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Developmental prospects of carrageenan-based wound dressing films: Unveiling techno-functional properties and freeze-drying technology for the development of absorbent films A review
This review explores the intricate wound healing process, emphasizing the critical role of dressing material selection, particularly for chronic wounds with high exudate levels. The aim is to tailor biodegradable dressings for comprehensive healing, focusing on maximizing moisture retention, a vital element for adequate recovery. Researchers are designing advanced wound dressings that enhance techno-functional and bioactive properties, minimizing healing time and ensuring cost-effective care. The study delves into wound dressing materials, highlighting carrageenan biocomposites superior attributes and potential in advancing wound care. Carrageenan's versatility in various biomedical applications demonstrates its potential for tissue repair, bone regeneration, and drug delivery. Ongoing research explores synergistic effects by combining carrageenan with other novel materials, aiming for complete biocompatibility. As innovative solutions emerge, carrageenan-based wound-healing medical devices are poised for global accessibility, addressing challenges associated with the complex wound-healing process. The exceptional physico-mechanical properties of carrageenan make it well-suited for highly exudating wounds, offering a promising avenue to revolutionize wound care through freeze-drying techniques. This thorough approach to evaluating the wound healing effectiveness of carrageenan-based films, particularly emphasizing the development potential of lyophilized films, has the potential to significantly improve the quality of life for patients receiving wound healing treatments. 2024 Elsevier B.V. -
Developmental Regulations to Conserve Catchment Area of an Urban Water Body: A Case of Upper Lake in the City of Bhopal, Madhya Pradesh, India
A lake reflects its catchment and provides a wide range of ecological services useful for the sustenance and betterment of human lives and other living organisms. The fast-paced developmental activities in the catchment area of lakes and over-consumption of available resources are resulting in the degradation of ecological services of the lake. The Upper Lake in Bhopal, a million-plus population city situated in the central part of India, has been selected for the study. The current regulatory regime for environmental conservation of the catchment area comprises a legislative framework and a set of regulatory institutions. Inadequacies in each accelerates environmental degradation of the Upper Lake, delays and escalates cost of developmental projects. This paper is an attempt to find out gaps in the existing institutional framework to conserve the catchment area. The present study focused on identifying and assessing major issues pertaining to the degradation of the Upper Lake, thereafter establishing a cause-and-effect relationship between anthropogenic activities and their environmental impacts. Subsequently, formulating developmental regulations forconserving theUpper Lake and its catchment area. Primarily, GIS application has been employed to delineate the catchment area of the Upper Lake by using catchment area assessment technique. Furthermore, a primary survey targeting the village stakeholders in the catchment area was conducted to analyze socio-economic profile and assess the agricultural practices adopted. Finally, formulated an institutional framework to conserve, govern and monitor the catchment area and the Upper Lake from the anthropogenic issues. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Developments in Feeds in Aquaculture Sector: Contemporary Aspects
Aquaculture, one of the largest protein food generating sectors, greatly relies on nutrition. Up to the present time, the dependency of the aqua feed sector on fish meal and fish oil as protein and lipid sources, respectively, was too high, which led to its inadequacy and over-exploitation of marine resources. Recently, numerous researches with locally available feed ingredients have been accomplished. There is urgency for a move from fish meal to plant/terrestrial animal/microbial proteins within the aquaculture industry as over-exploitation of wild fishes has negative ecological consequences. Plant proteins cannot successfully replace fish meal due to poor protein digestibility and essential amino acids imbalance, urging feed concerns to search for cheaper and nutritious fish meal alternatives from animal origin. In order to overcome the indiscriminate use of antimicrobial drugs, the concept of probiotics in aquaculture has received firm encouragement in recent years due to their wise inhibitory mechanisms and safeness. Negative perception for synthetic antioxidants among fish farmers and their unreliable nature has resulted in the research for non-synthetic, food derived antioxidants that could