Validation Tests Research Articles

Building Dual AI Models and Nomograms Using Noninvasive Parameters for Aiding Male Bladder Outlet Obstruction Diagnosis and Minimizing the Need for Invasive Video-Urodynamic Studies: Development and Validation Study.

Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS. These examinations' invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures. We conducted a retrospective study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed-1 based on the International Continence Society's definition (International Continence Society-defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies-diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model's performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making. Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models' predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS. The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.

Prediction of postoperative residual primary ovarian neoplasm or metastatic lesion close to rectum of serous ovarian carcinoma based on clinical and MR T1-DEI features.

The optimal primary debulking surgery outcome of serous ovarian carcinoma (SOC) is greatly affected by primary ovarian neoplasm or metastatic lesion close to the rectum. To study the risk factors affecting postoperative residual primary ovarian neoplasm or metastatic lesion close to the rectum of SOC. The clinical and MRI data of 164 patients with SOC eligible from institution A (training and test groups) and 36 patients with SOC eligible from institution B (external validation group) were collected and retrospectively analyzed. The clinical data included age, serum carbohydrate antigen 125 (CA-125), human epididymis protein 4, and neutrophil-to-lymphocyte ratio (NLR). Magnetic resonance imaging (MRI) data included ovarian mass distribution, maximum diameter of ovarian mass, ovarian mass features, degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion, and amount of ascites. A model was established using multivariate logistic regression. By univariate and multivariate logistic regressions, CA-125 (P = 0.024, odds ratio [OR] = 3.798, 95% confidence interval [CI] = 1.24-13.32), NLR (P = 0.037, OR = 3.543, 95% CI = 1.13-12.72), and degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion (P < 0.001, OR = 37.723, 95% CI = 7.46-266.88) were screened as independent predictors. The area under the curve values of the model in the training, test, and external validation groups were 0.860, 0.764, and 0.778, respectively. The clinical-radiological model based on T1-weighted dual-echo MRI can be used non-invasively to predict postoperative residual ovarian neoplasm or metastasis close to SOC in the rectum.

Predictive value of neck circumference for cardiometabolic risk in children

Objective: To investigate the predictive value of neck circumference on cardiometabolic risk in children. Methods: This was a cross-sectional study of natural sources. As the prediction cohort, clinical data were collected from 3 443 children aged 5-14 years who underwent physical examination in the Department of Child Healthcare, Children's Hospital of Nanjing Medical University from July 2021 to September 2022. As the validation cohort for external validation, clinical data were collected from 604 children aged 5-14 years who underwent physical examination in the Department of Child Healthcare, Children's Hospital of Nanjing Medical University from October 2022 to March 2023. Height, weight, neck circumference, waist circumference and body composition were measured in both groups, and body mass index, neck circumference to height ratio (NHtR), waist circumference to height ratio, body fat percentage and skeletal muscle percentage were calculated. Systolic blood pressure, diastolic blood pressure, fasting blood glucose, blood lipid and uric acid and other cardiovascular and metabolic risk indicators were collected in both groups. The prediction cohort was further stratified into clustered and non-clustered groups based on the clustering of cardiometabolic risk factors (CCRF). Various variables between these 2 groups were compared using the Mann-Whitney U test. Pearson correlation and binary Logistic regression were conducted to investigate the correlations between neck circumference and cardiovascular metabolic risk factors. The accuracy of NHtR in predicting the CCRF was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC). The cutoff value was determined using the Youden index. The validation cohort was then divided into groups above and below the cutoff value, and the detection rate of CCRF between the 2 groups was compared using the χ2 test for validation. Results: In the prediction cohort of 3 443 children (2 316 boys and 1 127 girls), 1 395 (40.5%) children were overweight or obese, and 1 157 (33.6%) children had CCRF. Pearson correlation analysis revealed all significant positive correlations (all P<0.01) between neck circumference and systolic blood pressure (r=0.47, 0.39), diastolic blood pressure (r=0.27, 0.21), uric acid (r=0.36, 0.30), and triglycerides (r=0.20, 0.20) after adjusting for age in both males and females. Among both males and females, neck circumference both showed significant negative correlation (both P<0.01) with high-density lipoprotein cholesterol (r=-0.27, -0.28), and no correlation with fasting glucose levels (r=0.03, -0.03, both P>0.05). After adjusting for gender, age, and body fat percentage, increased body mass index, neck circumference, or waist circumference increased the risks of hypertension (OR=1.23, 1.39, 1.07, all P<0.001), hyperuricemia (OR=1.16, 1.23, 1.05, all P<0.001), hypertriglyceridemia (OR=1.08, 1.16, 1.02, all P<0.01), low high-density lipoprotein cholesterol (OR=1.10, 1.27, 1.03, all P<0.01), and the CCRF (OR=1.51, 1.73, 1.15, all P<0.01). The areas under the ROC curves of NHtR in predicting CCRF was 0.73, with sensitivity and specificity at 0.66 and 0.71, respectively. The corresponding optimal cut-off value was 0.21. Validation with 604 children confirmed that the detection of CCRF in the NHtR≥0.21 group was 3.29 times (60.5% (112/185) vs. 18.7% (79/422),χ2=107.82, P<0.01) higher compared to the NHtR <0.21 group. Conclusions: Neck circumference is associated with cardiovascular metabolic risks such as hypertension, hyperlipidemia, hyperglycemia, and hyperuricemia in children. When the NHtR is ≥0.21, there is an increased likelihood of CCRF.

