Balancing the optimal sensitivity and specificity, the cut-off value of the optimal modified SBQ total scores for men was determined to be ≥4 and ≥3 for women.

Association of obesity, triglyceride-glucose and its derivatives index with risk of hyperuricemia among college students in Qingdao, China.

ObjectiveTo analyze and compare the associations of hyperuricemia (HUA) with obesity, triglyceride-glucose (TyG), and its derivatives in college students. To provide early guidance on risk predictors of HUA in college students.MethodsThis study was a cross-sectional survey including 23,411 participants (age: 17-20 years). Investigators conducted face-to-face interview surveys and physical examinations. Automated biochemical methods were used to detect biochemical indicators such as serum uric acid (UA). Calculation of obesity, TyG, and their derivatives indices were performed. Logistic regression was used to analyze the relationship between different indexes and hyperuricemia. OR value and 95% CI were also calculated. ROC curve was used for assessing the predictive ability of different indices of hyperuricemia.ResultsAfter adjusting for age, SBP, DBP, ALT, AST, TC, BUN, and CREA, multivariate logistic regression showed that the OR value of LAP in the obesity index was higher, especially in women (male OR: 4.347, 95%CI: 3.807, 4.964; female OR: 4.672, 95%CI: 3.800, 5.744). The other three quartiles of TyG derivatives were highly associated with hyperuricemia in men and women compared with the top quartile (all P< 0.05). The risk of hyperuricemia increased with an increase in quartiles. For college students, all indicators could distinguish the presence of hyperuricemia. For men, the area under the curve (AUC) of TyG-WC was the largest (AUC: 0.694; 95%CI: 0.684-0.704; P<0.05), according to the Maximum Youden index 0.290 with cut point value 477.853. In women, TyG-BMI showed a maximum AUC value of 0.702 (95%CI: 0.685-0.719; P<0.05), according to the maximum Youden index of 0.317 with cut point value 132.446. The TyG-WC, TyG-WHtR, TyG-LAP, and LAP indices also had relatively high AUC.ConclusionIn clinical practice, LAP, TYG, and their related derivatives may be used as sensitive indicators for HUA prediction in college students.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary center cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC-patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks were assessed. Next, we used EEG and fMRI data at study enrollment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel), to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS), at time of study enrollment and at ICU-discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC-patients (mean age, 50.0 ± 18 years, 43% women), 51 (59%) were ≤ UWS and 36 (41%) were ≥ MCS at study enrollment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrollment and ICU-discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrollment and ICU-discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Total serum bile acids predict therapy for HBeAg-negative chronic hepatitis B patients with borderline ALT and high HBV DNA.

The introduction of antiviral therapy in chronic hepatitis B (CHB) infection depends on precise evaluation of hepatic lesions. Total serum bile acids (TSBAs) are highly sensitive in monitoring liver dysfunction. We evaluated the predictive role of TSBAs for hepatic lesions in CHB patients with borderline alanine aminotransferase (ALT) and high level of hepatitis B virus (HBV) DNA copies. 328 CHB patients were enrolled, 241 were hepatitis B e antigen (HBeAg)-positive and 87 were HBeAg-negative. Patients were further divided into two entities according to inflammation/fibrosis evaluated by liver biopsy, low-grade (inflammation grade < 2 and fibrosis stage < 2) and high-grade (inflammation grade ≥ 2 or/and fibrosis stage ≥ 2) cohorts. TSBAs were compared with noninvasive tools including aspartate aminnotransferase (AST)-to-platelet ratio index (APRI), fibrosis-4 (FIB-4) and red cell distribution width (RDW)-to-platelet ratio (RPR) to predict high-grade hepatic lesions in CHB subgroups. TSBAs, APRI, FIB-4 and RPR were statistically different between low- and high-grade patients in HBeAg-positive cohort. Only TSBAs showed significant difference between low and high grade in HBeAg-negative patients. Similarly, APRI, FIB-4 and RPR were correlated with different division of inflammation/fibrosis only in HBeAg-positive while TSBAs were correlated with inflammation/fibrosis levels in both HBeAg-positive and HBeAg-negative groups. Of the four indicators, the receiver operating characteristic (ROC) curve analysis showed that TSBAs have the maximum AUC (area under the curve) in HBeAg-negative group but the minimum in HBeAg-positive cohort. TSBAs can be used for predicting antiviral therapy in CHB patients with HBeAg-negative, borderline ALT and high HBV DNA.

