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Background: Chemotherapy has increased survival rate in patients but is associated with systemic as well as cutaneous adverse effects. Among cutaneous adverse effects, nail changes caused by chemotherapy are often overlooked. But these changes are a major cause of cosmetic concern and negatively affect the quality of life (QoL) of patients. Aim: This study was conducted to evaluate the impact of chemotherapy-induced nail changes on QoL of patients with malignancy and to compare the severity of impact on QoL in various nail changes. Materials and Methods: This hospital-based cross-sectional observational study was conducted for a period of 12 months. Only patients on chemotherapy who developed nail changes were included in the study. A nail-specific QoL questionnaire was used. Statistical analysis was conducted to evaluate the impact of nail changes on QoL. Results: The mean QoL score of 188 patients was 32.37 ± 7.9. The mean QoL score of females was more than that of males. QoL was statistically more significant in younger age groups [ Conclusion: There is a significant impact of chemotherapy-induced nail changes on QoL of patients leading to psychological distress, reduced self-confidence and social limitation. This highlights the importance of proper counselling of patients beforehand regarding the possibility of associated nail changes during and after the chemotherapy regimens. Keywords: Chemotherapy, nail changes, quality of life, Author(s): Madhvi Trivedi (corresponding author) [1]; Rajesh D. Mehta [1]; B. C. Ghiya [1]; Prasoon Soni [1] Introduction Nail is an important appendage of integument. A pink and lustrous nail [...]

Background: Chemotherapy has increased survival rate in patients but is associated with systemic as well as cutaneous adverse effects. Among cutaneous adverse effects, nail changes caused by chemotherapy are often overlooked. But these changes are a major cause of cosmetic concern and negatively affect the quality of life (QoL) of patients. Aim: This study was conducted to evaluate the impact of chemotherapy-induced nail changes on QoL of patients with malignancy and to compare the severity of impact on QoL in various nail changes. Materials and Methods: This hospital-based cross-sectional observational study was conducted for a period of 12 months. Only patients on chemotherapy who developed nail changes were included in the study. A nail-specific QoL questionnaire was used. Statistical analysis was conducted to evaluate the impact of nail changes on QoL. Results: The mean QoL score of 188 patients was 32.37 ± 7.9. The mean QoL score of females was more than that of males. QoL was statistically more significant in younger age groups [

Background: Erbium-YAG laser has been the working horse in dermatology for years. Surprisingly, data on the efficacy and adverse effects of this novel resurfacing and ablative technique of erbium-YAG laser for superficial dermatoses in Indian skin is limited. Aim and Objective: To evaluate the efficacy and safety profile of erbium-YAG laser ablation in superficial cutaneous lesions. Materials and Methods: Two hundred and fifty patients of various superficial dermatoses, treatable by erbium-YAG laser, were recruited in the study. All the patients were subjected to erbium-YAG laser sessions. The number of laser sessions, fluence, frequency and other parameters were individualized as per the respective dermatosis. The clinical response was evaluated as grade 4 (100% lesion clearance), grade 3 (75–99%), grade 2 (50–75%) or grade 1 (

Background: The spectrum of cutaneous manifestations during the neonatal period varies from transient self-limiting conditions to serious life-threatening ones. Transient and pathological neonatal dermatoses should be differentiated from physiological changes to avoid unnecessary treatment and to relieve parent's stress. Purpose: The aim of this study was to determine the incidence and patterns of various dermatoses in early neonates and correlate these changes with maternal or neonatal factors; in the west region of Rajasthan, India. Methods: This hospital-based prospective study at a tertiary-level referral teaching medical center entailed recording detailed dermatological examination of 5000 early neonates having any physiological and/or pathological cutaneous changes. Results: A total of 5000 newborns were observed, there were 3248 (64.96%) male and 1752 (35.04%) female. Out of the total, 67.36% were full-term birth, whereas 30.44% were preterm and 2.2% were postterm newborns. Physiological skin changes (92.42%) were more commonly observed as compared to pathological changes (45.1%). The physiological skin changes observed in the order of frequency were sebaceous hyperplasia (65.32%), milia (51.12%), and physiological scaling (41.02%). Cutis marmorata (2.06%) and harlequin skin changes (1.06%) were seen more in preterms, weighing