Modeling the Discharge Rate of a Screw Conveyor Considering Hopper–Conveyor Coupling Parameters

Developing a flow rate model for the screw feeder and optimizing discharge performance are crucial for achieving automated intelligent precision feeding. This study constructs a mass flow rate model for screw conveyors, considering the coupled structural parameters of the hopper and screw conveyor. The model is developed using single-factor tests and central composite design (CCD) response surface tests and is validated through actual discharge tests. Results indicate that the discharge rate in the hopper–screw conveyor system is primarily influenced by the screw conveyor itself. Among the structural parameters, the hopper inclination angle and the hopper discharge opening length significantly affect the filling coefficient. Validation tests show an average error of 6.8% between the predicted and simulated mass flow rates and 5.0% with the actual mass flow rate, demonstrating the model’s high precision and accuracy.

Implementation of a Drift Flux Model into SAM with Development of a Verification and Validation Test Suite for Modeling of Noncondensable Gas Mixtures

The advanced thermal-hydraulic system code, System Analysis Module (SAM), was originally developed for the modeling of single-phase flow in advanced reactors. It has since been expanded to include a four-equation drift flux model for the modeling of two-phase flows containing a noncondensable gas. The model was expanded to support the modeling of molten salt reactor (MSR) designs in which the fuel is directly dissolved in the circulating coolant. These designs have shown that circulating gas bubbles can play an important role in the management of fission products and the operational behavior of the reactor. A drift flux model was implemented to more accurately capture the localized behavior of the void in the core and its impact on the mass transfer of fission products. A thorough assessment of the new model was performed by developing a verification and validation test suite. Verification problems were designed to test all major terms in the new governing equations. The new model converged to the correct solution at the expected order of accuracy for all verification cases. The validation cases included a wide range of flow and void conditions in different pipe geometries. Although higher void experiments show a slight underprediction of void by the drift flux model, experiments that aim to reproduce Molten Salt Reactor Experiment (MSRE) experimental conditions show good agreement with the model. The gas transport model was activated for a SAM model of the MSRE to demonstrate that it can be used in a more complex model. This gas transport model will be used along with an interfacial area transport equation being implemented in SAM for the prediction of mass transport behavior in MSR conditions.

Identification and validation of screening models for breast cancer with 3 serum miRNAs in an 11,349 samples mixed cohort.

The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels. Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability. Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models' robustness and stability. This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated.

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI.

The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.

Utilization of international normalized ratio-derived formula to predict plasma rivaroxaban level-Validation study and real-world experience.

