Firearm Injuries Presenting to a Tertiary Care Hospital of Karachi, Pakistan

Background: Violence is a public health problem in low and middle income countries. Our study attempted to define the circumstances, risk groups, extent and severity of firearm-related injuries in Patients coming to the Aga Khan University Hospital (AKUH) Karachi, Pakistan. Methods: This was a retrospective study conducted in the department of Emergency Medicine (EM) at AKUH Karachi, Pakistan. Past medical records of all Patients who were injured by firearms and were presented to the AKUH Emergency Department (ED) from June 2002 till May 2007 were reviewed. Data were recorded on the basic demographics of injured, length of hospital stay, body parts injured and the outcome (alive vs. dead). Results: Total of 286 Patients with firearm injuries were identified. Majority of them were males (92%, n=264). More than half of the Patients (63%) were in the age group of 21-40 years. Upon arrival to the hospital 85% (n=243) of Patients had Glasgow Coma Scale (GCS)\u3e= 13. The mean injury severity score (ISS) was found to be 6 (SD 4). The length of hospital stay of Patients ranged from 0 to 54 days with a mean of 7 days. Lower limb were the most affected body parts (30%, n=86) followed by abdomen pelvis (27%, n=77). Seven percent (n=21) of the Patient who were brought to the hospital were labeled as deceased on arrival . Most of the injuries were caused during the act of robbery (40%, n=103) in the city. Conclusions: Robbery was the most common cause of firearm injuries. Lower limb, abdomen and pelvis were the most affected body regions. Educational efforts, and individual, community and societal approaches are needed to alleviate firearm-related injuries

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. -e proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.Comment: 13 pages, 13 figure

A prototype of an energy-efficient MAGLEV train : a step towards cleaner train transport

The magnetic levitation (MAGLEV) train uses magnetic field to suspend, guide, and propel vehicle onto the track. The MAGLEV train provides a sustainable and cleaner solution for train transportation by significantly reducing the energy usage and greenhouse gas emissions as compared to traditional train transportation systems. In this paper, we propose an advanced control mechanism using an Arduino microcontroller that selectively energizes the electromagnets in a MAGLEV train system to provide dynamic stability and energy efficiency. We also design the prototype of an energy-efficient MAGLEV train that leverages our proposed control mechanism. In our MAGLEV train prototype, the levitation is achieved by creating a repulsive magnetic field between the train and the track using magnets mounted on the top-side of the track and bottom-side of the vehicle. The propulsion is performed by creating a repulsive magnetic field between the permanent magnets attached on the sides of the vehicle and electromagnets mounted at the center of the track using electrodynamic suspension (EDS). The electromagnets are energized via a control mechanism that is applied through an Arduino microcontroller. The Arduino microcontroller is programmed in such a way to propel and guide the vehicle onto the track by appropriate switching of the electromagnets. We use an infrared-based remote-control device for controlling the power, speed, and direction of the vehicle in both the forward and the backward direction. The proposed MAGLEV train control mechanism is novel, and according to the best of our knowledge is the first study of its kind that uses an Arduino-based microcontroller system for control mechanism. Experimental results illustrate that the designed prototype consumes only 144 W-hour (Wh) of energy as compared to a conventionally designed MAGLEV train prototype that consumes 1200 Wh. Results reveal that our proposed control mechanism and prototype model can reduce the total power consumption by 8.3 x as compared to the traditional MAGLEV train prototype, and can be applied to practical MAGLEV trains with necessary modifications. Thus, our proposed prototype and control mechanism serves as a first step towards cleaner engineering of train transportation systems

Social mining for sustainable cities: thematic study of gender-based violence coverage in news articles and domestic violence in relation to COVID-19

