Hand gesture recognition via deep data optimization and 3D reconstruction.

Hand gesture recognition (HGR) are the most significant tasks for communicating with the real-world environment. Recently, gesture recognition has been extensively utilized in diverse domains, including but not limited to virtual reality, augmented reality, health diagnosis, and robot interaction. On the other hand, accurate techniques typically utilize various modalities generated from RGB input sequences, such as optical flow which acquires the motion data in the images and videos. However, this approach impacts real-time performance due to its demand of substantial computational resources. This study aims to introduce a robust and effective approach to hand gesture recognition. We utilize two publicly available benchmark datasets. Initially, we performed preprocessing steps, including denoising, foreground extraction, and hand detection via associated component techniques. Next, hand segmentation is done to detect landmarks. Further, we utilized three multi-fused features, including geometric features, 3D point modeling and reconstruction, and angular point features. Finally, grey wolf optimization served useful features of artificial neural networks for hand gesture recognition. The experimental results have shown that the proposed HGR achieved significant recognition of 89.92% and 89.76% over IPN hand and Jester datasets, respectively.

Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays.

Our research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised sophisticated visual analysis techniques; such as deep learning (DL) algorithms, object recognition, and categorisation models. To create our model, we used a large training dataset of chest X-rays, which provided valuable information for visualising and categorising abnormalities. We also utilised various data augmentation methods; such as scaling, rotation, and imitation; to increase the diversity of images used for training. We adopted the widely used You Only Look Once (YOLO) v8 algorithm, an object recognition paradigm that has demonstrated positive outcomes in computer vision applications, and modified it to classify X-ray images into distinct categories; such as respiratory infections, tuberculosis (TB), and lung nodules. It was particularly effective in identifying unique and crucial outcomes that may, otherwise, be difficult to detect using traditional diagnostic methods. Our findings demonstrate that healthcare practitioners can reliably use machine learning (ML) algorithms to diagnose various lung disorders with greater accuracy and efficiency.

Noscapine hydrochloride (benzyl-isoquinoline alkaloid) effectively prevents protein denaturation through reduction of IL-6, NF-kB, COX-2, Prostaglandin-E2 in rheumatic rats.

Noscapine hydrochloride (benzyl-isoquinoline antitussive alkaloid) is an opium derivative and generally used as a cough suppressant. Numerous studies on noscapine hydrochloride have reported that it has potent anti-inflammatory activity. However, the mechanisms by which it exerts an anti-inflammatory function is not well understood. Protein denaturation is the primary step that leads to the organ destruction and permanent arthritic disability. The above-mentioned facts provided the ground to plan this study using different in-vitro and in-vivo approaches. RT-qPCR and ELISA assays were used to assess the inflammatory markers related to protein denaturation in complete adjuvant persuaded rheumatism in Sprague - Dawley rats. The results were collected as paw volume and body weight changes, arthritic scoring and serum antioxidant enzymes assays. These findings demonstrated that all doses of noscapine hydrochloride (10, 20 and 40 mg/kg) studied in this study, significantly (p < 0.001) decreased the protein denaturation by preventing the increase in levels of interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), nuclear factor-kB (NF-kB), cyclooxygenase-2 (COX-2) and prostaglandin E2. Noscapine hydrochloride significantly reduced the paw volume (p < 0.001), arthritic scoring and reversed the body mass as compared to arthritic control diseased rats.

Toxicity studies and anti-arthritic effect of mandelic acid (2-hydroxy -2-phenyl acetic acid) using in vitro and in vivo techniques.

The major concern to search for new anti-arthritic drugs is primarily to prevent systemic complications and to maintain quality of life. As these drugs are prescribed for long duration so the objective is to ensure their safety in terms of toxicity. By keeping in view this concept, the present study was investigated to determine new anti-arthritic potential using in-vitro and in-vivo methods. The in-vitro tests comprised of protein denaturation (BSA and egg albumin) and Human Red Blood cell (HRBC) membrane stabilization assays at 50-6400µg/mL, for in-vivo testing, formaldehyde-induced arthritic rats were treated with 40, 80 and 160mg/kg mandelic acid. Mandelic acid (MA) inhibited the protein denaturation and stabilized the membrane of HRBC in a concentration dependent manner. Likewise, mandelic acid exhibited dose dependent reduction in paw volume induced by formaldehyde. For acute and sub-acute treatment, MA did not show any sign of toxicity and mortality in each rat and LD50 might be greater than 2000mg/kg. In addition, histopathological assessment presented slight increased interstitial spaces in the kidney, disorganization of glomerulus, dilated sinusoids at highest dose 800mg/kg which were not observed in sub-chronic therapy. Hence, these results conclude that mandelic acid has the potential to treat rheumatoid arthritis with observed no significant signs of toxicity and should be tested further to determine anti-arthritic mechanism of drug action at cellular level.

Water pipe (shisha) smoking among male students of medical colleges in the eastern region of Saudi Arabia.

BACKGROUND AND OBJECTIVES: Shisha smoking, one of the commonest methods of smoking tobacco among Arabs, increases the risk of cardiovascular and respiratory diseases. The objective of this study was to determine the prevalence of shisha smoking among male students of three colleges and to identify factors associated with shisha smoking. METHODS: This cross-sectional study included 500 male students of three colleges (medicine, applied medical sciences and dentistry). Data were collected from 371 students using a self-administered questionnaire. RESULTS: The overall prevalence of shisha smoking was 12.6% (8.6% shisha only and 4.0% both shisha and cigarettes). Thirty students (63.8%) started shisha smoking at ages of 16 to 18 years. Seven students (15%) smoked shisha daily. Cafiotas or restaurants were the favorite places for smoking (70.2% of students). There was a high prevalence of shisha smoking among students whose mothers had a secondary (19.1%) and higher (53.3%) education. CONCLUSIONS: There was a high prevalence of shisha smoking among university students. The majority of students started shisha smoking at a young age. Public health measures, including the banning of smoking in public places are recommended.