Specific assays of plasma rivaroxaban level are not always readily available with short turnaround time, which hamper the management of urgent clinical situations. In this study, we aimed to build a predictive formula of plasma rivaroxaban levels from international normalized ratio (INR) value and validated in real world clinical situations. Ninety-four patients who were taking rivaroxaban participated in the study. Patients were randomized into testing cohort and validation cohorts. The prediction formula was built from the testing cohort and then validated in validation cohort. The predictive performance was further validated on real-world clinical requests. The root mean square error (RMSE) of the predictive formula for the testing and validation cohorts were 61.81 and 69.32 ng/mL, respectively. The sensitivity and specificity for the formula to predict the threshold plasma rivaroxaban level of 75 ng/mL were 95% (95% CI: 85.4%-100%) and 87.5% (95% CI: 71.3%-100%), respectively, in real-world clinical situations. Plasma rivaroxaban level of threshold level of 75 ng/mL can be calculated from prediction formula by INR value with satisfactory accuracy and it can be used to guide the decision for reversal.

Effects of Muddy Water Infiltration on the Hydraulic Conductivity of Soils

Despite the high sand content of Yellow River water in arid Northwest China, locals in the region opt to use muddy water to meet the demand for agricultural irrigation. Muddy water irrigation is a complex process and is still poorly understood. In this study, six sets of saturated soil column infiltration tests were designed, considering soil texture (silt loam, sandy loam, and sand) and muddy water sand content (3%, 6%, 9%, and 12%) as the influencing factors, with two sets of validation tests. Change in hydraulic conductivity (Kh), the average change rate of hydraulic conductivity (ΔK), and cumulative infiltration volume (I) were experimentally studied in the context of muddy water infiltration to respectively establish the separate functional models and developed to fit their relationship with time. The study results indicated that the hydraulic conductivity (Kh) decreased with increasing muddy water infiltration time. For silt loam and sandy loam, Kh stabilized at 0.0030 and 0.0109 cm/min, respectively, after 70 min of infiltration. In contrast, Kh in the saturated sandy soil column significantly declined throughout the muddy water infiltration, showing a 90.84% reduction after 90 min compared to the saturated hydraulic conductivity of the sandy soil. As the sand content of the muddy water increased from 3% to 12%, Kh decreased by 83.99%, 90.90%, 91.92%, and 92.21% for 3%, 6%, 9%, and 12% sand content, respectively, in the saturated sandy soil columns at the end of the infiltration period. The I values were 21.20, 9.29, 7.90, and 6.25 cm for 3%, 6%, 9%, and 12% sand content, respectively. The ΔK values were 0.0037, 0.0041, 0.0043, and 0.0044 cm/min2 for the respective sand contents, at an infiltration time of 80 min. The validation test demonstrated that the segmented function model accurately emulated the changes in hydraulic conductivity of sandy soil textures throughout the infiltration period. Results from this study provide a significant basis for understanding the mechanisms to hinder muddy water infiltration and to efficiently utilize muddy water for irrigation.

Development and validation of a patient-reported outcome measure for hair loss treatment: The HAIR-Q.

Patient-reported outcome measures (PROMs) for hair loss focus mainly on Alopecia Areata. We created a PROM (i.e., HAIR-Q) that is applicable to any hair loss condition. The HAIR-Q measures satisfaction with hair. Concept elicitation interviews were conducted and analyzed to develop a draft scale. Content validity was established through multiple rounds of patient and expert input. Psychometric properties of the scale were examined in an online sample (i.e., Prolific) using Rasch measurement theory (RMT) analysis. Test-retest reliability and tests of construct validation were examined. Content validity of a 22-item draft scale was established with input from 11 patients, 12 experts and an online Prolific sample of 59 people who had a variety of hair loss treatments. In the RMT analysis (n = 390), 8 items were dropped. Data for the 14-item scale fit the Rasch model (χ2 = 89.85, df = 70, p = 0.06). All 14 items had ordered thresholds and good item fit. Reliability was high with person separation index and Cronbach alpha values ≥0.91, and intraclass correlation coefficient of 0.94 based on a sample of 97 participants. Higher (better) scores on the scale were associated with having more hair, looking younger than ones' age, satisfaction with hair overall, being less bothered by hair loss, and for those who had a hair loss treatment in the past year, being more satisfied with their hair now than before treatment (p < 0.001). The HAIR-Q evidenced reliability and validity and can be used in research and to inform clinical care to measure satisfaction with hair from the patient perspective.

Automated segmentation of MRI white matter hyperintensities in 8,421 patients with acute ischemic stroke.

