PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Bone Loss after Solid Organ Transplantation: A Review of Organ-Specific Considerations.

This review article investigates solid organ transplantation-induced osteoporosis, a critical yet often overlooked issue, emphasizing its significance in post-transplant care. The initial sections provide a comprehensive understanding of the prevalence and multifactorial pathogenesis of transplantation osteoporosis, including factors such as deteriorating post-transplantation health, hormonal changes, and the impact of immunosuppressive medications. Furthermore, the review is dedicated to organ-specific considerations in transplantation osteoporosis, with separate analyses for kidney, liver, heart, and lung transplantations. Each section elucidates the unique challenges and management strategies pertinent to transplantation osteoporosis in relation to each organ type, highlighting the necessity of an organ-specific approach to fully understand the diverse manifestations and implications of transplantation osteoporosis. This review underscores the importance of this topic in transplant medicine, aiming to enhance awareness and knowledge among clinicians and researchers. By comprehensively examining transplantation osteoporosis, this study contributes to the development of improved management and care strategies, ultimately leading to improved patient outcomes in this vulnerable group. This detailed review serves as an essential resource for those involved in the complex multidisciplinary care of transplant recipients.

Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets.

Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.

Napyradiomycin B4 Suppresses RANKL-Induced Osteoclastogenesis and Prevents Alveolar Bone Destruction in Experimental Periodontitis.

The unique structure and beneficial biological properties of marine natural products have drawn interest in drug development. Here, we examined the therapeutic potential of napyradiomycin B4 isolated from marine-derived Streptomyces species for osteoclast-related skeletal diseases. Bone marrow-derived macrophages were treated with napyradiomycin B4 in an osteoclast-inducing medium, and osteoclast formation, osteoclast-specific gene expression, and nuclear factor of activated T-cells cytoplasmic 1 (NFATc1) localization were evaluated using tartrate-resistant acid phosphatase staining, real-time PCR, and immunostaining, respectively. Phosphorylation levels of signaling proteins were assessed by immunoblot analysis to understand the molecular action of napyradiomycin B4. The in vivo efficacy of napyradiomycin B4 was examined under experimental periodontitis, and alveolar bone destruction was evaluated by microcomputed tomography (micro-CT) and histological analyses. Among the eight napyradiomycin derivatives screened, napyradiomycin B4 considerably inhibited osteoclastogenesis. Napyradiomycin B4 significantly suppressed the receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast formation and disrupted the expression of NFATc1 and its target genes. Mitogen-activated extracellular signal-regulated kinase (MEK) and extracellular signal-regulated kinase (ERK) phosphorylation levels were reduced by napyradiomycin B4 in response to RANKL. Under in vivo experimental periodontitis, napyradiomycin B4 significantly attenuated osteoclast formation and decreased the distance between the cementoenamel junction and alveolar bone crest. Our findings demonstrate the antiosteoclastogenic activity of napyradiomycin B4 by inhibiting the RANKL-induced MEK-ERK signaling pathway and its protective effect on alveolar bone destruction.

Con7 is a key transcription regulator for conidiogenesis in the plant pathogenic fungus Fusarium graminearum.

