The Bioscan
Not a member yet
3512 research outputs found
Sort by
A STUDY TO EXPLORE THE KNOWLEDGE, ATTITUDE, AND QUALITY OF LIFE AMONG CHRONIC RENAL FAILURE PATIENTS UNDERGOING HEMODIALYSIS AT PANIMALAR MEDICAL COLLEGE HOSPITAL AND RESEARCH INSTITUTE, CHENNAI.
Full text linkackground: Chronic Kidney Disease (CKD) is a major global public health concern, with prevalence varying across regions. It is estimated that approximately 10–15% of the global adult population is affected by CKD. Evidence suggests that 30–60% of patients worldwide have inadequate knowledge regarding chronic renal failure (CRF), its progression, and the role of haemodialysis in disease management. Objectives: The objectives of the study were to assess the knowledge, attitude and quality of life of chronic renal failure patients undergoing haemodialysis. To Correlate knowledge, attitude and quality of life and to associate the level of knowledge, knowledge, attitude and quality of life with selected demographic variables chronic renal failure patients undergoing haemodialysis. Methods: An evaluative approach with a descriptive research design was adopted. A total of 134 patients undergoing haemodialysis were selected using a purposive non-probability sampling technique based on predefined inclusion criteria. Data on knowledge, attitude, and QoL were collected using a structured questionnaire and standardized tools. Statistical analysis was performed using descriptive and inferential statistics. Results: The findings revealed that the majority of participants 88 (65.67%) had inadequate knowledge, 35 (26.11%) had moderate knowledge, and only 11 (8.20%) demonstrated adequate knowledge regarding CRF and haemodialysis. Chi-square analysis demonstrated a highly significant association between knowledge level and age, education, marital status, and duration of dialysis (p < 0.0001), and a significant association with gender and employment status (p < 0.05). Attitude was significantly associated with age (p < 0.0001). QoL showed a significant association with marital status and duration of dialysis treatment (p < 0.0001). Conclusion: The study highlights that patients with chronic renal failure undergoing haemodialysis generally exhibit moderate levels of knowledge, attitude, and quality of life. Significant positive correlations were identified among knowledge, attitude, and QoL, with meaningful associations observed with selected demographic variables. The findings emphasize the importance of enhancing patient education and fostering positive attitudes, which may lead to improved quality of life and better health outcomes among patients undergoing haemodialysis
Heterologous expression and purification of novel antimicrobial peptide Odorranain-F-OW1 in Escherichia coli forbiophysical characterisation
Full text linkSmall cationic peptides having microbicidal qualities are known as antimicrobial peptides, or AMPs. All living things, including humans, plants, and insects, create AMPs as a major component of their immediate, non-specific defenses against diseases. AMPs range in length from six to fifty amino acids. By specifically interacting with bacterial membranes, these peptides ultimately destroy the germs and provide the host with protection. Natural antimicrobial peptides are abundant in amphibian skin. Odorranain-F-OW1, a 29-residue frog peptide, was selected for the investigation from the AMP database (https://aps.unmc.edu/home) (2). To achieve soluble expression, the chosen peptide gene was cloned into the pET-32a(+) vector and fused with a thioredoxin tag. The E. Coli BL21 (DE3) strain's peptide was isolated for biophysical analysis after being expressed via induction. The E. Coli BL21 (DE3) strain's peptide was isolated for biophysical analysis after being expressed via induction. The antibacterial qualities of the peptide were validated by the disc diffusion method and agar well.
KeywordsAntimicrobial peptides, antibiotic-resistant bacteria, multidrug resistance (MDR), recombinant peptide, expression, fusion protein purification, CD, MALDI, NMR
Intelligent Material Characterization: A ML Approach for Predicting Microstructure of Nanomaterials
Full text linkNanomaterials are important for many businesses today, such as computer chips and cloud storage devices. A lot of study is being done to make new nanomaterials at the same time, machine learning (ML) is being used more to solve problems in fields like physics, chemical engineering, and manufacturing sector. Because ML is capable of developing in both controlled and unstructured ways, it could help solve many problems in the real world. Using ML techniques for examining at images of nanomaterials is necessary to find out more about them and characterize and analyze their architecture and spectral data, according to the current state of the art. In order to achieve this, researchers presented in this study a ML based approach for analyzing Scanning transmission electron microscopy (STEM) images and spectral data from STEM images of nanomaterials. To analyze STEM images of a nanomaterial, researchers suggested an approach called Machine Learning for STEM Image Analysis (ML-SIA). In order to analyze the spectrum data of a STEM image of a nanomaterial, researchers have introduced an approach called ML for STEM Image spectrum Data Analysis (ML-SISDA). To execute the algorithms into practice and assess the suggested methods, researchers created a prototype ML application. According to experimental findings, ML-based methods are effective for characterizing nanomaterials. Therefore, by spurring more research in the field of material analysis using AI, this research assists in moving this into the future.