To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction, which is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction, using multisite datasets involving 8,421 patients with acute ischemic stroke. We included 8,421 stroke patients from 9 centers in Korea. 2D UNet and SE-Unet models were trained using 2,408 FLAIR MRI from 3 hospitals and validated using 6,013 FLAIR MRIs from 6 hospitals. WMH segmentation performance was assessed by calculating DSC, correlation coefficient, and concordance correlation coefficient compared to a human-segmented gold standard. In addition, we obtained an uncertainty index that represents overall ambiguity in the voxel classification for WMH segmentation in each patient based on the Kullback-Leibler divergence. In the training dataset, the mean age was 67.4±13.0 years and 60.4% were men. The mean (95% CI) DSCs for Unet in internal testing and external validation were respectively 0.659 (0.649-0.669) and 0.710 (0.707-0.714), which were slightly lower than the reliability between humans (DSC=0.744; 95% CI=0.738-0.751; P=.031). Compared with the Unet, the SE-Unet demonstrated better performance, achieving a mean DSC of 0.675 (0.666-0.685; P<.001) in the internal testing and 0.722 (0.719-0.726; P<.001) in the external validation; moreover, it achieved high DSC values (ranging from 0.672 to 0.744) across multiple validation datasets. We observed a significant correlation between WMH volumes that were segmented automatically and manually for the Unet (r=0.917, P<.0001) and even stronger for the SE-Unet (r=0.933, P<.0001). The SE-Unet also attained a high concordance correlation coefficient (ranging from 0.841 to 0.956) in external test datasets. In addition, the uncertainty indices in the majority of patients (86%) in the external datasets were below 0.35, with an average DSC of 0.744 in these patients. We developed and validated deep learning algorithms to segment WMH in patients with acute cerebral infarction using the largest-ever MRI datasets. In addition, we showed that the uncertainty index can be used to identify cases where automatic WMH segmentation is less accurate and requires human review. WMH = white matter hyperintensity; CNN = convolutional neural networks; SE = squeeze-and-excitation; KL = Kullback-Leibler; ReLU = rectified linear unit; LKW = last known well; mRS = modified Rankin Scale; NIHSS = National Institute of Health Stroke Scale; LAA = large artery atherosclerosis; SVO = small vessel occlusion; CE = cardioembolism.

TOTEM: a multi-cancer detection and localization approach using circulating tumor DNA methylation markers

BackgroundDetection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers.MethodsWe performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach.ResultsA total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort.ConclusionsTOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.

메타버스를 활용한 e-PBL 프로그램 개발 연구

Objectives The purpose of this study is to develop an e-PBL program using Metaverse and apply it to classes to verify its effectiveness. Methods For this purpose, this study developed a program according to the ADDIE design model procedure. First, after conducting literature and case analysis according to the content analysis method, design principles and support structures were derived through a categorization process. And a draft was completed based on the design principles. Second, improvements and supplements were derived through expert FGI on the draft program. After completing the final draft of the program by reflecting the results of FGI, validity verification was conducted. Third, after applying the final draft to the class, a satisfaction survey and an interview regarding open-ended survey items were conducted. Results The results of this study are as follows. First, five design principles and support structures were derived through the third categorization process of the content analysis method, and a draft program was derived by combining them. Second, expert FGI was conducted on the draft program to identify points of improvement and supplementation, and the final draft of the program was completed by reflecting these points. Third, validity (.88) and reliability (.76) were secured by conducting an expert validation (CVI) test and an inter-rater reliability test on the final draft of the program. Fourth, after applying the final plan of the program to the class, pre- and post-satisfaction was conducted, and it was confirmed that post-satisfaction (4.63) improved compared to pre-satisfaction (4.57). As a result of conducting individual interviews with five students in response to open survey questions, it was confirmed what was helpful, what was difficult, and what improvements or wishes were made in the program. Conclusions This study developed an e-PBL program using Metaverse for collaboration and problem-solving learning in virtual space required in the digital era, and investigated the effects of applying it to classes. It is expected that this will be able to provide meaningful information for the possibility of using the metaverse needed in future educational fields and for professorial research and development.

