ABSTRACT Parkinson’s Disease (PD) is a progressive neurological disorder that primarily affects movement, coordination, and motor control. Early diagnosis of Parkinson’s Disease is essential for timely treatment and improving the quality of life of patients. Traditional diagnosis methods mainly depend on clinical observation and medical expertise, which may delay early-stage detection. With the advancement of Artificial Intelligence, Deep Learning techniques have shown promising results in the automated detection of Parkinson’s Disease using medical data such as handwriting patterns, speech signals, gait analysis, MRI scans, and tremor measurements. This review paper presents an overview of various Deep Learning models used for Parkinson’s Disease detection, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid models such as CNN-LSTM. It also discusses commonly used datasets, preprocessing methods, feature extraction techniques, performance evaluation metrics, and challenges faced in implementation. Furthermore, the paper highlights recent advancements and future opportunities in real-time healthcare systems for Parkinson’s monitoring and prediction. The study concludes that Deep Learning models can significantly improve diagnostic accuracy and support healthcare professionals in early and efficient Parkinson’s Disease detection. Keywords : Parkinson’s Disease, Deep Learning, CNN, LSTM, Medical Diagnosis, AI in Healthcare, Disease Detection

In the 21st century, the desire for improved fuel efficiency of engines, lower fuel prices, and the need to reduce greenhouse gas emissions such as CO2 and NOx are leading the aviation industry to seek hybrid-electric jet engines for commercial aircraft. These aircraft will have greater maintenance challenges due to additional components requiring more reliable materials for the engine’s parts, such as turbine blades. Turbine blades must be composed of materials that have enhanced fatigue performance. Resistance to dynamic loads and high strength will be needed to ensure modern gas turbine blades are as reliable as possible. This review paper examines hybrid-electric engine turbine blades and subsequently introduces additive manufacturing (AM) and multi-principal element alloys (MPEAs) with a focus on laser powder bed fusion (LPBF), high-entropy alloys (HEAs), and medium-entropy alloys (MEAs). The tensile properties of LPBF HEAs range from 5 to 47% elongation and a UTS of 572–1640 MPa, while LPBF MEAs range from 8 to 73.9% and a UTS of 573–1382 MPa. This study focused on dynamic and fatigue properties while acknowledging gaps in high-temperature testing. The combination of mechanical properties with the ability to control internal geometry makes these AM alloys an attractive option for the next generation of gas turbine blades.

Background: Indigenous African fruits, like the African horned cucumber (Cucumis metuliferus), are abundant in nutrients and serve as a source of food and raw materials for manufacturing value-added products in both rural and urban areas. This review presents a comparative analysis of selected fruits in the Cucurbitaceae family, specifically in terms of the phytochemical, biochemical and mineral constituents, as well as nutritional contribution, and aims to explore how the African horned cucumber measures up to its counterparts by comparing their nutritional content against the recommended daily intake (RDI). Material and Methods: A literature search—using the keywords ‘African horned cucumber’, ‘Cucurbitaceae fruits’, ‘biochemical constituents’, ‘indigenous fruits’ and ‘recommended daily intake’—was used to gather credible data suitable for this review paper. Findings and Conclusions: The published peer-reviewed literature reveals that the African horned cucumber—with its nutrient-rich profile boasting high levels of calcium (19%), potassium (28%), magnesium (78.1%), sodium (10.7%), zinc (12.7%), beta carotene (15.5%), vitamin C (4.1%), vitamin E (15.2%), total flavonoids (0.28%), and total phenols (0.7%)—holds the promise of contributing significantly to the human diet while aligning with the RDI and dietary guidelines, as documented in studies, further underscoring its potential to meet nutritional needs and enhance health, thus supporting its consideration for commercialisation.

