Search Results (2,717)

Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents, which is also considered an aggressive disease due to its rapid growth rate, ability to metastasize early, and complex and heterogeneous tumor microenvironment (TME). Although we are developing improved surgical and chemotherapeutic approaches, the presence of metastatic or recurrent disease is still detrimental to the patient’s outcome. Major advances in understanding the molecular mechanisms of OS are needed to substantially improve outcomes for patients being treated for OS. This review integrates new data on the molecular biology, pathophysiology, and immune landscape of OS, as well as introducing salient areas of tumorigenesis underpinning these findings, such as chromothripsis; kataegis; cancer stem cell dynamics; and updated genetic, epigenetic, and glycosylation modifiers. In addition, we review promising biomarkers, diagnostic platforms, and treatments, including immunotherapy, targeted small molecule inhibitors, and nanomedicine. Using genomic techniques, we have defined OS for its significant genomic instability due to TP53 and RB1 mutations, chromosomal rearrangements, and aberrant glycosylation. The TME is also characterized as immunosuppressive and populated by tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, ultimately inhibiting immune checkpoint inhibitors. Emerging fields such as glycomics and epigenetics, as well as stem cell biology, have defined promising biomarkers and targets. Preclinical studies have identified that glycan-directed CAR therapies could be possible, as well as metabolic inhibitors and 3D tumor models, which presented some preclinical success and could allow for tumoral specificity and enhanced efficacy. OS is a biologically and clinically complex disease; however, advances in exploring the molecular and immunologic landscape of OS present new opportunities in biomarkers and the development of new treatment options with adjunctive care. Successful treatments in the future will require personalized, multi-targeted approaches to account for tumor heterogeneity and immune evasion. This will help us turn the corner in providing improved outcomes for patients with this resilient malignancy. Full article

Advanced therapy medicinal products (ATMPs), especially effective against cancer, remain costly due to their reliance on genetically modified T cells. Contamination during production is a major concern, as traditional quality control methods involve samplings, which can themselves introduce contaminants. It is therefore necessary to develop methods for detecting contamination without sampling and, if possible, in real time. In this article, we present a white light spectroscopy method that makes this possible. It is based on shape analysis of the absorption spectrum, which evolves from an approximately Gaussian shape to a shape modified by the 1/λ component of bacterial absorption spectra when contamination develops. A warning value based on this shape descriptor is proposed. It is demonstrated that a few hours are sufficient to detect contamination and trigger an alarm to quickly stop the production. This time-saving should reduce the cost of these new drugs, making them accessible to as many people as possible. This method can be used regardless of the type of contaminants, provided that the shape of their absorption spectrum is sufficiently different from that of pure T cells so that the shape descriptor is efficient. Full article

In today’s oncology, immunotherapy arises as a potent complement for conventional cancer treatment, allowing for obtaining better patient outcomes. B7-H3 (CD276) is a member of the B7 protein family, which emerged as an attractive target for the treatment of various tumors. The molecule modulates anti-cancer immune responses, acting through diverse signaling pathways and cell populations. It has been implicated in the pathogenesis of numerous malignancies, including melanoma, gliomas, lung cancer, gynecological cancers, renal cancer, gastrointestinal tumors, and others, fostering the immunosuppressive environment and marking worse prognosis for the patients. B7-H3 targeting therapies, such as monoclonal antibodies, antibody–drug conjugates, and CAR T-cells, present promising results in preclinical studies and are the subject of ongoing clinical trials. CAR-T therapies against B7-H3 have demonstrated utility in malignancies such as melanoma, glioblastoma, prostate cancer, and RCC. Moreover, ADCs targeting B7-H3 exerted cytotoxic effects on glioblastoma, neuroblastoma cells, prostate cancer, and craniopharyngioma models. B7-H3-targeting also delivers promising results in combined therapies, enhancing the response to other immune checkpoint inhibitors and giving hope for the development of approaches with minimized adverse effects. However, the strategies of B7-H3 blocking deliver substantial challenges, such as poorly understood molecular mechanisms behind B7-H3 protumor properties or therapy toxicity. In this review, we discuss B7-H3’s role in modulating immune responses, its significance for various malignancies, and clinical trials evaluating anti-B7-H3 immunotherapeutic strategies, focusing on the clinical potential of the molecule. Full article

