CMOS Impedance Measurement Array for Cell Sensing

Impedance measurement plays a vital role in determining the physical and chemical properties of live cells under different environmental conditions and aids in the development of cellular models for life science research and new medicines to fight disease. In order to improve the fidelity and spatial resolution of bio-impedance measurement systems, cell sensing platforms are being constructed using silicon chips where live cells interact with integrated microelectronic sensors through an on-chip electrode array. Our proposed complementary metal-oxide-semiconductor (CMOS) sensor array measures the impedance of complex cellular samples using a mixed-signal-based frequency response analysis (FRA) approach to extract and convert the real and imaginary parts of the cell impedance. The system is implemented using a synchronous voltage-to-frequency converter designed to operate over an input frequency range from 0.7 Hz to 2 kHz with a programmable nominal resolution up to 16 bits. Unlike previous work, we apply a switched-capacitor-based offset correction scheme to reduce the effect of multiplying integrator input offset on the sensor interface. The chip features an 8×6 surface electrode array of individually-addressable working electrodes connected to four independent impedance extraction channels for parallel data readout. The device is fabricated in a standard 0.18 µm CMOS technology, where each sensor channel consumes only 94 µW from a 1.8 V supply, and has been experimentally verified to provide linear conversion over an input current amplitude range from 40 pA to 60 nA.1 yea

Efficacy and safety of recombinant thrombomodulin for the prophylaxis of veno-occlusive complication in allogeneiccit hematopoietic stem cell transplantation: A systematic review and meta-analysis

Background: Hepatic veno-occlusive disease (VOD), also termed as sinusoidal obstruction syndrome (SOS), is a lethal complication after hematopoietic stem cell transplantation (HSCT). Various factors put patients undergoing allogeneic HSCT at an increased risk for VOD. Thrombomodulin (TM) is an important factor which has a wide range of effects, including anticoagulant, anti-inflammatory, angiogenic, and protective effect, on endothelial cells. It plays a role in preventing excessive coagulation and thrombosis by binding with thrombin and inhibiting the coagulation cascade. There are a limited number of options for the prevention of this fatal complication. Recombinant thrombomodulin (rTM), an endothelial anticoagulant co-factor, as prophylactic therapy might be able to prevent veno-occlusive complications after stem cell transplantation. Methods: A literature search was performed on PubMed, Embase, and Web of Science. We used the following Mesh terms and Emtree terms, Hepatic Veno-Occlusive Diseases OR Sinusoidal Obstruction OR Stem Cell Transplantations AND Thrombomodulin from the inception of data up to April 1, 2021. The PICO (Patient/Population, Intervention, Comparison and Outcomes) framework was used for the literature search. Results: For the VOD incidence after HSCTstem cell transplantation, the result was in favor of rTM with a risk ratio (RR) of 0.53 (I2 = 0%, 95% confidence interval [CI] = 0.32-0.89). The incidence of transplant-associated thrombotic microangiopathy (TA-TMA) after HSCT was reduced in rTM group. The RR for incidence of TA-TMA was 0.48 (I2 = 62%, 95% CI = 0.20-1.17) favoring rTM. The RR for incidence of graft-versus-host disease (GvHD) was also lower in rTM group, 0.48 (I2 = 64%, 95% CI = 0.32-0.72). Conclusion: In our meta-analysis, we evaluate the efficacy and safety of rTM in the prevention of SOS after HSCT. According to our results, rTM use led to a significant reduction in SOS episodes, TA-TMA, and GvHD after HSCT

Empathy quotient scores of 1st and 5th year medical students at a medical university in Pakistan.