Abstract Physically Based Rendering (PBR) is a fundamental approach in computer graphics that aims to generate highly realistic images by simulating the physical behavior of light. This review paper presents an in-depth analysis of five significant research works that have shaped the evolution of PBR. The study begins with the theoretical framework that defines light transport using radiometric principles and the rendering equation [1]. It further explores Monte Carlo integration techniques, which provide numerical solutions for complex light interactions [2]. Advanced sampling methods such as Metropolis Light Transport are examined for their ability to efficiently handle difficult lighting scenarios like caustics and indirect illumination.[7] The paper also discusses real-time advancements such as Spatiotemporal Reservoir Resampling (ReSTIR), which enables high-quality rendering at interactive frame rates. [8] Additionally, neural rendering approaches are analyzed for their integration of machine learning with physically based models to improve efficiency and reduce variance. [5]. A comparative study of these methods is presented based on objectives, methodologies, and outcomes. The review highlights the transition from traditional physics-based approaches to hybrid AI-driven techniques. Finally, the paper identifies future research directions in real-time rendering, neural sampling, and GPU optimization. Keywords—Physically Based Rendering, Global Illumination, Monte Carlo Integration, Light Transport, Neutral Rendering.

Abstract This comprehensive review paper examines deep learning and machine learning techniques applied to medical image analysis. The study systematically analyzes 26 major research papers from peer-reviewed venues, cataloging methodologies including convolutional neural networks (CNNs), generative adversarial networks (GANs), vision transformers, and other advanced architectures. These techniques are evaluated for disease detection, image segmentation, and classification across diverse imaging modalities such as CT, MRI, X-rays, and mammography. Through systematic analysis, the paper identifies key techniques, performance characteristics, challenges, and future directions in medical imaging. Findings demonstrate that deep learning models significantly enhance diagnostic accuracy and clinical decision support; however, critical challenges remain related to dataset quality, model interpretability, and regulatory compliance. The study concludes that integrating advanced machine learning techniques with medical expertise can substantially improve healthcare outcomes, while highlighting the necessity for continued research in explainable AI, federated learning, and privacy-preserving approaches for practical clinical deployment. Keywords: Deep learning, medical image analysis, convolutional neural networks (CNNs), generative adversarial networks (GANs), image segmentation, disease detection, and vision transformers.

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis.

Introduction Sleep is one of the most important biological functions performed every night by the entire animal kingdom. It is defined as a vital, reversible, and active state of reduced consciousness, metabolism, and sensory activity. And lack of sleep is the simplest definition for sleep deprivation. This review paper evaluates whether sleep deprivation poses significant risks to academic performance. This is becoming increasingly prevalent among adolescents and young adults in or entering higher education often due to academic pressures and social engagements leading to compromised rest and poor sleep hygiene. Objectives The primary objectives of this review paper are to methodically review literature on sleep deprivation in higher education, with a focused analysis on high pressure medical training environments. We assess documented consequences for memory, attention, and executive function, discuss the broader implications for student well-being and healthcare systems, and gather practical advice for educational institutions and students alike to improve sleep health and productivity. And finally we identify critical gaps to aid future research on this topic. Methodology A systematic search was conducted in PubMed, Scopus, and Google Scholar for studies examining sleep deprivation and cognitive function in medical students. Keywords included terms related to sleep, medical education, and cognitive/academic outcomes. Eligible studies were primary research or meta-analyses focusing on medical students, reporting cognitive outcomes, and published in English. Selection followed PRISMA guidelines. Conclusion Sleep deprivation clearly harms medical students’ focus, memory, and learning, and is linked to anxiety, lower grades, and poor well-being. To support the next generation of doctors, both students and medical schools must prioritize sleep through better habits, schedules, and policies that treat rest as essential to success and safety.

