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With the rise in popularity of Internet of Things (IoT), mobile computing, and wearable devices, a huge amount of data is being generated. Running complex tasks such as that are machine learning-based with minimum energy consumption is a challenge. It requires complex algorithms to run locally such as on middleware fog within the proximity of the devices generating data, or globally in a cloud to analyze the acquired data and create robust and smart applications. Deep learning is a technique used to analyze the raw, unstructured data coming from the sensors and mobile devices. However, it depends on the type of task execution policy applied at each level; local or global, to decide on energy and performance efficiency, since deep learning algorithms are high in complexity. Hence, task execution will be hierarchically distributed among the IoT nodes, fog, and cloud. We present in this paper a three-tier IoT-fog-cloud model by augmenting CloudSim toolkit. We argue that with distributed task execution, we can achieve high scalability of IoT services, and manage the global energy consumption as well. As a proof-of-concept, we evaluate our three-tier architecture by considering computational tasks for various applications in IoT related to medical, multimedia, location-based, and text.

Advanced nurse practitioners (ANPs) manage minor trauma in emergency departments; however, the quality of their care has not been objectively evaluated in comparison to orthopedic senior house officers (SHOs)/junior residents. We reviewed the management of 100 patients by ANPs. SHOs were tested as comparators. ANPs were then re-evaluated after receiving an education program. No difference between ANPs and novice SHOs was observed initially. After an education program, ANPs substantially improved with no statistical difference from experienced SHOs. ANPs provide a comparable standard of care to SHOs for minor trauma once they have completed an education program.

A sound knowledge of the elbow anatomy and biomechanics is critical to understanding the pathology of various elbow disorders and instigating appropriate management. The elbow joint is a trochoginglymoid joint: that is, it has flexion-extension [ginglymoid] motion at the ulnohumeral and radiocapitellar articulations and pronation and supination [trochoid] motion at the proximal radioulnar joint. Stability of the elbow joint is achieved through static and dynamic components. The aim of this article is to concisely describe the anatomy and biomechanics of the elbow joint relevant to the practice of trauma and orthopaedic surgeons.

Suprascapular neuropathy is often overlooked as a cause for shoulder pain in overhead athletes and can be misdiagnosed for many months, even years. With recent advancements in the understanding of the condition as well as its treatment methods, suprascapular neuropathy is now being diagnosed more frequently. However with a multitude of treatment options, it can be difficult to choose the optimal management. In this systematic review, we have carried out a detailed literature search about suprascapular neuropathy looking into evidence-based diagnostic workup and treatment modalities available. Repetitive overhead activities, rotator cuff tear and direct compression of the nerve by space-occupying lesion are important etiologies for suprascapular neuropathy. Whilst MRI is widely used for the identification of space-occupying lesions and rotator cuff injury; Electromyography (EMG) and Nerve Conduction Studies (NCS) remain gold standards for confirming injury to the nerve. Conservative treatment modalities including physiotherapy and activity modification, nerve blocks, arthroscopic and open surgical interventions are the main treatment options for suprascapular neuropathy.

Osteoarthritis of the wrist and hand can cause significant functional impairment and disability. Its management is as much an art as it is science, as one has to consider increasing patient expectations, functional demands, pain relief and preserving motion. A sound knowledge of the wrist and hand anatomy, biomechanics, appreciation of the specific location and grade of wrist and hand joints arthritis and various treatment options and their pros and cons is important to successfully manage patients with this condition. Wrist osteoarthritis can be idiopathic, however more commonly it is secondary to distal radius and/or carpal fractures and ligamentous injuries. A small proportion still can occur due to avascular necrosis of carpus or congenital and developmental causes. Osteoarthritis of the hand is often idiopathic with a high familial tendency, especially in younger patients but can be secondary to other causes such as trauma, gout and ligamentous laxity. Once the non-surgical management options such as analgesia, activity modifications, steroid injections and splints have been exhausted, then there are various surgical options available for wrist and hand osteoarthritis. These are tailored to the exact location of arthritis and patient factors. For the wrist, these include anterior and posterior interosseous nerve neurectomy, arthroscopic debridement, selective bony excisions, wrist arthroplasty and focal or total fusion. Whilst for finger osteoarthritis, surgical options include arthroplasty and arthrodesis. This article aims to describe the pathology of wrist and hand arthritis and various treatment options available.

Stroke is the second overall driving reason for human death and disability. Strokes are categorized into Ischemic and Hemorrhagic strokes. Ischemic stroke is 85% of strokes while hemorrhagic is 15%. An exact automatic lesion segmentation of ischemic stroke remains a test to date. A few machine learning techniques are applied previously to beat manual human observers yet slacks to survive. In this paper, we propose a completely automatic lesion segmentation of ischemic stroke in view of the Convolutional Neural Network (CNN). The dataset used as a part of this study is obtained from ISLES 2015 challenge, included four MRI modalities DWI, T1, T1c, and FLAIR of 28 patients. The CNN model is trained on 25 patient’s data while tested on the remaining 3 patients. As CNN is only used for classification, we convert segmentation to the pixel-by-pixel classification tasks. Dice Coefficient (DC) is used as a performance evaluation metric for assessing the performance of the model. The experimental results show that the proposed model achieves a comparatively higher DC rate from 4–5% than the considered state-of-the-art machine learning techniques.

This paper proposed Fuzzy-Sliding Mode Controller (F-SMC) for regulation of DC-link voltage and Proportional Resonant (PR) with Resonant Harmonic Compensator (RHC) for output current control of two stages 3 kW single-phase grid-tied Photovoltaic (PV) system. The dynamics of the system are tested with and without a feed-forward PV power loop. A SOGI phase lock loop is implemented that has harmonic insusceptibility, a fast-tracking accuracy, and rapid-dynamic response. The proposed controller enhances the dynamic and steady state performance of the overall system. Furthermore, the simulation results of PI controller is also presented to show the effectiveness of the proposed control strategy. A graphical and tabulated comparative assessment with a well-tuned PI controller authenticates the effectiveness, fastness and robustness of proposed controller. MATLAB/Simulink R2017b software is used as a design and implementation platform.

Background:Management of distal humerus fractures remains a challenge for trauma surgeons and advancements in treatment options continue to be made to achieve the best results for patients presenting with these complex fractures. Our aim in this article is to provide the surgeons with a detailed review of current literature to help them make an evidence based decision when faced with managing such complex injuries in their surgical practice.Methods:This is a comprehensive review of the current literature that details various aspects of distal distal humerus fractures such as classification, surgical anatomy, surgical approaches, treatment options, choices of devices, outcomes and complications.Results:With the advancements in techniques and equipment, there has been improvement in patients’ outcomes following surgical management of these fractures and a large proportion of these patients are able to achieve pre-injury level of function. The contoured locking plates have enabled successful fixation of many of these fractures that were previously considered unfixable. For those not amenable to surgical fixation, total elbow arthroplasty and elbow hemiarthroplasty are considered as good alternatives.Conclusion:Since the days where the ‘bag of bones’ technique was the preferred method of treating these complex injuries, techniques and outcomes have advanced greatly. However, they still present a significant technical challenge and need meticulous technique and experience to achieve optimal results.