Background: Atrial fibrosis is a key contributor to atrial cardiomyopathy and can be assessed invasively using mean left atrial voltage (MLAV) from electroanatomical mapping. However, the invasive nature of this procedure limits its clinical applicability. Machine learning (ML), particularly regression tree-based models, may offer a non-invasive approach for predicting MLAV using clinical and echocardiographic data, improving non-invasive atrial fibrosis characterisation beyond current dichotomous classifications. Methods: We prospectively included and followed 113 patients with paroxysmal or persistent atrial fibrillation (AF) undergoing pulmonary vein isolation (PVI) with ultra-high-density voltage mapping (uHDvM), from whom MLAV was estimated. Standardised two-dimensional transthoracic echocardiography was performed before ablation, and clinical and echocardiographic variables were analysed. A regression tree model was constructed using the Classification and Regression Trees—CART-algorithm to identify key predictors of MLAV. Results: The regression tree model exhibited moderate predictive accuracy (R² = 0.63; 95% CI: 0.55–0.71; root mean squared error = 0.90; 95% CI: 0.82–0.98), with indexed minimum LA volume and passive emptying fraction emerging as the most influential variables. No significant differences in AF recurrence-free survival were found among MLAV tertiles or model-based generated groups (log-rank p = 0.319 and p = 0.126, respectively). Conclusions: We present a novel ML-based regression tree model for non-invasive prediction of MLAV, identifying minimum LA volume and passive emptying fraction as the most significant predictors. This model offers an accessible, non-invasive tool for refining atrial cardiomyopathy characterisation by reflecting the fibrotic substrate as a continuum, a crucial advancement over existing dichotomous approaches to guide tailored therapeutic strategies. Full article

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

T-cell-engaging antibodies are a promising new type of treatment for patients with refractory or relapsed (R/R) diffuse large B-cell lymphoma, which has changed the prognosis and evolution of these patients in clinical trials. Bispecific antibodies (BsAbs) bind to two different targets (B and T lymphocytes) at the same time and in this way mimic the action of CAR (chimeric antigen receptor) T-cells. They are the T-cell-engaging antibodies most used in practice and are a solution for patients who do not respond to second- or later-line therapies, including chemoimmunotherapy, followed by salvage chemotherapy and hematopoietic stem cell transplantation. They are a therapeutic option for patients who are ineligible for CAR T-cell therapy and are also active in those with prior exposure to CAR T-cell treatment. A remarkable advantage of BsAbs is their rapid availability, even if the disease progresses rapidly, unlike CAR T-cell treatment, and they avoid the practical and financial challenges raised by autologous CAR T-cell therapies. CAR-T has been proven to have better efficacy compared to BsAbs, but cytokine release syndrome and neurotoxicity have appeared significantly more frequently in patients treated with CAR T-cells. The possibility of combining BsAbs with chemotherapy and their administration for relapses or as a frontline therapy is being studied to increase their efficacy. BsAbs are a life-saving therapy for many patients with diffuse large B-cell malignant non-Hodgkin’s lymphoma (NHL) who have a poor prognosis with classical therapies, but are not without adverse effects and require careful monitoring. Full article

Background: HIV-associated neurocognitive disorders (HANDs) continue to be a significant concern, despite the advancements in prognosis achieved through Combination Antiretroviral Therapy (cART). Neuropsychological assessment, recommended by international guidelines for HANDs diagnosis, can be resource-intensive. Brief screening tools, like the International HIV Dementia Scale (IHDS) and the Montreal Cognitive Assessment (MoCA), are crucial in facilitating initial evaluations. This study aims to assess the Italian IHDS (IHDS-IT) and evaluate its sensitivity and specificity in detecting cognitive impairment in HIV patients. Methods: This cross-sectional study involved 294 patients aged ≥30 years, evaluated at the Immunology Unit of the University of Cagliari. Cognitive function was assessed using the MoCA and IHDS. Laboratory parameters, such as CD4 nadir, current CD4 count, and HIV-RNA levels, were also collected. Statistical analyses included Spearman’s correlation, Receiver Operating Characteristic analysis, and the Youden J statistic to identify the optimal IHDS-IT cut-off for cognitive impairment detection. Results: The IHDS and MoCA scores showed a moderate positive correlation (Spearman’s rho = 0.411, p < 0.0001). ROC analysis identified an IHDS-IT cut-off of ≤9, yielding an Area Under the Curve (AUC) of 0.76, sensitivity of 71.7%, and specificity of 67.2%. At this threshold, 73.1% of patients with MoCA scores below 23 also presented abnormal IHDS scores, highlighting the complementary utility of both cognitive assessment instruments. Conclusions: The IHDS-IT exhibited fair diagnostic accuracy for intercepting cognitive impairment, with a lower optimal cut-off than previously reported. The observed differences may reflect this study cohort’s demographic and clinical characteristics, including advanced age and long-lasting HIV infection. Further, longitudinal studies are necessary to validate these findings and to confirm the proposed IHDS cut-off over extended periods. Full article