We argue that social computing and its diverse applications can contribute to the attainment of sustainable development goals (SDGs)—specifically to the SDGs concerning gender equality and empowerment of all women and girls, and to make cities and human settlements inclusive. To achieve the above goals for the sustainable growth of societies, it is crucial to study gender-based violence (GBV) in a smart city context, which is a common component of violence across socio-economic groups globally. This paper analyzes the nature of news articles reported in English newspapers of Pakistan, India, and the UK—accumulating 12,693 gender-based violence-related news articles. For the qualitative textual analysis, we employ Latent Dirichlet allocation for topic modeling and propose a Doc2Vec based word-embeddings model to classify gender-based violence-related content, called GBV2Vec. Further, by leveraging GBV2Vec, we also build an online tool that analyzes the sensitivity of Gender-based violence-related content from the textual data. We run a case study on GBV concerning COVID-19 by feeding the data collected through Google News API. Finally, we show different news reporting trends and the nature of the gender-based violence committed during the testing times of COVID-19. The approach and the toolkit that this paper proposes will be of great value to decision-makers and human rights activists, given the prompt and coordinated performance against gender-based violence in smart city context—and can contribute to the achievement of SDGs for sustainable growth of human societies

A multicenter comparison between Child Pugh and ALBI scores in patients treated with sorafenib for hepatocellular carcinoma

Background & aims: The ALBI grade was proposed as an objective means to evaluate liver function in patients with Hepatocellular Carcinoma (HCC). ALBI grade 1 vs 2 were proposed as stratification factors within the Child Pugh (CP) A class. However, the original publication did not provide comparison with the sub-classification by points (5 to 15) within the CP classification. Methods: We retrospectively analyzed data from patients treated with sorafenib for HCC from 17 centers in United Kingdom and France. Overall survival (OS) was analyzed with the Kaplan-Meier method and a Cox regression model. Discriminatory abilities of the classifications were assessed with the log likelihood ratio, Harrell’s C statistics and Akaike information criterion. Results: Data from 1,019 patients were collected, of which 905 could be assessed for both scores. 92% of ALBI grade 1 were CP A5 while ALBI 2 included a broad range of CP scores of which 44% were CP A6. Median OS was 10.2, 7.0 and 3.6 months for CP scores A5, A6 and >A6, respectively (P<0.001), Hazard Ratio (HR)=1.60 (95%CI: 1.35-1.89, P<0.001) for A6 vs A5. Median OS was 10.9, 6.6 and 3.0 months for ALBI grade 1, 2 and 3, respectively (P<0.001), HR=1.68 (1.43-1.97, P<0.001) for grade 2 vs 1. Discriminatory abilities of CP and ALBI were similar in the CP A population, but better for CP in the overall population. Conclusions: Our findings support the use CP class A as an inclusion criterion, and ALBI as a stratification factor in trials of systemic therapy

The C-Type Lectin Receptor CLECSF8/CLEC4D Is a Key Component of Anti-Mycobacterial Immunity

Open Access funded by Wellcome Trust: Under a Creative Commons license Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved. Acknowledgments We would like to thank S. Hardison, P. Redelinghuys, J. Taylor, C. Wallace, A. Richmond, S. Hadebe, A. Plato, F. Abbass, L. Fick, N. Allie, R. Wilkinson, K. Wilkinson, S. Cooper, D. Lang, and V. Kumar for reagents and assistance, and the animal facility staff for the care of our animals. This work was supported by the MRC (UK) and Wellcome Trust (G.D.B.); MRC (South Africa) and Sydney Brenner Fellowship (M.J.M.); Vici (M.G.N.), Vidi (R.v.C.), and Veni grants (T.S.P.) from the Netherlands Organization for Scientific Research; the Royal Netherlands Academy of Arts and Sciences (T.H.M.O.); EC FP7 projects (NEWTBVAC, ADITEC; T.H.M.O.); Carnegie Corporation and CIDRI (J.C.H.); and the University of Aberdeen (B.K.).Peer reviewedPublisher PD

A New Ensemble-Based Intrusion Detection System for Internet of Things

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification

From MDPI via Jisc Publications RouterHistory: accepted 2021-11-04, pub-electronic 2021-11-08Publication status: PublishedFunder: Medical Research Council; Grant(s): MR/M008959Funder: CRUK Manchester Centre award; Grant(s): C5759/A25254Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort’s pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651