Variation within and between digital pathology and light microscopy for the diagnosis of histopathology slides: blinded crossover comparison study.

Background: Digital pathology refers to the conversion of histopathology slides to digital image files for examination on computer workstations as opposed to conventional microscopes. Prior to adoption, it is important to demonstrate pathologists provide equivalent reports when using digital pathology in comparison to bright-field and immunofluorescent light microscopy, the current standard of care.

Objective: A multicentre comparison of digital pathology with light microscopy for reporting of histopathology slides, measuring variation within and between pathologists on both modalities.

Real-Time American Sign Language Interpretation Using Deep Learning and Keypoint Tracking.

Communication barriers pose significant challenges for the Deaf and Hard-of-Hearing (DHH) community, limiting their access to essential services, social interactions, and professional opportunities. To bridge this gap, assistive technologies leveraging artificial intelligence (AI) and deep learning have gained prominence. This study presents a real-time American Sign Language (ASL) interpretation system that integrates deep learning with keypoint tracking to enhance accessibility and foster inclusivity.

Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments.

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requiring the implementation of effective security solutions. This paper presents a novel intrusion detection system (IDS) specifically designed for IoMT networks.

High α-SMA expression in the tumor stroma is associated with adverse clinical parameters in mismatch repair-proficient colorectal cancers only.

Objectives: As mismatch repair status confers differential prognosis in colorectal cancers, this study aimed to determine associations of α-smooth muscle actin (α-SMA) protein expression in mismatch repair-proficient (pMMR) and mismatch repair-deficient (dMMR) colorectal tumors with clinicopathologic and prognostic features.

Methods: Tissue microarrays from patients with colorectal cancer, immunostained with α-SMA, were assessed through digital image analysis. Total (n = 962), pMMR (n = 782), and dMMR (n = 156) stromal H-scores were assessed for associations with clinicopathologic and survival data.

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices.

What factors influence cellular pathologists' confidence in case reporting?

Histopathology is a challenging interpretive discipline, and the level of confidence a pathologist has in their diagnosis is known to vary, which is conveyed descriptively in pathology reports. There has been little study to accurately quantify pathologists' diagnostic confidence or the factors that influence it. In this study involving sixteen pathologists from six NHS trusts, we assessed diagnostic confidence across multiple variables and four specialties.

Using Machine Learning Multiclass Classification Technique to Detect IoT Attacks in Real Time.

This paper presents a real-time intrusion detection system (IDS) aimed at detecting the Internet of Things (IoT) attacks using multiclass classification models within the PySpark architecture. The research objective is to enhance detection accuracy while reducing the prediction time. Various machine learning algorithms are employed using the OneVsRest (OVR) technique.

Advantages of automated immunostain analyses for complex membranous immunostains: An exemplar investigating loss of E-cadherin expression in colorectal cancer.

As pathology moves towards digitisation, biomarker profiling through automated image analysis provides potentially objective and time-efficient means of assessment. This study set out to determine how a complex membranous immunostain, E-cadherin, assessed using an automated digital platform fares in comparison to manual evaluation in terms of clinical correlations and prognostication. Tissue microarrays containing 1000 colorectal cancer samples, stained with clinical E-cadherin antibodies were assessed through both manual scoring and automated image analysis.

Improving Pediatric Hypertension Screening in an Academic Primary Care Setting.

Introduction: Adherence to the American Academy of Pediatrics clinical practice guidelines for screening and managing high blood pressure (BP) is low. This team sought to improve recognition and documentation of relevant diagnoses in patients aged 13-20 years who presented to general pediatric clinics.

Methods: The primary outcome measure was the proportion of office visits for patients ages 13-20 with a BP ≥ 120/80 with a visit or problem list diagnosis of hypertension or elevated BP.

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments.

The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS).

Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

Aims: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples.

Methods: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings.

Optimizing the selection of natural fibre reinforcement and polymer matrix for plastic composite using LS-SVM technique.

The manufacturing sector is paying close attention to plastic matrix composites (PMCs) reinforced with natural fibres for improving their products. Due to the fact that PMC reinforced with naturally occurring fibres is more affordable and has superior mechanical qualities. Based on the application material requirements, An important step in the production of PMC is choosing the right natural fibres for reinforcing and determining how much of each.

Deep Learning Technology to Recognize American Sign Language Alphabet.

Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet.

Investigating genomic, proteomic, and post-transcriptional regulation profiles in colorectal cancer: a comparative study between primary tumors and associated metastases.

Introduction: Approximately 50% of patients with primary colorectal carcinoma develop liver metastases. This study investigates the possible molecular discrepancies between primary colorectal cancer (pCRC) and their respective metastases.

Methods: A total of 22 pairs of pCRC and metastases were tested.

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning.

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy.

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things' Devices Security.

The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency.

Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer.

Purpose: High tumor production of the EGFR ligands, amphiregulin (AREG) and epiregulin (EREG), predicted benefit from anti-EGFR therapy for metastatic colorectal cancer (mCRC) in a retrospective analysis of clinical trial data. Here, AREG/EREG IHC was analyzed in a cohort of patients who received anti-EGFR therapy as part of routine care, including key clinical contexts not investigated in the previous analysis.

Experimental Design: Patients who received panitumumab or cetuximab ± chemotherapy for treatment of RAS wild-type mCRC at eight UK cancer centers were eligible.

In Vitro Culture and Histological Evaluation of 3D Organotypic Cultures.

Organotypic cultures allow cells to grow in a system which mimics in vivo tissue organization. Here we describe a method for establishing 3D organotypic cultures (using intestine as an example system), followed by methods for demonstrating cell morphology and tissue architecture using histological techniques and molecular expression analysis using immunohistochemistry, though the system is also amenable to molecular expression analysis, such as by PCR, RNA sequencing, or FISH.

Activated tissue resident memory T-cells (CD8+CD103+CD39+) uniquely predict survival in left sided "immune-hot" colorectal cancers.

Introduction: Characterization of the tumour immune infiltrate (notably CD8+ T-cells) has strong predictive survival value for cancer patients. Quantification of CD8 T-cells alone cannot determine antigenic experience, as not all infiltrating T-cells recognize tumour antigens. Activated tumour-specific tissue resident memory CD8 T-cells (T) can be defined by the co-express of CD103, CD39 and CD8.

BZR proteins: identification, evolutionary and expression analysis under various exogenous growth regulators in plants.

Backgrounds: The BRASSINAZOLE-RESISTANT (BZR) family of transcription factors affects a variety of developmental and physiological processes and plays a key role in multiple stress-resistance functions in plants. However, the evolutionary relationship and individual expression patterns of the BZR genes are unknown in various crop plants.

Methods And Results: In this study, we performed a genome-wide analysis of the BZR genes family in wheat and rice.