The Bioscan
Not a member yet
3512 research outputs found
Sort by
LIMNOLOGICAL STUDIES IN RELATION TO PHYTOPLANKTONS OF RIVER WARDHA NEAR EKONA, TALUKA WARORA, DISTRICT CHANDRAPUR, MAHARASHTRA STATE, INDIA
Get PDFOver a one year period, this study examined the whole impact of coal mines and power plants on entertained local phytoplankton community and physico-chemical characteristics of Wardha river near Ekona. These characteristics were studied for a period of 12 months from February 2022 – January 2023. In view of above, an attempt was made to study certain limnological parameters such as water temperature, conductivity, T.D.S., turbidity, pH, D.O., total alkalinity, B.O.D., free CO2, chloride, phosphate and nitrate. For examination of above all mentioned parameters American Public Health Association (APHA - 1985), American Water Works Association was made. Present research work concludes that Wardha river near Ekona was basically for irrigation purpose is now polluted water body due to continuous discharge of coal mines and power plants
PLANT-BASED BIOPLASTICS AND AN ECO-FRIENDLY ALTERNATIVE TO CONVENTIONAL PLASTICS
Get PDFThe increasing environmental burden of petroleum-based plastics has intensified the global search for sustainable alternatives. The widespread use of conventional plastics has led to severe ecological challenges, including excessive plastic waste, microplastic pollution, and greenhouse gas emissions, contributing to climate change. One promising solution is the development of plant-based bioplastics, which offer renewable, biodegradable, and eco-friendly characteristics compared to traditional plastics derived from fossil fuels. These bioplastics are synthesized from plant-based sources such as corn starch, sugarcane, cassava, cellulose, and algae, making them a viable alternative for packaging, agriculture, medical applications, and consumer goods. Recent advancements in biopolymer technology have led to the creation of various types of plant-derived plastics, including polylactic acid (PLA), polyhydroxyalkanoates (PHA), starch-based bioplastics, bio-polyethylene (bio-PE), and cellulose-based plastics. These materials exhibit improved biodegradability, reduced environmental toxicity, and enhanced functionality across industries.Despite their potential, the commercialization of plant-based bioplastics faces several hurdles. One of the major concerns is their high production cost, which makes them less competitive compared to petroleum-based plastics. Additionally, certain bioplastics require specific industrial composting conditions for degradation, making their disposal challenging in regions lacking proper composting infrastructure. The mechanical and thermal properties of some bioplastics also present challenges, as they may be less durable, heat-resistant, and water-resistant compared to synthetic polymers. Furthermore, concerns have been raised regarding the sustainability of large-scale production, as the cultivation of raw materials such as corn and sugarcane competes with food production, raising ethical and resource management issues.This paper explores the chemical composition, material properties, and production methods of plant-based bioplastics, along with their environmental benefits and industrial applications. It also discusses the challenges associated with their large-scale implementation and the regulatory frameworks that govern their production and disposal. Additionally, strategies for improving their cost-effectiveness, mechanical strength, and compostability are examined. With the growing emphasis on environmental conservation, circular economy practices, and sustainable manufacturing, plant-based bioplastics hold great promise in reducing plastic pollution and transitioning toward a more sustainable future. As industries and governments seek to mitigate the environmental impact of plastic waste, the adoption of bioplastics could serve as a crucial step in achieving global sustainability goals
Assessment of Caries Risk Using Salivary Biomarkers and Clinical Parameters in Pediatric Patients
Get PDFDental caries remains one of the most prevalent chronic conditions among children worldwide, reflecting a complex interplay between host factors, microbial ecology, diet, and salivary composition. Traditional risk assessment methods relying solely on clinical and dietary parameters often fail to predict disease onset accurately in pediatric populations. This study aims to evaluate the diagnostic utility of salivary biomarkers alongside clinical parameters to develop a more comprehensive, objective, and early predictive framework for caries risk assessment in children. A cross-sectional analytical study was conducted among pediatric patients aged 5 to 12 years, categorized into caries-free, moderate-risk, and high-risk groups based on the Decayed, Missing, and Filled Teeth (DMFT/dmft) index. Unstimulated whole saliva samples were collected under