Background: Owing to the myriad clinical presentations of COVID-19 vaccine-induced adverse events, clinicopathological correlation is the key to understanding the underlying pathophysiology of these reactions. However, there is still a dearth of such systematic studies across a wide spectrum of vaccine reactions. Aim and Objectives: This study provides a clinical and histopathological correlation of COVID-19 vaccine-induced mucocutaneous reactions. Materials and Methods: The study population included all the individuals developing any form of self-reported mucocutaneous adverse events within 4 weeks of receiving the COVID-19 vaccine. The clinical and histopathological features were recorded. Results: A total of 126 cases were identified. Seven histopathological patterns were recognized. The most common histopathological feature was spongiotic dermatitis, seen in all the patients with “vaccine-related eruption of papules and plaques” (VREPP). Other patterns included lichenoid or interface dermatitis, dermal hypersensitivity reaction, leukocytoclastic vasculitis, subepidermal blistering, psoriasiform hyperplasia, and dermal granulomatous reaction. Conclusion: Owing to such myriad clinical presentations, utilizing a histopathological classification could ease categorizing the vaccine-induced mucocutaneous eruptions.

Fiber-reinforced polymer is an advanced composite material composed of fibers and resin. It is a cost-effective and efficient material for repairing both existing and new structures. Additionally, these composite materials exhibit exceptional mechanical properties, including strength, Impact resistance, stiffness, load-bearing capacity and flexibility. This experimental research aims to investigate the behaviour of AFRP and CFRP materials wrapped in different layers when subjected to mechanical and non- destructive testing. Concrete confirming to the M30 grade is used for this experimental study. To ensure consistent concrete quality throughout the study, various test specimens were cast and subjected to standard tests, including compression tests, split tensile tests, modulus of rupture, modulus of elasticity, and impact tests on hardened concrete. Additionally, Rebound hammer and UPV test are two important NDT methods used to determine the quality of concrete. The tests were conducted using various specimens, including cubes (150mmx150mm), cylinders (150mmx300mm), prisms (100mmx100mmx500mm), and discs (63.5mm x 152.4mm). The experimental results indicate that concrete specimens confined with CFRP exhibit higher strength compared to those confined with AFRP and unconfined specimens, in both single and double ply wrappings.

Research on wireless body area networks (WBAN), also known as wireless body sensor networks (WBSN), has been increasingly important in medical applications recently and is now crucial for patient monitoring. To create a dependable body area network (BAN) system, several factors need to be considered at both the software and hardware levels. One such factor is the designing and implementation of routing protocols in the network layers. Protocols for routing can detect and manage the routing paths in a network to facilitate efficient data transmission between nodes. Therefore, the routing protocol is crucial in wireless sensor networks (WSN) to provide dependable communication among the sensor nodes. Different clustering methods can be used in WBAN systems. However, these techniques often produce many cluster heads (CHs), which leads to higher energy consumption. Increased consumption of energy reduces the lifespan of WBANs and raises costs of monitoring. This research proposes a recent metaheuristic algorithm to select the optimal clusters to provide an energy-effective protocol for healthcare monitoring. This research aims to minimize the energy utilization of WBANs by choosing the most suitable CHs based on the BWO. The proposed BWO-based routing protocol demonstrates superior performance in WBANs based on energy consumption, packet loss, packet delivery ratio, network lifetime, end-to-end delay, and throughput. It optimizes energy consumption by effectively selecting CHs and routing paths, leading to balanced energy usage and prolonged network operation. The BWO model significantly reduces end-to-end delay by ensuring data packets follow the shortest and least congested routes, which is significant for real-time health monitoring. It achieves a high packet delivery ratio, typically between 95% and 98%, indicating reliable data transmission, while maintaining a low packet loss rate, generally between 1% and 5%. Additionally, the BWO-based routing protocol extends network lifetime by preventing early node depletion and enhances network throughput by reducing congestion and packet collisions, thereby supporting continuous and robust health data monitoring. [ABSTRACT FROM AUTHOR]