A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

Risk mapping is a crucial part of spatial planning, as it optimizes the allocation of resources in its management. It is, therefore, of great interest to build tools that enhance its production. This work focuses on the implementation of a susceptibility model for different types of spatially distributed risk in a geographic information systems (GIS) Python plugin. As an example, the susceptibility model was applied to study the occurrence of wildfires in the municipality of Vila Nova de Foz Côa, Portugal. The plugin was developed to simplify the production and evaluation of susceptibility maps regarding the available geographical information. Regarding our case study, the data used corresponds to three training areas, ten years of burned areas and nine environmental variables. The model is applied to different combinations of these factors. The validation, performed with receiver operating characteristic (ROC) curves, resulted in an area under the curve (AUC) of 74% for a fire susceptibility model, calculated with the same environmental factors used in official Portuguese cartography (land use and slope) and with the optimal training area, years of information on burned area and level of land use classification. After experimenting with four variable combinations, a maximum AUC of 77% was achieved. This study confirms the suitability of the variables chosen for the production of official fire susceptibility models but leaves out the comparison between the official methodology and the methodology proposed in this work.

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

PurposeSocial networking platforms are increasingly using the Follower Link Prediction tool in an effort to expand the number of their users. It facilitates the discovery of previously unidentified individuals and can be employed to determine the relationships among the nodes in a social network. On the other hand, social site firms use follower–followee link prediction (FFLP) to increase their user base. FFLP can help identify unfamiliar people and determine node-to-node links in a social network. Choosing the appropriate person to follow becomes crucial as the number of users increases. A hybrid model employing the Ensemble Learning algorithm for FFLP (HMELA) is proposed to advise the formation of new follower links in large networks.Design/methodology/approachHMELA includes fundamental classification techniques for treating link prediction as a binary classification problem. The data sets are represented using a variety of machine-learning-friendly hybrid graph features. The HMELA is evaluated using six real-world social network data sets.FindingsThe first set of experiments used exploratory data analysis on a di-graph to produce a balanced matrix. The second set of experiments compared the benchmark and hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments show that the proposed (HMELA) method predicts missing links better than other methods.Practical implicationsA hybrid suggested model for link prediction is proposed in this paper. The suggested HMELA model makes use of AUC scores to predict new future links. The proposed approach facilitates comprehension and insight into the domain of link prediction. This work is almost entirely aimed at academics, practitioners, and those involved in the field of social networks, etc. Also, the model is quite effective in the field of product recommendation and in recommending a new friend and user on social networks.Originality/valueThe outcome on six benchmark data sets revealed that when the HMELA strategy had been applied to all of the selected data sets, the area under the curve (AUC) scores were greater than when individual techniques were applied to the same data sets. Using the HMELA technique, the maximum AUC score in the Facebook data set has been increased by 10.3 per cent from 0.8449 to 0.9479. There has also been an 8.53 per cent increase in the accuracy of the Net Science, Karate Club and USAir databases. As a result, the HMELA strategy outperforms every other strategy tested in the study.

The diagnostic value of pepsin concentration in saliva for laryngopharyngeal reflux disease.

To explore the diagnostic efficacy of pepsin concentration in saliva for laryngopharyngeal reflux (LPR) disease. In this study, we recruited 40 participants with abnormal sensation of throat into the study who visited our hospital from March 2020 to December 2020. The 24h multichannel intraluminal impedance and pH monitoring (24h MII-pH), reflux symptom index (RSI) and reflux finding score (RFS), pepsin concentration in saliva were collected. The Cohen's kappa test and receiver-operating characteristic (ROC) curves were performed to determine and compare the sensitivity and specificity of five diagnostic methods: RSI; RFS, pepsin concentration, RSI + RFS, RSI + RFS + pepsin concentration. The area under the curve (AUC) of RSI, RFS, pepsin concentration, RSI + RFS, RSI + RFS + pepsin concentration were 0.767, 0.733, 0.870, 0.750,0.867, respectively. That is, the pepsin concentration has maximum AUC (the cutoff point is 219.47 (ng/mL); the sensitivity and 1-specificity is 0.300, 0.933, respectively.). The positive predictive value was 90.3% (28/31), and the negative predictive value was 77.8% (7/9). The Cohen's kappa coefficients of the five diagnostic subgroups were: RSI 0.486 (95% CI 0.207-0.764, P = 0.001); RFS 0.333 (95% CI 0.021-0.644, P = 0.032); RSI + RFS: 0.517 (95% CI 0.205-0.829, P = 0.001); pepsin concentration: 0.699 (95% CI 0.379-0.931, P = 0.001); RSI + RFS + pepsin concentration: 0.500 (95% CI 0.181-0.819, P < 0.001). The pepsin concentration has the maximum AUC area and highest consistency with the 24h MII-pH. Therefore, it has certain value in the screening and diagnosis of diseases related to LPR disease.