Differentiation between renal cell carcinoma metastases to the pancreas and pancreatic neuroendocrine tumors in patients with renal cell carcinoma on CT or MRI.

Purpose: To determine whether renal cell carcinoma metastases (RCC-Mets) to the pancreas can be differentiated from pancreatic neuroendocrine tumors (PNETs) in patients with RCC on CT or MRI at presentation.

Methods: This retrospective study included patients with biopsy-proven RCC-Mets (n = 102) or PNETs (n = 32) at diagnosis or after nephrectomy for RCC. Inter-observer agreement (Cohen kappa) was assessed in 95 patients with independent reads by two radiologists, with discrepancies resolved by consensus for final analysis.

Stigma, Depression, and Attitudes Towards Seeking Psychological Help Among Nurses: The Role of Personal Stigma as an Inconsistent Mediator.

Rationale: Identifying whether perceived stigma or personal stigma more significantly affects nurses' attitudes towards seeking psychological help is essential for effectively addressing current challenges and facilitating early intervention for the well-being of nurses and their patients.

Aims And Objectives: The aim of this study was to explore the mediating roles of personal stigma and depression in the relationship between perceived stigma among nurses and their attitudes towards seeking psychological help.

Methods: The sample of this descriptive cross-sectional study consisted of 302 nurses working in a university hospital in southern Turkey, selected using the purposive sampling method, between April 1 and May 1, 2021.

A practical approach to the post esophagectomy CT: expected postoperative anatomy and anatomical approach to associated complication.

This pictorial review aims to provide a structured approach to the interpretation of post esophagectomy CT by reviewing the major esophagectomy surgeries and conduit reconstructions, along with their associated complications at key anatomical landmarks. This paper combines an image rich experience and evidence-based approach to common and rare complications. The paper begins with an overview of the conventional Ivor Lewis esophagectomy and the expected postoperative imaging appearance (with separate detailed tables on additional surgical reconstructions), followed by a focused review of various complications at specific anatomical sites in a systematic fashion.

A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence.

Background: Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data.

Methods: The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model.

Nuclear Imaging in Orthopaedic Practice: A Critical Analysis Review.

» Nuclear imaging techniques, including bone scintigraphy, labeled leukocyte scintigraphy, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) combined with computed tomography (CT), have wide applications in orthopaedics for evaluating trauma, painful total joint arthroplasty, musculoskeletal infection, and orthopaedic oncology.» Three-phase bone scintigraphy is a first-line, highly sensitive nuclear medicine study for evaluating orthopaedic pathology when initial studies are inconclusive. However, its specificity is limited, and findings may be falsely positive for up to 2 years after total joint arthroplasty because of physiologic bone remodeling.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach.

: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses.

Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma.

Background: The current study's objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors.

Methods: Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R.

A novel software for method comparison: MCS (method comparison software)-assessing agreement between estimated fetal weights calculated by Hadlock I-V formulas and birth weight.

Introduction: The evaluation of the performance of new methods, expected to provide cheaper and faster results than existing (reference) methods in the health field, is based on comparing the results obtained with this new method to those obtained with the existing method. The primary aim of this study is to examine the correlational and absolute agreement between measurement methods in clinical studies using Bland-Altman analysis and methodological (Ordinary Least Squares, Weighted Ordinary Least Squares, Deming, Weighted Deming, Passing-Bablok, Theil-Sen, and Passing-Bablok for Large Data Sets.) methods, and the secondary aim is to compare the accuracy and precision of Hadlock (I-V) formulas used for fetal weight estimation.

Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy.

Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM).

Deciphering ovarian torsion: insights from CT imaging analysis.

Purpose: In the milieu of emergency medicine, pelvic and lower abdominal pain present recurrently, with ovarian torsion posing a formidable diagnostic quandary amid multifarious etiologies. Given the burgeoning reliance on CT in acute care settings, it invariably assumes primacy as the principal imaging modality. This study endeavors to elucidate the CT imaging manifestations encountered by surgically confirmed ovarian torsion patients and utilizing CT to differentiate necrosis.

Corrigendum: Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.

[This corrects the article DOI: 10.3389/fmed.2024.

Next-Generation Healthcare: Artificial Intelligence Applications in Disease Management.

The quick and large development in the accumulation of medical data provides broad potential for the application of artificial intelligence technologies [...

Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS.

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.

Introduction: Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF.

Paroxysmal nocturnal hemoglobinuria-related thrombosis in the era of novel therapies: a 2043-patient-year analysis.

Thrombophilia is one of the principal features of paroxysmal nocturnal hemoglobinuria (PNH) and constitutes the main cause of disease morbidity/mortality. Anticomplement treatment has revolutionized the natural history of PNH, with control of the hemolytic process and abolition of thrombotic events (TEs). However, no guidelines exist for the management of thromboembolic complications in this setting, with type and duration of anticoagulation depending on individual practices.

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model.

This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria.

Determination of biomarker candidates for the placenta accreta spectrum by plasma proteomic analysis.

Placenta accreta spectrum (PAS) presents a significant obstetric challenge, associated with considerable maternal and fetal-neonatal morbidity and mortality. Nevertheless, it is imperative to acknowledge that a noteworthy subset of PAS cases remains undetected until the time of delivery, thereby contributing to an augmented incidence of morbidity among the affected individuals. The delayed identification of PAS not only hinders timely intervention but also exacerbates the associated health risks for both the maternal and fetal outcomes.

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.

An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites.

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities.

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.

Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC.

Methods: The current research is a retrospective case-control study.

Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits.

Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels.