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Background: Breast cancer is a malignancy characterized by chromosomal instability (CIN). This study aimed to examine the potential diagnostic value of chromosomal instability, detected by low-pass whole-genome sequencing (LPWGS), in the preoperative evaluation of sentinel lymph node metastasis (SLNM) in breast cancer. Methods: A retrospective investigation of clinical records from 29 patients with breast cancer revealed two distinct groups based on sentinel lymph node biopsy (SLNB) results: the SLN metastasis group (24 cases) and the SLN non-metastasis group (five cases). CIN and CIN scores were evaluated using LPWGS. An analysis of univariate data and binary logistic regression was employed to identify factors influencing SLNM, and a curve with receiver operating characteristics (ROC) was constructed to assess the diagnostic utility of CIN in predicting SLNM. Results: A significant association between the SLNM and CIN high groups was observed in breast cancer (P=0.011). The CIN score in the metastasis group (17,665.055 ± 8,630.691) was higher than that in the non-metastasis group (9,247.973 ± 3,692.873), demonstrating a significant difference (P=0.044). Univariate binary logistic regression analysis indicated that CIN was a significant predictor for SLNM (odds ratio: 4.036, 95% CI: 1.015-16.047, P=0.048). The AUC of CIN for preoperative diagnosis of SLNM was 0.808 (95% CI: 0.635-0.982, P=0.033), with a sensitivity value of 67.0% and specificity of 100.0% at a threshold of 13,563. Conclusion: Detecting CIN through LPWGS demonstrates diagnostic potential in predicting SLNM in patients with breast cancer before surgery. This approach offers a novel method for assessing axillary lymph node status in clinical practice. [ABSTRACT FROM AUTHOR]

AbstractObjectivesPatients and methodsResultsConclusionNeuropsychological tests (NPTs) are standard tools for assessing cognitive function. These tools can evaluate the cognitive status of a subject, which can be time-consuming and expensive for interpretation. Therefore, this paper aimed to optimize the systematic NPTs by machine learning and develop new classification models for differentiating healthy controls (HC), mild cognitive impairment, and Alzheimer’s disease dementia (ADD) among groups of subjects.A total dataset of 14,926 subjects was obtained from the formal 46 NPTs based on the Seoul Neuropsychological Screening Battery (SNSB). The statistical values of the dataset included an age of 70.18 ± 7.13 with an education level of 8.18 ± 5.50 and a diagnosis group of three; HC, MCI, and ADD. The dataset was preprocessed and classified in two- and three-way machine-learning classification from scikit-learn (www.scikit-learn.org) to differentiate between HC versus MCI, HC versus ADD, HC versus Cognitive Impairment (CI) (MCI + ADD), and HC versus MCI versus ADD. We compared the performance of seven machine learning algorithms, including Naïve Bayes (NB), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, and linear discriminant analysis (LDA). The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predictive value (NPV), area under the curve (AUC), confusion matrixes, and receiver operating characteristic (ROC) were obtained from each model based on the test dataset.The trained models based on 29 best-selected NPT features were evaluated, the model with the RF algorithm yielded the best accuracy, sensitivity, specificity, PPV, NPV, and AUC in all four models: HC versus MCI was 98%, 98%, 97%, 98%, 97%, and 99%; HC versus ADD was 98%, 99%, 96%, 97%, 98%, and 99%; HC versus CI was 97%, 99%, 92%, 97%, 97%, and 99% and HC versus MCI versus ADD was 97%, 96%, 98%, 97%, 98%, and 99%, respectively, in predicting of cognitive impairment among subjects.According to the results, the RF algorithm was the best classification model for both two- and three-way classification among the seven algorithms trained on an imbalanced NPTs SNSB dataset. The trained models proved useful for diagnosing MCI and ADD in patients with normal NPTs. These models can optimize cognitive evaluation, enhance diagnostic accuracy, and reduce missed diagnoses. [ABSTRACT FROM AUTHOR]

Cervical malignancy (CC) is the 2nd most prevalent malignancy among females, leading to cancer mortality. Primary detection of CC tumors results in an improved prognosis. CC is a malignant gynecological tumor, with few treatment options. New diagnostic and therapeutic agents are required to expand patient survival and quality of life. If CC tumors can be found at an early stage, the prognosis is much brighter. New diagnostic and therapeutic agents are needed to increase patient survival and quality of life. In this work, we performed whole-exome sequencing utilizing V5 (Illumina platform) 10 samples, 5 control and 5 CC tumour tissue, and we compared the results with transcriptome studies. KMT2C variations were shown to be among the most vicious in this analysis. From an Indian viewpoint, we found a plethora of SNVs and mutations, including those with known, unknown, and possible effects on health. Based on our findings, we know that the KMT2C gene is on chr. Seven and in exon 8, all three recognized variants are missense, synonymous, coding synonymous, non-coding variants, and GnomAD MAF (− 0.05). The variation at position (7:152265091, T > A, SNV 62478356) in KMT2C is unique, potent, and pathogenic. The missense coding transcript CIQTNF maps to chromosome 7 and displays T > C SNV. In addition, we performed single strand conformational polymorphism analysis on 64 samples and further confirmed them using Sanger sequencing to understand and verify the mutations. KMT2C shows a log FC value of − 1.16. Understanding emerging harmful mutations from an Indian viewpoint is facilitated by our bioinformatics-based, extensive correlation studies of WES analysis. Potentially harmful and new mutations were found in our preliminary analysis; among these ten top mutated genes, KMT2C and CIQTNF were altered in ten cases of CC with an Indian phenotype. [ABSTRACT FROM AUTHOR]

