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A dual-stage model for classifying Parkinson's disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson's disease is the primary neurodegenerative disorder that results in a gradual reduction in motor function. Early detection and monitoring of the disease progression is highly challenging due to the gradual progression of symptoms and the inadequacy of conventional methods in identifying subtle changes in mobility. The proposed dual-stage model utilized a hypertuned Random Forest Tree (RFT) to classify the subjects into PD and non-PD classes at Stage 1 and a hypertuned Ensemble Regressor (ER) to predict the severity of illness at Stage 2. Further, we have implemented the proposed model on the data signals gathered from both feet of 166 participants using Vertical Ground Reaction Force Sensors (VGRF). The dataset comprised 93 persons with Parkinson's disease and 73 healthy controls. The dataset (imbalance) collected from both feet is passed to the preprocessing phase (for balancing data using the SMOTE method), followed by the feature extraction phase to extract features related to time, frequency, spatial, and temporal features domains that are highly effective for detecting and assigning severity levels of PD. A Recursive Feature Elimination method is also used to select the optimal set of features to improve the model performance. It is acknowledged that the early detection of Parkinson's disease is contingent upon critical parameters, including stride length, stance duration, swing interval, double limb support, step time, and step length. The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. It attained an accuracy of 97.5 ± 2.1%, Sensitivity of 97% ± 2.5%, and average Specificity of 95% ± 2.2% in differentiating between PD and non-PD participants utilizing RFT and evaluated disease severity with an average accuracy of 96.4 ± 2.3%, an average mean absolute error of 0.065 ± 0.024, and a root mean square error of 0.080 ± 0.06. The results indicate that the proposed dual-stage model is exceptionally successful in the early detection and severity assessment of Parkinson's disease and demonstrates better efficacy than alternative models. [ABSTRACT FROM AUTHOR]

