Predictors of Non-Sentinel Nodal Involvement in Breast Cancer Patients with Positive Sentinel Nodes Undergoing Upfront Surgery.

Background & Objective: Sentinel lymph node (SLN) biopsy is performed to stage the axilla in clinically node negative breast cancer (BC) and is converted to axillary lymph node dissection (ALND) if found to be positive for tumor metastasis. A significant proportion of positive SLN cases do not reveal any positive non-SLN (NSLN) emphasizing the need to evaluate other clinicopathological factors which could better predict NSLN involvement and avoid unnecessary ALND in a select group of patients. We aimed to find the correlation of clinicopathological features of primary BC, positive SLN and NSLN after ALND in order to identify factors that may help in predicting involvement of NSLN.

A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.

Alzheimer's disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove to be possible through early diagnosis. The conventional machine learning models designed for AD detection work well only up to a certain point.

An Aggressive Angiomyxoma Presenting as a Vaginal Wall Carbuncle in a 31-Year-Old Female: A Case Report.

An aggressive angiomyxoma (AA) is a rare soft tissue neoplasm of the lower female genital tract. The incidence of vaginal involvement is low, so it is commonly misdiagnosed as a Bartholin cyst, lipoma, and abscess, among others. This is a case of a 31-year-old female patient presenting with anterior vaginal wall swelling measuring 1 x 1 cm, clinically diagnosed as vaginal carbuncle.

Correction: Efficiency, market concentration and bank performance during the COVID-19 outbreak: Evidence from the MENA region.

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

An empirical study for mitigating sustainable cloud computing challenges using ISM-ANN.

The significance of cloud computing methods in everyday life is growing as a result of the exponential advancement and refinement of artificial technology. As cloud computing makes more progress, it will bring with it new opportunities and threats that affect the long-term health of society and the environment. Many questions remain unanswered regarding sustainability, such as, "How will widely available computing systems affect environmental equilibrium"? When hundreds of millions of microcomputers are invisible to each other, what will society look like? What does this mean for social sustainability? This paper empirically investigates the ethical challenges and practices of cloud computing about sustainable development.

Accuracy and Comparison of Core Needle Biopsy Diagnoses With Excision Specimen for Diagnosing Fibroepithelial Lesions of the Breast.

Background Core needle biopsy (CNB) for fibroepithelial lesions (FELs) of the breast is commonly encountered by histopathologists. The distinction between fibroadenoma (FA) and phyllodes tumor (PT) can be challenging due to overlapping histological features and the limited nature of CNB material.  Objective This study aimed to assess the accuracy of CNB diagnosis of FA and PT by comparing it with a diagnosis on subsequent surgical excision specimen.

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts.

Diagnostic utility of anti-thyroid peroxidase immunohistochemistry in the identification of papillary thyroid carcinoma.

The routine histomorphological assessment of follicular thyroid neoplasms has been subject to interobserver or intraobserver variability among histopathologists. Anti-thyroid peroxidase (anti-TPO) has emerged as a useful immunohistochemical (IHC) marker, with its expression lost in papillary thyroid carcinoma (PTC). Our study aims to determine the diagnostic accuracy of anti-TPO IHC expression in the identifying PTC and its variants, particularly the Follicular variant of papillary thyroid carcinoma (FVPTC), with H&E assessment as the gold standard.

Role of SOX10 Immunohistochemical Expression in Diagnosing Triple Negative Breast Cancer and Its Correlation With Clinicopathological Features.

Background: Triple-negative breast cancer (TNBC) poses a diagnostic challenge for histopathologists due to the reduced frequency of breast-specific markers. SOX10 has emerged as a useful diagnostic marker for TNBC. The aim of our study was to determine the frequency of SOX-10 immunohistochemical (IHC) expression in our cohort and assess its correlation with clinicopathological and histological features.

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging.

Solitary Fibrous Tumor of Head and Neck Region: A Series of Three Cases at an Uncommon Location With a Review of the Literature.

Solitary fibrous tumors (SFTs) uncommonly involve the head and neck region. Head and neck SFTs (HNSFTs) exhibit diverse histological features and can mimic several neoplasms with different treatment and behavior. Herein, we report the clinicopathological features of three cases of HNSFT.

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.

Background: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making.

Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors.

Correction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

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

An Optimized IoT-enabled Big Data Analytics Architecture for Edge-Cloud Computing.

The awareness of edge computing is attaining eminence and is largely acknowledged with the rise of Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data.

Ensemble learning for multi-class COVID-19 detection from big data.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19.

IoT based battery energy monitoring and management for electric vehicles with improved converter efficiency.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications.

Solitary fibrous tumor of head and neck region; A clinicopathological study of 67 cases emphasizing the diversity of histological features and utility of various risk stratification models.

Background: Head and neck SFT (HNSFT) exhibit diverse histological features and can mimic various neoplasms with different treatment and behavior. While risk stratification systems have been developed for this tumor at various anatomic sites, a specific scheme for head and neck tumors is lacking. Our aim was to describe the histologic patterns present in HNSFT cases as well as assess the utility of risk assessment models in this location.

Evaluation of chemical characterization and antihyperlipidemic potential of orange peel powder (Citrus sinensis) in male human subjects.

Citrus sinensis is an important member of the genus Citrus which contains phenolic compounds and bioflavonoids which have antihyperlipidemic and antiatherogenic effects. It also has the potential to reduce oxidative stress. To investigate the antihyperlipidemic effect of orange peel powder was encapsulated and analyzed in hyperlipidemic patients.

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases.

Image Encryption Scheme Based on Orbital Shift Pixels Shuffling with ILM Chaotic System.

Image encryption techniques protect private images from unauthorized access while they are being transmitted. Previously used confusion and diffusion processes are risky and time-consuming. Therefore, finding a solution to this problem has become necessary.

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times.

Efficiency, market concentration and bank performance during the COVID-19 outbreak: Evidence from the MENA region.

This study aims to contribute to the existing literature that explores the impact of market concentration on bank efficiency in emerging economies. Using a sample of 225 banks in 18 countries in the Middle East and North Africa (MENA) region over the period 2006-2020, we empirically investigate the significance of this relationship. Since the evidence of concentration effect on efficiency during the COVID-19 outbreak is ambiguous, we test the hypothesis that the efficiency is positively affected by the level of banking market concentration in the MENA region.

A Case Report and Literature Review of Rectosigmoid Crohn's Disease: A Diagnostic Pitfall Ultimately Leading to Spontaneous Colonic Perforation.

Inflammatory bowel disease (IBD) is a chronic condition that affects the gastrointestinal tract, with ulcerative colitis (UC) and Crohn's disease (CD) as the two major entities. While these conditions share some similarities in clinical presentation, they have distinct histopathological features. UC is a mucosal disease affecting the left colon and rectum, while CD can affect any part of the gastrointestinal tract and all layers of the bowel wall.