Context Steatotic liver disease is common but overlooked in childhood obesity; diagnostic methods are invasive or expensive. Objective We sought to determine the diagnostic accuracy of vibration-controlled transient elastography (VCTE) compared with magnetic resonance imaging (MRI) in adolescents with obesity and high risk for hepatosteatosis. Methods Baseline data in 3 clinical trials enrolling adolescents with obesity were included (NCT03919929, NCT03717935, NCT04342390). Liver fat was assessed using MRI fat fraction and VCTE-based controlled attenuation parameter (CAP). Hepatosteatosis was defined as MRI fat fraction ≥5.0%. The area under the receiver-operating characteristic curves (AUROCs) for CAP against MRI was calculated, and optimal CAP using the Youden index for hepatosteatosis diagnosis was determined. Results Data from 82 adolescents (age 15.6 ± 1.4 years, body mass index 36.5 ± 5.9 kg/m2, 81% female) were included. Fifty youth had hepatosteatosis by MRI (fat fraction 9.3% ; 95% CI 6.7, 14.0), and 32 participants did not have hepatosteatosis (fat fraction 3.1%; 95% CI 2.2, 3.9; P <.001). The hepatosteatosis group had higher mean CAP compared with no hepatosteatosis (293 dB/m; 95% CI 267, 325 vs 267 dB/m; 95% CI 248, 282; P =.0120). A CAP of 281 dB/m had the highest sensitivity (60%) and specificity (74%) with AUROC of 0.649 (95% CI 0.51-0.79; P =.04) in the entire cohort. In a subset of participants with polycystic ovary syndrome (PCOS), a CAP of 306 dB/m had the highest sensitivity (78%) and specificity (52%) and AUROC of 0.678 (95% CI 0.45-0.90; P =.108). Conclusion CAP of 281 dB/m has modest diagnostic performance for hepatosteatosis compared with MRI in youth with significant obesity. A higher CAP in youth with PCOS suggests that comorbidities might affect optimal CAP in hepatosteatosis diagnosis. [ABSTRACT FROM AUTHOR]

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models. [ABSTRACT FROM AUTHOR]

BackgroundBreast cancer is a malignancy characterized by chromosomal instability (CIN). This study aimed to examine the potential diagnostic value of chromosomal instability, detected by low-pass whole-genome sequencing (LPWGS), in the preoperative evaluation of sentinel lymph node metastasis (SLNM) in breast cancer.MethodsA retrospective investigation of clinical records from 29 patients with breast cancer revealed two distinct groups based on sentinel lymph node biopsy (SLNB) results: the SLN metastasis group (24 cases) and the SLN non-metastasis group (five cases). CIN and CIN scores were evaluated using LPWGS. An analysis of univariate data and binary logistic regression was employed to identify factors influencing SLNM, and a curve with receiver operating characteristics (ROC) was constructed to assess the diagnostic utility of CIN in predicting SLNM.ResultsA significant association between the SLNM and CIN high groups was observed in breast cancer (P=0.011). The CIN score in the metastasis group (17,665.055 ± 8,630.691) was higher than that in the non-metastasis group (9,247.973 ± 3,692.873), demonstrating a significant difference (P=0.044). Univariate binary logistic regression analysis indicated that CIN was a significant predictor for SLNM (odds ratio: 4.036, 95% CI: 1.015–16.047, P=0.048). The AUC of CIN for preoperative diagnosis of SLNM was 0.808 (95%CI: 0.635–0.982, P=0.033), with a sensitivity value of 67.0% and specificity of 100.0% at a threshold of 13,563.ConclusionDetecting CIN through LPWGS demonstrates diagnostic potential in predicting SLNM in patients with breast cancer before surgery. This approach offers a novel method for assessing axillary lymph node status in clinical practice.

