Silicon balance in human volunteers; a pilot study to establish the variance in silicon excretion versus intake.

BACKGROUND: Accumulating evidence suggests a role for silicon in optimal connective tissue health. Further proof of its importance/essentiality may be provided by studies involving imposed depletion followed by 29Si challenge to estimate metabolic balance. Prior to conducting these expensive studies, we first established the variance of estimating normal Si excretion versus intake using a single oral dose of typical dietary Si, orthosilicic acid. METHODS: Healthy volunteers were recruited from Loei Rajabhat University, separated into two matched groups (three males and three females/group) and maintained on a standardized diet for the three study days. One group ingested 500 ml water containing orthosilicic acid (28.9 mg Si) and the other group received 500 ml water alone, all on a fasted stomach. Blood samples and total urine and faeces were collected over the 48 h post-dose period and 24 h before-hand (baseline) and analysed for silicon by inductively coupled plasma optical emission spectrometry. RESULTS: Serum Si analysis confirmed the ready absorption of silicon from the orthosilicic acid solution. Mean total urinary and faecal Si excretions over the 24 h post-dose period accounted for 57 ± 9.5% and 39 ± 9.4% of the ingested dose, respectively. Thus in total 96.3 ± 5.8% of the ingested dose was recovered in faecal plus urinary excretions over the 24 h post-dose period. CONCLUSIONS: We report that in healthy subjects (presumably in Si balance), the ingestion of a soluble dose of dietary Si results in the same quantity (within analytical error) being excreted within 24 h. It is currently not known if this all originated from the dose solution or if there was some exchange with the body Si pool but, given the low variance in these silicon balance data, isotopic studies are now merited

Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network

Coronary Artery Disease (CAD) diagnostic to be a major global cause of death, necessitating innovative solutions. Addressing the critical importance of early CAD detection and its impact on the mortality rate, we propose the potential of one-dimensional convolutional neural networks (1D-CNN) to enhance detection accuracy and reduce network complexity. This study goes beyond traditional diagnostic methodologies, leveraging the remarkable ability of 1D-CNN to interpret complex patterns within Electrocardiogram (ECG) signals without depending on feature extraction techniques. We explore the impact of varying sample lengths on model performance and conduct experiments involving layers reduction. The ECG data employed were obtained from the PhysioNet databases, namely the MIMIC III and Fantasia datasets, with respective sampling frequencies of 125 Hz and 250 Hz. The highest accuracy for unseen data obtained with a sample length of 250. These initial findings demonstrate the potential of 1D-CNNs in CAD diagnosis using ECG signals and highlight the sample size's role in achieving high accuracy

Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network

Coronary Artery Disease (CAD) diagnostic to be a major global cause of death, necessitating innovative solutions. Addressing the critical importance of early CAD detection and its impact on the mortality rate, we propose the potential of one-dimensional convolutional neural networks (1D-CNN) to enhance detection accuracy and reduce network complexity. This study goes beyond traditional diagnostic methodologies, leveraging the remarkable ability of 1D-CNN to interpret complex patterns within Electrocardiogram (ECG) signals without depending on feature extraction techniques. We explore the impact of varying sample lengths on model performance and conduct experiments involving layers reduction. The ECG data employed were obtained from the PhysioNet databases, namely the MIMIC III and Fantasia datasets, with respective sampling frequencies of 125 Hz and 250 Hz. The highest accuracy for unseen data obtained with a sample length of 250. These initial findings demonstrate the potential of 1D-CNNs in CAD diagnosis using ECG signals and highlight the sample size’s role in achieving high accuracy

Prevalence and Risk Factors for Ocular Complications in New-Onset Uveitis:A Study From a Tertiary Referral Center in Northern Thailand

Purpose: To determine the prevalence and identify risk factors of ocular complications in patients with uveitis. Methods: This retrospective study reviewed of 340 consecutive patients with a first episode of active uveitis from January 2015 to December 2019. Demographic and clinical data, including ocular complications were analyzed. Results: The mean age of the cohort was 47 years. Among them, 75 patients were HIV-positive (74% male), and 265 were HIV-negative (53% male). An Infectious etiology was identified in 52% of cases. Ocular complications, developed in 151 patients (44%), with their type strongly correlate to the anatomical location of uveitis. Multivariate analysis revealed chronic inflammation (risk ratio [RR]=18.9; 95% confidence interval [CI] 6.1–58.8), recurrent inflammation (RR=20.4; 95%; CI 6.5–64.3), and poor visual acuity (VA) at presentation (RR=3.6; 95% CI 1.4–9.2) as significant risk factors for complications. Conclusion: Nearly half of the patients with uveitis developed ocular complications, highlighting the importance of identifying risk factors. Understanding the relationship between the location of inflammation and specific complication patterns is essential for early detection and targeted prevention strategies.</p