Facial emotion recognition (FER) technology in autonomous vehicle drivers can considerably strengthen the efficiency and safety of the driving experience. The system can analyze facial expressions in real-time by employing advanced computer vision (CV) techniques, which identify emotions such as stress, fatigue, or distraction. This enables the vehicle to adapt its behavior, triggering interventions or alerts where applicable to alleviate possible threats. Ensuring the emotional well-being of the driver promotes a safer road environment, improving overall road safety and diminishing the possibility of accidents in the era of autonomous vehicles. FER using (Deep Learning) DL is an advanced technique that leverages deep neural network (DNN) to automatically interpret and identify emotions from facial expressions. DL algorithms, especially Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) have attained outstanding results in this field since they allow us to learn temporal dependencies hierarchy and features within the data. This research develops a novel Computer Vision with Optimal DL-based Emotion Recognition (CVODL-ER) model for Autonomous Vehicle Drivers. The CVODL-ER method concentrates on the automated classification of various sorts of emotions of autonomous vehicle drives. To accomplish this, the CVODL-ER technique makes use of the SE-ResNet model for learning intrinsic patterns from the driver's facial images. Besides, the hyper parameter tuning of the SE-ResNet model takes place via a quasi-oppositional Jaya (QO-Jaya) algorithm. For the recognition of driver emotions, the CVODL-ER system executes the deep belief network (DBN) algorithm. The performance analysis of the CVODL-ER technique takes place using a benchmark facial image database. The obtained results underline the improved efficiency of the CVODL-ER technique over other models. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in more than 65.5 million infections and 15,14,695 deaths in 212 countries over the last few months. Different drug intervention acting at multiple stages of pathogenesis of COVID-19 can substantially reduce the infection induced, thereby decreasing the mortality. Also population level control strategies can reduce the spread of the COVID-19 substantially. Motivated by these observations, in this work we propose and study a multi scale model linking both within-host and between-host dynamics of COVID-19. Initially the natural history dealing with the disease dynamics is studied. Later comparative effectiveness is performed to understand the efficacy of both the within-host and population level interventions. Findings of this study suggest that a combined strategy involving treatment with drugs such as Arbidol, remdesivir, Lopinavir/Ritonavir that inhibits viral replication and immunotherapies like monoclonal antibodies, along with environmental hygiene and generalized social distancing proved to be the best and optimal in reducing the basic reproduction number and environmental spread of the virus at the population level.

Mental health issues, such as anxiety, depression, and other psychological disturbances, have become a significant public health concern globally. Identifying individuals at risk of these conditions early on can lead to timely interventions and improved outcomes. With the rise of technology and the increasing use of smartphones and wearable devices, there is an opportunity to develop an innovative system that can monitor emotional health in real-time, helping to detect potential psychological disturbances before they escalate. Traditionally, mental health assessments relied on self-reporting and periodic check-ins with mental health professionals. These approaches often had limitations, as individuals may not always accurately report their emotional state, and there could be significant delays between assessments. Additionally, access to mental health services was not always readily available, leading to potential delays in diagnosis and treatment. Therefore, the need for an innovative system for monitoring emotional health arises from the desire to overcome the limitations of traditional approaches. By leveraging technology, such as machine learning, natural language processing, we can create a continuous and unobtrusive monitoring system. Such a system could gather real-time data on an individual's emotional state, behavior, and physiological responses. Early detection of emotional disturbances can lead to timely intervention and support, improving the overall mental well-being of individuals and reducing the burden on mental health services. This innovative monitoring system has the potential to significantly improve mental health outcomes on a broader scale. It offers a proactive approach to emotional well-being, empowering individuals to take control of their mental health and providing an invaluable tool for mental health professionals in identifying and supporting those at risk of psychological disturbances. [ABSTRACT FROM AUTHOR]

In this paper, an experimental evaluation of Mel-Frequency Cepstral Coefficients (MFCCs) for use in Electroencephalogram (EEG) signal classification is presented. The MFCC features are tested using CHB-MIT Scalp EEG Database. The objective is to classify the given EEG signal into normal or abnormal that is based on the MFCC representation of EEG signal. Initially, the QRS complex waves are detected using Pan Tompkins algorithm, and then the MFCC features are extracted. The performance of MFCC feature representation is analyzed in the context of an Artificial Neural Network (ANN) classification system in terms of sensitivity and specificity. The performance of EEG classification approach depends on the number of MFCC components used for the classification. When compared with 15 and 35 MFCC components, 25 MFCC components gives better result in terms of sensitivity (98%) and specificity (96%).