Diagnostics最新文献

Background: Glial fibrillary acidic protein (GFAP) is an important biomarker for neuroinflammatory conditions. Recently, advancements in the treatment of neurological diseases have highlighted the increasing importance of biomarkers, creating a demand for accurate and simple measurement systems for GFAP levels, which are essential for both research and clinical applications. This study presents the development and validation of a novel fully automated immunoassay for the quantitative determination of GFAP levels in biological samples.

Methods: We examined the analytical performance of the GFAP assay on the LUMIPULSE platform. The assay's parameters, including antibody concentrations, incubation times, and detection methods, were optimized to enhance sensitivity and specificity. GFAP levels were measured in 396 serum or plasma samples, comprising both healthy controls and patients with neurodegenerative diseases.

Results: In the analytical performance studies, intra- and inter-assay coefficients of variation (CV) were below 5%, indicating high reproducibility. Additionally, the assay demonstrated good linearity over the measurement range. The limit of quantification (LoQ) for this assay was 6.0 pg/mL, which is sufficient for measuring specimens from healthy controls. In clinical validation studies, GFAP levels were significantly elevated in patients with neurodegenerative diseases compared to healthy controls.

Conclusions: This automated GFAP assay provides a robust and reliable tool for GFAP measurement, facilitating further research into GFAP's role in neurological disorders and potentially aiding in the diagnosis and monitoring of these conditions.

Background/Objectives: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. Our aims are (1) to validate OSABayes prospectively, (2) to build a smartphone app based on the proposed model, and (3) to evaluate app usability. Methods: We prospectively included adult patients suspected of OSA, without suspicion of other sleep disorders, who underwent level I or III diagnostic PSG. Apnea-hypopnea index (AHI) and OSABayes probabilities were obtained and compared using the area under the ROC curve (AUC [95%CI]) for OSA diagnosis (AHI ≥ 5/h) and higher severity levels (AHI ≥ 15/h) prediction. We built the OSABayes app on 'App Inventor 2', and the usability was assessed with a cognitive walkthrough method and a general evaluation. Results: 216 subjects were included in the validation cohort, performing PSG levels I (34%) and III (66%). OSABayes presented an AUC of 83.6% [77.3-90.0%] for OSA diagnosis and 76.3% [69.9-82.7%] for moderate/severe OSA prediction, showing good response for both types of PSG. The OSABayes smartphone application allows one to calculate the probability of having OSA and consult information about OSA and the tool. In the usability evaluation, 96% of the proposed tasks were carried out. Conclusions: These results show the good discrimination power of OSABayes and validate its applicability in identifying patients with a high pre-test probability of OSA. The tool is available as an online form and as a smartphone app, allowing a quick and accessible calculation of OSA probability.

Background and Aims: Multiple non-invasive tests (NITs) for identifying advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) are available, but, due to the limitations of single NITs, the American Association for the Study of Liver Disease (AASLD) guidelines suggest a two-step strategy, combining the Fibrosis-4 Index (FIB-4) score with the Enhanced Liver Fibrosis (ELF) test to improve diagnostic accuracy and minimize unnecessary liver biopsies. However, few real-world studies have used such a sequential approach. We here evaluated the diagnostic accuracy of the ELF test in patients with recently established metabolic dysfunction-associated steatotic liver disease (MASLD) and assessed the clinical utility of applying a two-step strategy, including the ELF test following the FIB-4 score assessment, in patients with MASLD. Methods: We enrolled 153 patients diagnosed with MASLD who underwent liver biopsy at the Dong-A University Hospital between June 2018 and August 2023. The degree of fibrosis was determined based on liver biopsy results. Various NITs were used, including the Aminotransferase-to-Platelet Ratio Index (APRI), FIB-4 score, NAFLD Fibrosis score (NFS) and ELF test. The diagnostic efficacy of these NITs was evaluated based on the area under the receiver operating characteristic curve (AUROC). Additionally, the performance of each test was further examined both when applied individually and in a two-step approach, where FIB-4 was used followed by ELF testing. Key metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were used for this analysis. Results: Overall, 153 patients with MASLD (mean age: 46.62 years; 52.3% men; 28.1% with type 2 diabetes) were included. The performance of the NITs in identifying advanced fibrosis was as follows: the AUROC of the APRI, FIB-4, NFS, and ELF tests were 0.803 (95% confidence interval (CI), 0.713-0.863), 0.769 (95% CI, 0.694-0.833), 0.699 (95% CI, 0.528-0.796), and 0.829 (95% CI, 0.760-0.885), respectively. The combination of the FIB-4 score ≥ 1.30 and the ELF score ≥ 9.8 showed 67.86% sensitivity, 90.40% specificity, a PPV of 75.18%, an NPV of 86.78%, an accuracy of 83.64%, and an AUROC of 0.791 for predicting the diagnosis of advanced fibrosis. This approach excluded 28 patients (71.8%) from unnecessary liver biopsies. Conclusions: Our study demonstrated that ELF testing maintained diagnostic accuracy in assessing liver fibrosis in patients with MASLD in real-world practice. This test was used as a second step in the evaluation, reducing clinically unnecessary invasive liver biopsies and referrals to tertiary institutions. This approach allows assessment of MASLD severity in primary care settings without requiring additional equipment.