<p>SPSS data of first year and final year medical students at a medical university in Karachi, Pakistan, with their age, gender, Baron-Cohen and Wheelwright empathy quotient (EQ) score and EQ class (class 1, EQ< 33; class 2, 33-52; class 3, 53-63; class 4, 64-80).</p

Face Detection & Recognition from Images & Videos Based on CNN & Raspberry Pi

The amount of multimedia content is growing exponentially and a major portion of multimedia content uses images and video. Researchers in the computer vision community are exploring the possible directions to enhance the system accuracy and reliability, and these are the main requirements for robot vision-based systems. Due to the change of facial expressions and the wearing of masks or sunglasses, many face recognition systems fail or the accuracy in recognizing the face decreases in these scenarios. In this work, we contribute a real time surveillance framework using Raspberry Pi and CNN (Convolutional Neural Network) for facial recognition. We have provided a labeled dataset to the system. First, the system is trained upon the labeled dataset to extract different features of the face and landmark face detection and then it compares the query image with the dataset on the basis of features and landmark face detection. Finally, it compares faces and votes between them and gives a result that is based on voting. The classification accuracy of the system based on the CNN model is compared with a mid-level feature extractor that is Histogram of Oriented Gradient (HOG) and the state-of-the-art face detection and recognition methods. Moreover, the accuracy in recognizing the faces in the cases of wearing a mask or sunglasses or in live videos is also evaluated. The highest accuracy achieved for the VMU, face recognition, and 14 celebrity datasets is 98%, 98.24%, 89.39%, and 95.71%, respectively. Experimental results on standard image benchmarks demonstrate the effectiveness of the proposed research in accurate face recognition compared to the state-of-the-art face detection and recognition methods

Face Detection &amp; Recognition from Images &amp; Videos Based on CNN &amp; Raspberry Pi

The amount of multimedia content is growing exponentially and a major portion of multimedia content uses images and video. Researchers in the computer vision community are exploring the possible directions to enhance the system accuracy and reliability, and these are the main requirements for robot vision-based systems. Due to the change of facial expressions and the wearing of masks or sunglasses, many face recognition systems fail or the accuracy in recognizing the face decreases in these scenarios. In this work, we contribute a real time surveillance framework using Raspberry Pi and CNN (Convolutional Neural Network) for facial recognition. We have provided a labeled dataset to the system. First, the system is trained upon the labeled dataset to extract different features of the face and landmark face detection and then it compares the query image with the dataset on the basis of features and landmark face detection. Finally, it compares faces and votes between them and gives a result that is based on voting. The classification accuracy of the system based on the CNN model is compared with a mid-level feature extractor that is Histogram of Oriented Gradient (HOG) and the state-of-the-art face detection and recognition methods. Moreover, the accuracy in recognizing the faces in the cases of wearing a mask or sunglasses or in live videos is also evaluated. The highest accuracy achieved for the VMU, face recognition, and 14 celebrity datasets is 98%, 98.24%, 89.39%, and 95.71%, respectively. Experimental results on standard image benchmarks demonstrate the effectiveness of the proposed research in accurate face recognition compared to the state-of-the-art face detection and recognition methods

Association of metabolic syndrome with stroke, myocardial infarction, and other postoperative complications following carotid endarterectomy: A multicenter, retrospective cohort study

Background: Metabolic syndrome (MetS) is a constellation of hypertension, insulin resistance, obesity, and dyslipidemia and is known to increase the risk of postoperative morbidity. This study aimed to assess the impact of MetS on stroke, myocardial infarction, mortality, and other complications following carotid endarterectomy (CEA).Methods: We analyzed data from the National Surgical Quality Improvement Program. Patients undergoing elective CEA between 2011 and 2020 were included. Patients with American Society of Anesthesiologists status 5, preoperative length of stay (LOS) \u3e 1 day, ventilator dependence, admission from nonhome location, and ipsilateral internal carotid artery stenosis of \u3c 50% or 100% were excluded. A composite cardiovascular outcome for postoperative stroke, myocardial infarction, and mortality was generated. Multivariable binary logistic regression analyses were used to assess the association of MetS with the composite outcome and other perioperative complications.Results: We included 25,226 patients (3,613, 14.3% with MetS). MetS was associated with postoperative stroke, unplanned readmission, and prolonged LOS on bivariate analysis. On multivariable analysis, MetS was significantly associated with the composite cardiovascular outcome (1.320 [1.061-1.642]), stroke (1.387 [1.039-1.852]), unplanned readmission (1.399 [1.210-1.619]), and prolonged LOS (1.378 [1.024-1.853]). Other clinico-demographic factors associated with the cardiovascular outcome included Black race, smoking status, anemia, leukocytosis, physiologic risk factors, symptomatic disease, preoperative beta-blocker use, and operative time ≥ 150 min.Conclusions: MetS is associated with cardiovascular complications, stroke, prolonged LOS, and unplanned readmissions following CEA. Surgeons should provide optimized care to this high-risk population and strive to reduce operative durations

