Search Results (573)

Cancer is no longer considered as an isolated event. Rather, it occurs because of a complex biological drive orchestrating different cell types, growth factors, cytokines, and signaling pathways within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are the most populous stromal cells within the complex ecosystem of TME, with significant heterogeneity and plasticity in origin and functional phenotypes. Very enigmatic cells, CAFs determine the progress and outcomes of tumors through extensive reciprocal signaling with different tumors infiltrating immune cells in the TME. In their biological drive, CAFs release numerous chemical mediators and utilize various signaling pathways to recruit and modulate tumor-infiltrating immune cells. The CAF-induced secretome and exosomes render immune cells ineffective for their antitumor activities. Moreover, by upregulating immune inhibitory checkpoints, CAFs create an immunosuppressive TME that impedes the susceptibility of tumor cells to tumor-infiltrating lymphocytes (TILs). Further, by depositing and remodeling extracellular matrix (ECM), CAFs reshape the TME, which enhances tumor growth, invasion, metastasis, and chemoresistance. Understanding of CAF biology and its crosstalk with tumor-infiltrating immune cells is crucial not only to gain insight in tumorigenesis but to optimize the potential of novel targeted immunotherapies for cancers. The complex relationships between CAFs and tumor-infiltrating immune cells remain unclear and need further study. Herein, in this narrative review we have focused on updates of CAF biology and its interactions with tumor-infiltrating immune cells in generating immunosuppressive TME and resistance to cell death. Full article

Reparative tertiary dentinogenesis requires the recruitment and odontogenic differentiation of dental pulp stem cells (DPSCs). Extracellular vesicles (EVs) as bioactive molecules have gained attention in regenerative medicine for their ability to mediate tissue repair through intercellular communication, influencing cell recruitment, proliferation, and differentiation. This study aimed to evaluate the effects of EVs on DPSC homing and odontogenic differentiation for dentin regeneration. DPSC-derived EVs were cultured in either growth (EV-G) or odontogenic differentiation (EV-O) conditions and isolated using a modified precipitation method. EVs were characterized by nanoparticle tracking analysis, scanning electron microscopy, antibody array, and cellular uptake assay. Treatment with 5 × 10⁸ EVs/mL significantly enhanced DPSC chemotaxis and proliferation compared with a no-treatment control and a lower dosage of EV (5 × 10⁷ EVs/mL). Gene expression and biochemical analyses revealed that EV-O up-regulated odontogenic markers including collagen type 1A1 (COL1A1), runt-related transcription factor 2 (RUNX2), and alkaline phosphatase (ALP). EV-O enhanced dentin regeneration by approximately 55% over vehicle controls in a rabbit partial dentinotomy/pulpotomy model. We identified key microRNAs (miR-21-5p, miR-221-3p, and miR-708-3p) in EV-O involved in cell homing and odontogenesis. In conclusion, our EV-based cell homing and odontogenic differentiation strategy has significant therapeutic potential for dentin regeneration. Full article

This paper explores the oscillatory behavior of a class of second-order hybrid-type neutral delay difference equations. A novel approach is introduced to transform these complex trinomial equations into a simpler binomial form by utilizing solutions from an associated linear difference equation. By employing comparison techniques and summation-averaging methods, we establish new oscillation criteria which guarantee that all solutions exhibit oscillatory behavior. Our findings extend to an existing oscillation theory and are applicable even to non-neutral second-order equations. A couple of examples are presented to highlight the impact and novelty of the obtained results. Full article

This study examines the surface chemistry of platinum, palladium, rhodium, and ruthenium-substituted lanthanum strontium cobaltate perovskite catalysts in the context of the dry reforming of methane (DRM). The catalysts were synthesized by the solution combustion method and characterized by using a series of techniques. To explore the effect of noble metal ion substitution on the DRM, surface reaction was probed by CH₄/CO₂ TPSR using mass spectroscopy. It was recognized that La_1−xSr_xCo_1−yPd_yO₃ show the best activities for the reaction in terms of the temperature but became deactivated over time. CH₄/CO₂ temperature-programmed surface reactions (TPSRs) were set up to unravel the details of the surface phenomena responsible for the deactivation of the DRM activity on the LSPdCO. The CH₄/CO₂ TPSR analysis conclusively demonstrated the importance of lattice oxygen in the removal of carbon, which is responsible for the stability of the catalysts on the synthesized perovskites upon noble metal ion substitution. Full article

