Area Under The Curve Values Research Articles

Radiomics nomograms based on multi-sequence MRI for identifying cognitive impairment and predicting cognitive progression in relapsing-remitting multiple sclerosis

Rationale and ObjectivesTo build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS). Materials and MethodsWe retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n=149, for training and validation) and dataset2 (n=29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT. CI and CP were evaluated using SDMT scores at baseline and follow-up. The included DIR sequence aided in identifying cortical lesions. Lesion radiomics and structural features were extracted and selected from multi-sequence MRI, followed by the computation of radiomics and structural scores. The nomogram was developed through multivariate logistic regression, integrating clinical data, radiomics and structural scores to identify CI in patients. Moreover, a similar method was employed to further construct a nomogram predicting CP in patients. ResultsThe nomogram demonstrated superior performance in identifying patients with CI, with area under the curve (AUC) values of 0.937 (95% Conf. Interval: 0.898-0.975) and 0.876 (0.810-0.943) in internal and external validation sets, compared to models solely based on clinical data, lesion radiomics, and structural features. Furthermore, another nomogram constructed in predicting CP also exhibited outstanding performance, with an AUC value of 0.969 (0.875-1.000) in the validation set. ConclusionThese nomograms, integrating clinical data, multi-sequence lesions radiomics and structural features, enable more effective identification of CI and early prediction of CP in RRMS patients, providing important support for clinical decision-making.

Improving prediction accuracy of spread through air spaces in clinical-stage T1N0 lung adenocarcinoma using computed tomography imaging models

ObjectivesTo develop computed tomography (CT)-based models to increase the prediction accuracy of spread through air spaces (STAS) in clinical-stage T1N0 lung adenocarcinoma. MethodsThree cohorts of patients with stage T1N0 lung adenocarcinoma (n = 1258) were analyzed retrospectively. Two models using radiomics and deep neural networks (DNNs) were established to predict the lung adenocarcinoma STAS status. For the radiomic models, features were extracted using PyRadiomics, and 10 features with nonzero coefficients were selected using least absolute shrinkage and selection operator regression to construct the models. For the DNN models, a 2-stage (supervised contrastive learning and fine-tuning) deep-learning model, MultiCL, was constructed using CT images and the STAS status as training data. The area under the curve (AUC) was used to verify the predictive ability of both model types for the STAS status. ResultsAmong the radiomic models, the linear discriminant analysis model exhibited the best performance, with AUC values of 0.8944 (95% confidence interval [CI], 0.8241-0.9502) and 0.7796 (95% CI, 0.7089-0.8448) for predicting the STAS status on the test and external validation cohorts, respectively. Among the DNN models, MultiCL exhibited the best performance, with AUC values of 0.8434 (95% CI, 0.7580-0.9154) for the test cohort and 0.7686 (95% CI, 0.6991-0.8316) for the external validation cohort. ConclusionsCT-based imaging models (radiomics and DNNs) can accurately identify the STAS status of clinical-stage T1N0 lung adenocarcinoma, potentially guiding surgical decision making and improving patient outcomes.

CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy

PurposeTo establish a radiomics model using radiomics features from different region of interests (ROI) based on dosimetry-related regions in enhanced computed tomography (CT) simulated images to predict radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC). MethodsOur retrospective study was conducted based on a cohort of 236 NSCLC patients (59 of them with RP≥2) who were treated in 2 institutions and divided into the primary cohort (n = 182,46 of them with RP≥2) and external validation cohort (n = 54,13 of them with RP≥2). Radiomic features extracted from three ROIs were defined as the whole lung (WL), the dose volume histogram (DVH) of the lung V20 (V20_Lung) and the DVH of the V30 of lung minus the planning target volume (PTV) (V30 Lung-PTV). A total of 107 radiomics features were extracted from each ROIs. The U test, correlation coefficient and least absolute shrinkage and selection operator (LASSO) were performed for features selection. Six models based on different classification algorithms were developed to select the best radiomics model (R model).In addition, we built a dosimetry model then combined it with the best R model to create a mixed model (R+D model) The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the models. Decision curve analysis could benefit from the model proposals through the assessment of clinical utility. ResultsAmong the three ROIs, the best R model constructed from the LightGBM algorithm demonstrated the strongest discriminative ability in the ROI of V30 Lung-PTV. The corresponding area under the curve (AUC) value was 0.930 (95 % confidence interval (CI): 0.829–0.941). The D model, R model and R+D model achieved AUC values of 0.798 (95 %CI: 0.732–0.865), 0.930 (95 %CI: 0.829–0.941) and 0.940 (95 %CI: 0.906–0.974) in primary cohort, and in external validation cohort, the AUC values were 0.793 (95 %CI:0.637–0.949), 0.887 (95 %CI:0.810–0.993), 0.951 (95CI%:0.891–1.000). Decision curve demonstrate that R+D model could benefit for patients through the assessment of clinical utility. ConclusionThe radiomics model was able to predict the acute RP more effectively in comparison with the traditional dosimetry model. Especially the radiomics model based on the V30 Lung-PTV region was able to achieve a higher accuracy when compared to the other regions.

Predictive factors for COVID-19 severity and mortality in hospitalized children

BackgroundUnderstanding the factors influencing disease progression and severity in pediatric COVID-19 cases is essential for effective management and intervention strategies. This study aimed to evaluate the discriminative ability of clinical and laboratory parameters to identify predictors of COVID-19 severity and mortality in hospitalized children.MethodsIn this multicenter retrospective cohort study, we included 468 pediatric patients with COVID-19. We developed a predictive model using their demographic, clinical, and laboratory data. The performance of the model was assessed using various metrics including sensitivity, specificity, positive predictive value rates, and receiver operating characteristics (ROC).ResultsOur findings demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.818 for severity and 0.873 for mortality prediction. Key risk factors for severe COVID-19 in children include low albumin levels, elevated C-reactive protein (CRP), lactate dehydrogenase (LDH), and underlying medical conditions. Furthermore, ROC curve analysis highlights the predictive value of CRP, LDH, and albumin, with AUC values of 0.789, 0.752, and 0.758, respectively.ConclusionOur study indicates that laboratory values are valuable in predicting COVID-19 severity in children. Various factors, including CRP, LDH, and albumin levels, demonstrated statistically significant differences between patient groups, suggesting their potential as predictive markers for disease severity. Implementing predictive analyses based on these markers could aid clinicians in making informed decisions regarding patient management.

Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization

Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.

Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density weighted images with diagnostic support from the latest deep learning classification models?

This study aimed to clarify the performance of magnetic resonance imaging (MRI)-based deep learning classification models in diagnosing temporomandibular joint osteoarthritis (TMJ-OA) and to compare the developed diagnostic assistance with human observers. The subjects were 118 patients who underwent MRI for examination of TMJ disorders. One hundred condyles with TMJ-OA and 100 condyles without TMJ-OA were enrolled. Deep learning was performed with four networks (ResNet18, EfficientNet b4, Inception v3, and GoogLeNet) using five-fold cross validation. Receiver operating characteristics (ROC) curves were drawn for each model and diagnostic metrics were determined. The performances of the four network models were compared using Kruskal-Wallis tests and post-hoc Scheffe tests, and ROCs between the best model and human were compared using chi-square tests, with p < 0.05 considered significant. ResNet18 had areas under the curves (AUCs) of 0.91-0.93 and accuracy of 0.85-0.88, which were the highest among the four networks. There were significant differences in AUC and accuracy between ResNet and GoogLeNet (p = 0.0264 and p = 0.0418, respectively). The kappa values of the models were large, 0.95 for ResNet and 0.93 for EfficientNet. The experts achieved similar AUC and accuracy values ​​to the ResNet metrics, 0.94 and 0.85, and 0.84 and 0.84, respectively, but with a lower kappa of 0.67. Those of the dental residents showed lower values. There were significant differences in AUCs between ResNet and residents (p < 0.0001) and between experts and residents (p < 0.0001). Using a deep learning model, high performance was confirmed for MRI diagnosis of TMJ-OA.

Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis

This study presents a robust approach for the classification of ovarian cancer subtypes through the integration of deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages the powerful feature extraction capabilities of EfficientNet-B0, utilizing its deep features for subsequent fine-grained classification using the fine-KNN approach. The UBC-OCEAN dataset, encompassing histopathological images of five distinct ovarian cancer subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell ovarian carcinoma (CC), endometrioid carcinoma (EC), low-grade serous carcinoma (LGSC), and mucinous carcinoma (MC), served as the foundation for our investigation. With a dataset comprising 725 images, divided into 80% for training and 20% for testing, our model exhibits exceptional performance. Both the validation and testing phases achieved 100% accuracy, underscoring the efficacy of the proposed methodology. In addition, the area under the curve (AUC), a key metric for evaluating the model’s discriminative ability, demonstrated high performance across various subtypes, with AUC values of 0.94, 0.78, 0.69, 0.92, and 0.94 for MC. Furthermore, the positive likelihood ratios (LR+) were indicative of the model’s diagnostic utility, with notable values for each subtype: CC (27.294), EC (9.441), HGSC (12.588), LGSC (17.942), and MC (17.942). These findings demonstrate the effectiveness of the model in distinguishing between ovarian cancer subtypes, positioning it as a promising tool for diagnostic applications. The demonstrated accuracy, AUC values, and LR+ values underscore the potential of the model as a valuable diagnostic tool, contributing to the advancement of precision medicine in the field of ovarian cancer research.

Black Soldier Fly (Hermetia illucens L.) Larvae Oil Ointment to Accelerate Incision Wound Healing in Mice

Larvae of the Black Soldier Fly (BSF) are a natural material that has the potential to become a new alternative for accelerating wound healing due to their nutritional content. This study aims to analyze the potential of BSF larvae oil ointment in accelerating the healing of incisional wounds in mice. The study was conducted as an experimental research with six treatments and four repetitions, including a control group without treatment, ointment base, gentamicin ointment, BSF larvae oil ointment at 5% concentration, BSF larvae oil ointment at 10% concentration, and BSF larvae oil ointment at 15% concentration. The parameters observed included the percentage of wound healing, area under the curve (AUC) values, and anti-inflammatory efficacy percentage. The results showed no significant differences between groups based on statistical analysis (P&gt;0.05) for the observed parameters. However, the 5% BSF larvae oil ointment showed a 100% healing percentage on the fourteenth day. Based on AUC values and anti-inflammatory efficacy percentage, the 5% BSF larvae oil ointment had values close to commercial gentamicin ointment. This indicates that treatment with BSF larvae oil ointment, especially at the lowest dose, plays a role in accelerating incisional wound healing and has great potential to be developed as a commercial ointment in the future.

Analysis of risk factors for lymph node metastasis in 1096 patients with early gastric cancer and establishment of a predictive nomogram

Objective: To investigate the risk factors for lymph node metastasis in patients with early gastric cancer and establish a model for prediction of risk. Methods: The cohort of this retrospective observational study comprised 1096 patients who had undergone radical gastric cancer surgery combined with standard D1 lymphadenectomy and been diagnosed with early gastric cancer by postoperative pathology in Zhongshan Hospital affiliated with Fudan University from January 2016 to July 2022. The patients were allocated to groups with and without lymph node metastases. Clinicopathological characteristics were compared between the two groups and multi-factor logistic regression analysis used to identify independent risk factors for lymph node metastasis in patients with early gastric cancer. Indications for endoscopic resection in the Japanese Gastric Cancer Association (JGCA) guideline were also incorporated into construction of the model. The patient cohort was divided into training and validation sets in a 6:4 ratio. The identified independent risk factors were used to construct a predictive nomogram. Receiver operating characteristic curves were plotted separately and the difference between them in predictive efficacy was compared using the area under the curve (AUC). Results: A total of 1,096 patients with early gastric cancer were included, with 750 males and 346 females. Their average age was (61.4±10.9) years old, and the mean tumor diameter was (23.8±11.4) mm. Among them, 188 patients (17.2%) had positive lymph node metastasis, with 109 cases in N1 stage, 42 cases in N2 stage, and 37 cases in N3 stage. Additionally, 462 patients were in T1a stage, while 634 patients were in T1b stage. Univariate analysis showed that tumor diameter, location, Lauren classification, gross morphology, histological type, intravascular invasion, ulceration, differentiation type and tumor T stage were associated with lymph node metastasis after radical gastrectomy for early gastric cancer (all P<0.05). Multifactorial analysis showed that the presence of intravascular invasion (OR=14.822, 95%CI: 9.323-23.572, P<0.001), undifferentiated type (OR=3.095, 95%CI: 1.649-5.811, P<0.001), tumor T1b (OR=1.798, 95%CI: 1.053-3.079, P=0.032), and tumor diameter ≥2 cm (OR=1.229, 95%CI: 1.031-1.469, P=0.022) were independent risk factors for lymph node metastasis. The baseline data of the training set and validation set were consistent in terms of balance (all P>0.05). We used the above variables to establish a predictive nomogram for lymph node metastasis in patients with early gastric cancer. The AUC values obtained from the validation of the model in the training and validation sets were 0.880 (95%CI: 0.849-0.911) and 0.881 (95%CI: 0.841-0.921), respectively, and were significantly better than the predictive efficacy based on the JGCA guideline (AUC=0.777, 95%CI: 0.746-0.809, P<0.001). Conclusions: Patients with early gastric cancer and intravascular invasion, undifferentiated tumors, tumor T1b, and diameter ≥2 cm are at higher risk of postoperative lymph node metastasis than other patients. The predictive model developed in this study more accurately predicts lymph node metastasis in patients with early gastric cancer than previously proposed methods.

Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia

Land cover changes are a critical concern in a developing country where rapid urbanization and population growth intersect with environmental dynamics. Understanding the driving forces behind these changes is essential for sustainable development and effective land management. This study analyzes land cover changes in Demak-Jepara regency, Indonesia, over a 20-year period using Landsat data. The objective is to identify the dominant factors driving the increase in built-up areas, considering both natural and human-induced factors. Factors such as road distance, existing build area, and natural features are evaluated. Using ArcGIS and Idrisi Selva's Land Change Modeler, land cover data is processed, and Cellular Automata-Markov analysis is conducted. The study considers a cell size of 30 x 30 meters and a time step of 5 years from 2001 to 2009. Transition persistence analysis identifies significant factors, validated using 2020 land cover data through AUC (Area Under the Curve) and ROC (Relative Operating Characteristic) analysis. The combination of natural and human-induced factors (scenario-C) shows the highest AUC value of 0.9406, indicating better conformity with 2020 land cover. Dominant factors in scenario C include roads, existing built-up areas, river order, and slope gradient. Results reveal that road development and proximity to existing settlements are the primary drivers of land cover changes. Natural factors like river order and slope gradient have a lesser impact, while the coastline has minimal influence. These findings highlight the importance of considering both natural and human-induced factors in land use planning. They provide valuable insights for policymakers and land managers in making informed decisions for sustainable development and land use strategies. Keywords: Land Cover Changes; Driving Factors; Natural Factors; CA-Markov; Sustainable Development

The Consistency Between Symptom Scales and Multi-time Point Salivary Pepsin Test for Diagnosing Laryngopharyngeal Reflux Disease

ObjectiveTo investigate the screening diagnostic value of reflux symptom index (RSI), and reflux symptom score (RSS) for laryngopharyngeal reflux disease (LPRD). MethodsFifty-two patients hospitalized in the Department of Otolaryngology-Head and Neck Surgery between October 2022 and April 2023 were enrolled in the study. These patients completed the RSS, RSI scales, as well as underwent the multi-time point salivary pepsin test (MTPSPT). Cohen's kappa test and receiver operating characteristic analysis were utilized to assess and compare the diagnostic values of the RSI and RSS. ResultsA total of 52 patients, comprising 37 males and 15 females, with a mean age of 44 ± 12 years, were enrolled in the study. The Kappa values between RSS, RSI, and MTPSPT were found to be 0.403 (p < 0.05) and 0.192 (p < 0.05), respectively. When considering MTPSPT as the diagnostic criterion, the sensitivity and specificity of RSS were determined to be 76.70% and 76.90%, respectively, while those of RSI were 41.10% and 42.30%. Additionally, the negative predictive values for RSS and RSI stood at 77.70% and 64.70%, respectively, and their positive predictive values were 94.20% and 57.10%. Furthermore, the areas under the curves (AUC) for RSS and RSI were calculated to be 0.677 (95% CI: 0.464-0.742) and 0.609 (95% CI: 0.533-0.800), respectively. ConclusionRSS had a better concordance with multi-time point salivary pepsin test results and AUC values when screening for LPRD.