The mycelium of the plant pathogenic fungus Fusarium graminearum exhibits distinct structures for vegetative growth, asexual sporulation, sexual development, virulence, and chlamydospore formation. These structures are vital for the survival and pathogenicity of the fungus, necessitating precise regulation based on environmental cues. Initially identified in Magnaporthe oryzae, the transcription factor Con7p regulates conidiation and infection-related morphogenesis, but not vegetative growth. We characterized the Con7p ortholog FgCon7, and deletion of FgCON7 resulted in severe defects in conidium production, virulence, sexual development, and vegetative growth. The mycelia of the deletion mutant transformed into chlamydospore-like structures with high chitin level accumulation. Notably, boosting FgABAA expression partially alleviated developmental issues in the FgCON7 deletion mutant. Chromatin immunoprecipitation (ChIP)-quantitative PCR (qPCR) analysis confirmed a direct genetic link between FgABAA and FgCON7. Furthermore, the chitin synthase gene Fg6550 (FGSG_06550) showed significant upregulation in the FgCON7 deletion mutant, and altering FgCON7 expression affected cell wall integrity. Further research will focus on understanding the behavior of the chitin synthase gene and its regulation by FgCon7 in F. graminearum. This study contributes significantly to our understanding of the genetic pathways that regulate hyphal differentiation and conidiation in this plant pathogenic fungus. IMPORTANCE: The ascomycete fungus Fusarium graminearum is the primary cause of head blight disease in wheat and barley, as well as ear and stalk rot in maize. Given the importance of conidia and ascospores in the disease cycle of F. graminearum, precise spatiotemporal regulation of these biological processes is crucial. In this study, we characterized the Magnaporthe oryzae Con7p ortholog and discovered that FgCon7 significantly influences various crucial aspects of fungal development and pathogenicity. Notably, overexpression of FgABAA partially restored developmental defects in the FgCON7 deletion mutant. ChIP-qPCR analysis confirmed a direct genetic link between FgABAA and FgCON7. Furthermore, our research revealed a clear correlation between FgCon7 and chitin accumulation and the expression of chitin synthase genes. These findings offer valuable insights into the genetic mechanisms regulating conidiation and the significance of mycelial differentiation in this plant pathogenic fungus.

Gulp1 regulates chondrocyte growth arrest and differentiation via the TGF-ß/SMAD2/3 pathway.

Chondrocyte differentiation is crucial for cartilage formation. However, the complex processes and mechanisms coordinating chondrocyte proliferation and differentiation remain incompletely understood. Here, we report a novel function of the adaptor protein Gulp1 in chondrocyte differentiation. Gulp1 expression is upregulated during chondrogenic differentiation. Gulp1 knockdown in chondrogenic ATDC5 cells reduces the expression of chondrogenic and hypertrophic marker genes during differentiation. Furthermore, Gulp1 knockdown impairs cell growth arrest during chondrocyte differentiation and reduces the expression of the cyclin-dependent kinase inhibitor p21. The activation of the TGF-ß/SMAD2/3 pathway, which is associated with p21 expression in chondrocytes, is impaired in Gulp1 knockdown cells. Collectively, these results demonstrate that Gulp1 contributes to cell growth arrest and chondrocyte differentiation by modulating the TGF-ß/SMAD2/3 pathway.

Metabolomic Characteristics of Nontuberculous Mycobacterial Pulmonary Disease.

BACKGROUND: The therapeutic challenges posed by nontuberculous mycobacterial pulmonary disease (NTM-PD) contribute to an unmet medical need. In this study, we aimed to investigate NTM-PD-specific metabolic pathways using serum metabolomics to understand disease pathogenesis. METHODS: Mass spectrometry-based untargeted metabolomic profiling of serum from patients with NTM-PD (n = 50), patients with bronchiectasis (n = 50), and healthy controls (n = 60) was performed. Selected metabolites were validated by an independent cohort and subjected to pathway analysis and classification modeling. RESULTS: Leucine, tyrosine, inosine, proline, 5-oxoproline, and hypoxanthine levels increased in the NTM-PD group compared with the healthy control group. Furthermore, levels of antioxidant metabolites (ferulic acid, α-lipoic acid, biotin, and 2,8-phenazinediamine) decreased in patients with NTM-PD. These changes were associated with arginine- and proline-related metabolism, leading to generation of reactive oxygen species. Interestingly, the observed metabolic changes in the NTM-PD group overlapped with those in the bronchiectasis group. CONCLUSION: In NTM-PD, 11 metabolites linked to increased oxidative stress were significantly altered from those in healthy controls. Our findings enhance a comprehensive understanding of NTM-PD pathogenesis and provide insights for novel treatment approaches.

