ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System

Deep learning has emerged to be efficient Artificial Intelligence (AI) phenomena to solve problems in healthcare industry. Particularly Convolutional Neural Network (CNN) models have attracted researchers due to their efficiency in medical image analysis. According to World Health Organization (WHO), rapidly developing cerebral malfunction, brain stroke, is the second leading cause of death across the globe. Brain MRI scans, when analysed quantitatively, play vital role in diagnosis and treatment of stroke. There are many existing methods built on deep learning for stroke diagnosis. However, an automatic, reliable and faster method that not only helps in stroke diagnosis but also demarcate affected regions as part of Clinical Decision Support System (CDSS) is much desired. Towards this objective, we proposed an Automated Deep Learning based Brain Stroke Detection Framework (ADL-BSDF). It does not rely on expertise of healthcare professional in diagnosis and know the extent of damage enabling physician to make quick decisions. The framework is realized by two algorithms proposed. The first algorithm known as CNN-based Deep Learning for Brain Stroke Detection (CNNDL-BSD) focuses on accurate detection of stroke. The second algorithm, Deep Auto encoder for Stroke Severity Detection (DA-SSD), focuses on revealing extent of damage or severity of the stroke. The framework is evaluated against state of the art deep learning models such as EfficientNet, ResNet50 and VGG16

Enhanced Deep Learning Models for Efficient Stroke Detection Using MRI Brain Imagery

Deep learning models are widely used for solving problems in different applications. Especially Convolutional Neural Network (CNN) based models are found suitable for medical image analysis. As brain stroke is increasing in alarming rate, it is essential to have better approaches to detect it in time. Brain MRI is one of the medical imaging technologies widely used for brain imaging.we proposed certain advancements to well-known deep learning models like VGG16, ResNet50 and DenseNet121 for enhancing brain stroke detection performance. These models are optimized based on the brain stroke detection problem in hand as they are not specialized for a specific problem. We proposed an algorithm, named Deep Efficient Stroke Detection (ESD), that exploids enhanced deep learning models in pipeline. The experimental results revealed that there is performance improvement with optimized models. Highest accuracy is achieved by ResNet50 with 95.67%

3D Stacked Cache Data Management for Energy Minimization of 3D Chip Multiprocessor

In this model a runtime cache data mapping is discussed for 3-D stacked L2 caches to minimize the overall energy of 3-D chip multiprocessors (CMPs). The suggested method considers both temperature distribution and memory traffic of 3-D CMPs. Experimental result shows energy reduction achieving up to 22.88% compared to an existing solution which considers only the temperature distribution.  New tendencies envisage 3D Multi-Processor System-On-Chip (MPSoC) design as a promising solution to keep increasing the performance of the next-generation high performance computing (HPC) systems. However, as the power density of HPC systems increases with the arrival of 3D MPSoCs with energy reduction achieving up to 19.55% by supplying electrical power to the computing equipment and constantly removing the generated heat is rapidly becoming the dominant cost in any HPC facility

Association of ABO and Rh blood groups to HBV, HCV infections among blood donors in a blood bank of tertiary care teaching hospital in Southern India: A retrospective study

Background: ABO blood group has been found to be associated with the risk of several diseases. Infection with hepatitis B virus (HBV) and hepatitis C virus (HCV) are also the major health problems worldwide. This work was therefore aimed at assessing the ABO and Rh blood group antigens and its association with HBV and HCV seroreactive status among healthy blood donors.Methods: This is a retrospective cross-sectional analytical study carried out in the department of Transfusion Medicine of a tertiary care teaching hospital blood bank for a period of 6 years (January 2009 to December 2014). Data retrieved from blood bank records included the donors’ ABO group, Rh type and the result of HBV, HCV serology.Results: A total number of 41652 blood donors were registered and screened during the study period. The commonest blood group was O constituting 41.5% followed by B-32.6%, A-19.8%, AB-6.1% and Bombay-0.02%. Rh-D positive donors were 92.9% and remaining 7.1% were Rh-D negative. The overall prevalence of HBV and HCV were 2.4% and 0.4% respectively. Among total HBV and HCV seroreactive donors 41.7% and 37.9% were O blood group, B-30.9%, 32.7% A-21.6%, 21.2% and AB-5.7%, 8% respectively. Among the total HBV and HCV seroreactive group, 93.7% and 93.1% had Rh-D positive blood group and remaining 6.3% and 6.9% had Rh-D negative blood group respectively.Conclusion: In this study conducted to determine the predominant blood group antigen and its association with HBV and HCV seroreactivity, there was no association between blood group antigens with these infections.

