Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.

Peri-implant fractures after Intramedullary fixation for femoral trochanteric fracture: a multicenter (TRON Group) study.

BACKGROUND: With the rise in elderly populations, the incidence of femoral trochanteric fractures has also increased. Although intramedullary nail therapy is commonly used, the incidence of peri-implant fractures (PIFs) as a complication and its associated factors are not fully understood. The purpose of this study was to determine the incidence of PIFs and treatment strategies and outcomes. METHODS: A retrospective study across 11 hospitals from 2016 to 2020 examined 1855 patients with femoral trochanter fracture. After excluding 69 patients treated without intramedullary nailing, 1786 patients were analyzed. Parameters studied included age, sex, body mass index, medical history, and treatment methods. PIFs were categorized using the Chan classification. Treatment outcomes and patient mobility were assessed using the Parker Mobility Score, and postoperative complications and one-year survival data were compiled. RESULTS: The incidence of PIFs was 8 in 1786 cases. Chan classification showed 1 case of N1A, 6 of N2A, and 1 of N2B. Only the type N1 case was a transverse fracture, whereas all cases of type N2 were oblique fractures. Among these cases, five patients had fractures extending to the upper part of the femoral condyle. The patient with N1A and one bedridden patient with N2A fracture underwent conservative treatment, one patient with N2A in which the fracture did not extend to the condyle was treated with nail replacement, and 5 patients (N2A: 4, N2B: 1) with fractures extending to the condyle were treated with additional plate fixation. All patients had survived at one year after treatment for PIF, and no reoperations were required. CONCLUSIONS: The incidence of PIF was very low (0.45%). Of the 6 PIF cases, excluding the bedridden patients, the treatment of choice for PIF was an additional plate if the fracture line extended to the femoral condyle; otherwise, the nail was replaced. All patients achieved bony fusion. LEVEL OF EVIDENCE: Therapeutic Level IV.

Two-Stage Classification of Future Knee Osteoarthritis Severity After 8 Years Using MRI: Data from the Osteoarthritis Initiative.

Currently, there are no methods or tools available in clinical practice for classifying future knee osteoarthritis (KOA). In this study, we aimed to fill this gap by classifying future KOA into three severity grades: KL01 (healthy), KL2 (moderate), and KL34 (severe) based on the Kellgren-Lawrance scale. Due to the complex nature of multiclass classification, we used a two-stage method, which separates the classification task into two binary classifications (KL01 vs. KL234 in the first stage and KL2 vs. KL34 in the second stage). Our machine learning (ML) model used two Balanced Random Forest algorithms and was trained with gender, age, height, weight, and quantitative knee morphology obtained from magnetic resonance imaging. Our training dataset comprised longitudinal 8-year follow-up data of 1213 knees from the Osteoarthritis Initiative. Through extensive experimentation with various feature combinations, we identified KL baseline and weight as the most essential features, while gender surprisingly proved to be one of the least influential feature. Our best classification model generated a weighted F1 score of 79.0% and a balanced accuracy of 65.9%. The area under the receiver operating characteristic curve was 83.0% for healthy (KL01) versus moderate (KL2) or severe (KL34) KOA patients and 86.6% for moderate (KL2) versus severe (KL34) KOA patients. We found a statistically significant difference in performance between our two-stage classification model and the traditional single-stage classification model. These findings demonstrate the encouraging results of our two-stage classification model for multiclass KOA severity classification, suggesting its potential application in clinical settings in future.

Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization.