Remotely sensed data play a crucial role in monitoring the El Niño/La Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO events (1982–1983, 1997–1998, 2015–2016). In September 2022, La Niña entered its third year and was unlikely to continue through 2024. Instead, since 2022, forecasts have pointed to a transition from La Niña to a Neutral phase in the summer or late 2023. The onset of El Niño occurred around April 2023, and it is anticipated by sophisticated models to be a strong event through the Northern Hemisphere winter (December 2023–February 2024). The aim of this study is to demonstrate the ability of the combination of two new methods to improve the accuracy of the above claim because El Niño apart from climate anomalies, significantly impacts Earth’s ecosystems and human societies, regulating the spread of diseases by insects (e.g., malaria and dengue fever), and influencing nutrients, phytoplankton biomass, and primary productivity. This is done by exploring first the previous major El Niño events in the period January 1876–July 2023. Our calculations show that the ongoing 2023–2024 El Niño will not be the strongest.

Background: Paraneoplastic ischemic stroke has a poor prognosis. We have recently reported an algorithm based on the number of ischemic territories, C-reactive protein (CRP), lactate dehydrogenase (LDH), and granulocytosis to predict the underlying active cancer in a case-control setting. However, co-occurrence of cancer and stroke might also be merely incidental. Objective: To detect cancer-associated ischemic stroke in a large, unselected cohort of consecutive stroke patients by detailed analysis of ischemic stroke associated with specific cancer subtypes and comparison to patients with bacterial endocarditis. Methods: Retrospective single-center cohort study of consecutive 1612 ischemic strokes with magnetic resonance imaging, CRP, LDH, and relative granulocytosis data was performed, including identification of active cancers, history of now inactive cancers, and the diagnosis of endocarditis. The previously developed algorithm to detect paraneoplastic cancer was applied. Tumor types associated with paraneoplastic stroke were used to optimize the diagnostic algorithm. Results: Ischemic strokes associated with active cancer, but also endocarditis, were associated with more ischemic territories as well as higher CRP and LDH levels. Our previous algorithm identified active cancer-associated strokes with a specificity of 83% and sensitivity of 52%. Ischemic strokes associated with lung, pancreatic, and colorectal (LPC) cancers but not with breast and prostate cancers showed more frequent and prominent characteristics of paraneoplastic stroke. A multiple logistic regression model optimized to identify LPC cancers detected active cancer with a sensitivity of 77.8% and specificity of 81.4%. The positive predictive value (PPV) for all active cancers was 13.1%. Conclusion: Standard clinical examinations can be employed to identify suspect paraneoplastic stroke with an adequate sensitivity, specificity, and PPV when it is considered that the association of ischemic stroke with breast and prostate cancers in the stroke-prone elderly population might be largely incidental. [ABSTRACT FROM AUTHOR]

Background: Timely and accurate screening of malnutrition at the community level is essential to identifying malnourished children. The World Health Organization (WHO) guidelines classify non-oedematous acute malnutrition among children using mid-upper arm circumference (MUAC) or weight-for-height Z-score (WHZ). Study Design: A cross-sectional study. Methods: This study was conducted among children aged 6-60 months. After necessary exclusions, 433 participants were selected using a multi-stage simple random sampling method. Using WHO guidelines for global acute malnutrition (GAM) [WHZ < -2, MUAC < 12.5 cm], the sensitivity (Se), specificity (Sp), predictive values, likelihood ratios, Youden index, and receiver operating characteristic (ROC) curve were calculated for MUAC using WHZ as the criterion. Results: Out of 433 participants, 30% were diagnosed with GAM using WHZ, while 17.6% were found malnourished using MUAC measurements. As per WHO cut-offs, the Se, Sp, positive predictive value (PPV), negative predictive value (NPV), Youden index, positive likelihood ratio (LR +), and negative likelihood ratio (LR-) of MUAC were 48%, 96%, 83%, 81%, 0.44, 12, and 0.54, respectively. The ROC curve displayed an area under the curve of 0.86 (95% confidence interval = 0.83, 0.90) for MUAC < 12.5 cm. Bivariate Pearson correlation also demonstrated a positive linear relationship (R² = 0.302) between the WHZ and MUAC variables. Conclusion: Based on the findings, 48% of the children were correctly identified by the MUAC with an 83% probability of GAM (PPV = 0.83). Moreover, there was 96% Sp in non-malnourished children, with only 4% false positives. Therefore, personnel at the grassroots level can use MUAC for timely and accurate screening of children in Anganwadi centers (AWCs) due to its ease of use and simplicity. [ABSTRACT FROM AUTHOR]

Remotely sensed data play a crucial role in monitoring the El Niño/La Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO events (1982–1983, 1997–1998, 2015–2016). In September 2022, La Niña entered its third year and was unlikely to continue through 2024. Instead, since 2022, forecasts have pointed to a transition from La Niña to a Neutral phase in the summer or late 2023. The onset of El Niño occurred around April 2023, and it is anticipated by sophisticated models to be a strong event through the Northern Hemisphere winter (December 2023–February 2024). The aim of this study is to demonstrate the ability of the combination of two new methods to improve the accuracy of the above claim because El Niño apart from climate anomalies, significantly impacts Earth's ecosystems and human societies, regulating the spread of diseases by insects (e.g., malaria and dengue fever), and influencing nutrients, phytoplankton biomass, and primary productivity. This is done by exploring first the previous major El Niño events in the period January 1876–July 2023. Our calculations show that the ongoing 2023–2024 El Niño will not be the strongest. [ABSTRACT FROM AUTHOR]

