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"Devi, M M"
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The unspoken reality of gender bias in surgery: A qualitative systematic review
This study was conducted to better understand the pervasive gender barriers obstructing the progression of women in surgery by synthesising the perspectives of both female surgical trainees and surgeons.
Five electronic databases, including Medline, Embase, PsycINFO, CINAHL and Web of Science Core Collection, were searched for relevant articles. Following a full-text review by three authors, qualitative data was synthesized thematically according to the Thomas and Harden methodology and quality assessment was conducted by two authors reaching a consensus.
Fourteen articles were included, with unfavorable work environments, male-dominated culture and societal pressures being major themes. Females in surgery lacked support, faced harassment, and had unequal opportunities, which were often exacerbated by sex-blindness by their male counterparts. Mothers were especially affected, struggling to achieve a work-life balance while facing strong criticism. However, with increasing recognition of the unique professional traits of female surgeons, there is progress towards gender quality which requires continued and sustained efforts.
This systematic review sheds light on the numerous gender barriers that continue to stand in the way of female surgeons despite progress towards gender equality over the years. As the global agenda towards equality progresses, this review serves as a call-to-action to increase collective effort towards gender inclusivity which will significantly improve future health outcomes.
Journal Article
Entropy generation in two-immiscible MHD flow of pulsating Casson fluid in a vertical porous space with Slip effects
The unsteady two immiscible MHD free convective flow of Casson liquid through a vertical channel, with a porous medium, is studied numerically in this investigation. Using a double perturbation approach, the governing flow equations are reduced to a system of connected partial differential equations, which are then solved using the 4th-order numerical Runge–Kutta method coupled with the shooting approach in Mathematica. The velocity and thermal slip conditions have been accommodated in this model. The interaction of permeability of the porous medium, energy dissipation, Joule heating, and thermal radiation are taken into consideration. The computational upshot is also described explicitly to examine the consequences of pertinent parameters. The computational results are analyzed to investigate the effects associated with pertinent parameters which include the Hartmann number of two regions, Darcy number, a ratio of Porous medium permeability, Grashof number, Radiation parameter, heat source, and Prandtl number. The characteristics of the essential regulating parameters on flow frameworks of velocity, temperature, Entropy generation, Bejan number, and heat transfer rate are analyzed correctly via plots, and skin friction and flow rate are given in tabular form. As the radiation parameter rises both velocity and temperature of the Casson fluid decreases. The rate of entropy generation falls with a rise in the magnetic field. The Bejan number escalates as it moves forward from the lower wall to the channel’s center. In the later part of the channel, the Bejan number starts to drastically fall and reaches a minimum at the upper wall.
Journal Article
The perspectives of health professionals and patients on racism in healthcare: A qualitative systematic review
To understand racial bias in clinical settings from the perspectives of minority patients and healthcare providers to inspire changes in the way healthcare providers interact with their patients.
Articles on racial bias were searched on Medline, CINAHL, PsycINFO, Web of Science. Full text review and quality appraisal was conducted, before data was synthesized and analytically themed using the Thomas and Harden methodology.
23 articles were included, involving 1,006 participants. From minority patients' perspectives, two themes were generated: 1) alienation of minorities due to racial supremacism and lack of empathy, resulting in inadequate medical treatment; 2) labelling of minority patients who were stereotyped as belonging to a lower socio-economic class and having negative behaviors. From providers' perspectives, one theme recurred: the perpetuation of racial fault lines by providers. However, some patients and providers denied racism in the healthcare setting.
Implicit racial bias is pervasive and manifests in patient-provider interactions, exacerbating health disparities in minorities. Beyond targeted anti-racism measures in healthcare settings, wider national measures to reduce housing, education and income inequality may mitigate racism in healthcare and improve minority patient care.