Dental implants are the most acceptable treatment option for the replacement of missing teeth. Despite the high success rate, the presence of implant complications is increasing. Peri-implant mucositis and peri-implantitis are biologic implant complications induced by a dysbiotic shift in the microbiota. The main goal of the treatment of peri-implant diseases is to remove the bacterial biofilm on the implant surface and restore the ecological balance between the host and oral microbiota. For this purpose, both non-surgical and surgical treatments are applied. Non-surgical approaches based on mechanical debridement focus on biofilm control such as disruption/removal of submucosal biofilm and oral hygiene instructions. Mechanical debridement, non-ionizing radiation sources, antiseptics or antibiotics are included in the non-surgical treatment. Mechanical debridement is applied by the help of the curettes (titanium-coated, carbon fiber, Teflon and plastic), ultrasonic tips, various types of brushes and air-flow devices and allows the removal of soft and hard deposits from the implant surface. However, it remains unclear which treatment is the most effective and predictable for the management of peri-implant diseases. This review article aimed to overview, the non-surgical treatment options of peri-implant diseases and their microbial effectiveness. For this purpose, studies evaluating changes in the peri-implant microbiota before and after treatment using 16S ribosomal RNA (rRNA) sequencing were searched in databases (PubMed and Google Scholar).The literature search covered studies published between 2012 and May 2024 and 1202 of the 1209 articles searched were excluded. Seven studies that met the inclusion criteria were examined; these studies included a total of 107 patients with peri-implant mucositis and 182 patients with peri-implantitis for periods ranging from one to 60 months. Six studies evaluated the effectiveness of mechanical debridement alone or combined with other therapies while one study assessed the hyaluronic acid efficacy. Based on the literature review, it can be inferred that non-surgical treatment demonstrates effectiveness in the short term for managing peri-implant mucositis. However, its impact on the treatment of peri-implantitis appears to be considerably limited. Regarding the treatment of peri-implant mucositis, the application of antimicrobial agents (chlorhexidine or delmopinol) in addition to mechanical treatment (manually or ultrasonic devices) shows both clinical and microbiological effectiveness in the short term. After treatment, the microbial profile tends to shift towards a less dysbiotic form. According to the short-term follow-up findings, the effect of using chlorhexidine in addition to mechanical debridement in peri-implantitis therapy is contradictory. However, additional use of antimicrobial photodynamic therapy also appears to provide short-term benefits. The application of hyaluronic acid may decrease the microbial diversity. However, it is crucial to note that these findings are derived from a limited number of studies, emphasizing the need for future research to provide more comprehensive insights.

This review article critically examines the adoption of Environmental, Social, and Governance (ESG) practices in small and medium-sized enterprises (SMEs) by synthesizing the major theoretical perspectives, global barriers, and enabling strategies discussed in the literature. The study is grounded in stakeholder theory, the resource-based view, and institutional theory to explain how external pressures, internal capabilities, and regulatory environments shape ESG adoption in SMEs. The review finds that although ESG has become increasingly important for enhancing transparency, competitiveness, and long-term sustainability, SMEs continue to face significant obstacles in implementation. Key barriers include limited financial resources, lack of technical expertise, insufficient ESG knowledge, regulatory complexity, weak technological preparedness, short-term managerial orientation, and supply chain pressures. At the same time, the study highlights several enabling strategies that can facilitate ESG integration in SMEs, including access to green finance, sustainability-linked investments, capacity-building programs, peer learning, simplified ESG reporting frameworks, ethical leadership, digital transformation, and collaborative supply chain partnerships. The review further emphasizes that ESG adoption in SMEs is not shaped by a single factor but by the interaction of organizational, institutional, financial, and technological conditions. By bringing together fragmented insights from prior studies, this article offers a comprehensive understanding of how SMEs can overcome ESG adoption challenges and improve sustainability performance. The study contributes to the literature by providing an integrated conceptual perspective and practical insights for researchers, policymakers, and SME managers seeking to promote responsible and competitive business practices in a changing global environment

Oxalis corniculata Linn is a highly potent traditional medicinal plant that is widespread in tropical and subtropical regions of the world and is commonly known as woody climbing tamarind and belongs to the Oxalidaceae family. Human Disease, this overview shows different areas of previously isolated plant molecules, such as flavonoids, alkaloids, tannins, steroids, polyphenols, glycosidic compounds, lipids and volatile oils. It contains pharmaceutical substances such as isovitexin, flavonoids and the glycopyrunside Vitexin2ObetaD. It is a rich source of essential fatty acids like palmitic, oleic, linolenic and stearic acids, it has some important pharmacological properties like healing, anti-diarrhea, anti-cancer, anti-implantation and abortion. resistant, bacteriostatic, anti-inflammatory. This review article briefly describes the botanical properties, the ethnopharmacological use, the pharmacological properties and the phytochemical composition of this medicinal plant. and document the most important. Information on the various aspects of Oxalis corniculata and highlights research and development needs.

