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Journal of Clinical Medicine Jun 2024Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death....
Predicting Major Adverse Carotid Cerebrovascular Events in Patients with Carotid Stenosis: Integrating a Panel of Plasma Protein Biomarkers and Clinical Features-A Pilot Study.
Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death. The currently available treatment for CS is medical management through risk reduction, including control of hypertension, diabetes, and/or hypercholesterolemia. Surgical interventions are currently suggested for patients with symptomatic disease with stenosis >50%, where patients have suffered from a carotid-related event such as a cerebrovascular accident, or asymptomatic disease with stenosis >60% if the long-term risk of death is <3%. There is a lack of current plasma protein biomarkers available to predict patients at risk of such adverse events. In this study, we investigated several growth factors and biomarkers of inflammation as potential biomarkers for adverse CS events such as stroke, need for surgical intervention, myocardial infarction, and cardiovascular-related death. In this pilot study, we use a support vector machine (SVM), random forest models, and the following four significantly elevated biomarkers: C-X-C Motif Chemokine Ligand 6 (CXCL6); Interleukin-2 (IL-2); Galectin-9; and angiopoietin-like protein (ANGPTL4). Our SVM model best predicted carotid cerebrovascular events with an area under the curve (AUC) of >0.8 and an accuracy of 0.88, demonstrating strong prognostic capability. : Our SVM model may be used for risk stratification of patients with CS to determine those who may benefit from surgical intervention.
PubMed: 38929911
DOI: 10.3390/jcm13123382 -
Life (Basel, Switzerland) Jun 2024Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in... (Review)
Review
Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in synovitis and pannus formation. Timely detection and effective management of RA are pivotal for mitigating inflammatory arthritis consequences, potentially influencing disease progression. Nuclear medicine using radiolabeled targeted vectors presents a promising avenue for RA diagnosis and response to treatment assessment. Radiopharmaceutical such as technetium-99m (Tc), combined with single photon emission computed tomography (SPECT) combined with CT (SPECT/CT), introduces a more refined diagnostic approach, enhancing accuracy through precise anatomical localization, representing a notable advancement in hybrid molecular imaging for RA evaluation. This comprehensive review discusses existing research, encompassing in vitro, in vivo, and clinical studies to explore the application of Tc radiolabeled targeting vectors with SPECT imaging for RA diagnosis. The purpose of this review is to highlight the potential of this strategy to enhance patient outcomes by improving the early detection and management of RA.
PubMed: 38929734
DOI: 10.3390/life14060751 -
Animals : An Open Access Journal From... Jun 2024Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive...
Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.
PubMed: 38929416
DOI: 10.3390/ani14121797 -
International Journal of Environmental... May 2024Besides being vectors of the onchocerciasis parasite, blackflies are a source of nuisance in onchocerciasis-endemic communities. We investigated the experience of...
Community Perceptions of Blackfly Nuisance and Acceptability of the "Slash and Clear" Vector Control Approach in the Ntui Health District of Cameroon: A Qualitative Study.
Besides being vectors of the onchocerciasis parasite, blackflies are a source of nuisance in onchocerciasis-endemic communities. We investigated the experience of residents in the Ntui Health District (Cameroon) regarding blackfly nuisance and assessed their perceptions of a novel "Slash and Clear" (S&C) intervention for blackfly control. Focus group discussions were conducted before and after S&C implementation (respectively, in February 2022 and December 2023). Blackflies were known to emerge from the river areas and cause disease. To prevent blackfly bites, the population often covered their body with protective clothing and applied various substances (kerosene, oil, or lemon) to their skin. Post-intervention data showed reduced blackfly nuisance, and the willingness to sustain blackfly control in the long-term was unanimous among community leaders and members, including the village volunteers who implemented the S&C intervention. In conclusion, blackfly nuisance is evident in the Ntui onchocerciasis focus of Cameroon and led to a panoply of coping practices, some of which could be detrimental to their health. Implementing S&C for blackfly control is well accepted and could sustainably alleviate the nuisance caused by blackflies while simultaneously breaking the onchocerciasis transmission cycle.