Journal Article
SOLD-MMD: Wolf Pack Search Optimized Deep Learning Framework for Brain Disease Detection Using Multi-modal Data
Brain disease detection remains a complex challenge due to the heterogeneous nature of multimodal healthcare data, including medical images, electronic health records (EHR), and physiological signals. The effective integration of these diverse modalities is crucial to accurate and timely clinical decisions. Despite this, existing models in this domain have key shortcomings, such as poor data fusion strategies, redundant data storage, and insufficient exploitation of temporal and spatial dependencies. To address these drawbacks, a novel wolf pack Search Optimized deep Learning model for brain disease Detection using a Multi-Modal Data (SOLD-MMD) framework is proposed. The proposed approach creates a Merged Multi-Modal Set (M3Set) by combining clinical image, EHR, and signal data from the MIMIC database. A Rough Set-based Heterogeneous Method is used to detect and eliminate duplicate patient records by ensuring data consistency and reducing computational overhead. A hybrid Spiking Convolutional Neural Network-based Bidirectional Long Short-Term Memory (SCNN-BiLSTM) model is used to extract both spatial and sequential features. The hyperparameters of the SCNN-BiLSTM model are tuned using the Wolf Pack Search Optimization (WPSO) algorithm. SOLD-MMD is evaluated with several metrics, including accuracy, precision, and recall. SOLD-MMD achieves outstanding results across all evaluation metrics, with 98.59% Accuracy, 97.18% Specificity, 96.96% Precision, 97.41% Recall, and 96.10% F1 Score.
Journal Article
A Smart Approach for Intrusion Detection and Prevention System in Mobile Ad Hoc Networks Against Security Attacks
Design of intrusion detection and prevention scheme for improving MANET security, with considered energy efficiency, detection rate, delay, and false positive rate are major research issues. Most of the existing solutions have suffered to obtain accurate detection rate in minimal time execution and energy consumption. In this work we proposed a Smart approach for intrusion detection and prevention system (SA-IDPS) to mitigate attacks in MANET by machine learning methods. Initially, mobile users are registered in Trusted Authority using One Way Hash Chain Function. Each mobile user submits their following information to verify authentication: finger vein biometric, user id, and latitude and longitude. Intrusion detection is executed using four entities: Packet Analyzer, Preprocessing Unit, Feature Extraction Unit and Classification Unit. In packet analyzer, we verify whether any attack pattern is found or not. It is implemented using Type 2 Fuzzy Controller which considers information from packet header. In preprocessing unit, logarithmic normalization and encoding schemes are considered, which is time series and suitable for any application. In feature extraction unit, Mutual Information is used where we extracts optimum set of features for packets classification. In classification unit, Bootstrapped Optimistic Algorithm for Tree Construction with Artificial Neural Network is used for packets classification, which classifies packets five classes: DoS, Probe, U2R, R2L, and Anomaly, and then Association Rule Tree are used to classify whether the attack is Frequent or Rare. In this case, historical table is used for packets classification. Finally, experiments are conducted and tested for evaluating the performance of proposed SA-IDPS scheme in terms of Detection Rate (%), False Positive Rate (%), Detection Delay (s), and Energy Consumption (J).
Journal Article
In Silico computational screening of Kabasura Kudineer - Official Siddha Formulation and JACOM against SARS-CoV-2 spike protein
Siddha Medicine is a valuable therapeutic choice which is classically used for treating viral respiratory infections, this principle of medicine is proven to contain antiviral compounds.
The study is aimed to execute the In Silico computational studies of phytoconstituents of Siddha official formulation Kabasura Kudineer and novel herbal preparation - JACOM which are commonly used in treating viral fever and respiratory infectious diseases and could be affective against the ongoing pandemic novel corona virus disease SARS-CoV-2.
Cresset Flare software was used for molecular docking studies against the spike protein SARS-CoV-2 (PDB ID: 6VSB). Further, we also conducted insilico prediction studies on the pharmacokinetics (ADME) properties and the safety profile in order to identify the best drug candidates by using online pkCSM and SwissADME web servers.
Totally 37 compounds were screened, of these 9 compounds showed high binding affinity against SARS-CoV-2 spike protein. All the phytoconstituents were free from carcinogenic and tumorigenic properties. Based on these, we proposed the new formulation called as “SNACK–V”
Based on further experiments and clinical trials, these formulations could be used for effective treatment of COVID-19.
