Lightweight self-attention and deep gated neural network (LSA-DGNet) for multiple neurological disease detection.

Detecting neurological diseases is an important task in modern medicine, for which it is crucial to accurately model the temporal distributions of disease genesis. In prior methodologies, temporal patterns are used in feature effects and limiting assumptions such as proportionate risks. We introduce a new methodology for neural disease diagnosis, known as LSA-DGNet (Lightweight Self-Attention based on Deep Gated Network).

Satisfaction among edentulous patients before and after conventional complete denture.

Satisfaction levels in edentulous patients before and after receiving conventional complete dentures, focusing on smile confidence, eating comfort and pronunciation is of interest. Hence, a cross-sectional analysis of 100 patients (aged 52-75 years) using a structured questionnaire was completed to assess post-treatment improvements. Confidence in smiling increased from 8% to 62%, while eating comfort and pronunciation improved in 67% and 58% of patients, respectively.

Psychological assessment in edentulous patients before and after complete denture.

The psychological well-being of edentulous patients before and after receiving complete dentures, focusing on parameters such as confidence in smiling, ability to eat and pronunciation is of interest. Data shows substantial improvements in all assessed parameters are found. Confidence in smiling and eating showed the highest levels of improvement, while pronunciation difficulties significantly decreased.

Computation of the Retentive Forces of Ball and Magnet Attachments in a Two-Implant-Retained Mandibular Overdenture.

Background: Advancements in tooth replacement have led to increased use of implant overdentures, prompting the proposal of various attachment systems. Unsplinted options like ball, magnetic, and telescopic attachments offer different retention methods. Ball attachments are straightforward but require periodic O-ring replacement and are limited by implant angulation.

LG-TriCapsNet: A lightweight graph capsule framework with nearest neighbor graphs for multi-disease EEG classification.

EEG signal classification for neurological disorders is a very critical task in the healthcare field, demanding accuracy and efficiency. Due to the diversity of these disorders and the complexity of the EEG signals, the task of diagnosing these disorders is very challenging. Traditional methods usually cannot capture the intricate relationships between temporal and spatial features, which are inherent in EEG data.

Encoded in Titanium: The Digital DNA of Forensic Dentistry.

Forensic odontology greatly aids the identification of unknown people, particularly in situations where more conventional techniques like fingerprinting and iris recognition are impractical. This paper examines the increasing significance of dental implants in forensic research, emphasizing their special benefits, such as their resilience to high temperatures, longevity, and capacity to preserve identifying characteristics like batch or serial numbers. The article describes several identification approaches, including DNA profiling, radiographic imaging, lip and palatal print examination, and bite mark analysis, focusing on how dental implant analysis is incorporated into these techniques.

Enhanced EEG-based Alzheimer's disease detection using synchrosqueezing transform and deep transfer learning.

The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning due to memory loss, cognitive decline, and behavioral problems. By using synchrosqueezing representations of EEG signals classified by fine-tuned pre-trained convolutional neural networks, this paper presents an EEG-based classification model for Alzheimer's detection. EEG signals are converted into image patterns with time-varying oscillatory elements using the synchrosqueezing technique.

Evaluation of effect of low-level laser therapy on alleviation of pain in patients with implant-supported mandibular overdenture: A pilot study.

Aim: Rehabilitation of edentulous jaws with implant-supported overdenture has achieved a comprehensive acclamation across the globe due to compromised retention and stability in conventional complete dentures. In dentistry, low-level laser therapy (LLLT) has become a viable treatment option for lowering inflammation, easing pain, and speeding up wound healing. This study aimed to evaluate the effect of LLLT on alleviation of pain in patients with two implant-supported mandibular overdentures.

Synergistic Potential of Boenninghausenia albiflora (Hook.) Rchb. Ex Meisn. Essential Oil Blends Against Meloidogyne incognita and Spodoptera litura: A Natural Approach to Pest Management.

This study explored the chemical composition and synergistic interactions of essential oils (EOs) from Boenninghausenia albiflora collected from four locations (Boenninghausenia albiflora Harinagar EOs, BAHEO; Boenninghausenia albiflora Bhowali EOs, BABEO; Boenninghausenia albiflora Dunagiri EOs, BADEO and Boenninghausenia albiflora Munsyari EOs, BAMEO) in Uttarakhand, India, which focus on their pesticidal properties. To assess synergistic effects in pesticidal activities, EOs were combined in binary, ternary, and quaternary mixtures at equivalent ratios. The major compounds present in the EOs were identified.