In this short review paper, a detailed analysis of six two-dimensional quantum superintegrable systems in flat space is presented. It includes the Smorodinsky–Winternitz potentials I–II (the Holt potential), the Fokas–Lagerstrom model, the 3-body Calogero and Wolfes (equivalently, [Formula: see text] rational, or [Formula: see text]) models and the Tremblay–Turbiner–Winternitz (TTW) system with integer index k. It is shown that all of them are exactly-solvable, thus, confirming the Montreal conjecture (2001); they admit algebraic forms for the Hamiltonian and both integrals (all three can be written as differential operators with polynomial coefficients without a constant term), they have polynomial eigenfunctions with the invariants of the discrete symmetry group of invariance taken as variables, they have hidden (Lie) algebraic structure [Formula: see text] with various k and they possess a (finite order) polynomial algebras of integrals. Each model is characterized by infinitely-many finite-dimensional invariant subspaces, which form the infinite flag. Each subspace coincides with the finite-dimensional representation space of the algebra [Formula: see text] for a certain k. In all presented cases, the algebra of integrals is a 4-generated [Formula: see text] infinite-dimensional algebra of ordered monomials of degrees 2–5, which is a subalgebra of the universal enveloping algebra of the hidden algebra.

Aim In recent years, components of gingival crevicular fluid (GCF), including alkaline phosphatase (ALP), serotransferrin (TF), and vitamin D-binding protein (DBP), have attracted attention as biomarkers for determining stages of pubertal growth in orthodontic treatments. In this review article, we critically evaluated the reliability of biomarkers present in GCF for the accurate identification of the pubertal growth spurt. Materials and methods A systematic search of scientific sources was conducted in the PubMed, Web of Science, and Scopus databases. In this search, the relationship between biomarkers present in gingival crevicular fluid and key stages of skeletal development, particularly the mid-pubertal stages of jaw growth (CS3–CS4 stages in the CVM index), was examined. The main focus of this article was to determine the association of biomarkers such as ALP with specific maturation milestones in the cervical vertebrae. Results The results obtained from the literature search in this article showed that there is a significant association between the concentration of some GCF biomarkers and the peak growth velocity. Discussion If the obtained data are confirmed, it can be expected that the diagnostic accuracy and treatment timing in growth-modification-based orthodontic interventions will improve considerably. Conclusion This non-invasive diagnostic approach appears to have promising clinical potential. However, its current clinical application is limited due to heterogeneity in study methodologies. Standardized protocols need to be developed as a fundamental prerequisite for the reliable use of these biomarkers in routine orthodontic practice.

Through the use of unique nano-properties for targeted action and increased sensitivity, such as large surface area and quantum effects. By enabling precise manipulation of matter at the molecular level, nanotechnology revolutionizes biology and leads to advances in tissue engineering (biocompatible implants, 3D bioprinting scaffolds), gene editing, drug delivery (targeted cancer therapy, fewer side effects), and diagnostics (ultra-sensitive biosensors for early disease detection, imaging). Since nanotechnology primarily inhibits and destroys germs, it is a powerful tool against antibiotic resistance. By using nanosensors to enable early detection, enhanced imaging to improve diagnosis, and targeted drug delivery (nanocarriers like liposomes) to improve treatment. Currently, molecular imaging uses a variety of nanoparticles, and in recent decades, the use of nanoparticles for cancer detection and monitoring has attracted a lot of attention. By facilitating targeted drug delivery, painless oral insulin administration, more intelligent glucose monitoring, and regenerative medicine, going beyond injections to offer glucose-responsive systems.