Enhanced Coronary Artery Disease Classification through Feature Engineering and One-Dimensional Convolutional Neural Network

Coronary artery disease (CAD) diagnosis remains a significant contributor to global mortality rates, highlighting the need for novel approaches. Existing CAD diagnostic tools rely on costly and complex biomarkers and scanners. In this paper, using only electrocardiogram (ECG) signals, we propose a novel learning-based model for CAD diagnosis. The proposed method works based on a one-dimensional convolutional neural network (1D-CNN), offering a cost-effective alternative for sophisticated cardiac health monitoring. Furthermore, we introduce the concept of feature engineering to improve the quality of the model training process and mitigate the challenge of ill-conditioned ECG data. Unlike existing approaches, which often overlook signal quality, our model applies a smart feature engineering, ensuring that only diagnostically reliable signals are used. This design improves robustness, generalisability, and suitability for real-world clinical settings. Utilising one of the most complex publicly available datasets, i.e., MIMIC III, sourced from Physionet, the performance of the proposed model, along with existing ones in classifying potential cases of CAD and non-CAD is investigated. Our findings confirm that the proposed model exhibits outstanding performance, highlighting the effectiveness of our integrated feature engineering approach with the CNN model

Development of Acute Vogt–Koyanagi–Harada-like Syndrome during the Treatment Course with Vemurafenib for Metastatic Melanoma

Purpose: To report on ocular Vogt–Koyanagi–Harada (VKH)-like syndrome under vemurafenib treatment for metastatic melanoma. Design: A case report. Method: Description of clinical and imaging manifestations including fundus photography, fluorescein, and indocyanine green angiography. Results: A 46-year-old Thai female was diagnosed with metastatic melanoma of the skin and had been treated with multiple surgical excisions, radiotherapy, and vemurafenib (initial dose 480 mg orally twice daily, subsequently increased to maximum dose of 960 mg twice daily). After 6 months of vemurafenib use, she complained of bilateral redness and photophobia and was diagnosed with bilateral anterior uveitis, which was topically treated. Two weeks later, her visual acuity (VA) sharply deteriorated to 20/80 and counting fingers. Ocular examination at that stage stronly resembled acute VKH disease. She exhibited intraocular inflammation, and her fundus examination revealed bilateral optic disc swelling and serous retinal detachment. Fluorescein angiogram showed disc leakage and multiple pinpoint hyperfluorescence leakage spots and indocyanine green demonstrated multipl

Clinical Characteristics and Outcomes of Primary Vitreoretinal Lymphoma in Northern Thailand

OBJECTIVE This study aims to describe clinical characteristics and outcomes after treatment of primary vitreoretinal lymphoma (PVRL). METHODS Fifteen patients with a proven diagnosis of PVRL by histology, cytology and/or flow cytometry were analyzed. RESULTS The median age of the 15 patients was 59 years (range 41-71). Median follow-up time was 37 months (IQR 22.5-80) (range 4-106). Ophthalmic presentations of 25 eyes included vitritis (72%), chorioretinal infiltrations (60%), and retinal vasculitis (20%). Bilateral involvement was observed in 10 patients at presentation and in 4 patients during follow up. Ten patients (67%) developed brain involvement after ocular presentation with a median time of 22.5 months (range 2-84). Treatment modalities were included: 1) isolated intravitreal (IVT) methotrexate (6/15 patients; 40%) with a median number of injections of 4 (IQR 1,6) (range 1-16) 2) combined with IVT metho-trexate and/or rituximab and systemic chemotherapy and/or radiation (8/15; 53%) with a median of 6 injections (IQR 1,11) (range 1-16) and 3) systemic chemotherapy alone (1/15; 7%). Whole brain radiotherapy (WBRT) was performed in 10 of 15 patients (67%). Among the 6 patients who received isolated IVT methotrexate, 3 patients had complete remission (3/6; 50%), one died at 96 months after treatment, and one was lost to follow up after a single injection. Nine of 15 patients who received systemic chemotherapy with or without IVT chemotherapy and/or WBRT had complete remission (8/9; 89%). CONCLUSIONS Vitritis and chorioretinal infiltrations were the main ocular presentations of PVRL. Two-thirds of the patients developed brain involvement which resolved after treatment. Systemic chemotherapy tends to provide a higher rate of complete remission compared to local therapy alone