Multi-modality NDE fusion using encoder-decoder networks for identify multiple neurological disorders from EEG signals.

BackgroundThe complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.

Assessment of impact of COVID-19 on temporomandibular disorders and its prevalence in Central India post-COVID: a survey.

Background: Temporomandibular disorders (TMD) are characterized by pain and dysfunction affecting the temporomandibular joints and surrounding muscles. TMD prevalence in the general population is estimated at 5-12%, with psychological factors like anxiety and depression contributing to its onset and progression. TMD's impact extends to various health conditions and psychological distress.

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a number of diseases Due to inherent data uncertainties and Electroencephalogram (EEG) pattern overlap, conventional EEG diagnosis methods frequently encounter difficulties. This paper proposes a novel framework that integrates FLSNN to enhance the accuracy and robustness of multiple neurological disorder disease detection from EEG signals.

Composition of heavy metals in sediment, water, and fish of the Ganga and Yamuna Rivers in two major cities of India.

The rapid industrial development in the Indian capital region has led to significant waste generation, which, despite undergoing treatment prior to disposal, contributes substantially to water body contamination. Given the diverse nature of these wastes and their potential repercussions across the food chain, a study was conducted to evaluate heavy metal contamination levels in the Ganga and Yamuna Rivers of two major cities. Six heavy metals (Pb, Cd, Hg, Cu, Cr, and Zn) were analyzed in fish, water, and sediment samples by utilizing flame atomic absorption spectrophotometry (Avanta Σ) from March 2019 to February 2020.

A review on heavy metal biosorption utilizing modified chitosan.

Heavy metal pollution in water bodies is a global concern. The prominent source of metal contamination in aqueous streams and groundwater is wastewater containing heavy metal ions. Elevated concentrations of heavy metals in water bodies can have a negative impact on water quality and public health.

Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images.

Background: Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required.

Impact Strength of Various Types of Acrylic Resin: An Study.

Aim: The aim of this study is to evaluate and compare the impact strength of conventional acrylic resin, high-impact acrylic resin, high-impact acrylic resin reinforced with silver nanoparticles, and high-impact acrylic resin reinforced with a zirconium oxide powder.

Materials And Methods: A total of 60 samples were prepared of dimensions 60 mm length × 7 mm width × 4 mm thickness to test impact strength. Machined stainless steel dies of the same dimension were used to form molds for the fabrication of these samples.

Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images.

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming.

Evaluation of symptoms along with depression, anxiety and stress levels of Sars-CoV-2 positive patients during the second wave in the city of Indore, Central India - a cross sectional study.

Introduction: The consequences of the second wave hitting India have drastically laid a huge impact on the mental state of patients. The second wave had proven to be far more dangerous and hence the psychological evaluation needed to be conducted to know the scenario of patients suffering from SARS-CoV-2.

Objective: This study was undertaken to evaluate the symptoms of SARS-CoV-2 patients along with the existing depression, anxiety and stress levels amongst them.

Water quality of springs and lakes in the Kumaon Lesser Himalayan Region of Uttarakhand, India.

The scarcity of drinking water has become a bitter reality in many countries. The gap between demand and supply of water has been increasing exponentially year by year. Deforestation, vigorous use of groundwater for agricultural practices, and pollution of our present water resources such as rivers, lakes, and wells are triggering the freshwater scarcity problem.

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors.

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle.

Frequency, Prevalence, Interaction, and Gender Differences of Six Psychosocial Factors in Patients Presenting to a Psychiatric Emergency Service.

Introduction: The psychiatric emergency service (PES) has become an increasingly utilized patient care approach over the past 50 years. Psychosocial factors play an important role in PES utilization and disposition of patients in this environment. PES utilization in our region has increased 450% in the past 18 years, while the population has changed <1%.

Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction.

Background: The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment information. Since homologous details not available for all proteins.

A Survey on Medical Image Analysis in Capsule Endoscopy.

Background And Objective: Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality.

A review on traditional practice of tooth brushing in Ayurveda and its relevance in current era.

Ayurveda recommends sound and healthy life. It basically focuses on prevention rather than treatment. It includes a systematic lifestyle pattern which is composed of several principles and activities.