High-value competition horses represent significant financial investments, making proper documentation and assessment of injuries particularly important in insurance claim cases. This review paper examines the pathological manifestations, forensic investigation methods and protective measures relevant to these situations. The review paper explores common pathological conditions affecting performance horses, diagnostic approaches for disease progression and forensic veterinary analysis techniques for characterising injury patterns. The distinction between accidental trauma and non-accidental injuries is examined through tissue damage patterns and the relationship between injuries and reported incidents. Emphasis is placed on systematic pathological examination methods, evidence collection and documentation processes used for insurance and legal contexts. Special attention is given to soring practices and their diagnostic indicators. Soring involves the intentional infliction of pain to a horse's legs or hooves in order to force the horse to perform an artificial, exaggerated gait. Understanding injury types and their postmortem characteristics helps veterinarians working with insurance companies and legal authorities provide accurate assessments. This supports animal welfare protection and objective evaluation in these cases.

In recent years, processing naturally occurring materials, such as ilmenite, to develop titanium dioxide nanostructures has emerged as an economic, renewable, and eco-friendly approach. This review article focuses on various processing routes of ilmenite, namely, pre-treatment, extractive processing (sulfate and chloride methods), and the synthesis of the resulting titanium dioxide nanostructures. The resulting architectures, including nanofibers, nanotubes, nanowires, and nanoparticles, demonstrate significant potential in photocatalytic applications, particularly under visible light irradiation. While there have been encouraging outcomes at laboratory scales, significant challenges remain, such as the diversity in chemical compositions of ilmenite deposits and the comparatively high energy consumption associated with conventional processing routes. Addressing these challenges requires the development of energy-efficient processing routes (100-120°C) as an integral component of industrial scalability. Overall, utilizing ilmenite as a precursor for nanostructure synthesis holds immense potential for promoting green technologies in environmental, energy, and healthcare sectors.

Background: Cardiovascular diseases have become one among the leading global burden in the society with an estimated number of mortality and morbidity of 17.9 million lives per year. In the current era of modernization having sedentary life style, fat enriched diet, stressful jobs etc. further increase the incidence rate of cardiac disorder. Valvular Heart Disease (VHD) embodies the disorder afflicting cardiac valves. The patient is usually asymptomatic at the earlier stage but symptoms exacerbate with the severity of the disease. The contemporary science manages mild to moderate VHD with symptomatic medication and with the severity, surgical intervention for valvular repair or replacements are opted. The Surgical invasions are exceptionally high and also reported to have post-operative complications. Objective -The objective of this review article is to analyze the pathophysiological understanding of valvular heart disease bridging the various aspect of Hridroga (heart diseases) explained in the ancient ayurvedic classics. Materials and methods: The references related to hridroga and valvular heart diseases are collected from various ayurvedic texts, indexed journals and cardiology books. Keywords Valvular heart disease AND hridroga and VHD OR hridroga prevalence are used for searching the literature in PubMed. Results and Discussion: VHDs in ayurveda can be understood where the diseased valve may be stenosed or may develop laxity is usually due to improper kapha or vata condition. The risk factors and management are explained. Conclusion- Ayurveda can provide a novel approach in the management of Valvular Heart to enhance and improve the quality of life of VHD patients.

This scientific review article analyzes the epizootological situation regarding the prevalence of key bacterial pathogens in otitis media in dogs based on data from retrospective and prospective studies of the period 2020-2026. The dominant etiological agents identified were Staphylococcus pseudintermedius (41-48% of isolates), Pseudomonas aeruginosa (19-29%), Proteus mirabilis (8-11%), Escherichia coli (4-5%) and Streptococcus canis (6-10%). A comprehensive analysis of antimicrobial resistance (AMR) of isolates to the main classes of antibiotics, including beta-lactams (penicillins, cephalosporins), fluoroquinolones, aminoglycosides, macrolides, and tetracyclines, was performed. The analysis revealed resistance to amoxicillin/clavulanate (up to 70-93% for Pseudomonas spp.) and cephalexin (50-70% for Staphylococcus spp .), while maintaining high sensitivity to gentamicin (90-95%) and fluoroquinolones (80-85%) in most cases. Multidrug resistance was detected in 38-53% of isolates, especially among Pseudomonas aeruginosa . The need for cultural studies and sensitivity assessment for rational treatment is emphasized, taking into account the regional characteristics of antibacterial sensitivity.