Association Between Different Inflammatory Markers and Generalized Abdominal Obesity: A Cross-Sectional Study.

Inflammation is strongly correlated with obesity. However, very few studies have reported associations between novel inflammatory markers, such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), C-reactive protein (CRP), and C-reactive protein-to-albumin ratio (CAR), and different obesity types. Therefore, this study aimed to explore the associations of these inflammatory markers with generalized and abdominal obesity. This cross-sectional study included data from 2015 to 2018 obtained from the National Health and Nutrition Examination Survey. Multivariate regression analysis was performed to determine the associations between different inflammatory biomarkers and obesity. The discriminative capacities of the markers for obesity types were depicted using receiver operating characteristic (ROC) curves, with corresponding area under the curve (AUC) metrics quantifying this discrimination. After adjusting for confounding variables, generalized obesity was found to be positively associated with an increased risk of NLR by 35%, SII by 52%, CRP by 941%, and CAR by 925%, compared with the reference groups. In the model, the CRP concentration and CAR demonstrated high AUC values of 0.690 and 0.889, respectively, for the identification of generalized and abdominal obesity (P < 0.05). This study revealed associations between obesity and inflammatory biomarkers, such as the NLR, SII, CRP, and CAR. CRP is the most sensitive marker for generalized obesity, while CAR shows the strongest association with abdominal obesity. These findings suggest that inflammatory biomarkers may be useful for assessing and managing obesity-related health concerns.

Exploring multi-pollution variability in the urban environment: geospatial AI-driven modeling of air and noise

ABSTRACT This study addresses the critical need for comprehensive multi-pollution modeling in urban environments by employing Geospatial Artificial Intelligence (GeoAI) techniques. Specifically, it aims to elucidate the variability of air and noise pollution levels in Tehran, Iran, and develop precise multi-pollution susceptibility maps. A spatial database was compiled, encompassing annual average data (2019–2022) of the Air Quality Index (AQI) and Equivalent Continuous Sound Level (Leq), along with 19 influential factors related to both pollutants. The study utilized the CatBoost machine learning algorithm, enhanced with Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO) algorithms, to model and predict pollution patterns. Key quantitative achievements include a remarkable 15% reduction in Root Mean Square Error (RMSE) for air pollution and a 12% reduction in noise pollution when using the CatBoost-HHO configuration compared to the standalone CatBoost algorithm. Validation of the multi-pollution maps utilizing the Area Under the Curve (AUC) analysis showed high accuracy, with the CatBoost-HHO achieving AUC values of 0.943 for air pollution and 0.936 for noise pollution, outperforming CatBoost-GWO and standalone CatBoost models. These results underscore the efficacy of our approach in accurately identifying and mapping multi-pollution hotspots, contributing valuable insights for urban environmental management and public health preservation.

Dynamics of peripheral blood inflammatory index predict tumor pathological response and survival among patients with locally advanced non-small cell lung cancer who underwent neoadjuvant immunochemotherapy: a multi-cohort retrospective study.