Metal magnetic memory (MMM) method is a novel and potential nondestructive evaluation (NDE) technique to assess residual fatigue life of the ferromagnetic structure, such as hydraulic cylinder. In this paper, an evaluation framework was proposed. Firstly, the MMM signals under different fatigue cycles were recorded in the tensile-tensile experiment. A representative signal characteristic Hpp was defined as an evaluation index. The monotonic change trend and abrupt change point was investigated. Statistical results show that Hpp can be used to predict residual fatigue life. Except for a few specimens, almost all specimens produced an abrupt change at the normalized fatigue life coefficient k = 0.91. This can be used to distinguish different stages of fatigue life. The sensitivity, specificity, and diagnostic performance were analyzed using Receiver Operating Characteristic (ROC) curves. The statistical results show that when the coefficient k = 0.91, the diagnostic performance is relatively best with an area under the curve (AUC) is about 0.85, and Jorden's index is about 0.75.

Flash flood causes severe damage to the environment and human life across the world, no exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh in the early monsoon and pose a serious threat to every aspect of socioeconomic development and environmental sustainability. To manage the threat and reduce flood loss, the map of flash flood susceptible zones plays a key role. Thus, the aim of this research is to map the flash flood-susceptible areas of the northeastern haor areas of Bangladesh utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights of evidence (WoE), certainty factor (CF), Shanon’s entropy (SE) and information value (IV). Among the 250 identified flash flood locations, 80 % data was used for training purposes and 20 % data for testing purposes. Eleven selected conditioning factors of flash flood include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance to stream, stream density, rainfall and physiography. The calculated weights are assigned to the conditioning factors using ArcGIS environment to prepare the final flash flood maps. Results of AUC of ROC indicate WoE (success rate = 0.833 and prediction rate = 0.925) is the best model for flash flood susceptibility mapping followed by FR (success rate = 0.828 and prediction rate = 0.928) and SE (success rate = 0.827 and prediction rate = 0.923). According to the models, topographic (flat area) and hydrologic factors significantly control flash flood occurrence in the study area. The prepared flash flood susceptibility maps will be helpful for disaster managers and haor master planners of the study area.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps. [ABSTRACT FROM AUTHOR]

Geological structures have been shown by studies to have influence on the occurrence, storage and transportation of groundwater. Understanding the structural network of an area unearths a deep insight into the groundwater dynamics of the area. A geological structural analysis was carried out to reveal the geological structural network of Ghana's Voltaian basin. Using aeromagnetic data, structural density models were generated using the Center for Exploration Targeting grid analysis technique for two depth ranges (that is up to 100 m and 300 m) over the Voltaian basin. The total length of geological structures (lineaments) delineated at depths up to 100 m and 300 m were more than 5000 km and more than 8000 km, respectively. Given this, the study area was observed to be structurally dense at each of the aforementioned depths. The structural density models were discretized into five classes (very low, low, moderate, moderately high and very high regions), each of which was evaluated to determine their spatial association with known locations of groundwater occurrences within the study area using the frequency ratio technique. Frequency ratio results for both structural density models derived at 100 m and 300 m depths show the existence of a strong correlation between high structural density model classes and the known groundwater occurrences. The structural density models were further evaluated using the receiver operating characteristics curve. The area under the receiver operating characteristics curve scores indicates that, although both structural density models showed very good performance (with receiver operating characteristics scores greater than 0.7), the 300‐m depth structural density model performed better than the structural density model generated at a depth of 100 m (with their receiver operating characteristics scores being 0.721 and 0.715, respectively). The obtained results corroborate with literature assertion that groundwater occurrence within the Voltaian basin is mainly associated with structural features. It is expected that the outputs of this study would guide future groundwater exploration programmes within the study area. [ABSTRACT FROM AUTHOR]

Early detection of mesothelioma, a severe form of cancer commonly associated with asbestos exposure, is a significant challenge that greatly affects prognosis. This study addresses this issue using Machine Learning (ML) algorithms, including Gradient-Boosted Trees (GBT), Support Vector Machines (SVM), and Logistic Regression (LR). This study uses the mesothelioma dataset from the UCI Machine Learning Repository to evaluate the proposed models, achieving 100% accuracy and F1 score in detecting the disease, accurately classifying 98 patients with 30 true positives and 68 true negatives. Further analysis using the AUC-ROC score showed that the 'duration of symptoms' feature was most informative for the GBT model, with a score of 0.595. In contrast, 'C-reactive protein' was the most significant feature for the SVM and LR models, each achieving an AUC-ROC of 0.603. Despite these promising results, validating these findings with additional datasets is critical to confirm their generalisability. However, this study provides strong support for using ML algorithms in the early detection of mesothelioma, potentially leading to improved patient diagnoses. [ABSTRACT FROM AUTHOR]