Background: Trivector approach to total knee arthroplasty (TKA) is a quadriceps tendon sparing approach, whereas the medial parapatellar (MPP) approach involves making a longitudinal incision in quadriceps tendon. We postulated that quadriceps-sparing approach such as trivector should make postoperative rehabilitation easier and ultimately reduce the length of hospital stay. Objectives: The aim of the study is to compare the early postoperative outcomes of the TKA performed through the trivector and the MPP approaches. Methodology: We prospectively reviewed the results of 56 consecutive patients operated on by two knee surgeons: one routinely performs trivector and the other an MPP approach. Both the groups consisted of 28 patients each. We looked at operative times, the time taken to perform straight leg raise (SLR), range of movements achieved before discharge, and length of the hospital stay. Results: The mean time taken for performing the surgery through the MPP approach was 56 min, whereas, for the trivector approach, it was 54 min (P = 0.31). The mean time taken to perform SLR in the MPP approach group was 3.5 days, whereas in the trivector group, the meantime taken for SLR was 2.5 days (P = 0.003). The average extension at the time of discharge in the MPP group was 3.8°, and in the trivector group, it was 0.86° (P = 0.007). The average knee flexion at the time of discharge in the MPP group was 84.2°, whereas in the trivector group, it was 86.5° (P = 0.199). The average hospital stay in the MPP group was 5.96 days, whereas in the trivector group, it was 4.84 days (P = 0.11). Conclusions: The trivector approach patients took significantly less time to do SLR and achieved a better range of extension before discharge from the hospital. The patients with trivector approach were discharged early by one day, although it is not statistically significant, it is clinically significant.

In recent decade significantly rise in chronic diseases and ageing population ratio in world, demand efficient Health monitoring system (HMS) for comfort of people. This approach is not only cost effective but also a feasible alternative to traditional healthcare. The aged peoples and patients having chronic diseases refrain visit to health institutions (hospitals and nursing homes etc.) as a result it reduces burden on health institutions. In this paper we proposed IoT-based, intelligent HMS which continuously monitors patient’s health parameters like blood pressure, heart beat and ECG. Data from blood pressure sensor, heart rate sensor and ECG sensor automatically monitored by Arduino UNO and Pi-camera attached to raspberry pi for video. Arduino UNO sends sensor data to raspberry pi which fed data to server’s database using Wi-Fi, finally server sent data to webpage, which updates every 2 minutes. Doctor access data anywhere using internet and give feedback accordingly using text.

Ethyl alcohol or ethanol is an effective chemical in denaturing enveloped viruses in vitro, including SARS-CoV-2. It is found that exposure of only 30 seconds is sufficient to deactivate the virus. Alcohol has never been used for the treatment of viral lung infection. However, alcohol vapors by inhalation are used for acute respiratory distress syndrome and alcohol withdrawals. This article aims to discuss the possibility of using the alcohol vapors by inhalation in the treatment of COVID-19 and other viral lung infection. So far, alcohol vapors by inhalation have never been used before to treat viral infection in the lungs. Therefore, there is no data, or clinical trials are available to report the finding, but theoretically, it seems a possibility.

Since the outbreak of COVID-19 infection in December 2019, millions of people are infected, and thousands of people have died. Genetic shift and high infectivity rate made SARS-Cov-2 a pandemic. Doctors, researchers, and world leaders are scratching their heads, how to contained or treat the virus. Several treatment options are tried, but so far, there is no effective treatment available. Thousands of articles are published about COVID-19, and so much information is available that it is challenging for a practicing physician to review these articles in the limited time they have. This article summarized the treatment options for COVID-19 that have tried or are in clinical trials. The article also reviews other possibilities that are either briefly or not discussed in the literature but could play a role in the fight against COVID-19.

Identification of salt bodies buried under the earth's surface remains important for hydrocarbon industry as they form traps to hold oil and gas reservoirs in the subsurface. Seismic data interpretation on a large volume of data is labour-intensive and time-consuming task if performed manually. Many efforts have been made in the past to automate or semi-automate the process of extracting the salt bodies from migrated images using many proposed seismic attributes. There remains need of domain experts for picking the right geological features for interpretation and these attributes may not completely interpret the noise-contaminated seismic data. In the recent past, machine-learning algorithms have also been used to identify salt bodies using seismic attributes and they out-perform the existing methods. However, there is still need to have directly assisted processes of seismic data interpretation without the compulsion of providing seismic features. Recent researches have used deep learning methods for seismic facies classification and fault detection, which resulted with better outcomes than other machine learning algorithms. In this document, we propose a novel deep learning method based on U-Net plus Se-ResNet to identify salt bodies in seismic images. This method does not require domain knowledge expert and it is expected to extract required useful features automatically from the seismic data. After 10-fold cross-validation, a promising intersection over union (IOU) score has been achieved.

