Publications by authors named "J Kulpa"
A growing number of hemp-derived cannabidiol (CBD) products are available with negligible amounts (< 100 ppm) of delta-9-tetrahydrocannabinol (THC) due in part to consumer concerns regarding the risk of positive drug screens. There are, however, no published studies that report whether repeated use of these products may lead to positive urine drug tests for THC. There is also scant research on the effects of these products on physical and mental well-being.
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- Cannabidiol (CBD), a compound gaining popularity for its potential health benefits, is usually consumed through ingestion and inhalation, but the effects of inhaling CBD are less understood compared to oral consumption.
- In a study involving Sprague Dawley rats, researchers evaluated the effects of CBD inhalation at significantly higher doses than typically consumed by humans, finding that high doses led to inflammation and necrosis in the nasal area, while lower doses showed no adverse effects in other organs.
- The study suggests that when using over-the-counter vape products (which contain much lower doses of CBD), there is a substantial safety margin, indicating that typical use is much safer than the higher doses tested.
Article Synopsis
- Cardiac autonomic neuropathy (CAN) is a serious complication in diabetes that can lead to cardiovascular events, but it's not commonly diagnosed due to time constraints.
- A study explored using AI and deep learning to analyze retinal images from diabetic patients for diagnosing CAN, successfully applying techniques like ResNet 18 and Multiple Instance Learning.
- The results showed high accuracy, with the AI model identifying 93% of CAN cases and 89% of non-CAN cases, particularly excelling in distinguishing severe CAN stages, indicating a promising diagnostic tool for clinical practice.
Rheumatoid arthritis (RA) is one of the most common autoimmune diseases. Inflammation of the synovial fluid propagates the pathological process of angiogenesis. Semaphorins play a crucial role in the context of endothelial cell function, and their pleiotropic nature has various effects on the further development of RA.
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- The study aimed to develop machine learning algorithms to analyze electrocardiograms (ECGs) for diagnosing cardiac autonomic neuropathy (CAN) in diabetic patients.
- Researchers utilized motif and discord extraction techniques along with long short-term memory networks to evaluate the effectiveness of these methods using various performance metrics via 10-fold cross-validation.
- The findings showed high accuracy in detecting severe CAN (dsCAN) with an accuracy of 0.92, while detecting any stage of CAN had a lower accuracy of 0.65, suggesting the potential of machine learning in improving CAN diagnosis and screening.