Gamified inoculation interventions designed to improve the detection of online misinformation are becoming increasingly prevalent. Two of the most notable interventions of this kind are Bad News and Go Viral!. To assess their efficacy, prior research has typically used pre-post designs in which participants rated the reliability or manipulativeness of true and fake news items before and after playing these games, while most of the time also including a control group who played an irrelevant game (Tetris) or did nothing at all. Mean ratings were then compared between pre-tests and post-tests and/or between the control and experimental conditions. Critically, these prior studies have not separated response bias effects (overall tendency to respond "true" or "fake") from discrimination (ability to distinguish between true and fake news, commonly dubbed discernment). We reanalyzed the results from five prior studies using receiver operating characteristic (ROC) curves, a method common to signal detection theory that allows for discrimination to be measured free from response bias. Across the studies, when comparable true and fake news items were used, Bad News and Go Viral! did not improve discrimination, but rather elicited more "false" responses to all news items (more conservative responding). These novel findings suggest that the current gamified inoculation interventions designed to improve fake news detection are not as effective as previously thought and may even be counterproductive. They also demonstrate the usefulness of ROC analysis, a largely unexploited method in this setting, for assessing the effectiveness of any intervention designed to improve fake news detection. [ABSTRACT FROM AUTHOR]

Abstract Background Presepsin is produced during the phagocytosis of bacteria by granulocytes. Presepsin increases at the site of infection; however, the significance of urinary presepsin in pyelonephritis is unknown. This study aimed to evaluate whether measuring urinary presepsin can distinguish between pyelonephritis and nonpyelonephritis. Methods A cross-sectional study of patients with suspected pyelonephritis was conducted. Urinary presepsin at admission was compared between the pyelonephritis and nonpyelonephritis groups using the Mann–Whitney test. The predictive accuracy of urinary presepsin for diagnosing pyelonephritis was evaluated by the area under the receiver operating characteristics (ROC) analysis curve. Results A total of 35 eligible participants were included in the pyelonephritis group and 25 in the nonpyelonephritis group. The median urinary presepsin level was 2232.0 (interquartile range [IQR], 1029.0–3907.0) pg/mL in the pyelonephritis group and 1348.0 (IQR, 614.5–2304.8) pg/mL in the nonpyelonephritis group. Urinary presepsin concentrations were significantly higher in the pyelonephritis group than in the nonpyelonephritis group (P = 0.023). ROC analysis of urinary presepsin revealed a cutoff value of 3650 pg/mL to distinguish between the pyelonephritis and nonpyelonephritis groups. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the diagnosis of pyelonephritis were 0.40 (95% confidence interval [CI], 0.24–0.58), 0.96 (95% CI, 0.79–1.00), 0.93 (95% CI, 0.68–1.00), 0.52 (95% CI, 0.37–0.68), 9.60 (95% CI, 1.35–68.23), and 0.62 (95% CI, 0.47–0.83), respectively. Conclusions The measurement of urinary presepsin is useful in differentiating pyelonephritis from other diseases.

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals. [ABSTRACT FROM AUTHOR]

The best–worst method (BWM), though a recently developed multi-criteria decision-making (MCDM) technique, has its application evolving in recent years in addressing MCDM problems. By employing the BWM technique, this study generated a mineral prospectivity map (MPM), primarily essential for the delineation of prospective regions of gold (Au) deposits over the central part of the Southern Kibi-Winneba belt. The MPM produced was generated by integrating seven geoscientific thematic layers derived from geophysical and geological datasets within the study area. These thematic layers were used as the main criteria for the generation of MPM, whose respective weights were determined based on the BWM technique by three decision-makers with in-depth knowledge of the use of geoscientific datasets for mineral exploration. The assigned weights obtained from the three decision-makers for each of the seven thematic layers were combined and normalised to obtain BWM weight for each evidential layer. These weighted evidential layers were subsequently integrated to produce the mineral prospectivity map over the study area. The mineral prospectivity map produced based on the BWM was evaluated based on known areas of reported Au occurrence and subsequently evaluated based on the frequency ratio technique to ascertain the coherence between locations of known mineral occurrence and the delineated prospectivity classes obtained. The performance of the prospectivity model produced was further assessed using the receiver operating characteristics (ROC) curve. An ROC score of 0.808 was obtained for the mineral prospectivity model (MPM) generated. The ROC score obtained indicates that the MPM produced exhibits a conscionable outcome for gold exploration over the central part of the southern Kibi-Winneba belt. [ABSTRACT FROM AUTHOR]