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI's ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as 'artificial intelligence', 'machine learning', 'sports injury', and 'risk prediction'. While AI's predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.

Background: While tricuspid annuloplasty (TAP) is an effective treatment option for tricuspid regurgitation (TR), understanding the echocardiographic factors contributing to recurrent TR can help in developing more effective preventive measures to reduce the rate of recurrent TR after TAP.

Methods: This study was designed as a prospective observational cohort study to investigate factors contributing to recurrent TR following surgical tricuspid valve (TV) repair in patients with moderate or severe functional TR caused by left heart valvular disease, with severe mitral regurgitation as the dominant pathology. The study included 66 patients who underwent preoperative two-dimensional (2D) and three-dimensional (3D) echocardiographic assessments. Patients were divided into two groups based on TAP outcomes: the effective TAP group and the recurrent TR group.

Results: The analysis revealed that 3D-derived both septal-lateral diastolic and systolic tricuspid annulus (TA) diameter (odds ratio (OR) 1.77; 95% confidence interval (CI) 1.17-2.68 and OR 1.62; 95% CI 1.14-2.29, respectively), and major axis diastolic TA diameter (OR 1.59; 95% CI 1.15-2.2) had the highest OR among all echocardiographic parameters. The further univariate analysis of predefined echocardiographic values unveiled that the combined effect of heightened 3D-measured TA major axis diastolic diameter and increased right ventricle (RV) basal diameter exhibited the highest OR at 12.8 (95% CI 2.3-72.8) for a recurrent TR. Using ROC analysis, diastolic major axis (area under the curve (AUC) 0.848; cut-off 48.5 mm), septal-lateral systolic (AUC 0.840; cut-off 43.5 mm) and diastolic (AUC 0.840; cut-off 46.5 mm) TA diameter demonstrated the highest predictive value for recurrent TR from all TV parameters.

Conclusions: Recurrent moderate or severe TR after TAP is associated with preoperative TA size, right atrium and RV geometry, but not with changes of RV function. The predictive capacity of 2D-assessed echocardiographic parameters was found to be lower when compared to their corresponding 3D parameters.

Background and objectives: Multiple primary malignant tumors represent a small percentage of the total number of oncological cases and can involve either metachronous or synchronous development and represent challenges in diagnosis, staging, and treatment planning. Our purpose is to present a rare case of bladder adenocarcinoma in a female patient with multiple primary malignant tumors and to provide systematic review of the available literature.

Materials and methods: A 67-year-old female patient was admitted with altered general condition and anuria. The past medical history of the patient included malignant melanoma (2014), cervical cancer (2017), colon cancer (2021), obstructive anuria (2023), and liver metastasectomy (2023). Transurethral resection of bladder tumor was performed for bladder tumors.

Results: Contrast CT highlighted multiple pulmonary metastases, a poly nodular liver conglomerate, retroperitoneal lymph node, II/III grade left ureterohydronephrosis, and no digestive tract tumor masses. The pathological result of the bladder resection showed an infiltrative adenocarcinoma.

Conclusions: The difference between primary bladder adenocarcinoma tumor and metastatic colorectal adenocarcinoma is the key for the future therapeutic strategy. Identification and assessment of risk factors such as viral infection, radiotherapy, chemotherapy, smoking, and genetics are pivotal in understanding and managing multiple primary malignant tumors. Personalized prevention strategies and screening programs may facilitate the early detection of these tumors, whether synchronous or metachronous. The use of multicancer early detection (MCED) blood tests for early diagnosis appears promising. However, additional research is needed to standardize these techniques for cancer detection.

Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. These factors include demographic attributes, medical history, lifestyle elements, and physiological metrics. Method: An effective random sampling method is proposed to handle the highly biased data of stroke. The stroke pre-diction using optimized boosting machine learning algorithms is supported with explainable AI using LIME and SHAP. This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. Results: The performance of three boosting algorithms is studied for stroke prediction, which include Gradient Boosting (GB), AdaBoost (ADB), and XGBoost (XGB) with XGB achieved the best outcome overall with a training accuracy of 96.97% and testing accuracy of 92.13%. Conclusions: Through this approach, the study seeks to uncover actionable insights to guide healthcare practitioners in devising personalized treatment strategies for stroke patients.