Comparative interactome analysis of α-arrestin families in human and Drosophila.

The α-arrestins form a large family of evolutionally conserved modulators that control diverse signaling pathways, including both G-protein-coupled receptor (GPCR)-mediated and non-GPCR-mediated pathways, across eukaryotes. However, unlike ß-arrestins, only a few α-arrestin targets and functions have been characterized. Here, using affinity purification and mass spectrometry, we constructed interactomes for 6 human and 12 Drosophila α-arrestins. The resulting high-confidence interactomes comprised 307 and 467 prey proteins in human and Drosophila, respectively. A comparative analysis of these interactomes predicted not only conserved binding partners, such as motor proteins, proteases, ubiquitin ligases, RNA splicing factors, and GTPase-activating proteins, but also those specific to mammals, such as histone modifiers and the subunits of V-type ATPase. Given the manifestation of the interaction between the human α-arrestin, TXNIP, and the histone-modifying enzymes, including HDAC2, we undertook a global analysis of transcription signals and chromatin structures that were affected by TXNIP knockdown. We found that TXNIP activated targets by blocking HDAC2 recruitment to targets, a result that was validated by chromatin immunoprecipitation assays. Additionally, the interactome for an uncharacterized human α-arrestin ARRDC5 uncovered multiple components in the V-type ATPase, which plays a key role in bone resorption by osteoclasts. Our study presents conserved and species-specific protein-protein interaction maps for α-arrestins, which provide a valuable resource for interrogating their cellular functions for both basic and clinical research.

Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization.

Malaria disease can indeed be fatal if not identified and treated promptly. Due to advancements in the malaria diagnostic process, microscopy techniques are employed for blood cell analysis. Unfortunately, the diagnostic process of malaria via microscopy depends on microscopic skills. To overcome such issues, machine/deep learning algorithms can be proposed for more accurate and efficient detection of malaria. Therefore, a method is proposed for classifying malaria parasites that consist of three phases. The bilateral filter is applied to enhance image quality. After that shape-based and deep features are extracted. In shape-based pyramid histograms of oriented gradients (PHOG) features are derived with the dimension of N × 300. Deep features are derived from the residual network (ResNet)-50, and ResNet-18 at fully connected layers having the dimension of N × 1,000 respectively. The features obtained are fused serially, resulting in a dimensionality of N × 2,300. From this set, N × 498 features are chosen using the generalized normal distribution optimization (GNDO) method. The proposed method is accessed on a microscopic malarial parasite imaging dataset providing 99% classification accuracy which is better than as compared to recently published work.

Correction: Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

[This corrects the article DOI: 10.1371/journal.pone.0292601.].

Effects of uric acid on ischemic diseases, stratified by lipid levels: a drug-target, nonlinear Mendelian randomization study.

Although uric acid-lowering agents such as xanthine oxidase inhibitors have potential cardioprotective effects, studies on their use in preventing cardiovascular diseases are lacking. We investigated the genetically proxied effects of reducing uric acid on ischemic cardiovascular diseases in a lipid-level-stratified population. We performed drug-target Mendelian randomization (MR) analyses using UK Biobank data to select genetic instruments within a uric acid-lowering gene, xanthine dehydrogenase (XDH), and construct genetic scores. For nonlinear MR analyses, individuals were stratified by lipid level. Outcomes included acute myocardial infarction (AMI), ischemic heart disease, cerebral infarction, transient cerebral ischemic attack, overall ischemic disease, and gout. We included 474,983 non-gout individuals with XDH-associated single-nucleotide polymorphisms. The XDH-variant-induced uric acid reduction was associated with reduced risk of gout (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.78-0.93; P < 0.001), cerebral infarction (OR, 0.86; 95% CI, 0.75-0.98; P = 0.023), AMI (OR, 0.79; 95% CI, 0.66-0.94; P = 0.010) in individuals with triglycerides ≥ 188.00 mg/dL, and cerebral infarction in individuals with low-density lipoprotein cholesterol (LDL-C) ≤ 112.30 mg/dL (OR, 0.76; 95% CI, 0.61-0.96; P = 0.020) or LDL-C of 136.90-157.40 mg/dL (OR, 0.67; 95% CI, 0.49-0.92; P = 0.012). XDH-variant-induced uric acid reduction lowers the risk of gout, AMI for individuals with high triglycerides, and cerebral infarction except for individuals with high LDL-C, highlighting the potential heterogeneity in the protective effects of xanthine oxidase inhibitors for treating AMI and cerebral infarction depending on the lipid profiles.

GULP1 deficiency reduces adipogenesis and glucose uptake via downregulation of PPAR signaling and disturbing of insulin/ERK signaling in 3T3-L1 cells.

Obesity and metabolic disorders caused by alterations in lipid metabolism are major health issues in developed, affluent societies. Adipose tissue is the only organ that stores lipids and prevents lipotoxicity in other organs. Mature adipocytes can affect themselves and distant metabolism-related tissues by producing various adipokines, including adiponectin and leptin. The engulfment adaptor phosphotyrosine-binding domain-containing 1 (GULP1) regulates intracellular trafficking of glycosphingolipids and cholesterol, suggesting its close association with lipid metabolism. However, the role of GULP1 in adipocytes remains unknown. Therefore, this study aimed to investigate the function of GULP1 in adipogenesis, glucose uptake, and the insulin signaling pathway in adipocytes. A 3T3-L1 cell line with Gulp1 knockdown (shGulp1) and a 3T3-L1 control group (U6) were established. Changes in shGulp1 cells due to GULP1 deficiency were examined and compared to those in U6 cells using microarray analysis. Glucose uptake was monitored via insulin stimulation in shGulp1 and U6 cells using a 2-NBDG glucose uptake assay, and the insulin signaling pathway was investigated by western blot analysis. Adipogenesis was significantly delayed, lipid metabolism was altered, and several adipogenesis-related genes were downregulated in shGulp1 cells compared to those in U6 cells. Microarray analysis revealed significant inhibition of peroxisome proliferator-activated receptor signaling in shGulp1 cells compared with U6 cells. The production and secretion of adiponectin as well as the expression of adiponectin receptor were decreased in shGulp1 cells. In particular, compared with U6 cells, glucose uptake via insulin stimulation was significantly decreased in shGulp1 cells through the disturbance of ERK1/2 phosphorylation. This is the first study to identify the role of GULP1 in adipogenesis and insulin-stimulated glucose uptake by adipocytes, thereby providing new insights into the differentiation and functions of adipocytes and the metabolism of lipids and glucose, which can help better understand metabolic diseases.

Phase I study targeting newly diagnosed grade 4 astrocytoma with bispecific antibody armed T cells (EGFR BATs) in combination with radiation and temozolomide.

PURPOSE: The purpose of this study was to determine the safety, feasibility, and immunologic responses of treating grade 4 astrocytomas with multiple infusions of anti-CD3 x anti-EGFR bispecific antibody (EGFRBi) armed T cells (EGFR BATs) in combination with radiation and chemotherapy. METHODS: This phase I study used a 3 + 3 dose escalation design to test the safety and feasibility of intravenously infused EGFR BATs in combination with radiation and temozolomide (TMZ) in patients with newly diagnosed grade 4 astrocytomas (AG4). After finding the feasible dose, an expansion cohort with unmethylated O6-methylguanine-DNA methyltransferase (MGMT) tumors received weekly EGFR BATs without TMZ. RESULTS: The highest feasible dose was 80 × 109 EGFR BATs without dose-limiting toxicities (DLTs) in seven patients. We could not escalate the dose because of the limited T-cell expansion. There were no DLTs in the additional cohort of three patients with unmethylated MGMT tumors who received eight weekly infusions of EGFR BATs without TMZ. EGFR BATs infusions induced increases in glioma specific anti-tumor cytotoxicity by peripheral blood mononuclear cells (p < 0.03) and NK cell activity (p < 0.002) ex vivo, and increased serum concentrations of IFN-γ (p < 0.03), IL-2 (p < 0.007), and GM-CSF (p < 0.009). CONCLUSION: Targeting AG4 with EGFR BATs at the maximum feasible dose of 80 × 109, with or without TMZ was safe and induced significant anti-tumor-specific immune responses. These results support further clinical trials to examine the efficacy of this adoptive cell therapy in patients with MGMT-unmethylated GBM. CLINICALTRIALS: gov Identifier: NCT03344250.