Parking space prediction is a significant aspect of smart cities. It is essential for addressing traffic congestion challenges and low parking availability in urban areas. The present research mainly focuses on proposing a novel scalable hybrid model for accurately predicting parking space. The proposed model works in two phases: in first phase, auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are integrated. Further, in second phase, backpropagation neural network (BPNN) is used to improve the accuracy of parking space prediction by reducing number of errors. The model utilizes the ARIMA model for handling linear values and the LSTM model for targeting non-linear values of the dataset. The Melbourne Internet of Things (IoT) based dataset, is used for implementing the proposed hybrid model. It consists of the data collected from the sensors that are employed in smart parking areas of the city. Before analysis, data was pre-processed to remove noise from the dataset and real time information collected from different sensors to predict the results accurately. The proposed hybrid model achieves the minimum mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.32, 0.48, and 0.56, respectively. Further, to verify the generalizability of the proposed hybrid model, it is also implemented on the Harvard IoT-based dataset. It achieves the minimum MSE, MAE, and RMSE values of 0.31, 0.47, and 0.56, respectively. Therefore, the proposed hybrid model outperforms both datasets by achieving minimum error, even when compared with the performance of other existing models. The proposed hybrid model can potentially improve parking space prediction, contributing to sustainable and economical smart cities and enhancing the quality of life for citizens. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer is a major health concern globally, and finding ways to improve treatment outcomes is crucial. This review explores the role of biomarkers—biological indicators that can predict how a patient will respond to treatment or indicate the likely course of the disease—in managing colorectal cancer. By examining both well-established and emerging biomarkers, we hope to provide a clearer understanding of how these markers can guide personalized treatment plans. The findings from this research could help doctors make more informed decisions, ultimately improving patient care and outcomes in colorectal cancer. Colorectal cancer (CRC) remains the second leading cause of cancer-related mortality worldwide. While immune checkpoint inhibitors have significantly improved patient outcomes, their effectiveness is mostly limited to tumors with microsatellite instability (MSI-H/dMMR) or an increased tumor mutational burden, which comprise 10% of cases. Advancing personalized medicine in CRC hinges on identifying predictive biomarkers to guide treatment decisions. This comprehensive review examines established tissue markers such as KRAS and HER2, highlighting their roles in resistance to anti-EGFR agents and discussing advances in targeted therapies for these markers. Additionally, this review summarizes encouraging data on promising therapeutic targets and highlights the clinical utility of liquid biopsies. By synthesizing current evidence and identifying knowledge gaps, this review provides clinicians and researchers with a contemporary understanding of the biomarker landscape in CRC. Finally, the review examines future directions and challenges in translating promising biomarkers into clinical practice, with the goal of enhancing personalized medicine approaches for colorectal cancer patients. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases represent one of the utmost imperative well-being health issues and apprehensions due to their escalating incidence of mortality. Natural derivatives are more efficacious in various preclinical models of neurodegenerative illnesses. These natural compounds include phytoconstituents in herbs, vegetables, fruits, nuts, and marine and freshwater flora, with remarkable efficacy in mitigating neurodegeneration and enhancing cognitive abilities in preclinical models. According to the latest research, the therapeutic activity of natural substances can be increased by adding phytoconstituents in nanocarriers such as nanoparticles, nanogels, and nanostructured lipid carriers. They can enhance the stability and specificity of the bioactive compounds to a more considerable extent. Nanotechnology can also provide targeting, enhancing their specificity to the respective site of action. In light of these findings, this article discusses the biological and therapeutic potential of natural products and their bioactive derivatives to exert neuroprotective effects and some clinical studies assessing their translational potential to treat neurodegenerative disorders. Common mechanisms, therapeutic targets, and molecular pathogenesis of neurodegeneration. It is focused on the biological and therapeutic potential of natural products and their bioactive derivatives to exert a neuroprotective effect on the pathologies of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Objective: To propose a theoretical formulation of engeletin-nanostructured lipid nanocarriers for improved delivery and increased bioavailability in treating Huntington's disease (HD). Methods: We conducted a literature review of the pathophysiology of HD and the limitations of currently available medications. We also reviewed the potential therapeutic benefits of engeletin, a flavanol glycoside, in treating HD through the Keap1/nrf2 pathway. We then proposed a theoretical formulation of engeletinnanostructured lipid nanocarriers for improved delivery across the blood-brain barrier (BBB) and increased bioavailability. Results: HD is an autosomal dominant neurological illness caused by a repetition of the cytosine-adenine-guanine trinucleotide, producing a mutant protein called Huntingtin, which degenerates the brain's motor and cognitive functions. Excitotoxicity, mitochondrial dysfunction, oxidative stress, elevated concentration of ROS and RNS, neuroinflammation, and protein aggregation significantly impact HD development. Current therapeutic medications can postpone HD symptoms but have long-term adverse effects when used regularly. Herbal medications such as engeletin have drawn attention due to their minimal side effects. Engeletin has been shown to reduce mitochondrial dysfunction and suppress inflammation through the Keap1/NRF2 pathway. However, its limited solubility and permeability hinder it from reaching the target site. A theoretical formulation of engeletin-nanostructured lipid nanocarriers may allow for free transit over the BBB due to offering a similar composition to the natural lipids present in the body a lipid solubility and increase bioavailability, potentially leading to a cure or prevention of HD. Conclusion: The theoretical formulation of engeletin-nanostructured lipid nanocarriers has the potential to improve delivery and increase the bioavailability of engeletin in the treatment of HD, which may lead to a cure or prevention of this fatal illness. [ABSTRACT FROM AUTHOR]

The neurodegenerative condition known as Parkinson's disease (PD) is brought on by the depletion of dopaminergic neurons in the basal ganglia, which is the brain region that controls body movement. PD occurs due to many factors, from which one of the acknowledged effects of oxidative stress is pathogenic pathways that play a role in the development of Parkinson's disease. Antioxidants, including flavonoids, vitamins E and C, and polyphenolic substances, help to reduce the oxidative stress brought on by free radicals. Consequently, this lowers the risk of neurodegenerative disorders in the long term. Although there is currently no cure for neurodegenerative illnesses, these conditions can be controlled. The treatment of this disease lessens its symptoms, which helps to preserve the patient's quality of life. Therefore, the use of naturally occurring antioxidants, such as polyphenols, which may be obtained through food or nutritional supplements and have a variety of positive effects, has emerged as an appealing alternative management strategy. This article will examine the extent of knowledge about antioxidants in the treatment of neurodegenerative illnesses, as well as future directions for research. Additionally, an evaluation of the value of antioxidants as neuroprotective agents will be provided. [ABSTRACT FROM AUTHOR]

Introduction: Black pregnant individuals endure a disproportionate burden of preventable morbidity and mortality due to persistent, racially mediated social and systemic inequities. As patient advocates, Black community‐based doulas help address these disparities via unique services not provided by conventional doulas. However, Black doulas themselves may encounter obstacles when providing care to Black perinatal clients. We characterized the barriers encountered by Black community‐based doulas in Los Angeles, California. Methods: We partnered with a Black community‐based doula program to conduct semistructured interviews with its community doulas and program directors, covering the following topics: motivations for becoming a doula, services provided, and challenges faced as a Black doula in perinatal settings. Interview transcripts were reviewed via directed content analysis, with attention to the influence of systemic racism on service provision. Additionally, our research team used Camara Jones' Levels of Racism, which describes race‐associated differences in health outcomes to code data. Results: We interviewed 5 Black community‐based doulas and 2 program directors, who all shared experiences of inequitable care and bias against Black clients that could be addressed with the support and advocacy of culturally congruent doulas. The community doulas shared experiences of stigma as Black doulas, compounded by racial prejudice. Interviewees noted sources of structural racism affecting program development, instances of interpersonal racism as they interacted with the health care system, and internalized racism that was revealed during culturally based doula trainings. Additionally, the doulas emphasized the importance of cultural concordance, or a shared identity with clients, which they considered integral to providing equitable care. Conclusion: Despite facing institutionalized, interpersonal, and internalized forms of racism, Black community‐based doulas provide avenues for Black birthing individuals to navigate systemic racism experienced during the perinatal process. However, these forms of racism need to be addressed for Black community doulas to flourish. [ABSTRACT FROM AUTHOR]

Activin A receptor type I (ACVR1), a transmembrane serine/threonine kinase, belongs to the transforming growth factor-β superfamily, which signals via phosphorylating the downstream effectors and SMAD transcription factors. Its central role in several biological processes and intracellular signaling is well known. Genetic variation in ACVR1 has been associated with a rare disease, fibrodysplasia ossificans progressive, and its somatic alteration is reported in rare cancer diffuse intrinsic pontine glioma. Furthermore, altered expression or variation of ACVR1 is associated with multiple pathologies such as polycystic ovary syndrome, congenital heart defects, diffuse idiopathic skeletal hyperostosis, posterior fossa ependymoma and other malignancies. Recent advancements have witnessed ACVR1 as a potential pharmacological target, and divergent promising approaches for its therapeutic targeting have been explored. This review highlights the structural and functional characteristics of receptor ACVR1, associated signaling pathways, genetic variants in several diseases and cancers, protein–protein interaction, gene expression, regulatory miRNA prediction and potential therapeutic targeting approaches. The comprehensive knowledge will offer new horizons and insights into future strategies harnessing its therapeutic potential. [ABSTRACT FROM AUTHOR]

Successful implementations of Software Development Methodologies significantly improve software efficiency, collaboration and security. Most companies are moving away from traditional development methodologies towards DevOps for faster and better software delivery. DevOps, which is a primary need of the IT industry, brings development and operation teams together to overcome communication gaps responsible for software failures. It relies on different sets of automation tools to robotize the tasks of software development from continuous integration, to testing, delivery, and deployment. The existence of several automation tools in each development phase raises the need for an integrated set of tools to reduce development time. For this purpose, we used the DevOps-based hybrid model Integrated Tool Chain (ITC), along with three sample java-based projects or code repositories to quantify the results. This paper evaluates and compares measurement metrics of java projects using traditional development methodologies and DevOps, and the results are shown in tabular and graphical format. The latest Google and Stack Overflow Trends have also been included to retrieve the best performer development methodology. This comparative and evaluative performance analysis will be beneficial to young researchers that study the metrics of software development, while also they will be introduced to the automotive environment of DevOps, the latest emerging buzzword in software development. [ABSTRACT FROM AUTHOR]