Insulin-like growth factor 1 (IGF-1) is a crucial protein that participates in cell growth and development. The actions of growth hormone (GH) are primarily regulated by IGF-1. The anterior pituitary gland is responsible for the production of GH, which is then delivered in the bloodstream and triggers the liver for the production of IGF-1. The growth-promoting effects of IGF-1 then occur in nearly every cell of the human body, including skeletal muscle tissues, kidneys, skin, bone, cartilage, nerve tissues, bone marrow, and lungs. While the liver is responsible for secreting about 75% of the circulating IGF-1, some target tissues can produce and express IGF-1 locally through autocrine and paracrine roles. These tissues include muscle, cartilage, bone, kidneys, and brain. IGF-1 can influence cell growth and development, particularly in nerve cells, along with the synthesis of DNA within cells, in addition to its insulin-like effects. The importance of IGF-1 in cell proliferation and inhibition of cell death (apoptosis) has been demonstrated. This review article aimed to determine and present the effect of IGF-1 in murine craniofacial growth in the presence of decreased masticatory load.

Background: Intranasal drug delivery has emerged as one of the clinically significant non-invasive route for the administration of sedatives and the anaesthetic agents, offering rapid onset, improved patient compliance, and avoidance of the first-pass metabolism. This research paper has addressed critical review, Pharmacokinetics, Pharmacodynamics and Clinical use of three common intranasal agents which are used in midazolam, dexmedetomidine and ketamine. Nares mucosa happens to be a highly vascularized mucosa and thus is efficient in its absorption of systems and is also likely to bypass to the central nervous system either through olfactory and trigeminal pathways. Methodology: It is a literature review article that is concerned with the comparison of bioavailability of such agents, duration of action, time of onset, and safety profile of such agents in children and in adults. The methodology is assessed on a comparative basis, which is based on their effectiveness as a therapeutic tool in multiple clinical scenarios as premedication, procedural sedation, emergency care, and pain management. Results: It is demonstrated that intranasal midazolam acts fast on the anxiolytic and anticonvulsant effect, dexmedetomidine is the best in the sedation with the least respiratory depression, and ketamine is good in analgesic or dissociative effect of sedation and excellent safety profile. Nevertheless, intranasal with a parameter of dosing and delivering it has been clinically effective. These limitations comprise mucosal irritation, absorption variability and non-standardized formulations. Conclusion: The article concludes that this type of streamlining delivery procedures and now gigantic size randomized controlled trials is an imperative that would, in turn, be necessary to further implement the accuracy and safety of the dose in the long-term. Anaesthetic Intranasal drug delivery is a revolutionary product in the practice of anaesthetic especially when there is scarce resource and emergency is the topic of discussion

To limit energy costs and environmental impacts related to road lighting, luminance-based designing is the best solution to provide the right amount of light on the surfaces to be lit. But this exercise requires knowledge of the reflection properties of the road surface, which are generally unknown. Therefore, the design is often done in terms of illuminance, and luminance is estimated using the standard r -tables provided by the CIE 50 years ago. This review article presents the current state of knowledge on the reflection properties of road surfaces and the external factors (age, traffic, climatic conditions) that may influence them for road lighting applications. These factors are closely connected, and they were not addressed in isolation. However, this article illustrates the evolving and dynamic nature of the reflection properties of road surfaces over time. To characterise a road surface in a state representative of most of its life, it is preferable to wait 2 years after application, especially for bituminous roads with no initial surface treatment. Furthermore, spatial heterogeneity could be considered by taking measurements in the centre track and in the wheel track.