Since airborne microplastics (AMPs) are a recent and unexplored field of study, there are several unresolved issues regarding their effects on plants. The accumulating potential of AMPs and their effect on the biochemical parameters of ten different plant species in an Indian city environment were assessed. The four types of AMPs deposited in the phyllosphere—fragment (30.76%), film (28.95%), fiber (22.61%), and pellet (17.68%)—were examined using stereomicroscopy and fluorescence microscopy. The air pollution tolerance index (APTI) was determined, and other biochemical parameters such as proline, phenol, malondialdehyde, carotenoids, superoxide dismutase, catalase, and peroxidase were also measured. The findings showed that in the case of polymers type, PE (30%) was more abundant than others, followed by PET (17%), PP (15%), PVC (13%), PVA (10%), PS (7%), ABS (5%), and PMMA (3%). Clerodendrum infortunatum L., Calotropis procera (Aiton) W.T. Aiton, and Mangifera indica L. all showed a strong APTI and also exhibited significantly higher amounts of AMP accumulation. Principal component analysis showed a stronger association between phyllospheric AMPs and biochemical parameters. Additionally, the correlation analysis revealed that the presence of accumulated AMPs may significantly influence the biochemical parameters of the plants. Thus, it can be concluded that the different plant species are uniquely specialized in AMP accumulation, which is significantly impacted by the plants’ APTI as well as other biochemical parameters. Full article

In recent times, fiber reinforced polymer composite materials have become more popular due to their remarkable features such as high specific strength, high stiffness and durability. Particularly, Carbon Fiber Reinforced Polymer (CFRP) composites are one of the most prominent materials used in the field of transportation and building engineering, replacing conventional materials due to their attractive properties as mentioned. In this work, a CFRP laminate is fabricated with carbon fiber mats and epoxy by a hand layup technique. Alumina (Al₂O₃) micro particles are used as a filler material, mixed with epoxy at different weight fractions of 0% to 4% during the fabrication of CFRP laminates. The important objective of the study is to investigate the influence of alumina micro particles on the mechanical performance of the laminates through characterization for various physical and mechanical properties. It is revealed from the results of study that the mass density of the laminates steadily increased with the quantity of alumina micro particles added and subsequently, the porosity of the laminates is reduced significantly. The SEM micrograph confirmed the constituents of the laminate and uniform distribution of Al₂O₃ micro particles with no significant agglomeration. The hardness of the CFRP laminates increased significantly for about 60% with an increase in weight % of Al₂O₃ from 0% to 4%, whereas the water gain % gradually drops from 0 to 2%, after which a substantial rise is observed for 3 to 4%. The improved interlocking due to the addition of filler material reduced the voids in the interfaces and thereby resist the absorption of water and in turn reduced the plasticity of the resin too. Tensile, flexural and inter-laminar shear strengths of the CFRP laminate were improved appreciably with the addition of alumina particles through extended grain boundary and enhanced interfacial bonding between the fibers, epoxy and alumina particles, except at 1 and 3 wt.% of Al₂O₃, which may be due to the pooling of alumina particles within the matrix. Inclusion of hard alumina particles resulted in a significant drop in impact strength due to appreciable reduction in softness of the core region of the laminates. Full article

Energy storage and conversion units have been considered the backbone of modern energy science and technology. In recent years, the Ni-based sulfides (NS) and mixed sulfides (NMS) have been significantly utilized as promising electrodes for various energy-related applications. This article summarizes the recent progress of NS and NMS materials in the fields of energy storage (supercapacitors) and conversion (oxygen evolution reactions). The synthetic approaches have been thoroughly discussed. A brief overview of the electrochemical performance of these materials as the electrodes for energy storage and conversion is systematically represented in the article. For such applications, these materials are frequently combined with other advanced materials, such as metal oxides, metal sulfides, and carbonaceous materials. The article ends with the existing challenges and future research directions in these research fields. Full article

