Psychometric properties of Child Sensory Profile-2 (CSP-2) among children with spastic cerebral palsy.

Unlabelled: Cerebral palsy (CP) is not just caused by neuromuscular abnormalities; it is also a result of an impaired sensory system. Since there is not a standardized measure to evaluate sensory processing of children with spastic CP, therefore an in-depth assessment of sensory processing deficits in children with spastic CP would require an understanding of the validity and reliability of the Child Sensory Profile-2 (CSP-2) in children with spastic CP. The sample of this study included 230 children with spastic CP aged 3 to 14 years who referred to different paediatric rehabilitation centres of Delhi-NCR from April 2021 and September 2023 by using the snowball sampling procedure.

Enhanced Water Splitting with Sulfur-Doped Nickel Ferrite for Green Hydrogen at Industrial Current Density.

The main challenge for water electrolysis is that continuous and effective hydrogen evolution at high current densities is unattainable due to the quick degradation of performance that occurs with extended large-current operation. In this work, sulfur-doped nickel ferrite nanocomposites were prepared using simple hydrothermal method with the objective of improving electrocatalytic green hydrogen production at industrial current densities. X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) were used to analyse the crystalline structure, morphology, and chemical composition of the synthesised nanocomposites.

Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.

Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diagnose pneumonia.

An eco-friendly polycaprolactone/graphite composite as a robust freestanding electrode platform for supercapacitive energy storage.

We present the successful development and characterization of a novel eco-friendly polycaprolactone-graphite (PCLGr) composite as a freestanding platform, serving as a bulk conducting chip electrode for supercapacitor applications. Notably, this is the first report of using this biodegradable polymer for making such a self-standing conductive platform. Traditional polymer and carbon-based electrodes often rely on insulating supports or non-eco-friendly materials, which we have addressed in our work.

Germination and probiotic fermentation: a way to enhance nutritional and biochemical properties of cereals and millets.

Probiotics have become increasingly popular as consumers demand balanced nutrition and health benefits from their diet. However, lactose intolerance and allergies to milk proteins may make dairy-based probiotics unsuitable for some individuals. Thus, probiotics derived from cereals and millets have shown promise as an alternative to dairy probiotics.

Optimization of Lurasidone HCl-Loaded PLGA Nanoparticles for Intramuscular Delivery: Enhanced Bioavailability, Reduced Dosing Frequency, Pharmacokinetics, and Therapeutic Outcomes.

in vitro in vivo

Advancement in Biopolymer Assisted Cancer Theranostics.

Applications of nanotechnology have increased the importance of research and nanocarriers, which have revolutionized the method of drug delivery to treat several diseases, including cancer, in the past few years. Cancer, one of the world's fatal diseases, has drawn scientists' attention for its multidrug resistance to various chemotherapeutic drugs. To minimize the side effects of chemotherapeutic agents on healthy cells and to develop technological advancement in drug delivery systems, scientists have developed an alternative approach to delivering chemotherapeutic drugs at the targeted site by integrating it inside the nanocarriers like synthetic polymers, nanotubes, micelles, dendrimers, magnetic nanoparticles, quantum dots (QDs), lipid nanoparticles, nano-biopolymeric substances, etc.

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization.

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment.

Machine learning-based approach to predict ice meltdown in glaciers due to climate change and solutions.

The fate of humankind and all other life forms on earth is threatened by a foe, known as climate change. All parts of the world are affected directly or indirectly by this phenomenon. The rivers are drying up in some places and in other places, it is flooding.

A novel artificial intelligence-based predictive analytics technique to detect skin cancer.

One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage.

Impact of technology on the relation between disaggregated energy consumption and CO emission in populous countries of Asia.

All around the world, but particularly in developing nations, carbon dioxide emissions are on the rise, and climate change and global warming are brought on by an increase in CO emissions. This article provides an overview of the technological effect on energy consumption in the residential, transport, and industrial sector and its ultimate effect on the environment. Using the STIRPAT-Kaya-EKC model for the years 1990 to 2020, this study looked at the threshold impact of technological advancements on the link between disaggregated energy use and CO emissions for a panel of 10 Asian countries using the panel threshold regression.

Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy.

A novel multistage ensemble approach for prediction and classification of diabetes.

Diabetes mellitus is a metabolic syndrome affecting millions of people worldwide. Every year, the rate of occurrence rises drastically. Diabetes-related problems across several vital organs of the body can be fatal if left untreated.

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment.

In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance.

Aerial Separation and Receiver Arrangements on Identifying Lung Syndromes Using the Artificial Neural Network.

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases.

Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification.

Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients' multimodal pictures and labels served as training sets, whereas the remaining 75 patients' multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used.

Neural correlates in functional brain mapping among breast cancer survivors receiving different chemotherapy regimens: a qEEG/HEG-based investigation.

Background: Post-chemotherapy cognitive impairment commonly known as 'chemobrain' or 'chemofog' is a well-established clinical disorder affecting various cognitive domains including attention, visuospatial working memory, executive function, etc. Although several studies have confirmed the chemobrain in recent years, scant experiments have evaluated the potential neurotoxicity of different chemotherapy regimens and agents. In this study, we aimed to evaluate the extent of attention deficits, one of the commonly affected cognitive domains, among breast cancer patients treated with different chemotherapy regimens through neuroimaging techniques.

Comparative Analysis of the Chemotherapy-Related Cognitive Impairments in Patients with Breast Cancer: A Community-Based Research.

This study aimed to evaluate the effects of two common chemotherapy regimens on breast cancer (BC) survivors' cognition. The participants comprised 35 patients with BC who underwent two chemotherapy regimens, AC-T and TAC, and 24 matched healthy volunteers. The participants were assessed regarding cognitive function through Addenbrooke's Cognitive Examination and Cambridge Brain Science tests.

Paludisphaera rhizosphaereae sp. nov., a new member of the family Isosphaeraceae, isolated from the rhizosphere soil of Erianthus ravennae.

Two axenic cultures of Planctomycetota were isolated from distinct geographical locations of India. Strain JC665 was isolated from a rhizosphere soil of Loktak lake, Manipur, whereas strain JC747 was isolated from a soil sediment at Pallikkara village, Kerala, India. The two closely related strains shared the highest 16S rRNA gene sequence identity (94.

In silico docking and ADME study of deketene curcumin derivatives (DKC) as an aromatase inhibitor or antagonist to the estrogen-alpha positive receptor (Erα): potent application of breast cancer.

Unlabelled: Regardless of many extensive studies, hormonal-based breast cancer is the most common cause of cancer-related mortality of females worldwide. Indeed, estrogen receptor-positive (ER +) is the communal subtype in breast cancer. To treat this, three types of medications are typically used: selective estrogen receptor modulators (SERMs), selective estrogen receptor down modulators (SERDMs), and aromatase inhibitors (AIs), all of which directly interact with the activation of the estrogen signaling pathway and its formation.

In silico approach: docking study of oxindole derivatives against the main protease of COVID-19 and its comparison with existing therapeutic agents.

Objectives: Presently, the pandemic of COVID-19 has worsened the situation worldwide and received global attention. The United States of America have the highest numbers of a patient infected by this disease followed by Brazil, Russia, India and many other countries. Moreover, lots of research is going on to find out effective vaccines or medicine, but still, no potent vaccine or drug is discovered to cure COVID-19.