COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

there are increasing concerns about the danger that water-borne pathogens and pollutants pose to the public. of particular importance are those that disrupt the plasma membrane, since loss of membrane integrity can lead to cell death. currently, quantitative assays to detect membrane-disrupting (lytic) agents are done offsite, leading to long turnaround times and high costs, while existing colorimetric point-of-need solutions often sacrifice sensitivity. Thus, portable and highly sensitive solutions are needed to detect lytic agents for health and environmental monitoring. Here, a lipid-based electrochemical sensing platform is introduced to rapidly detect membrane-disrupting agents. the platform combines benchtop fabricated microstructured electrodes (MSes) with lipid membranes. the sensing mechanism of the lipid-based platform relies on stacked lipid membranes serving as passivating layers that when disrupted generate electrochemical signals proportional to the membrane damage. the MSe topography, membrane casting and annealing conditions were optimized to yield the most reproducible and sensitive devices. We used the sensors to detect membrane-disrupting agents sodium dodecyl sulfate and Polymyxin-B within minutes and with limits of detection in the ppm regime. This study introduces a platform with potential for the integration of complex membranes on MSEs towards the goal of developing Membrane-on-chip sensing devices.Bacterial pathogens, pesticides, and parasitic vectors are common water-borne risks that are currently detected and quantified using methods such as ELISA, chromatography, and mass spectrometry. These methods are reliable and offer high precision and accuracy, but are expensive, require highly trained technicians, and are time consuming 1-3 , which precludes their use in resource-limited environments 3-6 . Thus, there is an increasing demand for diagnostic tools that do not compromise affordability, sensitivity, and portability for applications in point-of-care (PoC) diagnostics 3,7 , personalized medicine 8,9 , food quality assessment 10,11 , and water testing 12 . Biosensors are attractive routes to address these needs because they leverage biorecognition elements to offer rapid and low-cost solutions for the detection of potentially harmful agents and can be adapted to portable platforms [13][14][15][16] . Particularly relevant to food safety, environmental testing and biosecurity areas is the development of biosensors that can detect the disruption of the cell plasma membrane -a hallmark of the presence of pathogenic microorganisms or toxins that can pose serious threats to human health.The cell plasma membrane is a complex structure that separates the internal cellular components from external environments. Apart from protecting the cell from its surroundings, the plasma membrane mediates ion and small molecule transport, adhesion, motility, and the uptake of larger foreign bodies through endocytosis. The membrane is primarily composed of a phospholipid bilayer, within which sterols, carboh...

Osteochondroma, often referred to as exostosis, is the most common benign bone tumor characterized by a bony protuberance surrounded by a cartilaginous surface. Most osteochondromas are found on the metaphysis of long bones, with the dorsal aspect of the scapula being a rare site of occurrence for an exostosis. Radiographic imaging, preferably through MRI or CT, assists in the identification of benign growth; however, a definitive diagnosis requires a biopsy. Open surgical resection and arthroscopic excision are the definitive treatment modalities of the nidus. Postoperative care requires immobilization of the limb for two months, with at least four months being the appropriate timeline for complete recovery.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is best known for causing febrile pneumonia with lung parenchymal involvement. However, that is often not the only disease presentation, as many studies have shown that coronavirus disease 2019 (COVID-19) can present with other complications involving the cardiovascular and neurologic systems. Here, we report a case of COVID-19 pneumonia presenting with a peculiar finding of unilateral diaphragmatic paralysis. The patient presented with dyspnea requiring oxygen support via a nasal cannula. He was managed with the hospital's COVID-19 treatment protocols and clinically improved within 14 days of admission. This case helps shine some light on the neuroinvasive potential of SARS-CoV-2.

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was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, and predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspectives. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

A neurofibroma is a benign, non-encapsulated neoplasm of the peripheral nerve sheath. These tumors are a notorious manifestation of the autosomal dominant condition known as neurofibromatosis type 1, where they present as multiple, cutaneous masses with high malignant potential. On the contrary, benign solitary retroperitoneal neurofibromas (SRN) occur without any associated conditions and have rarely been documented. Our case is of a 40-year-old male who presented with a three-month history of painful calf swelling, refractory to over-the-counter painkillers which was later diagnosed as deep vein thrombosis (DVT). A computed tomography (CT) angiogram was done which revealed a mass in the retroperitoneum impinging on the inferior vena cava (IVC). Approximately one month later, the whole mass was surgically excised and histopathology confirmed the diagnosis of a neurofibroma. This case presentation proved to be novel as it highlights the evaluation and management of a rare SRN which resulted in extensive DVT.

Federated learning (FL) is known to perform machine learning tasks in a distributed manner. Over the years, this has become an emerging technology, especially with various data protection and privacy policies being imposed. FL allows for performing machine learning tasks while adhering to these challenges. As with the emergence of any new technology, there will be challenges and benefits. A challenge that exists in FL is the communication costs: as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates, this can create a communication bottleneck. This paper presents the state-of-the-art of the conducted works on communication constraints of FL while maintaining the secure and smart properties that federated learning is known for. Overall, current challenges and possible methods for enhancing the FL models’ efficiency with a perspective on communication are discussed. This paper aims to bridge the gap in all conducted review papers by solely focusing on communication aspects in FL environments.