Development of video mind mapping-based teaching materials on relations and functions to increase junior high school students' learning motivation

This study aims to produce teaching materials based on video mind mapping on relations and functions to increase student learning motivation. The research is a type of Research and Development (R&amp;D) using the ADDIE development model but is only limited to the Development stage. The research instruments used in this study were validation sheets, test questions, and interviews. The subjects of this study were three students of class VIII at MTsN 1 Majalengka with high, medium, and low ability levels. The results indicate that the developed video mind mapping-based teaching materials have met valid criteria, making them suitable as alternative teaching materials to increase student learning motivation. The implementation of video mind mapping-based teaching materials shows promise in enhancing students' motivation to learn relations and functions, suggesting potential broader application in educational settings.

Automated, image-based quantification of peroxisome characteristics with perox-per-cell.

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. Supplementary data are available at Bioinformatics online.

The Performance of the Current Algorithm of HIV Diagnosis

Background: We aimed to evaluate the performance of the current algorithm the HIV diagnosis that has been performed for four years. Results of HIV Ag/Ab tests, anti-HIV 1/2 confirmatory tests, HIV-1 RNA tests and the time for official results to be approved were evaluated. Methods: The relationship of HIV Ag/Ab tests, anti-HIV 1/2 confirmation tests and HIV-1 RNA PCR tests, their result times and suitability to the algorithm were examined at Izmir Dokuz Eylül University between May 2017 and June 2021. Results: HIV Ag/Ab ELISA was reactive repetitively in 165/54628 (0.30%) serum samples. Anti-HIV 1/2 confir-mation test was reactive in 54.42% (80/147) of samples. The most common pattern (18.2%) in the con-firmation tests was the positivity of the antibodies against gp160 - gp41 envelope glycoproteins together. The mean reporting time of the confirmation test result was 3h 50 min after the ELISA test. The mean reporting time of the HIV-1 RNA PCR was 12.79 d (±10.22) after the ELISA test and 12.63 (± 10.12) day after the confirmation test. In ROC analysis, the estimated rate of the ELISA test for the confirmation test was highest when S/CO was &gt;13.16 (sensitivity: 97.59 %, specificity: 97.59%). Conclusion: The confirmation test in the current algorithm enabled the rapid test results, early diagnosis of HIV and early antiretroviral therapy. To use the new algorithm effectively, decentralization of the validation tests would be appropriate.

Numerical implementation of an extended multi-resolution SPH-PD coupling model for fluid-structure interaction problem with impacting failure

Considering the fluid-structure impacting failure problems with large size, this paper presents an extended multi-resolution smoothed particle hydrodynamics (SPH)-peridynamics (PD) coupling model. The influence domain and the interpolation smoothing length are innovatively introduced to reconstruct governing equations for interactions between particles of diverse resolutions, ensuring system-wide momentum conservation. Then, an adaptive multi-level cell neighborhood search (AMCNS) algorithm is presented, designed to reduce the time of neighborhood search in multi-resolution simulations. Several 2D and 3D validation tests demonstrate the accuracy of the multi-resolution SPH-PD model in describing solid deformation and fluid impact pressure. The multi-resolution SPH-PD model significantly enhances computational efficiency and can depict the process of structural failure under conditions where a wide difference in scale between waves and structures.

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer.

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy. The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making. Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

Developing an eyeball positioning method in the eye orbit for craniofacial identification in Korean population

We analysed the skulls and faces of Korean subjects using anthropometric methods to understand the anatomical characteristics of the eyeball and eye orbit region of Korean population and to determine the correlations between the hard and soft tissues around the eyeball and eye orbit region. In total, 82 sections in the region were measured to determine the correlations; among them, 34 showed significant differences by sex, and 6 showed significant differences by age. As the distance from the centre of the eye lens to the eye orbit is calculated as a ratio, we determined that the centre of the eye lens is located relatively on the lateral and superior position in each eye orbit in front view. Fourteen sections that could be used for craniofacial reconstruction/approximation in men and women were selected. Regression equations were derived according to the correlation of each section, and their reliabilities were verified by out of sample validation tests. Therefore, our results increase the accuracy of eyeball position determination, which would be useful for more efficient craniofacial reconstruction/approximation of the Korean population and should improve the efficiency of facial recognition.

Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study

Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.