There are no pharmacological interventions to prevent the development of epilepsy, although many promising compounds have been identified in the animal laboratory. Clinical trials to validate their effectiveness, however, would currently be prohibitively expensive due to the large subject population and duration of follow-up necessary. There is, therefore, the need to identify biomarkers of epileptogenesis that could identify patients at high risk for epilepsy following a potential epileptogenic insult to enrich the subject population, as well as biomarkers that could determine the effectiveness of therapeutic intervention without the need to wait for seizures to occur. Putative biomarkers under investigation for epileptogenesis and its treatment include genetic, molecular, cellular, imaging, and electrophysiological measures that might reliably predict the development or progression of an epileptic condition, the effects of antiepileptogenic treatment, or cure after surgery. To be clinically useful for most purposes, ideal biomarkers should be noninvasive, and it is anticipated that a profile of multiple biomarkers will likely be required. Ongoing animal research involves a number of experimental models of epileptogenesis, with traumatic brain injury, offering the best potential for translational clinical investigations. Collaborative and multicenter research efforts by multidisciplinary teams of basic and clinical neuroscientists with access to robust, well-defined animal models, extensive patient populations, standardized protocols, and cutting-edge analytical methodologies are likely to be most successful. Such biomarker research should also provide insights into fundamental neuronal mechanisms of epileptogenesis suggesting novel targets for antiepileptogenic treatments. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Face perception is more difficult when faces are inverted compared to when they are upright. However, it is not known whether face inversion disrupts the ability to make perceiving-based discriminations (i.e., the ability to identify a specific feature change), or sensing-based discriminations (i.e., the ability to detect there was a change without the ability to identify what changed). In the current study, we used confidence-based receiver operating characteristics (ROCs) in a change detection test to examine the effect of face inversion on perceiving and sensing. In Experiment 1, face inversion led to a reduction in the probability of perceiving but did not impact sensing-based discriminations. In Experiment 2, we replicated these results, and verified that the findings based on ROC estimates paralleled participants' phenomenological experiences of perceiving and sensing. Furthermore, the perceiving-based face inversion effect was found to reflect a reduction in the ability to accurately report specific feature changes. These findings indicate that face inversion does not reduce the ability to sense there was a change in the absence of identification, but rather it reduces the ability to consciously identify specific characteristics of faces in service of perceiving-based discriminations. In addition, they suggest that sensing responds to global differences across the visual image, rather than to changes in holistic processing of the visual input. These results further our understanding of the face inversion effect and clarify the nature of the processes underlying visual perception. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background: Presepsin is produced during the phagocytosis of bacteria by granulocytes. Presepsin increases at the site of infection; however, the significance of urinary presepsin in pyelonephritis is unknown. This study aimed to evaluate whether measuring urinary presepsin can distinguish between pyelonephritis and nonpyelonephritis. Methods: A cross-sectional study of patients with suspected pyelonephritis was conducted. Urinary presepsin at admission was compared between the pyelonephritis and nonpyelonephritis groups using the Mann–Whitney test. The predictive accuracy of urinary presepsin for diagnosing pyelonephritis was evaluated by the area under the receiver operating characteristics (ROC) analysis curve. Results: A total of 35 eligible participants were included in the pyelonephritis group and 25 in the nonpyelonephritis group. The median urinary presepsin level was 2232.0 (interquartile range [IQR], 1029.0–3907.0) pg/mL in the pyelonephritis group and 1348.0 (IQR, 614.5–2304.8) pg/mL in the nonpyelonephritis group. Urinary presepsin concentrations were significantly higher in the pyelonephritis group than in the nonpyelonephritis group (P = 0.023). ROC analysis of urinary presepsin revealed a cutoff value of 3650 pg/mL to distinguish between the pyelonephritis and nonpyelonephritis groups. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the diagnosis of pyelonephritis were 0.40 (95% confidence interval [CI], 0.24–0.58), 0.96 (95% CI, 0.79–1.00), 0.93 (95% CI, 0.68–1.00), 0.52 (95% CI, 0.37–0.68), 9.60 (95% CI, 1.35–68.23), and 0.62 (95% CI, 0.47–0.83), respectively. Conclusions: The measurement of urinary presepsin is useful in differentiating pyelonephritis from other diseases. [ABSTRACT FROM AUTHOR]

Landslides are the nation's hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development. [ABSTRACT FROM AUTHOR]

The American Society of Anesthesiologists Physical Status (ASA-PS) grade better risk stratifies geriatric trauma patients, but it is only reported in patients scheduled for surgery. The Charlson Comorbidity Index (CCI), however, is available for all patients. This study aims to create a crosswalk from the CCI to ASA-PS. Geriatric trauma cases, aged 55 years and older with both ASA-PS and CCI values (N = 4223), were used for the analysis. We assessed the relationship between CCI and ASA-PS, adjusting for age, sex, marital status, and body mass index. We reported the predicted probabilities and the receiver operating characteristics. A CCI of zero was highly predictive of ASA-PS grade 1 or 2, and a CCI of 1 or higher was highly predictive of ASA-PS grade 3 or 4. Additionally, while a CCI of 3 predicted ASA-PS grade 4, a CCI of 4 and higher exhibited greater accuracy in predicting ASA-PS grade 4. We created a formula that may accurately situate a geriatric trauma patient in the appropriate ASA-PS grade after adjusting for age, sex, marital status, and body mass index. In conclusion, ASA-PS grades can be predicted from CCI, and this may aid in generating more predictive trauma models. [ABSTRACT FROM AUTHOR]