A prospective study of role of doppler in pregnancy and the perinatal outcome

Background: The development of doppler ultrasonographic technology has provided an opportunity to obtain a qualitative and quantitative assessment of maternal and foetal circulation using a non-invasive method. It has been proved by many studies that doppler has a very important role in screening of high-risk pregnancies. Objective of this study was to evaluate the role of colour doppler study in normal and high-risk pregnancy in relation to perinatal outcome.Methods: A prospective study was done including 75 women with high risk pregnancy and 75 normal pregnant women during the period October 2018 to September 2019 in hospitals attached to Bangalore Medical College and Research Institute. Doppler examination was done after recording patients’ history, clinical examination and ultrasound. Results were analysed and conclusions were made.Results: Out of the 22 patients with PIH, 20 patients had abnormal umbilical artery S/D ratio and all 22 had abnormal MCA PI. Out of 12 patients with diabetes, 10 had abnormal umbilical artery S/D ratio. All the patients with IUGR had abnormal umbilical artery S/D ratio and abnormal MCA PI.Conclusions: Colour doppler flow velocimetry done repeatedly can predict adverse foetal events with a great degree of accuracy

First and second trimester bleeding and pregnancy outcome: a prospective study in a tertiary government hospital

Background: Bleeding in first and second trimester of pregnancy is one of the common complications of pregnancy. there is evidence from various prospective and retrospective studies that first and second trimester vaginal bleeding which continue with pregnancy is associated with adverse pregnancy outcome, including preterm delivery, low birth weight babies, perinatal death and congenital anomalies. Objective of this study was to know the outcome of pregnancies who have bleeding in first and second trimester of pregnancy.Methods: This study was prospective study done in the department of obstetrics and gynaecology, Vanivilas Hospital, Bangalore from September 2018 to August 2019.Results: This study concludes that I trimester vaginal bleeding are at increased risk of abortion than in II trimester vaginal bleeding. Risk of placenta previa was more in II trimester vaginal bleeding than in I trimester vaginal bleeding.Conclusions: This study concludes that I trimester vaginal bleeding are at increased risk of abortion than in II trimester vaginal bleeding. Risk of placenta previa was more in II trimester vaginal bleeding than in I trimester vaginal bleeding. Bleeding in I trimester and II trimester call for special attention in view of increased risk of preterm birth and perinatal death. Recognition of these association will be useful for detection and follow up of pregnancies being at high risk

A study to identify the prevalence of vulvovaginal candidiasis in second trimester

Background: Vaginal candidial infections are due to excessive growth of Candida. These are normally present in the vagina in small numbers. Vaginal infections are typically caused by the yeast species Candida albicans. It is found that candidial infection increases the risk of preterm labour. Aims and objectives of the study were to determine the prevalence of vulvovaginal candidiasis and influence of maternal age, parity and weeks of 2nd trimester on its occurrence among pregnant women in 2nd trimester, attending the antenatal clinic in our hospital.Methods: A prospective study conducted in BMCRI for a period of 3 months (October 2019-December 2019) on patients in second trimester. Consent of patients taken. High vaginal swabs were collected from the pregnant patients in second trimester and sent for culture. Candida positive cases were noted and results were analysed.Results: A total of 100 high vaginal swabs were collected and reported in our study. Among them 54 swabs were positive for Candida growth (54%) and 46 swabs were negative for growth (46%). Culture positive patients’ clinical details were analysed and tabulated.Conclusions: Our study concluded that candidiasis is more prevalent in pregnant women but there was no statistical significance in occurrence of vaginal candidiasis among various age groups, parity or trimester. Hence it is better to screen all the patients in I early II trimester in order to find out and treat positive cases early to prevent preterm births attributed to vaginal candidiasis