Economic and social costs of violence against women in Pakistan: Technical report

Violence against women and girls (VAWG) is widely recognised as a violation of human rights and a challenge to public health. VAWG also has economic and social costs that have not been adequately recognised. These costs not only impact individual women and their families but ripple through society and the economy at large. The threat VAWG poses to the social fabric of the country and its impacts on economic development have not been adequately investigated, analysed or quantified in Pakistan. The Department of International Development (UK) funded a five year (2014-2019) research project to examine the costs of VAWG in South Sudan, Ghana and Pakistan. The research in Pakistan was led by researchers at the National University of Ireland Galway in collaboration with Ipsos Mori (UK/ Pakistan), the International Centre for Research on Women (Washington D.C.), and the Social Policy and Development Centre (Pakistan). A National Advisory Committee composed of stakeholders and policy makers within Pakistan also inputted into the project. The research explores the tangible and intangible costs of violence to individuals, families, communities and businesses in Pakistan. It further estimates costs of VAWG at the national level. Although such estimates cannot account for the totality of costs of violence, many of which occur over generations or which have ripple effects that the methods used here cannot capture, the study demonstrates significant impacts from VAWG in Pakistan, and makes the economic case for investment by government and donors in the prevention of VAWG. Methodology To ascertain the costs of VAWG in Pakistan, this study used a mixed method approach including both quantitative surveys of individual women, households and businesses, and qualitative inquiry methods including key informant interviews, participatory focus groups and individual in-depth interviews. An overall sample of 2998 women was drawn from across the main provinces of Punjab, Sind, Balochistan, Khyber Pakhtunkhwa and Islamabad Capital Territory. 532 employees and 25 managers across 100 businesses in Karachi, Lahore and Faisalabad took part in business surveys. In addition, over 100 individuals took part in qualitative interviews and Focus Group Discussions in the agricultural district of Sargodha and the city of Islamabad. A range of analysis methods were used including thematic content analysis, econometrics, and statistical analysis to generate findings and produce estimates of the costs of VAWG. Assumptions and Limitations An important assumption in the study is that any type of violence (economic, psychological, physical or sexual) has negative impacts for women experiencing such behaviours. The analysis thus explores the economic impacts of any behaviour of violence across the different locations that women experience violence. The study also has several limitations that need to be acknowledged. First, there is a strong possibility of significant underreporting by women respondents about their experiences of violence, given the stigma surrounding such issues in Pakistan. Second, the costs estimated in this study are not comprehensive given the narrow focus on tangible costs. Third, national estimates extrapolated from sample data can result in overestimates or underestimates depending on the representativeness of the sample as well as cell size for variables of interest. Thus, given these limitations, the estimates provide only an indication of the significance of the costs that are incurred due to VAWG in Pakistan. Nevertheless, the contribution of knowledge from this project on the social and economic costs of violence, though incomplete, is an essential first step in making the economic case for investment in activities to prevent, reduce or eliminate VAWG.This report has been supported by funding from U.K. Department for International Development as part of the overall ‘What Works to Prevent Violence Against Women and Girls’ Research and Innovation Programme.peer-reviewe