Globally, under-five child mortality is a substantial health problem. In developing countries, reducing child mortality and improving child health are the key priorities in health sectors. Despite the significant reduction in deaths of under-five children globally, developing countries are still struggling to maintain an acceptable mortality rate. Globally, the death rate of under-five children is 41 per 1000 live births. However, the death rate of children in developing nations like Pakistan and Ethiopia per 1000 live births is 74 and 54, respectively. Such nations find it very challenging to decrease the mortality rate. Data analytics on healthcare data plays a pivotal role in identifying the trends and highlighting the key factors behind the children deaths. Similarly, predictive analytics with the help of Internet of Things based frameworks significantly advances the smart healthcare systems to forecast death trends for timely intervention. Moreover, it helps in capturing hidden associations between health-related variables and key death factors among children. In this study, a predictive analytics framework has been developed to predict the death rates with high accuracy and to find the significant determinants that cause high child mortality. Our framework uses an automated method of information gain to rank the information-rich mortality variables for accurate predictions. Ethiopian Demographic Health Survey and Pakistan Demographic Health Survey data sets have been used for the validation of our proposed framework. These real-world data sets have been tested using machine learning classifiers, such as Naïve Bayes, decision tree, rule induction, random forest, and multi-layer perceptron, for the prediction task. It has been revealed through our experimentation that Naïve Bayes classifier predicts the child mortality rate with the highest average accuracy of 96.4% and decision tree helps in identifying key classification rules covering the factors behind children deaths.

Websites are part of our everyday life and are used to exchange and convey information between user communities. Despite website proliferation, assessment of site quality remains a challenging area of research. Scientific literature identifies several aspects, or criteria, of quality, which are often aggregated to content, structure, design, navigation and multimedia but they cannot determine what the user is thinking about the content while browsing. Previous researches supported the notion that the internet users do not read the websites. Only thing that attract them is the visual contents. This project aims to develop an EEG (Electroencephalogram) based application to rate interest in presented visual content. The idea is to analyze the feeling difference of a human mind as compare to the subjects' verbal interest. Some popular websites are selected for this purpose having similarities in the main contents. Brain signals collected through EEG device are send to an application for processing. It compares results of all the websites observed by the user and produces a rating which is displayed on the user interface of the application as a feed back to the user. Knowledge from the results can be basically used when designing websites and marketing campaigns online.

Introduction: Patients with scapulothoracic syndrome present with pain in the scapulothoracic area aggravated by overhead and repetitive shoulder movements. The aim of our study was to assess the outcome of scapulothoracic arthroscopic treatment in patients with painful snapping scapula in our institution. Methods: Fourteen patients underwent scapulothoracic arthroscopic treatment for painful snapping scapula. Pre-operatively, all these patients had a trial of conservative treatment modalities for at least 6 months. Two portals along the medial border of scapula were used for arthroscopy and instrumentation. In three cases a superior portal was also used. The arm was placed in the “chicken wing” position so that the scapula lifted up from the chest wall. Outcome was assessed using pre and postoperative pain visual analogue score and Oxford Shoulder Score. Results: Of the fourteen patients included in our study, ten were female and four were male patients. Mean age at the time of surgery was 27.6 years. Mean follow up was 35.7 months. Pain visual analogue score improved significantly from a mean of 8.8 preoperatively to 2.5 postoperatively (P value 0.00002). There was also a significant improvement in Oxford Shoulder Score from a mean of 10.8 to 40.9 (P= 0.00001). Mean crepitus score significantly decreased from 2.6 to 0.21 (p < 0.00001). Crepitus completely resolved in eleven patients. In three there was residual palpable crepitus but they had good pain relief. Conclusion: Arthroscopic scapulothoracic treatment provides significant pain relief and functional improvement for painful snapping scapula symptoms not responding to non-surgical treatment modalities.