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•In silico Docking Studies of Kabasura Kudineer-Official Siddha Formulation and JACOM against SARS-CoV-2 spike protein.•37 Phytochemical constituents were docked to spike glycoprotein of SARS-COV-2 (PDB ID: 6VSB) by using Cresset Flare software.•Chrysoeriol and Luteolin from Kabasura Kudineer and Quercetin from JACOM shown the highest dock score values of above -11.00.•In silico ADME and drug Likeliness and synthetic accessibility were also carried out for phytoconstituents.
Journal Article
An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map
Speech recognition is one of the entrancing fields in the zone of computer science. Exactness of speech recognition framework may decrease because of the nearness of noise exhibited by the speech signal. Consequently, noise removal is a fundamental advance in automatic speech recognition (ASR) system. ASR is researched for various languages in light of the fact that every language has its particular highlights. Particularly, the requirement for ASR framework in Tamil language has been expanded broadly over the most recent couple of years. In this work, bidirectional recurrent neural network (BRNN) with self-organizing map (SOM)-based classification scheme is suggested for Tamil speech recognition. At first, the input speech signal is pre-prepared by utilizing Savitzky–Golay filter keeping in mind the end goal to evacuate the background noise and to improve the signal. At that point, Multivariate Autoregressive based highlights by presenting discrete cosine transformation piece to give a proficient signal investigation. And in addition, perceptual linear predictive coefficients likewise separated to enhance the classification accuracy. The feature vector is shifted in measure, for picking the right length of feature vector SOM utilized. At long last, Tamil digits and words are ordered by utilizing BRNN classifier where the settled length feature vector from SOM is given as input, named as BRNN-SOM. The experimental analysis demonstrates that the suggested conspire accomplished preferable outcomes looked at over exist deep neural network–hidden Markov model algorithm regarding signal-to-noise ratio, classification accuracy, and mean square error.
Journal Article
Epidemiology of neurological disorders in India: review of background, prevalence and incidence of epilepsy, stroke, Parkinson's disease and tremors
Growth and development of neuroepidemiology in India during the last four decades has been documented highlighting the historical milestones. The prevalence rates of the spectrum of neurological disorders from different regions of the country ranged from 967-4,070 with a mean of 2394 per 100,000 population, providing a rough estimate of over 30 million people with neurological disorders (excluding neuroinfections and traumatic injuries). Prevalence and incidence rates of common disorders including epilepsy, stroke, Parkinson's disease and tremors determined through population-based surveys show considerable variation across different regions of the country. The need for a standardized screening questionnaire, uniform methodology for case ascertainment and diagnosis is an essential requiste for generating robust national data on neurological disorders. Higher rates of prevalence of neurological disorders in rural areas, 6-8 million people with epilepsy and high case fatality rates of stroke (27-42%) call for urgent strategies to establish outreach neurology services to cater to remote and rural areas, develop National Epilepsy Control Program and establish stroke units at different levels of health care pyramid.
Journal Article
Fault detection in satellite power system using convolutional neural network
Satellite failures account for heavy, irreparable damages, especially when associated with the Power System which is the heart of a satellite. Anomalies in Satellite Power System (SPS) can lead to complete failure of the mission. This demands the need to understand the causes of power system related failures. Huge number of sensors installed in a satellite system conveys information regarding the health of the system. The conventional manual level checking of sensors can be augmented with data driven fault diagnosis approach to reduce the false alarm and burden on operating personnel. The latter has the advantage of exploiting the interrelationship between sensor measurements for fault diagnosis. In this work, Convolutional Neural Network (CNN) is trained on satellite telemetry data for sensor fault detection in SPS. Various processing schemes in time and frequency domains were explored to process the input data to CNN. Promising results were obtained with combination of Stockwell transform (S-transform) and CNN for data processing and classification, respectively. Advanced Diagnostics and Prognostics Testbed (ADAPT), a publicly-available dataset was analysed and used for validating the proposed algorithm, yielding an accuracy as high as 96.7%, precisison of 0.9, F1 score of 0.95 and AUC equal to 0.976.
Journal Article