Functionalized MCM-48 as Carrier for In Vitro Controlled Release of an Active Biomolecule, L-Arginine

The present chapter describes the synthesis, characterizations, and application of MCM-48 functionalized by an inorganic moiety, as a carrier. MCM-48 functionalized by 12-tungstophophoric acid (TPA) (TPA-MCM-48) and L-arginine was loaded into pure as well as functionalized MCM-48. Both the materials were characterized by various physicochemical techniques and evaluated for in vitro release of L-arginine at body temperature under different conditions. A study on release kinetics was carried out using first-order release kinetic model, while the mechanism were by Higuchi model. Further, to see the influence of TPA on release rate, release profile obtained from pure and functionalized MCM-48 was compared

Plant Disease Detection Using Sequential Convolutional Neural Network

The main warning in the area of food preservation and care is on topmost are crop diseases. It has been recognized speedily, but it is not as easy as in any area of the world because no required framework exists. Both the healthy and diseased plant leaves were gathered and collected under the condition and circumstances. For this purpose, a public set of information was used. It was 20,639 images of plants that were infected and healthy. In order to recognize three different crops and 12 diseases, a sequential convolutional neural network from Keras was trained and applied. The perfection and exactness was 98.18 % onset of information of the above trained mentioned model using CNN . It has also indicated the probability and possibility of this strategy and procedure. The over-fitting occurs and neutralizes by putting the dropout value to 0.25

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Extreme weather events and global climate change have exacerbated the problem of evaporation rates. Thus, accurately predicting soil moisture evaporation rates affecting soil cracking becomes crucial. However, less is known about how novel feature engineering techniques and machine-learning predictions may account for estimating the soil moisture evaporation rate. This research focuses on predicting the evaporation rate of soil using machine learning (ML) models. The dataset comprised twenty-one ground-based parameters, including temperature, humidity, and soil-related features, used as features to predict evaporation potential. To tackle the high number of features and potential uncorrelated features, a novel guided backpropagation-based feature selection technique was developed to rank the most relevant features. The top-10 features, highly correlated with evaporation rate, were selected for ML model input, alongside the top-5 and all features. Several ML models, including multiple regression (MR), K-nearest neighbor (KNN), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg), random forest (RF), and a novel K-Nearest Oracles (KNORA) ensemble, were constructed for the purpose of forecasting the evaporation rate. The average error of these models was assessed using the root mean squared error (RMSE). Experimental results showed that the KNORA ensemble model performed the best, achieving a 7.54 mg/h RMSE in testing with the top-10 features. MLP was followed closely by a 25.1 mg/h RMSE in the same testing. An empirical model using all features showed a higher RMSE of 1319.1 mg/h, indicating the superiority of the ML models for accurate evaporation rate predictions. We highlight the implications of our results for climate-induced soil cracking in the real world

Addressing class imbalance in soil movement predictions

Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat. Accurately predicting soil movement from monitoring data is challenging due to its complexity and inherent class imbalance. This study proposes developing machine learning (ML) models with oversampling techniques to address the class imbalance issue and develop a robust soil movement prediction system. The dataset, comprising 2 years (2019–2021) of monitoring data from a landslide in Uttarakhand, has a 70:30 ratio of training and testing data. To tackle the class imbalance problem, various oversampling techniques, including the synthetic minority oversampling technique (SMOTE), K-means SMOTE, borderline-SMOTE, and adaptive SMOTE (ADASYN), were applied to the training dataset. Several ML models, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), category boosting (CatBoost), long short-term memory (LSTM), multilayer perceptron (MLP), and a dynamic ensemble, were trained and compared for soil movement prediction. A 5-fold cross-validation method was applied to optimize the ML models on the training data, and the models were tested on the testing set. Among these ML models, the dynamic ensemble model with K-means SMOTE performed the best in testing, with an accuracy, precision, and recall rate of 0.995, 0.995, and 0.995, respectively, and an F1 score of 0.995. Additionally, models without oversampling exhibited poor performance in training and testing, highlighting the importance of incorporating oversampling techniques to enhance predictive capabilities.</p

LATICIFEROUS PLANT PROTEASES IN WOUND CARE

Context: Since antiquity, different parts of plants such as bark, stem and leaves have been used in wound healing. Around 10% of the angiosperm plants produce a natural polymer from specialized laticiferous cells called latex. The major role played by the latex is in wound healing and defensive mechanism against infectious diseases in plants.Objective: This paper emphasizes the role of various plant latex proteases in wound healing. The review also emphasizes on the methodology to be adopted in accessing the proteases studied for procoagulant and thrombolytic activities.Methods: This review conglomerates the reports of laticiferous plants of different families viz., Altingiaceae, Amaranthaceae, Apocyanaceae, Asclepiadaceae, Asteraceae, Caricaceae, Dipterocarpaceae, Euphorbiaceae, Lamiaceae, Moraceae, Papaveraceae, Plumbaginaceae, and Solanaceae involved in wound healing. Emphasis was given on the all possible reports on laticiferous plants in wound healing with thorough literature survey.Results: A number of proteases have been studied from plant latex proteases for their role in wound healing. Some have been extensively studied with characterization while some are yet to be explored. This review enables a detailed up-to-date knowledge of laticiferous plants studied scientifically for wound care.Conclusion: In the past 20 years, with biochemical and pharmacological characterization of plant latex it has come to light that proteases are involved in wound healing. However, research on latex protease is still in budding stage. Adopting the proteases having promising applicability in wound care needs to be focussed.Â