KeywordsMachine Learning, Nanomaterials, STEM Images, Spectral Data, Chemical Engineering, Manufacturing Secto
Effect on Foliar Feeding of Plant Growth Regulators and Micronutrients on Flowering attributes of Ber (Ziziphus mauritiana Lamk) fruits cv. Gola
Full text linkThe study was carried out on six-year-old Ber (Ziziphus mauritiana Lamk.) trees cultivated under sodic soil conditions at the Production Processing of Fruits and User Waste Land, Akma. The research was undertaken within the Department of Fruit Science, College of Horticulture and Forestry, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.), during the academic years 2023–24 and 2024–25. The objective of the investigation was to evaluate the impact of foliar application of plant growth regulators and micronutrients on fruiting character of Ber cv. Gola fruits. The experiment was laid out in a RBD (Randomized Block Design) and data were collected on key preharvest parameters including Flowering initiation time, Number of flowers per shoot, Days taken to 50 % flowering, Days taken for full bloom. Among the treatments, T12 (GA₃ 20 ppm + NAA 30 ppm + ZnSO₄ 0.5% + Borax 0.5%) consistently minimized the time required for flowering initiation, increased the number of flowers per shoot, and reduced the days taken to reach 50% flowering as well as full bloom, across both years and in the pooled mean, compared with the control.
KeywordsBer, Randomized Block Design, Flower and Bora
Deep Learning for Corrosion Monitoring Virtual Sensor and Predictive Modelling Approaches in Industrial Water Pipeline
Full text linkIndustrial water pipeline corrosion constitutes major risks in maintenance expenses, protection & performance. Physical indicators & periodic examinations were the core of conventional tracking approaches which might be expensive as well as ineffective. The present study examines corrosive monitoring in corporate pipelines through predictive modelling techniques & artificial detectors powered by deep learning. Machine learning algorithms have the potential of accurately forecasting the rates of corrosion along with spotting irregularities by employing real-time information using existing detectors comprising pressure, temperature & fluid flow. Different kinds of designs are investigated regarding their capacity to detect rusting trends which consists of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The proposed approach makes it possible to perform in progress, safe monitoring that minimizes maintenance expenses & increases pipeline durability. The outcomes compared to studies highlight how machine learning systems may enhance earlier detection of defects & erosion prediction, leading to higher robustness and efficient pipeline architecture.
KeywordsIndustrial water pipeline corrosion, Recurrent Neural Networks, Convolutional Neural Networks, Machine Learning, Predictive modelling techniques and artificial detectors
COMPARATIVE ANALYSIS OF INDIGENOUS BACTERIAL AND FUNGAL CONSORTIA FOR ENHANCED CHROMIUM DETOXIFICATION AND ENVIRONMENTAL SAFETY
Full text linkThe research concentrates on the development and characterization of mixed bacterial–fungal consortia derived from industrial effluents and contaminated soils for the biodegradation and detoxification of hexavalent chromium [Cr(VI)]. This study examines the synergistic interactions between indigenous bacteria (Bacillus subtilis, Lysinibacillus macroides) and filamentous fungi (Aspergillus niger, Penicillium chrysogenum), in contrast to previous studies that focused solely on bacterial communities. The detoxification efficiency, assessed at 50, 100, and 200 mg/L Cr(VI), demonstrated a removal rate of up to 87% within 72 hours under optimized conditions (pH 7, temperature 35°C). The results show that microbial synergism is a long-term and cost-effective way to get rid of heavy metals with little risk to the environment.
KeywordsChromium detoxification, microbial consortium, Bacillus subtilis, Aspergillus niger, synergistic bioremediation, environmental safety
Analysis of the Impact of Polycystic Ovarian Syndrome on the Reproductive Health of Married Women in Tamil Nadu
Full text linkPolycystic Ovarian Syndrome (PCOS) is a frequently occurring endocrine condition among women of reproductive age and a primary contributor to subfertility associated with anovulation. Moreover, insufficient awareness of PCOS, its management, and essential lifestyle modifications adversely affects clinical outcomes. This qualitative study explores women’s understanding and perceptions of the syndrome, its treatment options, and the lifestyle changes required for effective management. A total of 120 individuals with PCOS were selected through purposive sampling from Vriddhachalam, and telephonic interviews were also conducted. The themes derived from the analysis encompassed women’s understanding of PCOS; its causes, complications, and risk factors; treatment-related perceptions; and the perceived significance of health-promoting behaviors such as physical activity, sleep habits, and societal support. The respondents further emphasized the importance of nutrition, regular physical activity, and a wellness-oriented lifestyle. While medications assisted participants in achieving consistent menstrual cycles, they also produced side effects, which are discussed in the subsequent section. A small number of respondents reported being unaware of PCOS at the time of their diagnosis during adolescence. Overall, this study enhances the understanding of PCOS from a qualitative perspective that incorporates cultural context alongside relevant clinical and lifestyle considerations.
KeywordsPolycystic Ovarian Syndrome, Mensural Impact, Treatment, Life style changes
‘‘A study to assess the growth and development of preschool children (age 3-4 years) attending Anganwadi in selected rural area of Haryana.’’