Chimeric antigen receptor (CAR)-T immunotherapy has revolutionized the management of patients with relapsed/refractory B-cell hematological malignancies. There is emerging evidence that CAR-engineered cells—not only T cells, but also natural killers and macrophages—might have a crucial role in the treatment of autoimmune disorders and solid tumors. Moreover, given the burden of chronic infectious diseases, the mortality and morbidity of infections in immunocompromised individuals, and the development of multidrug-resistant pathogens, including bacteria, fungi, and mycobacteria, a need for novel and personalized therapeutics in this field is emerging. To this end, the development of CAR cells for the management of chronic infections has been reported. In this literature review, we summarize the ongoing clinical and pre-clinical data about CAR cell products in the field of infectious diseases. Currently, clinical studies on CAR immunotherapy for infections mainly concern human immunodeficiency virus infection treatment, and data regarding other infections largely originate from preclinical in vitro and in vivo models. In the era of personalized medicine, effective and safe therapies for the management of chronic infections and infectious complications in immunocompromised patients are crucial. Full article

Background: Transfection is vital for gene therapy, mRNA treatments, CAR-T cell therapy, and regenerative medicine. While viral vectors are effective, non-viral systems like lipid nanoparticles (LNPs) offer safer, more flexible alternatives. This work explores emerging non-viral transfection technologies to improve delivery efficiency and therapeutic outcomes. Methods: This review synthesizes the current literature and recent advancements in non-viral transfection technologies. It focuses on the mechanisms, advantages, and limitations of various delivery systems, including lipid nanoparticles, biodegradable polymers, electroporation, peptide-based carriers, and microfluidic platforms. Comparative analysis was conducted to evaluate their performance in terms of transfection efficiency, cellular uptake, biocompatibility, and potential for clinical translation. Several academic search engines and online resources were utilized for data collection, including Science Direct, PubMed, Google Scholar Scopus, the National Cancer Institute’s online portal, and other reputable online databases. Results: Non-viral systems demonstrated superior performance in delivering mRNA, siRNA, and antisense oligonucleotides, particularly in clinical applications. Biodegradable polymers and peptide-based systems showed promise in enhancing biocompatibility and targeted delivery. Electroporation and microfluidic systems offered precise control over transfection parameters, improving reproducibility and scalability. Collectively, these innovations address key challenges in gene delivery, such as stability, immune response, and cell-type specificity. Conclusions: The continuous evolution of transfection technologies is pivotal for advancing gene and cell-based therapies. Non-viral delivery systems, particularly LNPs and emerging platforms like microfluidics and biodegradable polymers, offer safer and more adaptable alternatives to viral vectors. These innovations are critical for optimizing therapeutic efficacy and enabling personalized medicine, immunotherapy, and regenerative treatments. Future research should focus on integrating these technologies to develop next-generation transfection platforms with enhanced precision and clinical applicability. Full article

Multiple myeloma (MM) is predominantly a disease of the elderly. In recent years, a surge of highly effective plasma cell therapies has revolutionized the care of elderly multiple myeloma (MM) patients, for whom frailty and age-related competing causes of mortality determine management. Traditionally, the treatment of newly diagnosed elderly patients has centered on doublet or triplet combinations composed of immunomodulators (IMIDs), proteasome inhibitors (PIs), anti-CD38 monoclonal antibodies (mAbs), and corticosteroids producing median progression-free survival (PFS) rates between 34 and 62 months. However, recently, a series of large phase III clinical trials examining quadruplet regimens of PIs, IMIDs, corticosteroids, and anti-CD38 mAbs have shown exceptional outcomes, with median PFS exceeding 60 months, albeit with higher rates of peripheral neuropathy (≥Grade 2: 27% vs. 10%) when PIs and IMIDs are combined, and infections (≥Grade 3: 40% vs. 29–41%) with the addition of anti-CD38mAbs. The development of T-cell redirecting therapies including T-cell engagers (TCEs) and CAR-T cells has further expanded the therapeutic arsenal. TCEs have shown exceptional activity in relapsed disease and are being explored in the newly diagnosed setting with promising early results. However, concerns remain regarding the logistical challenges of step-up dosing, which often necessitates inpatient admission, the infectious risks, and the financial burden associated with TCEs in elderly patients. CAR-T, the most potent commercially available therapy for MM, offers the potential of a ‘one and done’ approach. However, its application to elderly patients has been tempered by significant concerns of cytokine release syndrome, early and delayed neurological toxicity, and its overall tolerability in frail patients. Robust data in frail patients are still needed. How CAR-T and TCEs will be sequenced among the growing therapeutic armamentarium for elderly MM patients remains to be determined. This review explores the safety, efficacy, cost, and logistical barriers associated with the above treatments in elderly MM patients. Full article