Landslide hazards give rise to considerable demolition and losses to lives in hilly areas. To reduce the destruction in these endangered regions, the prediction of landslide incidents with good accuracy remains a key challenge. Over the years, machine learning models have been used to increase the accuracy and precision of landslide predictions. These machine learning models are sensitive to the data on which they are applied. Feature selection is a crucial task in applying machine learning as meticulously selected features can significantly improve the performance of the machine learning model. These selected features decrease the learning time of the model and increase comprehensibility. In this paper, we have considered three feature selection methods namely chi-squared, extra tree classifier and heat map. The paper substantiates that feature selection can significantly increase the performance of the model. The study was carried out on the landslide data of the Kullu to Rohtang Pass transport corridor in Himachal Pradesh, India. The classification score and receiver operating characteristics (ROC) curves were used to evaluate the model performance. Results exhibited that eliminating one or more features using different feature selection methods increased the comprehensibility of the model by reducing the dimensionality of the dataset. The model achieved an accuracy of 90.74% and an area under the ROC curve (AUROC) value of 0.979. Furthermore, it can be deduced that with a reduced number of features model learns faster without affecting the actual result. [ABSTRACT FROM AUTHOR]

The transition period is important in cows. This study was conducted to evaluate the effects of retained fetal membranes (RFM), clinical mastitis, and metritis on biochemical and selected mineral levels in Simmental cows. Cows were divided into five groups; cows with RFM (n=17), clinical mastitis (n=25), metritis (n=21) and postpartum healthy cows (n=21) within 21 days postpartum, and also prepartum healthy cows (n=20) in the 15±5 days before the expected parturition date. The activity of alkaline phosphatase (ALP) (85.18±15.83 U/L), aspartate transaminase (AST) (123.02±19.15 U/L), and gamma-glutamyl transferase (GGT) (28.18±2.66 U/L) in the metritis group increased compared to the prepartum healthy cows. Moreover, GGT (41.83±14.61 U/L) and a myocardial band of creatine kinase (CK-MB) (155.25±27.85 U/L) activities were highest in the RFM group, while creatine kinase - N-acetyl-cysteine activity (CKNAC) (540.45±157.67 U/L) and creatinine concentration (2.29±0.88 mg/dL) were observed in the metritis group. Total protein (6.39±0.38 g/dL) concentration was highest in the case of mastitis. Urea, on the other hand, was highest in the metritis group with a concentration of 61.40±17.38 mg/dL. Our results showed changes in the biochemical profile of cows with RFM, clinical mastitis, and metritis. Biomarker profiles were determined using receiver-operator characteristic (ROC) curves. It was determined that activities of = 89 (U/L) AST, = 24 (U/L) GGT, and = 106 (U/L) CK-MB for metritis, = 21(U/L) GGT for RFM, and = 105 (U/L) CK-MB for mastitis can be used in the preliminary diagnosis. Also, further studies with a larger cow cohorts are recommended. [ABSTRACT FROM AUTHOR]

A growing body of research indicates that the medial temporal lobe (MTL) is essential not only for long-term episodic memory but also for visual working memory (VWM). In particular, recent work has shown that the MTL is especially important for VWM when complex, high-resolution binding is required. However, all of these studies tested VWM for multiple items which invites the possibility that working memory capacity was exceeded and patient impairments instead reflected deficits in long-term memory. Thus, the precise conditions under which the MTL is critical for VWM and the type of working memory processes that are affected by MTL damage are not yet clear. To address these issues, we examined the effects of MTL damage on VWM for a single item (i.e., a square that contained color, location, and orientation information) using confidence-based receiver operating characteristic methods to assess VWM discriminability and to separate perceiving- and sensing-based memory judgments. This approach was motivated by dual-process theories of cognition that posit distinct subprocesses underlie performance across perception, working memory, and long-term memory. The results indicated that MTL patients were significantly impaired in VWM for a single item. Interestingly, the patients were not impaired at making accurate high-confidence judgments that a change had occurred (i.e., perceiving), rather they were impaired at making low-confidence judgments that they sensed whether or not there had been a change in the absence of identifying the exact change. These results demonstrate that the MTL is critical in supporting working memory even for a single item, and that it contributes selectively to sensing-based discriminations.