standardized conditions, and biochemical analyses were performed to quantify salivary flow rate, pH, buffer capacity, total protein concentration, calcium, phosphate, and levels of specific biomarkers, including lactoferrin, α-amylase, secretory IgA, and Streptococcus mutans count. Clinical parameters such as plaque index, dietary habits, and oral hygiene practices were also evaluated and correlated with biochemical findings using multivariate regression models. The results demonstrated a statistically significant association between elevated bacterial load, reduced salivary flow, and lower buffer capacity with higher caries activity. Among the biomarkers, lactoferrin and secretory IgA exhibited notable variations, indicating their potential as early predictors of caries susceptibility. Salivary calcium and phosphate levels were inversely correlated with caries severity, underscoring their protective role in enamel remineralization. The combined assessment of salivary and clinical parameters enhanced the predictive accuracy of caries risk models by nearly 30% compared to conventional diagnostic indices alone. These findings affirm that salivary biomarkers, when interpreted in conjunction with clinical data, offer a powerful, non-invasive, and reproducible method for assessing caries risk in pediatric patients. The incorporation of biomarker-based screening into routine pediatric dental examinations could enable clinicians to identify at-risk children earlier, individualize preventive care strategies, and monitor the efficacy of intervention programs. This integrative approach aligns with precision dentistry principles, promoting a shift from restorative to preventive and personalized oral health management in pediatric populations.
KEYWORDS:
Caries risk assessment, Salivary biomarkers, Pediatric dentistry, Secretory IgA, Streptococcus mutan
EFFICACY OF A PSYCHO-CORRECTIONAL PROGRAM IN PREPARING CHILDREN WITH ATTENTION DEFICIT AND HYPERACTIVITY DISORDER FOR SCHOOL
Get PDFBackground: Children with Attention Deficit and Hyperactivity Disorder (ADHD) often face significant challenges in adapting to the structured school environment, necessitating specialized preparatory interventions. This study investigates the efficacy of a novel psycho-correctional program aimed at enhancing school readiness in this population.
Methods: The program employed a multi-faceted approach, with a particular emphasis on emotional and behavioral correction techniques tailored to the needs of children with ADHD.
Results: Post-intervention assessment revealed marked improvements in key areas of school readiness among the participants, including emotional self-regulation and classroom preparedness.
Conclusion: The findings confirm that a targeted psycho-correctional program is an effective means of preparing children with ADHD for the academic and social demands of school.
KEYWORDS:
Attention deficit disorder and hyperactivity, psychological readiness for school education
ASSESSING KNOWLEDGE REGARDING POLYCYSTIC OVARIAN SYNDROME AMONG ADOLESCENT GIRLS
Get PDFPolycystic Ovarian Syndrome (PCOS) is a common endocrine disorder that significantly affects women of reproductive age, including adolescents. It presents with a wide range of symptoms such as irregular menstruation, hirsutism, acne, and obesity, and can lead to long-term complications like infertility, type 2 diabetes, and cardiovascular disorders. Despite its increasing prevalence, awareness among adolescent girls remains limited. The aim of the study is to assess the level of knowledge regarding PCOS among adolescent girls in a selected university and the examine associations between their knowledge and selected Socio-demographic variables. This study adopted a quantitative research design. In this study total 150 adolescent girls aged 12-19 years were selected using a non-probability purposive sampling technique. Data were collected using structured questionnaire that included demographic variables and knowledge-based questions related to PCOS. Descriptive and inferential statistics were used for data analysis. Findings revealed that 70% of participants had heard of PCOS; however, only 38% demonstrated excellent knowledge, 17.3% good knowledge, 13.3% average knowledge, and 31.3% poor knowledge. A statistically significant association was observed between family monthly income and knowledge levels (p = 0.026), indicating that higher income was linked with better awareness. Other variables, such as age, education, family type, and menstrual history, showed no significant association. The study concludes that although basic awareness about PCOS exists, a substantial knowledge gap remains. The findings highlight the need for structured, school-based health education programs focusing on reproductive health and PCOS awareness. Strengthening such interventions can empower adolescent girls to seek timely medical attention and adopt preventive strategies.