In the evolving healthcare landscape, recommender systems have gained significant importance due to their role in predicting and anticipating a wide range of health-related data for both patients and healthcare professionals. These systems are crucial for delivering precise information while adhering to high standards of quality, reliability, and authentication. Objectives: The primary objective of this research is to address the challenge of class imbalance in healthcare recommendation systems. This is achieved by improving the prediction and diagnostic capabilities of these systems through a novel approach that integrates linear discriminant wolf (LDW) with convolutional neural networks (CNNs), forming the LDW-CNN model. Methods: The LDW-CNN model incorporates the grey wolf optimizer with linear discriminant analysis to enhance prediction accuracy. The model's performance is evaluated using multi-disease datasets, covering heart, liver, and kidney diseases. Established error metrics are used to compare the effectiveness of the LDW-CNN model against conventional methods, such as CNNs and multi-level support vector machines (MSVMs). Results: The proposed LDW-CNN system demonstrates remarkable accuracy, achieving a rate of 98.1%, which surpasses existing deep learning approaches. In addition, the model improves specificity to 99.18% and sensitivity to 99.008%, outperforming traditional CNN and MSVM techniques in terms of predictive performance. Conclusions: The LDW-CNN model emerges as a robust solution for multidisciplinary disease prediction and recommendation, offering superior performance in healthcare recommender systems. Its high accuracy, alongside its improved specificity and sensitivity, positions it as a valuable tool for enhancing prediction and diagnosis across multiple disease domains.

Background: Pancreatic cancer is among the malignancies with the poorest prognosis, largely due to its aggressive nature and resistance to conventional therapies. Case Summary: This report describes the case of a 69-year-old male patient with stage IV primary lung adenocarcinoma presenting with high levels of programmed death-ligand 1 (PD-L1). Simultaneously, abdominal computed tomography (CT) showed a dilated pancreatic duct at the level of the pancreatic head and a hypodense lesion in the uncinate process involving the superior mesenteric artery. Fine-needle aspiration (FNA) of the pancreatic lesions was negative. After three cycles of chemoimmunotherapy, positron emission tomography-computed tomography (PET-CT) showed complete remission of the lung nodules, lymphadenopathy, and pleural thickening, as well as a decrease in the size of the pancreatic lesion. After another six months, a PET-CT scan showed a focal increased uptake in the pancreatic mass in the same location, indicating disease progression. A core biopsy of the pancreatic tumor showed atypical spindle cell morphology with positive staining for vimentin, characteristic of mesenchymal differentiation with no apparent epithelial features. Comprehensive molecular profiling through Caris Molecular Intelligence^® revealed four genes with actionable mutations in the pancreatic tissue, including KRAS (p.G12D) and TP53 (p.R175H). These molecular findings suggested the diagnoses of sarcomatoid carcinoma and conventional pancreatic ductal adenocarcinoma with epithelial-mesenchymal transition. Primary mesenchymal tumors and neuroendocrine neoplasms were excluded because immunohistochemistry was negative for anaplastic lymphoma kinase (ALK), smooth muscle actin (SMA), desmin, CD34, signal transducer and activator of transcription 6 (STAT6), S100, HMB45, CD117, discovered on GIST-1 (DOG1), CD56, progesterone, and synaptophysin. However, despite multiple rounds of systemic chemotherapy, immunotherapy, and radiation, his pancreatic disease rapidly deteriorated and metastasized to the liver and bone. Conclusions: Despite multiple lines of treatment, the patient's condition worsened and he succumbed to his pancreatic malignancy. This study highlights the clinical characteristics, diagnosis, and treatment of rare pancreatic cancer, emphasizing the importance of molecular testing and histopathological biomarkers in personalizing treatment. It also provides insights into promising therapeutic approaches for similar cases with an unusual presentation.

Background/Objectives: Alcohol can directly damage the liver, causing steatosis, steatohepatitis, cirrhosis, and hepatocellular cancer. The aim of this study was to examine 28-day survival in hospitalized patients with alcohol-related liver disease (ALD) cirrhosis, as well as to develop and validate a new survival prediction model. Methods: A total of 145 patients with ALD cirrhosis were included; 107 were diagnosed with acute decompensation (AD) and 38 with acute-on-chronic liver failure (ACLF). The new liver mortality inpatients (LIV-IN) score was calculated using the following variables: hepatic encephalopathy (HE), hepatorenal syndrome (HRS), ascites, systemic inflammatory response syndrome (SIRS), community-acquired infection (CAI), and fibrinogen. The diagnostic accuracy of the LIV-IN score was tested, along with the model for end-stage liver disease (MELD), model for end-stage liver disease-sodium (MELD-Na), albumin-bilirubin (ALBI), neutrophil-to-lymphocyte ratio (NLR), chronic liver failure consortium-C acute decompensation (CLIF-C AD), and chronic liver failure consortium-acute-on-chronic liver failure (CLIF-C ACLF). Results: Lethal outcome occurred in 46 (31.7%) patients. The mortality rate was higher in the ACLF group (n = 22, 57.9%) compared to the AD group (n = 24, 22.4%) (p < 0.01). The highest predictive power for short-term mortality was observed for the LIV-IN score (AUC 73.4%, p < 0.01). In patients with AD, the diagnostic accuracy of the CLIF-C AD score was better than for the LIV-IN score (AUC 0.699; p = 0.004, AUC 0.686; p = 0.007, respectively). In patients with ACLF, only the LIV-IN score had statistically significant discriminative power in predicting 28-day survival. Conclusions: The liver mortality inpatients prognostic score is a new, reliable prognostic model in predicting 28-day mortality.