Forensic science standards often require the analyst to report in categorical terms. Categorical reporting without reference to the strength of the evidence, or the strength threshold that must be met to sustain or justify the decision, obscures the decision-making process, and allows for inconsistency and bias. Standards that promote reporting in probabilistic terms require the analyst to report the strength of the evidence without offering a conclusive interpretation of the evidence. Probabilistic reporting is often based on a likelihood ratio which depends on calibrated probabilities. While probabilistic reporting may be more objective and less open to bias than categorical reporting, the report can be difficult for a lay jury to interpret. These reporting methods may appear disparate, but the relationship between the two is easily understood and visualized by a simple decision theory construct known as the receiver operating characteristic (ROC) curve. Implementing ROC-facilitated reporting through an expanded proficiency testing regime may provide transparency in categorical reporting and potentially obviate some of the lay jury interpretation issues associated with probabilistic reporting. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven approach is utilized for bearing condition monitoring involving the classification of different operating states by processing the raw vibration data. The vibration responses are analyzed and preprocessed before input to 1D-RCNN (one-dimensional residual convolutional neural network). The comparison results are based on commonly implemented evaluation indices such as precision, recall, F1-score, and ROC plots. Hence, the results revealed the superiority of the proposed methodology and its efficacy in segregating the bearing lifetime data into different operating conditions. Furthermore, t-SNE (t-distributed stochastic neighbor embedding) technique is implemented to represent the precise discriminative learning ability of different layers of the network. The overall classification accuracy values are obtained as 97.2% for 1D-RCNN, 95.31% for 1D-CNN, 86.2%, 86.42%, and 87.4% for SVM, KNN, and DNN, respectively. Hence, the proposed methodology may be effectively implemented for bearing health monitoring utilizing deep learning networks as classifiers. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is a malignancy characterized by chromosomal instability (CIN). This study aimed to examine the potential diagnostic value of chromosomal instability, detected by low-pass whole-genome sequencing (LPWGS), in the preoperative evaluation of sentinel lymph node metastasis (SLNM) in breast cancer., Methods: A retrospective investigation of clinical records from 29 patients with breast cancer revealed two distinct groups based on sentinel lymph node biopsy (SLNB) results: the SLN metastasis group (24 cases) and the SLN non-metastasis group (five cases). CIN and CIN scores were evaluated using LPWGS. An analysis of univariate data and binary logistic regression was employed to identify factors influencing SLNM, and a curve with receiver operating characteristics (ROC) was constructed to assess the diagnostic utility of CIN in predicting SLNM., Results: A significant association between the SLNM and CIN high groups was observed in breast cancer ( P =0.011). The CIN score in the metastasis group (17,665.055 ± 8,630.691) was higher than that in the non-metastasis group (9,247.973 ± 3,692.873), demonstrating a significant difference ( P =0.044). Univariate binary logistic regression analysis indicated that CIN was a significant predictor for SLNM (odds ratio: 4.036, 95% CI: 1.015-16.047, P =0.048). The AUC of CIN for preoperative diagnosis of SLNM was 0.808 (95%CI: 0.635-0.982, P =0.033), with a sensitivity value of 67.0% and specificity of 100.0% at a threshold of 13,563., Conclusion: Detecting CIN through LPWGS demonstrates diagnostic potential in predicting SLNM in patients with breast cancer before surgery. This approach offers a novel method for assessing axillary lymph node status in clinical practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Xu, Li, Lin and Hao.)

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi‐class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi‐site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi‐class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave‐one‐out and k‐fold cross‐validation. Metabolites identified as crucial in tumour classification include myo‐inositol (P < 0.05, AUC=0.81±0.01), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC=0.78±0.01) and total creatine (P < 0.05, AUC=0.77±0.01) for the 1.5 T cohort, and glycine (P < 0.05, AUC=0.79±0.01), total N‐acetylaspartate (P < 0.05, AUC=0.79±0.01) and total choline (P < 0.05, AUC=0.75±0.01) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave‐one‐out cross‐validation was 85% for 1.5 T 1H‐MRS through support vector machine and 75% for 3 T 1H‐MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours. [ABSTRACT FROM AUTHOR]

Landslides are the nation’s hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development.

The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we elaborate on the fact that, in the context of internal/relative clustering validation as we consider, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a much more efficient algorithmic procedure. Our theoretical findings are supported by experimental results. These results show that, in addition to an effective and robust quantitative evaluation provided by AUCC, visual inspection of the ROC curves themselves can be useful to further assess a candidate clustering solution from a broader, qualitative perspective as well. [ABSTRACT FROM AUTHOR]

In recent years, cyber-physical systems (CPSs) have received a lot of attention because of their wide range of applications. However, because CPSs rely heavily on communication networks, they are vulnerable to deliberate cyberattacks. As a result, there are a wide variety of attack detection methods that have been proposed to protect CPSs. This research examines the behavior of malicious acts and anomalies within the cyber physical system using Higuchi Fractal Dimension (HFD). The model was constructed and evaluated using the HFD algorithm. The model categorizes various types of anomalous events, such as sabotage, cyber-attacks, and hardware failures. Furthermore, our proposed model notifies the operator by calculating the probability of an anomaly occurring, which aids in the mitigation process. A real-world dataset is used to train and test the model. [ABSTRACT FROM AUTHOR]