KEYWORDS:
Assess, Knowledge, Polycystic ovarian syndrome, Adolescent girl
ML/DL Techniques to analyze EMG signal across various domains: Exhaustive Review
Get PDF
Research on Machine Learning (ML) and Deep Learning (DL) applied to Electromyography (EMG) signal analysis shows widespread use in medical and rehabilitation applications for prosthetic control, diagnosis of neuromuscular disorders, and gait analysis, as well as in Human-Computer Interaction (HCI) for gesture recognition and user input systems.
Purpose
The central goal of employing ML and DL for EMG analysis is to accurately and robustly decode human motor intent and neuromuscular activity. Applications include controlling prosthetic devices, diagnosing neuromuscular disorders, guiding rehabilitation, and enabling human-computer interaction.
Methods
The analysis aims to overcome inherent challenges of EMG signals, such as noise, variability due to electrode placement, and changes with muscle fatigue, to achieve reliable, real-time performance.
The standard methodology involves a multi-stage pipeline:
1. 1. Data Acquisition: Non-invasive surface EMG (sEMG) using electrodes placed on the skin over target muscles. Invasive intramuscular EMG (iEMG) offers higher selectivity for non-clinical applications.
2. Preprocessing: Raw EMG signals are noisy and require filtering to remove artifacts like power-line interference, motion artifacts, and baseline drift, band-pass filtering (e.g., 10–500 Hz), notch filtering (e.g., at 50/60 Hz), rectification, and segmentation.
3. Feature Extraction: from frequency domain (e.g., Mean/Median Frequency), or time-frequency domain (e.g., Wavelet Transform coefficients), the Deep learning models, in contrast, and extract these high-level features automatically from raw or minimally processed data.
4. Classification/Regression: using ML/DL models for either discrete classification (e.g., gesture recognition) or continuous regression (e.g., joint angle or force estimation).
Common techniques include Random Forests, Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Support Vector Machines (SVM)
5. Evaluation: Models are evaluated using metrics with ongoing research focused on improving accuracy, reducing latency, handling data imbalance, and enabling real-time control on embedded systems.
Results
High Accuracy: Both ML and DL models with very high accuracy in controlled settings, with some applications classification accuracies exceeding 99%.
DL Advances: Deep learning models, particularly hybrid CNN-LSTM architectures automatically learning more abstract and robust features more efficient.
Real-time Performance: DL models have been optimized for real-time applications in fields like prosthetic control, enabling responsive and intuitive human-machine interfaces.
Domain-Specific Outcomes:
Prosthetics and Robotics: DL classifies hand and arm gestures, allowing more naturalistic and precise control of robotic hands and exoskeletons.
Clinical Diagnostics: healthy muscle activity and signals from patients with neuromuscular disorders like myopathy and Amyotrophic Lateral Sclerosis (ALS), achieving high precision.
Rehabilitation: provides biofeedback for guided rehabilitation exercises.
These advanced methods, combined with techniques like feature extraction in the time and frequency domains (e.g., RMS, MAV, Wavelet Transform) and noise reduction, allow for accurate, real-time detection and classification of muscle activity, leading to more intuitive and responsive control systems.
Conclusion
ML and DL techniques have revolutionized EMG signal analysis, enabling accurate and robust decoding of human intent across diverse domains especially in complex, real-world scenarios. The continued development of these techniques, alongside improved data acquisition hardware, promises more intuitive and effective human-machine interfaces for prosthetics, advanced clinical diagnostics, and targeted rehabilitation therapies.
Keywords
ML, DL, EMG, prosthetic control, HCI, gesture recognition, CNN, RNN, LSTM, SVM
Bioflx: A Novel Pediatric Dental Crown
Get PDFBioflx crowns represent an innovative advancement in pediatric dentistry, addressing the growing demand for aesthetic and functional solutions in the restoration of primary molars. Combining the desirable features of traditional stainless steel crowns and zirconia crowns, bioflx crowns are designed to be highly flexible, visually appealing, and durable. This paper reviews the characteristics, benefits, and clinical applications of bioflx crowns in pediatric dental practice.