This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate. Using a three-phase framework, this study integrates data-driven diagnostics, AI modeling, and real-world validation. In the first phase, a systematic analysis of current packaging inefficiencies was conducted through secondary data, benchmarking, and cost modeling. Findings revealed significant waste caused by over-packaging, suboptimal box-sizing, and poor alignment between product characteristics and logistics strategy. In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. This model was supported by AI simulation tools that enabled virtual testing of material performance, space efficiency, and damage risk. Results demonstrated measurable improvements in packaging optimization, cost reduction, and emission mitigation. The third phase validated the AI framework using practical case studies—ranging from a college textbook to a fragile kitchen dish set and a high-volume children’s bicycle. The model successfully recommended right-sized packaging for each product, resulting in reduced material usage, improved shipping density, and enhanced protection. Simulated cost-saving scenarios further confirmed that smart packaging and AI-generated configurations can drive efficiency. The research concludes that AI-based packaging systems offer substantial strategic value, including cost savings, environmental benefits, and alignment with regulatory and consumer expectations—providing scalable, data-driven solutions for e-commerce enterprises such as Amazon and others. Full article

Organoids are three-dimensional tissue culture models derived from stem cells, and they have become one of the most valuable tools in biomedical research. These self-organizing miniature organs mimic the structure−function properties of their in vivo counterparts and offer an exceptional prospective for disease modeling, drug discovery, and regenerative medicine. By replicating the complexity of human tissue, organoids enable the study of disease pathophysiology, tissue development, and cellular interactions in a highly controlled and manipulable environment. Recent developments in organoid technology have enabled the production of functional organoids of various tissues. These systems have proven to be highly promising tools for personalized medicine. In addition, organoids have also raised hopes for the development of functional transplantable organs, transforming the study of regenerative medicine. This review provides an overview of the current state of organoid technology and its application and prospects and focuses on the transformative impact of organoid technology on biomedical research and its contribution to human health. Full article

The most common size measurements for agricultural produce, including fruits and vegetables, are length and width. While the length of any agricultural produce can be unique, the width varies continuously along its length. Single-width measurements alone are insufficient for accurately characterizing varying width profiles, resulting in an inaccurate representation of the shape or mean dimension. Consequently, the manual measurement of multiple mean dimensions is laborious or impractical, and no information in this domain is available. Therefore, an efficient alternative computer vision measurement tool was developed utilizing ImageJ (Ver. 1.54p). Twenty sample sets, comprising fruits and vegetables, with each representing different shapes, were selected and measured for length and multiple widths. A statistically significant minimum number of multiple widths was determined for practical measurements based on an object’s shape. The “aspect ratio” (width/length) was identified to serve as an effective indicator of the minimum multiple width measurements. In general, 50 multiple width measurements are recommended; however, even 15 measurements would be satisfactory ($\mathrm{1.0\%}\pm \mathrm{0.6\%}$ deviation from 50 widths). The developed plugin was fast (734 ms ± 365 ms CPU time/image), accurate (>99.6%), and cost-effective, and it incorporated several user-friendly and helpful features. This study’s outcomes have practical applications in the characterization, quality control, grading and sorting, and pricing determination of agricultural produce. Full article

The present study focused on a comparative analysis of NaOH-treated and untreated Sida acuta fiber-reinforced composites with respect to their wear behavior and compressive strength. The Sida acuta fibers were treated with 5% NaOH, while the untreated fibers were used directly as reinforcement, both comprising 32 ± 1 wt.% of the epoxy matrix composites. The composites were further characterized based on their average density, hardness, and compressive strength. Additionally, weight loss, volume loss, and wear rate were examined under dry wear test conditions across various loads and sliding velocities. The results indicate that the alkali-treated fiber-reinforced composite exhibits superior hardness (84.3 ± 2.0) and compressive strength (99.89 ± 3.92 MPa), representing improvements of 12.57% and 13.5%, respectively, over the untreated fiber-reinforced composite. Moreover, the 5% NaOH-treated fiber-reinforced composite demonstrated lower wear rates compared to its untreated counterpart. Scanning Electron Microscopy (SEM) was employed to examine the dry wear surface morphology of both composite laminates, providing insights that support the observed test results. Overall, the developed Sida acuta composite exhibits promising properties, making it suitable for lightweight and medium-strength structural applications. Full article