Sulfur metabolism-mediated fungal glutathione biosynthesis is essential for oxidative stress resistance and pathogenicity in the plant pathogenic fungus Fusarium graminearum.

IMPORTANCE: Fusarium graminearum is a destructive fungal pathogen that causes Fusarium head blight (FHB) on a wide range of cereal crops. To control fungal diseases, it is essential to comprehend the pathogenic mechanisms that enable fungi to overcome host defenses during infection. Pathogens require an oxidative stress response to overcome host-derived oxidative stress. Here, we identify the underlying mechanisms of the Fgbzip007-mediated oxidative stress response in F. graminearum. ChIP-seq and subsequent genetic analyses revealed that the role of glutathione in pathogenesis is not dependent on antioxidant functions in F. graminearum. Altogether, this study establishes a comprehensive framework for the Fgbzip007 regulon on pathogenicity and oxidative stress responses, offering a new perspective on the role of glutathione in pathogenicity.

A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris.

Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.

Colon histology slide classification with deep-learning framework using individual and fused features.

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.

YAP targetome reveals activation of SPEM in gastric pre-neoplastic progression and regeneration.

Peptic ulcer disease caused by environmental factors increases the risk of developing gastric cancer (GC), one of the most common and deadly cancers in the world. However, the mechanisms underlying this association remain unclear. A major type of GC uniquely undergoes spasmolytic polypeptide-expressing metaplasia (SPEM) followed by intestinal metaplasia. Notably, intestinal-type GC patients with high levels of YAP signaling exhibit a lower survival rate and poor prognosis. YAP overexpression in gastric cells induces atrophy, metaplasia, and hyperproliferation, while its deletion in a Notch-activated gastric adenoma model suppresses them. By defining the YAP targetome genome-wide, we demonstrate that YAP binds to active chromatin elements of SPEM-related genes, which correlates with the activation of their expression in both metaplasia and ulcers. Single-cell analysis combined with our YAP signature reveals that YAP signaling is activated during SPEM, demonstrating YAP as a central regulator of SPEM in gastric neoplasia and regeneration.

Identification of Essential Genes for the Establishment of Spray-Induced Gene Silencing-Based Disease Control in Fusarium graminearum.

As resistance to chemical fungicides continues to increase inFusarium graminearum, there is a growing need to develop novel disease control strategies. To discover essential genes that could serve as new disease control targets, we selected essential gene candidates that had failed to be deleted in previous studies. Thirteen genes were confirmed to be essential, either by constructing conditional promoter replacement mutants or by employing a clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-mediated editing strategy. We synthesized double-stranded RNAs (dsRNAs) targeting these essential genes and analyzed their protective effects in plants using a spray-induced gene silencing (SIGS) method. When dsRNAs targeting Fg10360, Fg13150, and Fg06123 were applied to detached barley leaves prior to fungal inoculation, disease lesions were greatly reduced. Our findings provide evidence of the potential of essential genes identified by a SIGS method to be effective targets for the control of fungal diseases.

Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research.

AIM: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. METHOD: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the "black box" problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. RESULTS: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p ≤ 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) genes were also determined to increase the risk of metastasis in BC. CONCLUSION: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning.

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.