encounter and neutralize the detrimental effects of free radicals. In line with this, there is a strong captivation to research on the individual or synergistic effects of protein hydrolysates, peptide fragments, and free amino acids that could innately exhibit antioxidative activity. Commercialization of economical feeds with antioxidative feed ingredients could strengthen the profits of feed processors. Recent development in the aquaculture sector propelled by the application of biotechnological methods has clearly highlighted the need for development of functional feeds through incorporation of bioactive molecules. In the last few decades, the global aqua feed industry has witnessed milestones in the development of feed ingredients from waste raw materials and sustainable alternatives to antibiotics and chemicals used to tackle the disease outbreak in aquaculture. As the health and immunity of fishes primarily depend on their nutritional pattern, a great research interest is extended on incorporation of biomolecules like single-cell proteins, animal proteins, plant metabolites, biopolymers, and enzymes as feed ingredients to enhance the nutritive quality and immune tolerance in fishes. Besides the focus on the feed ingredients, the modification and development of fermentation strategies for producing probioticbased feed and enzyme-assisted bioconversion into valuable feed ingredients is also gaining more importance. This chapter deals with the recent development in the aqua feed industry with specific reference to the incorporation of non-conventional feed ingredients like animal/microbial proteins, biopolymers, enzymes, and other immunostimulatory compounds in aqua feed and their impact analysis in improving the growth profile and pathogen tolerance in fishes. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
DFT electronic structure calculations, spectroscopic studies, and normal coordinate analysis of 2-[(5-nitro-1,3-thiazol-2-yl)carbamoyl]phenyl acetate
The solid phase FTIR and FT-Raman spectra of 2-[(5-nitro-1,3-thiazol-2-yl)carbamoyl]phenyl acetate (25N2LCPA) have been recorded 450-4000 cm-1 and 100-4000 cm-1 respectively. The normal coordinate analysis was carried out to confirm the precision of the assignments. DFT calculations have been performed giving energies, optimized structures, harmonic vibrational frequencies and IR intensities. The structure of the molecule was optimized and the structural characteristics were determined by density functional theory (DFT) using B3LYP method with 6-31+G(d,p) basis set. The detailed interpretation of the vibrational spectra has been carried out with aid of normal coordinate analysis (NCA) following the scaled quantum mechanical force field methodology. The Vibrational frequencies are calculated in the above method and are compared with experimental frequencies which yield good agreement between observed and calculated frequencies. Stability of the molecule arising from hyper conjugative interactions, charge delocalization has been analyzed using natural bond orbital (NBO) analysis. In addition, Frontiers molecular orbital and molecular electrostatic potential were computed by using Density Functional Theory (DFT) B3LYP/6-31+G(d,p) basis set. The calculated HOMO and LUMO energies show that charge transfer occurs in the molecule. 2014 Elsevier B.V. All rights reserved. -
DFT studies on D?A substituted bis-1,3,4-oxadiazole for nonlinear optical application
In the present work, we have synthesized novel D?A substituted bis-1,3,4-oxadiazoles derivatives and studied nonlinear optical properties using density functional theory (DFT). The FT-IR and 1H NMR data confirmed the structure of the molecule. The HOMOLUMO, energy band gap, molecular electrostatic potential map, and global chemical reactivity descriptors were estimated using the DFT and TD-DFT with B3LYP, CAM-B3LYP and WB97XD using 6-31G (d) levels basis set and results show all synthesized molecules have excellent chemical hardness, chemical potential, excellent chemical strength, and excellent chemical stability. The static and dynamic linear polarizability, first hyperpolarizability and second hyperpolarizability components were estimated using time-dependent density functional theory. The first-order hyperpolarizability ? (2x; x, x) computed at a wavelength of 1064nm was found to be 55 times greater than the urea molecule. The dynamic molecular second-order hyperpolarizabilities ? (?3x;x,x,x) suggested good nonlinear properties for the designed molecule. The Author(s), under exclusive licence to The Optical Society of India 2024. -
DFT, spectroscopic studies, NBO, NLO and Fukui functional analysis of 1-(1-(2,4-difluorophenyl)-2-(1H-1,2,4-triazol-1-yl)ethylidene) thiosemicarbazide