Keywords
Primary teeth, Bioflx crown, SSC, Zirconia Crow
Seasonal Variation and Regional Distribution of Spider Families Across Akole, Parner and Sangamner
Get PDFSpiders play a crucial role in terrestrial ecosystems, particularly in regulating pest populations. This study investigates how weather parameters-temperature, relative humidity, and rainfall- affect spider diversity in the agricultural landscapes of western Ahmednagar district, Maharashtra. We found that Araneidae (orb-weaving spiders) are positively correlated with humidity and rainfall, especially during the rainy season, while Lycosidae and Salticidae, as active hunters, show stronger correlations with temperature and sunshine hours, indicating their resilience to changes in humidity and rainfall. These findings align with existing research on spider ecology and highlight the significant role of microclimatic factors in shaping web architecture, species behavior, and spider abundance in agro-ecosystems. Seasonal distribution patterns across the locations of Akole, Parner and Sangamner were found to be strongly influenced by local weather conditions. Akole, with its higher humidity and rainfall, supports a diverse range of spiders, including web-builders like Araneidae and hunting spiders like Lycosidae during the winter. Parner with warmer, drier conditions, favors hunting spiders, such as Lycosidae and Salticidae, while Sangamner shares similar trends but supports some web-builders during the rainy season due to slightly higher moisture levels. Overall, Akole’s humid environment supports greater spider diversity, while Parner and Sangamner favor active hunters, reflecting the strong influence of local climate on spider diversity and ecosystem dynamics. Future studies should further explore the impact of climate change on spider populations and their role in ecosystem services in agro-ecosystems.
KEYWORDS:
Araneae, Agro-ecosystem, Ecology, Humidity, Temperature, Rainfall, Ahmednagar
The Role of Gut Microbiota in Health of Animals and Development: A Zoological Perspective
Get PDFThe gut microbiota, the diverse community of microorganisms residing in the gastrointestinal tract, plays an essential role in animal health, development, physiology, and evolution. Recent advances in molecular and metagenomic techniques have revealed complex host–microbe interactions that influence digestion, immune system maturation, metabolism, and even behavior. From invertebrates to mammals, the gut microbiota contributes to nutrient acquisition, protection against pathogens, and modulation of host development. In this review, we discuss the composition, functions, and evolutionary significance of gut microbiota across animal taxa. We also highlight the influence of environmental factors, diet, and host genetics on microbial community structure, as well as the implications of dysbiosis for animal health. Understanding these intricate host–microbe relationships provide a zoological framework for improving animal welfare, sustainable livestock production, and biodiversity conservation.
KEYWORDS
Gut microbiota, host–microbe interactions, animal development, symbiosis, immune regulation, evolution, zoolog
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN MEDICAL DEVICES: FDA REGULATORY PATHWAYS, TRENDS, AND THE CASE OF NEMOSCAN 510(K) CLEARANCE
Get PDFWith the introduction of artificial intelligence (AI) and machine learning (ML) in medical device software, in particular software as a medical device (SaMD), healthcare continues to be reshaped. This review provides a historical overview of AI and the foundational principles of ML as well as the regulatory framework it currently undergoes to achieve all the medical devices enabled by AI/ML. It outlines 510(k), De Novo, and PMA regulatory pathways and recent efforts made to muster Good Machine Learning Practice (GMLP) on a global scale. The study also explores the tendencies of FDA approvals, especially in terms of device classification and manufacturer impact, as well as regulatory openness. Described in this case study is how the NemoScan (K232698) dental planning software has been brought to light as an example of practical FDA 510(k) clearance of the substantial equivalence. To ensure that upcoming technological changes and further use of AI/ML-based medical hardware turn out to be not only safe but also effective, the review culminates by mentioning disaster-based regulation and empirical data, as well as interdisciplinary collaboration.
KEYWORDS:
510(k) Clearance, Artificial Intelligence, Machine Learning, Medical Devices, Clinical Trial