Conventional immunotherapy, including immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, has revolutionized cancer therapy over the past decade. Yet, the efficacy of these therapies is limited by tumor resistance, antigen escape mechanisms, poor persistence, and T-cell exhaustion, particularly in the treatment of solid tumors. The emergence of unconventional immunotherapies offers novel opportunities by leveraging diverse immune cell subsets and synthetic biologics. This review explores various immunotherapy platforms, including gamma delta T cells, invariant natural killer T cells, mucosal-associated invariant T cells, engineered regulatory T cells, and universal CAR platforms. Additionally, it expands on biologics, including bispecific and multispecific antibodies, cytokine fusions, agonists, and oncolytic viruses, showcasing their potential for modular engineering and off-the-shelf applicability. Distinct features of unconventional platforms include independence from the major histocompatibility complex (MHC), tissue-homing capabilities, stress ligand sensing, and the ability to bridge adaptive and innate immunity. Their compatibility with engineering approaches highlights their potential as scalable, efficient, and cost-effective therapies. To overcome translational challenges such as functional heterogeneity, immune exhaustion, tumor microenvironment-mediated suppression, and limited persistence, novel strategies will be discussed, including metabolic and epigenetic reprogramming, immune cloaking, gene editing, and the utilization of artificial intelligence for patient stratification. Ultimately, unconventional immunotherapies extend the therapeutic horizon of cancer immunotherapy by breaking barriers in solid tumor treatment and increasing accessibility. Continued investments in research for mechanistic insights and scalable manufacturing are key to unlocking their full clinical potential. Full article

Classical Hodgkin lymphoma (cHL) is a biologically and clinically unique malignancy characterized by rare Hodgkin and Reed–Sternberg (HRS) cells surrounded by a dense and diverse inflammatory infiltrate. These malignant cells actively reshape the tumor microenvironment (TME) through metabolic reprogramming and immune evasion strategies. This review synthesizes current knowledge on how metabolic alterations contribute to tumor survival, immune dysfunction, and therapeutic resistance in cHL. We discuss novel therapeutic approaches aimed at disrupting these processes and examine the potential of combining metabolic interventions with immune-based strategies—such as immune checkpoint inhibitors (CPIs), epigenetic modulators, bispecific antibodies, and CAR-T/CAR-NK cell therapies—which may help overcome resistance and enhance anti-tumor responses. Several agents are currently under investigation for their ability to modulate immune cell metabolism and restore effective immune surveillance. Altogether, targeting metabolic vulnerabilities within both tumor and immune compartments offers a promising, multifaceted strategy to improve clinical outcomes in patients with relapsed or refractory cHL. Full article

Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by rapid progression, profound heterogeneity, and resistance to conventional therapies. This review provides an integrated overview of GBM’s pathophysiology, highlighting key mechanisms such as neuroinflammation, genetic alterations (e.g., EGFR, PDGFRA), the tumor microenvironment, microbiome interactions, and molecular dysregulations involving gangliosides and sphingolipids. Current diagnostic strategies, including imaging, histopathology, immunohistochemistry, and emerging liquid biopsy techniques, are explored for their role in improving early detection and monitoring. Treatment remains challenging, with standard therapies—surgery, radiotherapy, and temozolomide—offering limited survival benefits. Innovative therapies are increasingly being explored and implemented, including immune checkpoint inhibitors, CAR-T cell therapy, dendritic and peptide vaccines, and oncolytic virotherapy. Advances in nanotechnology and personalized medicine, such as individualized multimodal immunotherapy and NanoTherm therapy, are also discussed as strategies to overcome the blood–brain barrier and tumor heterogeneity. Additionally, stem cell-based approaches show promise in targeted drug delivery and immune modulation. Non-conventional strategies such as ketogenic diets and palliative care are also evaluated for their adjunctive potential. While novel therapies hold promise, GBM’s complexity demands continued interdisciplinary research to improve prognosis, treatment response, and patient quality of life. This review underscores the urgent need for personalized, multimodal strategies in combating this devastating malignancy. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

This paper presents a novel dynamic modeling framework for gantry crane systems based on the cart double pendulum with dual cables (CDPD) model. The CDPD model systematically incorporates the effects of dual suspension cables, equalizer beams, and closed-chain kinematic constraints, enabling an accurate simulation of both symmetric and asymmetric lifting scenarios. Utilizing Kane’s method, the model efficiently handles redundant coordinates and holonomic constraints, resulting in a compact and numerically robust formulation. Validation results demonstrate strict energy conservation and consistency with traditional models in limiting cases. The proposed approach provides a unified and extensible foundation for the advanced analysis, control, and optimization of large-scale gantry crane operations. Full article