We present a case of multifocal plasmacytoma, one of the rarest hematological malignancies, with an unusual presentation of cervical myelopathy. A 60-year-old male presented with features of cervicalgia and radiculomyelopathy. There was a history of significant weight loss over 2 months with no sphincter involvement. Neurological examination showed spastic quadriparesis with extensor plantars. Radiological workup showed multiple lytic lesions from C3 to C6 with cord compression and myelomalacic changes. The patient underwent three-level corpectomy (C5–C7) with cord decompression. Reconstruction was done with an expandable titanium cage, and anterior cervical plating from C4 to T1 was done. The histopathology and immunohistochemistry markers enabled in clinching this rare diagnosis of multifocal plasmacytoma. It is essential to distinguish early between various entities among plasmacytoma as each one has a different clinical course and prognosis. Multiple solitary plasmacytoma presenting de novo with cord compression and myelopathy is an extremely rare presentation.

OBJECTIVE To determine the clinical outcome and mean length of hospital stay of paediatric patients with severe blunt traumatic head injury (THI) receiving 3% hypertonic saline (HTS) in the Emergency Department (ED). Methodology This case series study was conducted at the Department of Emergency Medicine, Aga Khan University Hospital, Karachi, from 2014 to 2015 via chart review of 105 patients. Detailed history and clinical examination of all paediatric patients aged 2-16 years was recorded which included moderate to severe head injury as classified by the Glasgow Coma Scale (GCS) by the Brain Trauma Foundation. As per routine care after admission of such a patient, for resuscitation 3% HTS was administered. GCS was recorded at 6 hours and at the time of discharge. RESULTS Of the 105 patients, 76 (72.4%) were male and 29 (27.6%) were female, and the mean age was 61.6+45.9 months. Traumatic brain injury (TBI) was found moderate in 60 (57.1%) cases and severe in 45 (42.9%) of our patients as per the GCS. Six hours after resuscitation with 3% hypertonic saline, 45 (43%) patients normalised as per GCS, 39 (37%) patients had moderate TBI and 21 (20%) had severe TBI. Forty five patients had a hospital stay of 2-3 days. The GCS improved after resuscitation with 3% hypertonic saline in emergency department, with a mean length of stay of 4.6+3.9 and 12.6+10.7 days in moderate and severe head injury respectively with a P value of

Old methods of training and medical education are coming at halt considering chances of errors, neglect and communication gaps amongst medical teams associated with the traditional methods, leading to avoidable patient mortality. Simulation based learning is slowly replacing the old methods given its provision of safe environment to medical professionals to polish their skills and knowledge without the risk of any loss of patient lives. This article discusses the experience of simulation based learning as it is tried to being introduced at one of the hospitals of Pakistan, a resource limited nation and the challenges that has to be faced in trying to incorporate simulation in the education system in near future

Depression is a one of the leading cause of disability. It is estimated that approximately 17% of the world’s population and 18% of the US population experience major depression in their lifetime. Women are almost twice as likely to experience major depression as men. The suicidal rate is four times higher among those with depression than those without depression. Use of herbal medicine is an important part of the evidence-based treatment of depression. Patients are more inclined to use herbal products than ever before and physicians are often asked about herbal medicine. Most of our curriculum in medical school does not or only briefly touch on this topic. Several clinical studies have shown that St. John’s wort and SAMe may be an option in the treatment of mild to moderate depression or where patient prefer herbal over conventional treatment.

BACKGROUND Motor vehicle crash (MVC) related injury has been identified as a major public health concern. Child restrain (CR) seat belts can minimize the mortality and morbidity from MVC. The use for seat belts is substantially low in developing countries like Pakistan even though its use has been shown to decrease morbidity and mortality by a significant extent. METHODS This was a case control study with cases from the general population (GP) and controls from the Aga Khan University (AKU) employees in a 3:1 ratio. The study questionnaire was based on parameters like gender, education level, awareness and presence of CR and also assessed the frequency of usage, reasons for not using CR and the source of knowledge regarding CR use. RESULTS Out of 848 respondents, 212 were from AKU and 636 were from the GP. 96.7% from AKU had at least a bachelor's degree while less than half (42.6%) of those from the GP were graduate or above (P