Static tumor features before initiating anti-tumor treatment were insufficient to distinguish responding from non-responding tumors under the selective pressure of immuno-therapy. Herein we investigated the longitudinal dynamics of peripheral blood inflammatory indexes (dPBI) and its value in predicting major pathological response (MPR) in non-small cell lung cancer (NSCLC). A total of 147 patients with NSCLC who underwent neoadjuvant immunochemotherapy were retrospectively reviewed as training cohort, and 26 NSCLC patients from a phase II trial were included as validation cohort. Peripheral blood inflammatory indexes were collected at baseline and as posttreatment status; their dynamics were calculated as their posttreatment values minus their baseline level. Least absolute shrinkage and selection operator algorithm was utilized to screen out predictors for MPR, and a MPR score was integrated. We constructed a model incorporating this MPR score and clinical predictors for predicting MPR and evaluated its predictive capacity via the area under the curve (AUC) of the receiver operating characteristic and calibration curves. Furthermore, we sought to interpret this MPR score in the context of micro-RNA transcriptomic analysis in plasma exosomes for 12 paired samples (baseline and posttreatment) obtained from the training cohort. Longitudinal dynamics of monocyte-lymphocyte ratio, platelet-to-lymphocyte ratio, platelet-to-albumin ratio, and prognostic nutritional index were screened out as significant indicators for MPR and a MPR score was integrated, which was further identified as an independent predictor of MPR. Then, we constructed a predictive model incorporating MPR score, histology, and differentiated degree, which discriminated MPR and non-MPR patients well in both the training and validation cohorts with an AUC value of 0.803 and 0.817, respectively. Furthermore, micro-RNA transcriptomic analysis revealed the association between our MPR score and immune regulation pathways. A significantly better event-free survival was seen in subpopulations with a high MPR score. Our findings suggested that dPBI reflected responses to neoadjuvant immuno-chemotherapy for NSCLC. The MPR score, a non-invasive biomarker integrating their dynamics, captured the miRNA transcriptomic pattern in the tumor microenvironment and distinguished MPR from non-MPR for neoadjuvant immunochemotherapy, which could support the clinical decisions on the utilization of immune checkpoint inhibitor-based treatments in NSCLC patients.

Multi-resolution assessment of heart rate variability signals during yogic and normal breathing using machine learning modules

In recent times, yoga has gained global prominence due to its wide-ranging health benefits. Heart rate variability (HRV) analysis has become a widely embraced tool to assess the impact of yoga and meditation on individuals. The effects of yoga can vary significantly among individuals based on their prior expertise, skills, and the interplay of external and internal dynamics. This variability complicates the task of extracting distinctive characteristics from raw data to categorize yogic and pre-yogic states. This paper introduces a new approach that enhances the screening of yogic states through multi-resolution analysis of HRV data combined with a machine learning module. The empirical Fourier decomposition (EFD) technique is harnessed to decompose input data into distinct multi-resolution modes. Various statistical characteristics are then extracted from these modes, allowing for a comprehensive analysis of the immediate effects of yoga breathing on HRV signals. To discern between yogic and pre-yogic HRV signals, a selection of significant features is ranked using a chi-square score and subsequently inputted into machine-learning modules. Notably, employing normalized features with the decision medium tree classifier yields an area under the curve (AUC) value of 0.99, while using direct features with an ensemble bagged tree classifier results in an AUC value of 0.82. Furthermore, this research underscores the importance of assessing outcomes from various metrics to compute the underlying dynamics of HRV data during breathing practices. This approach facilitates precise monitoring, personalized feedback, and intervention in yoga machine interfacing systems, ultimately enriching the overall experience and well-being of yoga practitioners.

Exploring the role of ferroptosis-related genes as biomarkers in acute kidney injury.

Acute kidney injury (AKI) is a severe condition with high morbidity and mortality. Innovative biomarkers and treatments are essential for improving patient outcomes. This study aims to investigate the role of ferroptosis-related genes (FRGs) in AKI for identifying potential biomarkers and therapeutic targets. We analyzed mRNA expression profiles from the Gene Expression Omnibus (GEO: GSE139061) dataset, comparing 36 AKI samples with 9 normal samples. Differentially expressed genes (DEGs) were identified using the R software package limma. Functional enrichment analyses were conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Key biomarkers were validated through area under the curve (AUC) values, and immune cell infiltration was analyzed using CIBERSORT. We identified 78 differentially expressed FRGs, with 27 up-regulated and 51 down-regulated genes. Key signaling pathways included MAPK, ferroptosis, and p53. Five genes-NR4A1, GLRX5, USP35, AEBP2, and MDM4-were identified as potential biomarkers, each demonstrating AUC values greater than 0.800. Specifically, MDM4 showed significant potential by promoting the phosphorylation of p53 at Ser46, enhancing mitochondrial apoptotic activity. Immune analysis revealed a significant elevation of M0 macrophages in AKI samples compared to normal samples (P < 0.01). Our findings highlight the critical role of ferroptosis-related genes in AKI, identifying NR4A1, GLRX5, USP35, AEBP2, and MDM4 as key biomarkers with high diagnostic potential. These results provide novel insights into the molecular mechanisms of AKI.

Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments.

Clinical Characteristics, Predictors for Mortality and Comparison of the Birmingham Vasculitis Activity Score and the Five-Factor Score on Survival in ANCA-Associated Vasculitis in Hong Kong

Objective: To describe the clinical profile and predictors of mortality of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) patients in Hong Kong. To compare the accuracy of the latest Five-Factor Score (FFS-2009) and the Birmingham Vasculitis Activity Score (BVAS) in prediction of survival with this local cohort. Methods: A retrospective observational study on newly diagnosed AAV patients, from January 1, 2011 to March 31, 2022, managed in the Kowloon West Cluster (KWC) hospitals in Hong Kong. Demographic and baseline characteristics, clinical profile, and treatment profile were reviewed. Factors associated with mortality were analyzed with the Cox proportional hazards model. The performances of FFS and BVAS in mortality prediction were analyzed by receiver operating characteristic (ROC) curves. Results: A total of 83 AAV patients were included in the study. The median age was 70.5 years at diagnosis. Microscopic polyangiitis (MPA; 69.9%) was the most common AAV subtype. The median FFS and BVAS were 2 and 20, respectively. The overall mortality was 45.6% across the study period. Multivariate Cox regression identified age at diagnosis (HR 1.043, [Formula: see text]), stabilized peak serum creatinine (HR 1.002, [Formula: see text]), hemoglobin level (HR 0.754, [Formula: see text]), cardiac involvement (HR 3.862, [Formula: see text]), and use of maintenance therapy (HR 0.261, [Formula: see text]) as independent predictors of overall survival. Both FFS and BVAS were significant predictors of overall survival. The areas under the curve (AUC) of ROC curves suggested FFS was a good prediction tool for early mortality in 1 year, with an AUC value of 0.874. Conclusion: Despite the advances in treatment, AAV still carried significant morbidities with high mortality. Clinical predictors and existing scoring systems showed good predictive power on mortality.

Prognostic impact of the high-sensitivity modified glasgow prognostic score on patients undergoing radical surgery for hepatocellular carcinoma : Authorship.

Our study aimed to assess the ability of high-sensitivity modified Glasgow prognostic Score (HS-mGPS) predicting survival in patients undergoing radical surgery for hepatocellular carcinoma (HCC) and to compare the impact with other Inflammation-Based prognostic scoring systems including Glasgow prognostic Score (GPS) and modified GPS (mGPS). Our study evaluated 293 patients with HCC who had undergone hepatectomy at the Third Affiliated Hospital of Soochow University between 2010 and 2018. The HS-mGPS, mGPS, and GPS were calculated based on particular cut-off values of preoperative C-reactive protein and albumin, and the correlations between HS-mGPS and clinicopathological parameters were evaluated. Univariate and multivariate survival analyses were conducted by Kaplan-Meier method and Cox proportional hazards model. To evaluate the discrimination ability of each prognostic score, the receiver operating characteristic (ROC) curve were generated and the areas under the curve (AUC) were measured and compared. The study results indicated a correlation between elevated HS-mGPS scores and adverse clinical factors, including higher BCLC stage, C-P grade, multiple tumors, and larger tumor diameter. Kaplan-Meier and univariate survival analyses revealed that higher scores of HS-mGPS, GPS, and mGPS were all associated with significantly reduced overall survival (OS) (all p < 0.001). In multivariate survival analysis, HS-mGPS emerged as an independent risk factor for poor OS in patients undergoing hepatectomy for HCC (p = 0.010), along with factors including maximal tumor diameter (p < 0.001), microvascular invasion (MVI) (p = 0.008), and BCLC stage (p = 0.001). The analysis of ROC curves and the AUC values indicated that HS-mGPS outperforms GPS and mGPS in predicting the long-term prognosis of patients with resectable HCC. Preoperative HS-mGPS proves superior in predicting adverse long-term outcomes in HCC patients undergoing radical surgery.