Full text linkGrowth and development are critical indicators of health and well-being during early childhood, particularly in the preschool years when rapid physical and developmental changes occur. This study aimed to assess the growth and development of preschool children (3–4 years) attending Anganwadi centres in selected rural areas of Haryana, to determine the association between growth and development with selected demographic variables, and to identify the correlation between growth and developmental domains. A quantitative research approach with a descriptive survey design was adopted. The study sample comprised 150 preschool children aged 3–4 years, selected using a non-probability convenient sampling technique. Data were collected through a structured interview schedule administered to parents. Anthropometric measurements including height, weight, and mid-upper arm circumference (MUAC) were assessed using standard measurement tools. Data analysis was performed using descriptive and inferential statistics. The demographic profile revealed that the majority of children were aged 3.7–3.9 years (33.33%), male (59.3%), Hindu (75%), from nuclear families (46.7%), and belonged to families with moderate monthly income. Most fathers had secondary-level education (39.34%) and were private employees (34.66%), while the majority of mothers were homemakers (66.66%). Anthropometric assessment showed that 59.3% of children had average height, 72.7% had average weight, and 86.0% had average MUAC. Significant associations were observed between height and fathers’ education and meal pattern; weight with child’s age and family income; and MUAC with age, birth order, and family income. Developmental assessment revealed significant associations across gross motor, fine motor, self-care, cognitive, language, psychosocial, and psychosexual domains with variables such as age, parental education, occupation, and meal pattern. A positive correlation was identified between overall growth and developmental status of preschool children. The study highlights the influence of socio-demographic and nutritional factors on the growth and development of preschool children in rural settings, emphasizing the need for targeted interventions through Anganwadi services.
KeywordsPreschool children, growth, development, Anganwadi, assessment
EFFECTIVENESS OF WASH METHOD ON MENSTRUAL HEALTH AMONG ADOLESCENT GIRLS AT SELECTED SCHOOL, CHENNAI
Full text linkBackground Menstrual health management remains a major concern for adolescent girls, particularly in low-resource school environments where water, sanitation, and hygiene (WASH) facilities are inadequate. Poor knowledge and lack of proper hygiene practices during menstruation negatively affect girls’ school attendance, participation, and overall well-being. Structured educational interventions and improved WASH accessibility can significantly enhance menstrual health knowledge and reduce related stigma. Aim of the Study: The atudy aimed to assess the effectiveness of a structured WASH-based information booklet on improving menstrual health knowledge among adolescent girls. Methods: A quantitative one-group pre-test and post-test research design was adopted among 100 adolescent girls aged 12–18 years who had attained menarche at a selected school. A structured questionnaire assessed demographic variables and menstrual health knowledge. After the pre-test, an information booklet on WASH practices was administered, followed by a post-test. Data were analyzed using descriptive and inferential statistics, including paired t-test and chi-square test.Results: Before the intervention, 58% of the participants demonstrated poor menstrual health knowledge, while none achieved an extremely effective level. Following the WASH intervention, 59% reached an extremely effective knowledge level and no student remained in the poorly effective category. The mean score increased from 10.74 in the pre-test to 21.02 in the post-test, showing a highly significant improvement (p < 0.001). Educational standard and age at menarche showed significant association with post-test knowledge levels. Conclusion: The WASH intervention significantly improved menstrual health knowledge among adolescent girls. Culturally appropriate WASH-based education programs in schools can enhance menstrual health practices and support improved academic participation and well-being.
KeywordsMenstrual health, WASH intervention, adolescent girls, school hygiene, knowledge improvemen
Assessment of Lumbar Vertebrae Morphometry Across Different Age Groups: A Computed Tomographic Study
Full text linkBackground: Lumbar vertebral morphometry plays a critical role in spinal biomechanics, radiological interpretation, and surgical planning. Although several studies have evaluated lumbar vertebral dimensions, data on age-related morphometric changes in the Indian population remain limited.
Aim: To establish age-specific morphometric standards for lumbar vertebrae (L1–L5) and to assess age-related changes using computed tomography (CT).
Materials and Methods: This multicentric, cross-sectional study included 1000 CT scans of the lumbar spine from individuals aged 18–50 years. Thin-slice (1 mm) CT images were analyzed using a DICOM viewer to measure pedicle dimensions, vertebral body dimensions, canal parameters, disc-related indices, and interlaminar angles at levels L1–L5. Participants were categorized into four age groups. Statistical analysis was performed using ANOVA, with p < 0.05 considered significant.
Results: Significant age-related differences were observed across most morphometric parameters at all lumbar levels (p < 0.001). Transverse dimensions, interpedicular distance, and disc parameters increased progressively from L1 to L5, while vertebral heights showed a relative decline with advancing age. The greatest remodeling was observed at L4 and L5.
Conclusion: Lumbar vertebral morphometry demonstrates significant age- and level-dependent variation, with maximal structural adaptation at the lower lumbar levels. These findings provide valuable normative data for the Indian population and have important implications for radiological evaluation, implant design, and spinal surgery.
KeywordsLumbar spine; Vertebral morphometry; CT scan; Age variation; Spinal biomechanics; Surgical plannin