Reliable and trustworthy artificial intelligence (AI), particularly in high-stake medical diagnoses, necessitates effective uncertainty quantification (UQ). Existing UQ methods using model ensembles often introduce invalid variability or computational complexity, rendering them impractical and ineffective in clinical workflow. We propose a UQ approach based on deep neuroevolution (DNE), a data-efficient optimization strategy. Our goal is to replicate trends observed in expert-based UQ. We focused on language lateralization maps from resting-state functional MRI (rs-fMRI). Fifty rs-fMRI maps were divided into training/testing (30:20) sets, representing two labels: "left-dominant" and "co-dominant." DNE facilitated acquiring an ensemble of 100 models with high training and testing set accuracy. Model uncertainty was derived from distribution entropies over the 100 model predictions. Expert reviewers provided user-based uncertainties for comparison. Model (epistemic) and user-based (aleatoric) uncertainties were consistent in the independently and identically distributed (IID) testing set, mainly indicating low uncertainty. In a mostly out-of-distribution (OOD) holdout set, both model and user-based entropies correlated but displayed a bimodal distribution, with one peak representing low and another high uncertainty. We also found a statistically significant positive correlation between epistemic and aleatoric uncertainties. DNE-based UQ effectively mirrored user-based uncertainties, particularly highlighting increased uncertainty in OOD images. We conclude that DNE-based UQ correlates with expert assessments, making it reliable for our use case and potentially for other radiology applications.

Benchmarking PathCLIP for Pathology Image Analysis.

Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pre-training (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, pathology-dedicated CLIP (PathCLIP) has been developed, utilizing over 200,000 image and text pairs in training. While the performance the PathCLIP is impressive, its robustness under a wide range of image corruptions remains unknown. Therefore, we conduct an extensive evaluation to analyze the performance of PathCLIP on various corrupted images from the datasets of osteosarcoma and WSSS4LUAD. In our experiments, we introduce eleven corruption types including brightness, contrast, defocus, resolution, saturation, hue, markup, deformation, incompleteness, rotation, and flipping at various settings. Through experiments, we find that PathCLIP surpasses OpenAI-CLIP and the pathology language-image pre-training (PLIP) model in zero-shot classification. It is relatively robust to image corruptions including contrast, saturation, incompleteness, and orientation factors. Among the eleven corruptions, hue, markup, deformation, defocus, and resolution can cause relatively severe performance fluctuation of the PathCLIP. This indicates that ensuring the quality of images is crucial before conducting a clinical test. Additionally, we assess the robustness of PathCLIP in the task of image-to-image retrieval, revealing that PathCLIP performs less effectively than PLIP on osteosarcoma but performs better on WSSS4LUAD under diverse corruptions. Overall, PathCLIP presents impressive zero-shot classification and retrieval performance for pathology images, but appropriate care needs to be taken when using it.

Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice.

The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.

A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning.

Cerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatment becomes evident upon diagnosis, as the severity of the disease guides the course of action. Cerebral aneurysms are particularly vulnerable in the circle of Willis and pose a significant concern due to the potential for rupture, which can lead to irreversible consequences, including fatality. The primary objective of this study is to predict the rupture status of cerebral aneurysms. To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. Diagnosing and predicting aneurysm rupture based solely on brain scans is a significant challenge with limited reliability, even for experienced physicians. However, by employing statistical methods and machine learning techniques, we can assist physicians in making more confident predictions regarding rupture likelihood and selecting appropriate treatment strategies. To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. The dataset comprised 3 clinical features and 35 morphological parameters, including 8 novel morphological features introduced for the first time in this study. Our models demonstrated exceptional performance in predicting cerebral aneurysm rupture, with accuracy ranging from 0.76 to 0.82 and precision score from 0.79 to 0.83 for the test dataset. As the data are sensitive and the condition is critical, recall is prioritized as the more crucial parameter over accuracy and precision, and our models achieved outstanding recall score ranging from 0.85 to 0.92. Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. The ellipticity index, size ratio, and shape irregularity are pivotal features in predicting aneurysm rupture, respectively, contributing significantly to our understanding of this complex condition. Among the multitude of parameters under investigation, these are particularly important. In this study, the ideal roundness parameter was introduced as a novel consideration and ranked fifth among all 38 parameters. Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.

Efficacy of buccal pad fat as a new approach in the treatment of gingival recession: a systematic review.