Feasibility of breast crawl in a tertiary care teaching institute

Background: Early initiation of breastfeeding has many beneficial effects for both the mother and the baby. The breast crawl has been&nbsp;established as the ideal method for promoting early skin-to-skin contact and early initiation of breastfeeding. Objective: The objective&nbsp;of the study was to assess the feasibility of breast crawl in a busy tertiary care institute. Materials and Methods: An observational&nbsp;study was performed including 50 mother-baby pairs, admitted to the labor room of Sassoon General Hospital, Pune, from January2018 for 6 months. Observations were made on patient and nurse attitude and behavior and were further analyzed. Results: Of the&nbsp;total subjects, 23 (46%) mothers had not received any counseling about breastfeeding during the antenatal period and none of them&nbsp;were familiar with the idea of breast crawl from the antenatal period. Of the 50 mothers, 27 mothers (54%) were concerned about&nbsp;privacy, 8 (16%) were concerned about environmental cold, 6 (12%) were worried about exposure, and 9 mothers (18%) were&nbsp;concerned about the baby, whereas 6 mothers (12%) had no concerns and 6 mothers had more than one concern. The attitude of the&nbsp;nursing staff was favorable (enthusiastic) 19 times, indifferent 23 times, reluctant 6 times, and unfavorable (uncooperative) 2 times.&nbsp;Conclusion: While it is highly desirable to implement breast crawl as a routine practice, there are several roadblocks such as lack&nbsp;of antenatal counseling, lack of awareness and motivation, lack of specific guidelines and instructions, skewed staff-to-patient and&nbsp;bed-to-patient ratio, and lack of privacy

Unveiling transfusions: Analyzing blood product utilization patterns in a leading tertiary care center in Madhya Pradesh, India

Background: Blood transfusions play a pivotal role in medical care, saving millions of lives annually. The timely provision of safe blood is critical in various clinical scenarios, necessitating a careful balance between supply and demand. Despite advanced blood banking facilities globally, challenges persist in ensuring appropriate blood component utilization, prompting a need for clinical audits and optimization strategies. Aims and Objectives: This study aims to analyze the patterns of blood product utilization in a tertiary care hospital in Madhya Pradesh over 1 year, focusing on transfusion requests, cross-match-to-transfusion (C/T) ratios, transfusion indices (TIs), and indications for transfusions. Materials and Methods: A retrospective study was conducted at Shyam Shah Medical College from January 01, 2023, to December 31, 2023. Data from transfusion and cross-match requests in various departments were collected. C/T ratios, TI, and non-usage probability were computed to assess blood utilization efficiency. Results: Out of 16,682 cross-matched units, 71.93% were transfused. The overall C/T ratio was 1.39. The department of medicine demonstrated the most efficient blood usage with a C/T ratio of 1.16. Obstetrics and gynecology had the highest TI (1.06), while surgery had the lowest (0.71). Indications for transfusion included anemia (29.9%), pre-operative (17.2%), intraoperative (21.8%), and post-operative (31.1%). Conclusion: This study provides valuable insights into blood utilization patterns, offering a foundation for refining transfusion practices and enhancing the efficiency of blood management in the studied tertiary care hospital

Circular Economy Pathways for Municipal Wastewater Management in India: A Practitioner’s Guide

The 2030 Water Resources Group is a unique publicprivate- civil society partnership that helps governments to accelerate reforms that will ensure sustainable water resource management for the long term development and economic growth of their country. It does so by helping to change the “political economy” for water reform in the country through convening a wide range of actors and providing water resource analysis in ways that are digestible for politicians and business leaders. The 2030 WRG was launched in 2008 at the World Economic Forum and has been hosted by the International Finance Corporation (IFC) since 2012

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Implementation of No-Reference Image Quality Assessment in Contourlet Domain

In image processing, efficiency term refers to the ability in capturing significant information that is sensitive to human visual system with small description. Natural images or scenes that contain intrinsic geometrical structures (contours) are key features of visual information. The existing transform methods like Fourier transformation, wavelets, curvelets, ridgelets etc., have limitations in capturing directional information in an image and their compatibility with compression methods. Hence, to capture the directional information or natural scene statistics of an image and to handle the compatibility over distortion methods, Contourlet Transform (CT) can be a promising approach. The goal of no-reference image quality assessment using contourlet transform (NR IQACT) is to establish a rational computational model to predict the visual quality of an image. In this thesis we implemented an improved Natural Scene Statistics (NSS) model that blindly measures image quality using the concept of Contourlet Transform (CT). In fact, natural scenes contain nonlinear dependencies that can be disturbed by a compression process. This disturbance can be quantified and related to human perception of quality.0091-8089080211; 0091-991281927