A novel triazole derivative 1-(1-(2,4-difluorophenyl)-2-(1H-1,2,4-triazol-1-yl)ethylidene) thiosemicarbazide was synthesized and subjected to density functional theory (DFT) studies employing B3LYP/6-31+G(d,p) basis set. Characterization was done by FT-IR, Raman, mass, 1H NMR and 13C NMR spectroscopic analyses. The stability of the molecule was evaluated from NBO studies. Delocalization of electron charge density and hyper-conjugative interactions were accountable for the stability of the molecule. The dipole moment (?), mean polarizabilty (??) and first order hyperpolarizability (?) of the molecule were calculated. Molecular electrostatic potential studies, HOMO-LUMO and thermodynamic properties were also determined. HOMO and LUMO energies were experimentally determined by Cyclic Voltammetry. 2018 Elsevier B.V. -
Dharma as a binary identity
The idea of Dharma has different connotation in History from that of religion as is popularly understood. While it is accepted as righteousness, it transcends the notion that Dharma represents piety, spirituality, belief and nobility. On the contrary, History is replete with instances of how religion, an institutionalized aspect of Dharma, was constantly articulated as representing Authority, Power, Status and Hierarchy. Due to these interpretations Dharma often was projected as a tool for realization of the above by various institutions, be they, political, social, cultural or economic, and Dharma provided legitimacy and justified their identities. The present paper juxtaposes this articulation in the context of Ancient and Medieval India, spanning a period approximately from 3rd century BCE to 10th century CE. It argues that the different trajectories that flowed between Dharma and various other secular institutions constantly witnessed divergence as well as assimilation at various points of time. 2015 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bengaluru), ISSN: 0253-7222. -
Dharma in Manusmrti: Agent of social cohesion and equilibrium
Dharma, through its role as a moral ideal and through its manifestations that permeate every part of the society, binds all the individuals in Manu's society as envisaged in Manusmrti. It serves as a common value and dictates common goals for the people that could be attained by functioning for the good of the institutions they belong to and, ultimately, for the survival of the society. Social institutions like marriage, family, varna system, as?rama system, political system and legal system were structured with accurate positions and roles for their efficient and smooth functioning. They were made to function compatibly with each other to ensure the survival of the society. Manu carefully avoided conflicts and competitions in the society. Thus, by acting as a cohesive agent, Dharma, as a foundational moral principle, integrates both individuals and the institutions to maintain the equilibrium of Manu's state. 2013 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore), ISSN: 0253-7222. -
Dhat syndrome and its perceived impact on psychological well-being
Background: Dhat syndrome is a culture-bound syndrome originating in the Indian subcontinent, primarily among men characterized by the fear of loss of semen. Objective: The article discusses the perceived impact of Dhat syndrome on the overall psychological well-being of the individual. Method: Four patients from hospitals in Kolkata, West Bengal, were screened using MINI and then interviewed using semi-structured interview to assess presenting concerns, interventions, psychological well-being, attitude toward sex and masturbation, and their sociodemographic details. The data were then categorized based on the dimensions of the questionnaire, which was then analyzed individually and separately based on the dimensions. The differences and commonalities between the dimensions as conveyed by the participants were then reported. Results: The analysis showed that the participants reported lower levels of psychological well-being based on the categories of Seligman's PERMA model and attributed it to the symptoms experienced by them. They traced the beginning of the hindrances to achieving optimal well-being to the onset of symptoms. Conclusion: This article proposes the incorporation of integrative therapeutic interventions and advocacy of sex education to address the psychological well-being over the current symptom reduction interventions used. 2019 Indian Journal of Social Psychiatry | Published by Wolters Kluwer - Medknow. -
Di-cationic ionic liquid catalyzed synthesis of 1,5-benzothiazepines
A simple and elegant method for the synthesis of 1,5-benzothiazepines has been developed using di-cationic liquid as a solvent cum catalyst by the reaction of o-aminothiophenol with a variety of chalcones under mild reaction conditions. Furthermore the reusability of the catalyst has also been studied for three cycles. All the reactions are proposed to proceed through a 1,4-conjugate Michael addition followed by a cyclo-condensation reaction. 2018, Chemical Publishing Co. All rights reserved. -
Diabe Maigre and Diabe Gras Revisited
[No abstract available] -
Diabetes Mellitus Classification Using Machine Learning Algorithms with Hyperparameter Tuning
Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care. 2024 IEEE. -
Diabetes mellitus prediction using machine learning within the scope of a generic framework
Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Diabetic retinopathy detection using convolutional neural networka study
Detection and classification of Diabetic Retinopathy (DR) is a challenging task. Automation of the detection is an active research area in image processing and machine learning. Conventional preprocessing and feature extraction methods followed by classification of a suitable classifier algorithm are the common approaches followed by DR detection. With the advancement in deep learning and the evolution of Convolutional Neural Network (CNN), conventional preprocessing and feature extraction steps are rapidly being replaced by CNN. This paper reviews some of the recent contributions in diabetic retinopathy detection using deep architectures. Further, two architectures are implemented with minor modifications. Experiments are carried out with different sample sizes, and the detection accuracies of the two architectures are compared. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested. 2024 Taylor & Francis Group, LLC. -
Diabetic Retinopathy Diagnosis Using Retinal Fundus Images through MobileNetV3
Diabetic Retinopathy (DR) is a major disease throughoutthe world. Diagnosis of diabetes at an early stage is so critical and could help save several lifestyles. One out of two individuals experiencing diabetes has been determined to have some phase of DR. Recognition of DR symptoms in time can turn away the vision weakness inmost the cases, nonetheless, such disclosure is troublesome with present devices and strategies. Existingmethods for determining whether a person is suffering from diabetes or maybe the chances of acquiring diabetesrely heavily on examiners. Most of the time, it can be treated if caught during the early stages. There is a need for creating models that are efficient and robust to detect DR holistically. In recent times the advent of Deep learning models has been used extensively in various Bio medical applications. In this work, we utilize a Hyper parameter tuned MobileNet-V3 model based on a multi-stage Convolutional Neural Network (CNN) to efficiently classify images from the IRDID dataset. A Multiclass classification model involving images collated from various sources were trained, validated and tested for classification accuracy. The network was evaluated based on parameters and the network was able to achieve an accuracy of 88.6% 2023 IEEE. -
Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture
Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F-measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key. 2022 Abhilash Pati et al. -
Diagnosis and prediction of iigps countries bubble crashes during brexit
We herein employ an alternative approach to model the financial bubbles prior to crashes and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal, and Spain) during Brexit. These countries represent the five financially troubled economies of the Eurozone that have suffered the most during the Brexit referendum. It was found that all 77 crashes across the five IIGPS nations from 19 January 2015 until 17 February 2020 strictly followed a log-periodic power law or other LPPL signature. They all had a speculative bubble phase (following the power law growth) that was then followed by a sudden crash immediately after reaching a critical point. Furthermore, their pattern coefficients were similar as well. This study would surely assist policymakers around the Eurozone to predict future crashes with the help of these parameters. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Diagnosis of Autism Spectrum Disorder: A Review of Three Focused Interventions
Autism is a neurological developmental disorder that impacts a persons physical, social, and emotional behavior. This disorder develops over time and is characterized by social deficits and repetitive behavior. Although there is no cure for this disorder, an early diagnosis and intervention can do significant wonders and can help the subject to become active functioning members of the family and society. The aim of this study is to minimize the diagnostic period by finding an optimal diagnosis procedure from the existing diagnosis tools. The diagnosis of autism can be done in three ways: 1. clinical evaluation; 2. screening tools; 3. brain images. In this review paper, we have thoroughly gone through all three types of diagnostic procedures and found that there was no single diagnostic tool to confirm the disorder. We also found that the diagnosis period was too long. As the result of this review, we found an ASD diagnosis triad which helps to choose the right diagnosis procedure based on the subjects age which reduces the diagnostic period and helps to aid early diagnosis by eliminating the chaos in choosing the diagnostic tools. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.