OBJECTIVE: This systematic review evaluates the efficacy of buccal pad fat (BPF) as an autologous graft in the treatment of gingival recession (GR). Thus, the research question explores if the BPF can serve as a viable alternative to the gold standard connective tissue graft. MATERIALS AND METHODS: Only seven studies met the inclusion criteria were critically appraised including the randomized controlled clinical trials, and case series. The inclusion criteria were systemically healthy individuals in the age range (18-65 years old) with Miller's classification of GR either class I, II, III, or IV while exclusion criteria were patients with poor oral hygiene, pregnant and lactating patients, teeth with caries, any prior surgery in the relevant regions, and use of medications. RESULTS: The review included 117 patients with 136 GR defects. The age of participants ranges from 20 to 65 years old with the higher percentage of root coverage (%RC) at 6 months in the pedicled BPF group which was 89.30%while the lowest (%RC) at 6 months in the same group was 46.78%. The BPF group's width of keratinized gingiva (WKG) values indicate a notable improvement, suggesting a positive impact on WKG compared to the control group. CONCLUSIONS: BPF can be considered as a promising graft to augment gingival tissues at different sites in the oral cavity with different Miller's classes of GR providing a new era in GR treatment.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN: The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS: Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS: We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Missing data imputation using classification and regression trees.

Background: Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART). Methods: We consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit approval data for illustration. Results: The method that performs the best strongly depends on the correlation between variables. For imputing missing ordinal categorical variables, the rpart package with surrogate variables is recommended under correlations larger than 0 with missing completely at random (MCAR) and missing at random (MAR) conditions. Under missing not at random (MNAR), chi-squared test methods and the rpart package with surrogate variables are suggested. For imputing missing quantitative variables, the iterative imputation method is most recommended under moderate correlation conditions.

A systematic literature review on meta-heuristic based feature selection techniques for text classification.

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

Comparative evaluation of machine learning algorithms for phishing site detection.

The advent of Internet technologies has resulted in the proliferation of electronic trading and the use of the Internet for electronic transactions, leading to a rise in unauthorized access to sensitive user information and the depletion of resources for enterprises. As a consequence, there has been a marked increase in phishing, which is now considered one of the most common types of online theft. Phishing attacks are typically directed towards obtaining confidential information, such as login credentials for online banking platforms and sensitive systems. The primary objective of such attacks is to acquire specific personal information to either use for financial gain or commit identity theft. Recent studies have been conducted to combat phishing attacks by examining domain characteristics such as website addresses, content on websites, and combinations of both approaches for the website and its source code. However, businesses require more effective anti-phishing technologies to identify phishing URLs and safeguard their users. The present research aims to evaluate the effectiveness of eight machine learning (ML) and deep learning (DL) algorithms, including support vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), logistic regression (LR), convolutional neural network (CNN), and DL model and assess their performances in identifying phishing. This study utilizes two real datasets, Mendeley and UCI, employing performance metrics such as accuracy, precision, recall, false positive rate (FPR), and F-1 score. Notably, CNN exhibits superior accuracy, emphasizing its efficacy. Contributions include using purpose-specific datasets, meticulous feature engineering, introducing SMOTE for class imbalance, incorporating the novel CNN model, and rigorous hyperparameter tuning. The study demonstrates consistent model performance across both datasets, highlighting stability and reliability.

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions.