Topics: Cameroon; Animals; Simuliidae; Humans; Onchocerciasis; Insect Control; Female; Male; Adult; Insect Vectors; Middle Aged; Focus Groups; Health Knowledge, Attitudes, Practice; Young Adult; Insect Bites and Stings
PubMed: 38928904
DOI: 10.3390/ijerph21060658 -
Diagnostics (Basel, Switzerland) Jun 2024Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for...
Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for effective treatment and management of this condition. To overcome this difficulty, we created a state-of-the-art IoT-Based Ambulatory Blood Pressure Monitoring System that provides real-time blood pressure readings, systolic, diastolic, and pulse rates at predefined intervals. This unique technology comes with a module that forecasts CHD's early warning score. Various machine learning algorithms employed comprise Naïve Bayes, K-Nearest Neighbors (K-NN), random forest, decision tree, and Support Vector Machine (SVM). Using Naïve Bayes, the proposed model has achieved an impressive 99.44% accuracy in predicting blood pressure, a vital aspect of real-time intensive care for CHD. This IoT-based ambulatory blood pressure monitoring (IABPM) system will provide some advancement in the field of healthcare. The system overcomes the limitations of earlier BP monitoring devices, significantly reduces healthcare costs, and efficiently detects irregularities in chronic heart diseases. By implementing this system, we can take a significant step forward in improving patient outcomes and reducing the global burden of CHD. The system's advanced features provide an accurate and reliable diagnosis that is essential for treating and managing CHD. Overall, this IoT-based ambulatory blood pressure monitoring system is an important tool for the early identification and treatment of CHD in the field of healthcare.
PubMed: 38928712
DOI: 10.3390/diagnostics14121297 -
Diagnostics (Basel, Switzerland) Jun 2024The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19...
The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients' gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.
PubMed: 38928699
DOI: 10.3390/diagnostics14121284 -
Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum.Brain Sciences Jun 2024Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological...
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls ( < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment ( = 0.726, < 0.001), AD ( = 0.845, < 0.001), and AD continuum ( = 0.797, < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
PubMed: 38928575
DOI: 10.3390/brainsci14060575 -
Brain Sciences Jun 2024This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by...
This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.
PubMed: 38928565
DOI: 10.3390/brainsci14060565 -
International Journal of Molecular... Jun 2024(, ; CHIKV) is a mosquito-borne global health threat. The main urban vector of CHIKV is the mosquito, which is found throughout Brazil. Therefore, it is important to...
(, ; CHIKV) is a mosquito-borne global health threat. The main urban vector of CHIKV is the mosquito, which is found throughout Brazil. Therefore, it is important to carry out laboratory tests to assist in the virus's diagnosis and surveillance. Most molecular biology methodologies use nucleic acid extraction as the first step and require quality RNA for their execution. In this context, four RNA extraction protocols were evaluated in experimentally infected with CHIKV. Six pools were tested in triplicates (n = 18), each containing 1, 5, 10, 20, 30, or 40 mosquitoes per pool (72 tests). Four commercial kits were compared: QIAamp, Maxwell, PureLink, and PureLink with TRIzol. The QIAamp and PureLink with TRIzol kits had greater sensitivity. Two negative correlations were observed: as the number of mosquitoes per pool increases, the Ct value decreases, with a higher viral load. Significant differences were found when comparing the purity and concentration of RNA. The QIAamp protocol performed better when it came to lower Ct values and higher RNA purity and concentration. These results may provide help in CHIKV entomovirological surveillance planning.
Topics: Chikungunya virus; Aedes; Animals; RNA, Viral; Mosquito Vectors; Chikungunya Fever; Viral Load
PubMed: 38928410
DOI: 10.3390/ijms25126700 -
Cancers Jun 2024High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck...
Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients.
High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models-penalized Cox model, random forest, gradient boosted model and support vector machine-to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.
PubMed: 38927901
DOI: 10.3390/cancers16122195