IN SILICO TARGET IDENTIFICATION OF NOOTROPIC BIOACTIVE COMPOUNDS FROM AYURVEDIC HERBS

Numerous plants are listed in the Ayurvedic pharmacopoeia, and several different plant parts are being used in Ayurvedic formulations. The bioactive ingredients of many of these medicinal have been identified. A key step in Ayurvedic drug development is the identification and validation of biological targets of these bioactive ingredients. Most of the experimental techniques involving genomic, proteomic and metabolomic approaches for target identification are laborious and expensive. Computational approaches allow an efficient alternative approach for in silico target prediction of bioactive compounds. Here, we have used computational methods to predict the target proteins of major bioactive compounds present in seven medicinal plants (Bacopa monnieri, Centella asiatica, Clitoria ternatea, Acorus calamus Glycyrrhiza glabra Celastrus paniculatus  Nardostachys jatamansi) known for their nootropic properties. These plants/plant parts are being used in various traditional Ayurveda formulations intended for cognitive enhancement and memory boosting. Even though these plants are widely used in the treatment of cognitive deficits, their scientific evaluation is lacking. Till date, very few studies have attempted to elucidate the targets or to explain the mode of action of bioactive ingredients in nootropic medicinal plants. We have chosen three databases for target prediction- ChEMBL, Swiss Target Prediction, and Binding DB. Based on available literature, we also examined if any of the predicted target proteins have brain-related functions. Pertinent to the nootropic properties of the medicinal plants, our study revealed several potential target proteins such as CYP19A1, MAPT, PTGS1, ACHE, SLC6A2, SLC6A3, MAOA and MAOB implicated in neurodevelopment, neuroprotection, learning and memory.

A study to establish the prevalence of urinary tract infection in preterm labour

Background: Urinary tract infection (UTI) is one of the many etiological factors of preterm labour. Preterm labour is the onset of labour after 28 weeks and before 37 weeks of gestation. Preterm labour is a significant cause for perinatal morbidity and mortality. Hence early diagnosis and management of etiological factors is necessary. The most common bacterial infection encountered during pregnancy is UTIs. Early detection and management of UTIs may effectively prevent complications of preterm labour including preterm birth. Aims and objectives of the study was to estimate the prevalence of UTIs in preterm labour.Methods: cross sectional study with a total of 250 patients carried out in the department of obstetrics and gynaecology, Vanivilas hospital, BMCRI for a period of 6 moths-Aug 2019-Feb 2020. Patients in preterm labour i.e., cervical dilatation ≥1 cm, cervical effacement ≥80% with true labour pains were included in the study after taken informed consent form the patient. Detailed clinical history including age of patient, level of education, duration of antenatal care, parity, and obstetrical history was taken. Gestational age was calculated by LMP or early ultrasound. General examination, systemic examination and obstetric examination was done. Routine investigations were done along with clean catch midstream urine sample in a sterile container. Two samples were collected: 1st sample for microscopic examination, 2nd sample for culture and sensitivity.Conclusions: Untreated UTI can be associated with obstetric complications. The most common bacterial infection during pregnancy is UTIs. All women should be screened for UTI at the first antenatal visit. Once diagnosed it should be promptly treated with suitable antibiotic which is sensitive yet safest

Towards bio-encapsulation of chitosan-silver nanocomplex? Impact on malaria mosquito vectors, human breast adenocarcinoma cells (MCF-7) and behavioral traits of non-target fishes

In this study, we synthesized and bio-encapsulated a chitosan-silver nanocomplex (Ch-AgNPs), characterizing it by UV–Vis spectroscopy, FTIR, EDX, SEM, XRD and Zeta potential analyses. The bio-encapsulated chitosan-Ag nanocomplex (BNC) was efficient as scavenger of free radicals (DPPH and ABTS), if compared to Ch-AgNPs. In toxicity assays against breast cancer cells (MCF-7) the BNC triggered apoptotic pathways, leading to a decline of MCF-7 cell viability with IC50 of 17.79 μg/mL after 48 h of exposure. LC50 of BNC on Anopheles stephensi ranged from 54.65 (larva I), to 98.172 ppm (pupa) while Ch-AgNPs LC50 ranged from 4.432 (I) to 7.641 ppm (pupa). In the field, the application of Ch-AgNP (10 × LC50) lead to A. stephensi larval reduction to 86.2, 48.4 and 100% after 24, 48, and 72 h, while the BNC nanocomplex exhibited 68.8, 36.4 and 100% larval reduction, respectively. Both Ch-AgNPs and the BNC reduced longevity and fecundity of A. stephensi. As regards to non-target effects on fish behavioral traits, in standard conditions, Poecilia reticulata predation on A. stephensi larvae was 70.25 (II) and 46.75 larvae per day (III), while post-treatment with sub-lethal doses of BNC, predation was boosted to 88.5 (II) and 70.25 (III) larvae per day