Background: There is lack of discrimination as to traditional imaging diagnostic methods of cystic renal lesions (CRLs). This study aimed to evaluate the value of machine learning models based on clinical data and contrast-enhanced computed tomography (CECT) radiomics features in the differential diagnosis of benign and malignant CRL. Methods: There were 192 patients with CRL (Bosniak class ≥ II) enrolled through histopathological examination, including 144 benign cystic renal lesions (BCRLs) and 48 malignant cystic renal lesions (MCRLs). Radiomics features were extracted from CECT images taken during the medullary phase. Using the light gradient boosting machine (LightGBM) algorithm, the clinical, radiomics and combined models were constructed. A comprehensive nomogram was developed by integrating the radiomics score (Rad-score) with independent clinical factors. Receiver operating characteristic (ROC) curves were plotted. The corresponding area under the curve (AUC) value was worked out to quantify the discrimination performance of the three models in training and validation cohorts. Calibration curves were worked out to assess the accuracy of the probability values predicted by the models. Decision curve analysis (DCA) was worked out to assess the performance of models at different thresholds. Results: Maximum diameter and Bosniak class were independent risk factors of patients with MCRL in the clinical model. Twenty-one radiomics features were extracted to work out a Rad-score. The performance of the clinical model in the training cohort was AUC =0.948, 95% confidence interval (CI): 0.917-0.980, and the performance in the validation cohort was AUC =0.936, 95% CI: 0.859-1.000 (P<0.05). The performance of the radiomics model in the training cohort was AUC =0.990, 95% CI: 0.979-1.000, and the performance in the validation cohort was AUC =0.959, 95% CI: 0.903-1.000 (P<0.05). Compared with the above models, the combined radiomics nomogram had an AUC of 0.989 (95% CI: 0.977-1.000) in the training cohort and an AUC of 0.962 (95% CI: 0.905-1.000) in the validation cohort (P<0.05), showing the best diagnostic efficacy. Conclusions: The radiomics nomogram integrating clinical independent risk factors and radiomics signature improved the diagnostic accuracy in differentiating between BCRL and MCRL, which can provide a reference for clinical decision-making and help clinicians develop individualized treatment strategies for patients.

Clinical and translational implications of immunotherapy in sarcomas.

Immunotherapy has emerged as promising treatment in sarcomas, but the high variability in terms of histology, clinical behavior and response to treatments determines a particular challenge for its role in these neoplasms. Tumor immune microenvironment (TiME) of sarcomas reflects the heterogeneity of these tumors originating from mesenchymal cells and encompassing more than 100 histologies. Advances in the understanding of the complexity of TiME have led to an improvement of the immunotherapeutic responsiveness in sarcomas, that at first showed disappointing results. The proposed immune-classification of sarcomas based on the interaction between immune cell populations and tumor cells showed to have a prognostic and potential predictive role for immunotherapies. Several studies have explored the clinical impact of immune therapies in the management of these histotypes leading to controversial results. The presence of Tumor Infiltrating Lymphocytes (TIL) seems to correlate with an improvement in the survival of patients and with a higher responsiveness to immunotherapy. In this context, it is important to consider that also immune-related genes (IRGs) have been demonstrated to have a key role in tumorigenesis and in the building of tumor immune microenvironment. The IRGs landscape in soft tissue and bone sarcomas is characterized by the connection between several tumor-related genes that can assume a potential prognostic and predictive therapeutic role. In this paper, we reviewed the state of art of the principal immune strategies in the management of sarcomas including their clinical and translational relevance.

Gray-tone appearances on 4-hour delayed gadolinium-enhanced magnetic resonance imaging indicate severe inner ear pathology and symptoms in sudden sensorineural hearing loss.

Objective: Hybrid of reversed image of positive endolymph signal and negative image of perilymph signal (HYDROPS) in delayed gadolinium-enhanced magnetic resonance imaging (MRI) typically depicts normal inner ear as "white-tone" and endolymphatic hydrops as "black-transparent" appearances, whereas ears with auditory and vestibular disorders are occasionally depicted as "gray-tone." This study aimed to investigate the pathological basis of sudden sensorineural hearing loss (SSNHL) patients with "gray-tone" appearances on HYDROPS. Methods: Delayed gadolinium-enhanced MRI examinations were conducted on 29 subjects with unilateral SSNHL. We mainly analyzed positive perilymph image (PPI) and positive endolymph image (PEI), which were components HYDROPS. Results: On PPI, signal intensity (SI) values extracted from the cochlear and vestibular region of interest (ROI) were higher in the SSNHL ears with dizziness/vertigo symptom at the first visit compared to the healthy ear. Additionally, the PPI/PEI enhancement pattern in the vestibule was associated with a high prevalence of hearing and vestibular deteriorations at the first visit and poor hearing improvement after treatment. Conclusion: Enhancement on PPI/PEI may result from leakage of gadolinium into the inner ear following breakdown of the blood-labyrinth barrier, with high SI being correlated with the amount of leakage. Particularly, a significant leakage into the endolymphatic space, defined as PPI+/PEI+, indicates severe inner ear pathology. Ultimately, we emphasize that the "gray-tone" appearance in the inner ear on HYDROPS comprises enhancements on both PPI and PEI and propose a new classification for evaluating SSNHL Peri- and Endolymphatic image Enhancement pattern in Delayed gadolinium-enhanced MRI (SPEED). Level of Evidence: 4.

