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Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this proposed work, we have tested a Convolution Neural Network (CNN) based on You Only Look Once (YOLO) variants on NVIDIA Jetson Xavier to identify compatibility between the GPU and the YOLO models. Furthermore, the performance of the YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv5s models is evaluated during the training using our PowerEdge Dell R740 Server. We have successfully demonstrated that YOLOV5s is a good benchmark for object detection, classification, and traffic congestion using the Jetson Xavier GPU board. The YOLOv5s achieved an average precision of 95.9% among all YOLO variants and the highest success rate achieved is 98.89.

Photoplethysmography (PPG) has emerged as an increasingly attractive signal for noninvasive physiological measurements, owing to its simplicity, cost-effectiveness, and broad applicability spanning cardiovascular to respiratory systems. The burgeoning interest in PPG signal processing has facilitated its extensive incorporation in wearable devices, thus stimulating active research in this field. The present study undertakes a comprehensive evaluation to discern the optimal index finger (right or left) for PPG data acquisition and subsequent filtration, appraised through the lens of the signal-to-noise ratio (SNR) of the filtered signal. An analysis conducted on signals contaminated with white Gaussian noise unveiled that the Savitzky-Golay filter (a polynomial filter) with a window size of three outperformed other window lengths, rendering the highest SNR. Among the Infinite Impulse Response (HR) filters compared; the Chebyshev I filter emerged as superior. Interestingly, the right index finger consistently demonstrated a higher mean SNR across filters: 0.49% for the Savitzky-Golay filters, 4.32% for the Butterworth (order 6), 7.71 % for the Chebyshev I (order 10), and 4.02% for the Chebyshev II (order 4), relative to the left index finger for PPG signals perturbed by white Gaussian noise. These findings provide an insightful perspective for future research and development in wearable devices, suggesting potential superiority of the right index finger for PPG signal acquisition and filtration.

Fire is one of the most prominent threat to safety of human and property, in both domestic and industrial setups. Efficiently combating a fire threat, usually, depends on how early the fire is detected. This paper reports work for development of a low cost wireless sensor-based system for surveillance and early fire detection, using machine vision technique. The system consists of an on-board camera node, capable of transmitting videos over wireless network to a remote host computer that runs an image processing based fire detection algorithm. The system is standalone and portable with the capability of transmitting videos to virtually anywhere in the world. Prototype of the system has been successfully tested, performing video streaming alongwith segmentation of fire regions using HSI features of the retrieved images. Future work will inlcude automatic fire detection and alarm generation alongwith the extension of the system on multiple and widely scattered transmission nodes.

Traumatic Brain Injury (TBI) remains one of the prevailing disorders that affect millions of people around the globe. There is a cascade of secondary attributes attached to TBI including excitotoxicity, axonal degeneration, neuroinflammation, oxidative stress, and apoptosis. Neuroinflammation is caused due to the activation of microglia along with pro-inflammatory cytokines. The activation of microglia triggers TNF-α which sequentially results in the triggering and upregulation of NF-kB. The aim of the current research was to investigate vitamin B1’s potential as neuroprotective agent against TBI-induced neuroinflammation arbitrated memory impairment together with pre- and post-synaptic dysfunction in an adult albino male mice model. TBI was induced using the weight-drop method which caused the microglial activation resulting in neuroinflammation along with synaptic dysfunction leading to the memory impairment of the adult mice. Vitamin B1 was administered for seven days via the intraperitoneal pathway. To analyze the memory impairment and efficacy of vitamin B1, Morris water maze and Y-maze tests were performed. The escape latency time and short-term memories of the experimental mice treated with vitamin B1 were significantly different from the reference mice. The western blot results showed that vitamin B1 has reduced neuroinflammation by downregulating proinflammatory cytokines (NFκ-B, TNF- α). Vitamin B1 also proved its worthiness as a convincing neuroprotective agent by reducing memory dysfunction and recovering the activities of pre- and post-synapse via upregulation of synaptophysin and Postsynaptic density protein 95 (PSD-95).

Fragile-X syndrome (FXS) is the most common form of inherited intellectual disability (ID) and affects 0.7-3.0% of intellectually compromised population of unknown etiology worldwide. It is mostly caused by repeat expansion mutations in the FMR1 at chromosome Xq27.3. The present study aimed to develop molecular diagnostic tools for a better detection of FXS, to assess implementation of diagnostic protocols in a developing country and to estimate the prevalence of FXS in a cohort of intellectually disabled subjects from Pakistan. From a large pool of individuals with below normal IQ range, 395 subjects with intellectual disability of unknown etiology belonging to different regions of the country were recruited. Conventional-PCR, modified-PCR and Southern blot analysis methods were employed for the detection of CGG repeat polymorphisms in the FMR1 gene. Initial screening with conventional-PCR identified 13 suspected patients. Subsequent investigations through modified PCR and Southern blot analyses confirmed the presence of the FMR1 mutation, suggesting a prevalence of 3.5% and 2.8% (mean 3.3%) among the male and female ID patients, respectively. These diagnostic methods were further customized with the in-house conditions to offer robust screening of referral patients/families for diagnostics and genetic counseling. Prescreening and early diagnosis are crucial for designing a prudent strategy for the management of subjects with ID. Outcome of the study recommends health practitioners for implementation of molecular based FXS diagnosis in routine clinical practice to give a better care for patients similar to the ones included in the study.