Object detection is a mature problem in autonomous driving, with pedestrian detection being one of the first commercially deployed algorithms. It has been extensively studied in the literature. However, object detection is relatively less explored for fisheye cameras used for surround-view near-field sensing. The standard bounding-box representation fails in fisheye cameras due to heavy radial distortion, particularly in the periphery. In this paper, a generic object detection framework is implemented using the base YOLO (You Only Look Once) detector to systematically explore various object representations using the public WoodScape dataset. First, we implement basic representations, namely the standard bounding box, the oriented bounding box, and the ellipse. Secondly, we implement a generic polygon and propose a novel curvature-adaptive polygon, which obtains an improvement of 3 mAP (mean average precision) points. A polygon is expensive to annotate and complex to use in downstream tasks; thus, it is not practical to use it in real-world applications. However, we utilize it to demonstrate that the accuracy gap between the polygon and the bounding box representation is very high due to strong distortion in fisheye cameras. This motivates the design of a distortion-aware optimal representation of the bounding box for fisheye images, which tend to be banana-shaped near the periphery. We derive a novel representation called a curved box and improve it further by leveraging vanishing-point constraints. The proposed curved box representations outperform the bounding box by 3 mAP points and the oriented bounding box by 1.6 mAP points. In addition, the camera geometry tensor is formulated to provide adaptation to non-linear fisheye camera distortion characteristics and improves the performance further by 1.4 mAP points. Full article

Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios. Full article

Background/Objectives: AML is a heterogeneous hematological malignancy distinguished by the clonal expansion of immature myeloid progenitor cells. Despite advances in therapy, relapse rates remain high, and outcomes are poor. The WT1 gene has emerged as a potential contributor to leukemogenesis, but its clinical relevance at the transcriptional level is not fully understood. This study employed RNA sequencing as a discovery tool to identify WT1 gene expression in AML and further investigated its role in diagnosis, prognosis, and treatment response. Methods: Between 2020 and 2024, 345 diagnostic, 259 post-induction, and 70 relapse-stage BM or PB samples were prospectively collected from de novo AML patients at AIIMS, New Delhi. RNA sequencing was initially performed on five paired diagnosis-relapse samples to profile transcriptomic changes and assess WT1 expression dynamics. WT1 expression was further validated by qPCR. The relationship between WT1 expression and various clinical parameters was evaluated using Cox regression analysis to determine its impact on prognosis. Results: RNA sequencing and qPCR confirmed WT1 overexpression at diagnosis, which significantly declined following induction therapy. High WT1 expression at diagnosis was linked with adverse clinical characteristics, including elevated WBC counts and higher blast percentages and predicted poor survival outcomes. WT1 expression was identified as a significant prognostic marker, correlating with OS and EFS. Conclusions: By integrating RNA sequencing with targeted validation, this study highlights WT1 expression as a critical biomarker for AML diagnosis, prognosis, and treatment response. The findings suggest that WT1 expression may serve as a valuable tool for monitoring disease status, risk stratification, and guiding treatment decisions in AML, with potential applications for WT1-targeted precision therapies. Full article

Diagnosing tuberculosis (TB) in children presents significant challenges, necessitating the identification of reliable biomarkers for accurate diagnosis. In this study, we investigated plasma matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) as potential diagnostic markers. A prospective case–control study involved 167 children classified into confirmed TB, unconfirmed TB, and unlikely TB control groups. Plasma levels of MMPs (MMP 1, 2, 3, 7, 8, 9, 12, and 13) and TIMPs (TIMP 1, 2, 3, and 4) were measured using multiplex assays. Elevated baseline levels of MMP-1, MMP-2, MMP-7, MMP-9, TIMP-1, TIMP-2, TIMP-3, and TIMP-4 were observed in active TB cases compared to unlikely TB controls. Receiver operating characteristics (ROC) analysis identified MMP-1, MMP-2, MMP-9, and TIMP-1 as potential biomarkers with over 80% sensitivity and specificity. A three-MMP signature (MMP-1, MMP-2, and MMP-9) demonstrated 100% sensitivity and specificity. The findings suggest that a baseline MMP signature could serve as an accurate biomarker for diagnosing pediatric TB, enabling early intervention and effective management. Full article