Why do generation X customers use wearable fitness technology equipment after recovering from coronavirus? The role of perceived health risks.

The health sector has prioritized the physical health of vulnerable Generation X individuals at high Coronavirus risk. Despite vaccination efforts, both infected and healthy people continue facing health threats. Unlike other industries devastated by COVID-19, wearable fitness technology equipment (WFTE) is essential for health-focused individuals. This research examined customers' intention to use WFTE using an adapted Technology Acceptance Model (TAM) framework. A key contribution is the inclusion of perceived health risk and its impact on WFTE value perceptions and usage attitudes post-pandemic. The study gathered qualitative data from coronavirus patients and survey data from 513 participants. Structural equation modeling analysis supported the theoretical model. While the standard TAM evaluated intent to use WFTE, this study uniquely examined how WFTE's functional, hedonic, and symbolic value shapes its perceived value. Perceived health risk was found to significantly impact perceived WFTE value and usage attitudes after the pandemic recovery. Findings offer managerial implications to boost WFTE adoption among the vulnerable Generation X demographic.

Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.

BACKGROUND: Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM: To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS: The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION: Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 × : 95.50%, and BreakHis 100 × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net's aptness for agricultural growth as well as human cancer classification.

Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family.

PURPOSE: In this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is achieved through a deep neural network model that relies on convolutional neural networks (CNNs). Since viruses within the same family share similar genetic and structural characteristics, the classification process becomes more challenging, necessitating more robust models. With the rapid evolution of viral genomes and the increasing need for timely classification, we aimed to provide a robust and efficient tool that could increase the accuracy of viral identification and classification processes. Contribute to advancing research in viral genomics and assist in surveilling emerging viral strains. METHODS: Based on a one-dimensional deep CNN, the proposed tool is capable of training and testing on the Coronaviridae family, including SARS-CoV-2. Our model's performance was assessed using various metrics, including F1-score and AUROC. Additionally, artificial mutation tests were conducted to evaluate the model's generalization ability across sequence variations. We also used the BLAST algorithm and conducted comprehensive processing time analyses for comparison. RESULTS: DeepVirusClassifier demonstrated exceptional performance across several evaluation metrics in the training and testing phases. Indicating its robust learning capacity. Notably, during testing on more than 10,000 viral sequences, the model exhibited a more than 99% sensitivity for sequences with fewer than 2000 mutations. The tool achieves superior accuracy and significantly reduced processing times compared to the Basic Local Alignment Search Tool algorithm. Furthermore, the results appear more reliable than the work discussed in the text, indicating that the tool has great potential to revolutionize viral genomic research. CONCLUSION: DeepVirusClassifier is a powerful tool for accurately classifying viral sequences, specifically focusing on SARS-CoV-2 and other subtypes within the Coronaviridae family. The superiority of our model becomes evident through rigorous evaluation and comparison with existing methods. Introducing artificial mutations into the sequences demonstrates the tool's ability to identify variations and significantly contributes to viral classification and genomic research. As viral surveillance becomes increasingly critical, our model holds promise in aiding rapid and accurate identification of emerging viral strains.