Efficacy of prophylactic tranexamic acid administration in prevention of postpartum hemorrhage in placenta previa cesarean section: an interventional study

Background: Postpartum hemorrhage accounts for the major part of the mortality as well as morbidity like severe anemia, need for blood transfusion, hospital stay and infection. Aim and objectives of the study were to determine the efficacy and safety of prophylactic tranexamic acid and intravenous tranexamic acid in preventing postpartum hemorrhage in women undergoing caesarean section for placenta previa.Methods: Seventy women with placenta previa over 1 year, randomized into 2 groups: group 1 (n=35): Women who received 10 IU oxytocin intravenous infusion after placental delivery and group 2 (n=35): Women who received 1 gm (10 ml) tranexamic acid IV before skin incision plus 10 IU oxytocin intravenous infusion after placental delivery.Results: The mean age was similar in 2 groups i.e., 26.34±4.78 years in group 1 and 27.31±5.62 years in group 2. Most women in the present study presented with type IV placenta previa i.e., 34.3% in group 1 and 48.6% in group 2. Mean pre-operative hemoglobin was 9.57±1.54 g/dl in group 1 and 9.59±1.35 g/dl in group 2. Intra-operative mean blood loss was 729.31±172.45 ml in intravenous oxytocin group and 464.86±28.00 ml in intravenous tranexamic acid group. A total of 74.3% women in group 1 and 20% women in group 2 developed postpartum hemorrhage. Mean post-operative hemoglobin was 8.04±1.34 g/dl in group 1 and 8.85±1.26 g/dl in group 2. In group 1, 5.7% neonates were born with very low birth weight and while none in group 2. 51.4% neonates in group 1 and 45.7% in group 2 had low birth weight.Conclusions: It is concluded that tranexamic acid used prophylactically intravenously before skin incision in patients undergoing cesarean section for placenta previa significantly reduces intra-operative blood loss.

People’s Perception about Gender Equity at RHTC, Naila, Jaipur

Gender equality refers to the equal rights, responsibilities, and opportunities of women and men, as well as girls and boys (United Nations Women, 2012). Women in India have suffered gender disparities since ages; although addressed at all fronts (social, political) for last few decades yet we can find scars here and there in the form of gender violence, honor-killing, rape, and social policing. Changes toward equitable gender roles and relations in the community as well as household are a prerequisite to gender equality Promotion of gender equality and empowering of women is one of the eight Millennium Development Goals (MDG) to which India is a signatory. Gender equality and women‘s empowerment are two sides of the same coin: progress toward gender equality requires women‘s empowerment and women‘s empowerment requires increases in gender equality evident by pairing of them in MDG

Red Tacton

All the user-friendly services require technologies that enable communication between people and objects in close proximity. This paper describes a model of human area networking technology that enables communication by touching, a technology we call Red Tacton. It is a Human Area Networking technology, which is developed by Robin Gaur Jind, that uses the surface of the human body as a safe, high speed network tran smission path.It is completely distinct from wireless and infrared technologies as it uses the minute electric field emitted on the surface of the human body. RedTacton involves initiating communication with a touch that could result in a wide range of actions in response. It does not rely on electromagnetic or a light wave to transmit data. A transmission path is formed at the moment a part of the human body comes in contact with a RedTacton transceiver. Communication is possible using any body surfaces, such as the hands, fingers, arms, feet, face, legs etc. RedTacton works through shoes and clothing as well. When the physical contact gets separated, the communication is ended. The data transfer between RedTacton enabled devices does not require any dialing or log-in, the data transfer would be practically instantaneous. While it is true that similar personal area networks are alre ady accessible by using radio-based technologies like Wi-Fi or Bluetooth, they are often hampered by intermittent service and will eventually be replaced by "human area network

RED TACTON

As electronic devices become smaller, lower in power requirements, and less expensive, we have begun to adorn our bodies with personal information and communication appliances. Such devices include cellular phones, personal digital assistants(pdas), pagers and many more. Currently there is no method for these devices to share data. Networking these devices can reduce functional I/O redundancies and allow new conveniences and services. RedTacton was introduced by Nippon Telegraph and Telephone Corporation(NTT). RedTacton is a break-through technology that, uses the surface of the human body as a safe, high speed network transmission path. So we, in this paper are explaining the unique new functional features and enormous potential of RedTacton as a Human Area Networking technology. In this paper I have described features, applications advantages , disadvantages of redtacton.evels of connectivity: Wide Area Networks(WAN), Local Area Networks(LAN), and Human Area Networks(HAN) for connectivity to personal information, media and communication appliances within the much smaller sphere of ordinary daily activities[6]

Knowledge and attitude of peripheral health workers regarding Non-Communicable diseases in a Rural area of Rajasthan

Background: Mortality due to Non communicable disease (NCD) has increased from 50% to 60% in India from 2004 to 2014. Increasing mortality due to NCD has compelled Government of India to launch a national program (NPCDCS). This program has involved peripheral health workers hence this study was conducted to assess level of knowledge and attitude of peripheral health workers working in rural area of CHC Naila regarding NCDs. Methods: Present study was conducted at CHC Naila, Rajasthan, during June to Dec 2019. All (38) peripheral health staff working under CHC Naila were assessed and categorised regarding NCD and NPCDCS program. Results: Majority (77%) peripheral health workers had more than ten years of field experience. All have heard about NPCDCS program and type of NCDs covered under it. they were aware of sign & symptoms of common NCDs, however 18.42% of these were not aware of their role of community awareness about risk factors of NCDs and conducting regular screening. Conclusion: Though the level of awareness of health workers regarding type of NCDs, its consequences and risk factors was good however skill development training is needed so that they can screen people effectively and motivate them for healthy life style for optimum result

Host ICAMs play a role in cell invasion by Mycobacterium tuberculosis and Plasmodium falciparum

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Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients