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Immunotherapeutic approaches in EBV-associated nasopharyngeal carcinoma.

Epstein-Barr virus (EBV) was the first tumor virus in humans. Nasopharyngeal carcinoma (NPC) accounts for approximately 60% of the 200,000 new tumor cases caused by EBV infection worldwide each year. NPC has an insidious onset and is highly malignant, with more than 70% of patients having intermediate to advanced disease at the time of initial diagnosis, and is strongly implicated in epithelial cancers as well as malignant lymphoid and natural killer/T cell lymphomas. Over 90% of patients with confirmed undifferentiated NPC are infected with EBV. In recent decades, much progress has been made

Predictive Modeling of Pharmacokinetic Drug-Drug and Herb-Drug Interactions in Oncology: Insights From PBPK Studies.

Physiologically based pharmacokinetic (PBPK) modeling is increasingly used to anticipate, quantify, and strategically manage drug-drug (DDI) and herb-drug (HDI) interactions that can alter the exposure of chemotherapy agents together with co-administered phytochemicals or nutraceuticals. To evaluate current knowledge, we performed a comprehensive Google Scholar search (2003-2024) and selected studies that employed PBPK platforms, reported quantitative validation, and focused on chemotherapy-related interactions. From these reports, key modeling parameters, validation metrics, and clinically re

Current and Future Role of Medical Imaging in Guiding the Management of Patients With Relapsed and Refractory Non-Hodgkin Lymphoma Treated With CAR T-Cell Therapy.

Chimeric antigen receptor (CAR) T-cells are a novel immunotherapy available for patients with refractory/relapsed non-Hodgkin lymphoma. In this indication, clinical trials have demonstrated that CAR T-cells achieve high rates of response, complete response, and long-term response (up to 80%, 60%, and 40%, respectively). Nonetheless, the majority of patients ultimately relapsed. This review provides an overview about the current and future role of medical imaging in guiding the management of non-Hodgkin lymphoma patients treated with CAR T-cells. It discusses the value of predictive and prognos

An evaluation based on the analytic hierarchy process and GGEbiplot on French fry potato genotypes in Yunnan, China.

A total of 33 potato (Solanum tuberosum L.) cultivars and breeding clones imported from the United States and two local cultivars (Yunshu 401 and Cooperation 88, CK) were planted and evaluated. To determine their suitability for processing into French fries at five locations (e1-e5) in Yunnan Province, China, we developed a comprehensive evaluation system using the analytical hierarchy process (AHP). Eleven evaluation indicators for French fry quality, yield, and agronomic characteristics with a relative importance (weight coefficients) of 0.483, 0.301 and 0.216, respectively, were used to ana

Development of a machine learning-based predictive risk model combining fatty acid metabolism and ferroptosis for immunotherapy response and prognosis in prostate cancer.

Prostate cancer (PCa) remains a leading cause of cancer-related mortality, necessitating robust prognostic models and personalized therapeutic strategies. This study integrated bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics to construct a prognostic model based on genes shared between ferroptosis and fatty acid metabolism (FAM). Using the TCGA-PRAD dataset, we identified 73 differentially expressed genes (DEGs) at the intersection of ferroptosis and FAM, of which 19 were significantly associated with progression-free survival (PFS). A machine learning-

A New Deep Learning Algorithm for Detecting Spinal Metastases on Computed Tomography Images.

Study designRetrospective diagnostic study.ObjectiveTo automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model.Summary of background dataRadiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end

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… Burns et al. 21 used a combination of a watershed segmentation algorithm and a support vector machine classifier. Hammon et al. 22 used three consecutive random forest classifiers, each processing local image features, to assess candidate regions for bone metastases. …

Micro and nanotechnologies: The little formulations that could.

The first publication of micro- and nanotechnology in medicine was in 1798 with the use of the Cowpox virus by Edward Jenner as an attenuated vaccine against Smallpox. Since then, there has been an explosion of micro- and nanotechnologies for medical applications. The breadth of these micro- and nanotechnologies is discussed in this piece, presenting the date of their first report and their latest progression (e.g., clinical trials, FDA approval). This includes successes such as the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines from Pfizer, Moderna, and Janssen (

:: Анализ тенденций в области выявления множественных склерозов и анализа терапевтических тенденций на основе графиков знаний: реальные данные, полученные с помощью two - крупные системы здравоохранения.

Площадь склероза (MS) эволюционировала со временем. Мы провели основанный на использовании графиков анализ тенденций, связанных с рецептурной терапией, направленной на модификацию конкретных заболеваний (ДМТ), с использованием продольных реальных клинических данных. Мы проанализировали связанные с реестром данные электронной медицинской записи (ЭЗ) two между крупными независимыми системами здравоохранения 2004 и 2022, включая как академическую, так и общинную практику. Мы разработали неконтролируемый фенотификационный алгоритм, который использует общедоступный график знаний и информативные эле

Advanced Technologies for Large Scale Supply of Marine Drugs.

Marine organisms represent a source of unique chemical entities with valuable biomedical potentialities, broad diversity, and complexity. It is essential to ensure a reliable and sustainable supply of marine natural products (MNPs) for their translation into commercial drugs and other valuable products. From a structural point of view and with few exceptions, MNPs of pharmaceutical importance derive from the so-called secondary metabolism of marine organisms. When production strategies rely on marine macroorganisms, harvesting or culturing coupled with extraction procedures frequently remain t

Nanomedicine for cancer patient-centered care.

Cancer is a leading cause of morbidity and mortality worldwide, and an increase in incidence is estimated in the next future, due to population aging, which requires the development of highly tolerable and low-toxicity cancer treatment strategies. The use of nanotechnology to tailor treatments according to the genetic and immunophenotypic characteristics of a patient's tumor, and to allow its targeted release, can meet this need, improving the efficacy of treatment and minimizing side effects. Nanomedicine-based approach for the diagnosis and treatment of cancer is a rapidly evolving field. Se

Exploiting artificial intelligence in precision oncology: an updated comprehensive review.

Precision oncology considers the genetic makeup of both the tumor and the cancer patient, medical history, clinical metadata, lifestyle and environmental factors. Thus, adoption of precision cancer medicine mandates sophisticated integrative analysis. Being a master in sorting and solving the puzzle pieces, artificial intelligence (AI)-powered frameworks emerged to fill the gap via executing multimodal analysis and generating meaningful outputs. Herein, we systematically discussed the opportunities and challenges of exploiting AI-assisted models in preclinical cancer research and in clinical o

Designing optimal allocations for cancer screening using queuing network models.

Cancer is one of the leading causes of death, but mortality can be reduced by detecting tumors earlier so that treatment is initiated at a less aggressive stage. The tradeoff between costs associated with screening and its benefit makes the decision of whom to screen and when a challenge. To enable comparisons across screening strategies for any cancer type, we demonstrate a mathematical modeling platform based on the theory of queuing networks designed for quantifying the benefits of screening strategies. Our methodology can be used to design optimal screening protocols and to estimate their

Immunodiagnosis - the promise of personalized immunotherapy.

Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. H

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… Machine Learning (ML) is a branch of AI, which involved the use of algorithms such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines. …

Перевозка и сдерживание движения sphingosine-1-phosphate Экспортеры SPNS2.

Sphingosine-1-phosphate (S1P) — сигнал лизолипида, имеющий решающее значение для развития сердца, иммунитета и слуха. Соответственно, мутации в переносчике S1P SPNS2 связаны с уменьшением числа белых клеток и нарушениями слуха. СПНС2 также экспортирует S1P-mimicking FTY720-P (Финголимод) и, таким образом, является центральным элементом фармакокинетики этого препарата при лечении множественного склероза. Здесь мы используем комбинацию крио-электронной микроскопии, иммунофлюоресценции, связывания in vitro и в vivo S1P-оценки экспорта, а также молекулярное моделирование динамики для исследования

Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives.

Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order to control its occurrence. This is the first critical state-of-the-art review article to discuss all the commonly used biosensor types and the reasons that can give rise to potential false results. Furthermore, AI is discussed in parallel with biosensors and their misdiagnoses, and again some reasons for possible false results

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… Moreover, various machine learning algorithms—including support vector machines, principal components analysis, hierarchical cluster analysis, artificial neural networks and decision trees—have been used in AI biosensors for efficacious and precise decision making; SVMs are supervised ML techniques designed …

Virtual Screening of FDA-Approved Compounds: Exploring New Alternatives for HIV Treatment.

Human immunodeficiency virus (HIV) infection remains a significant public health challenge, particularly because of the emergence of drug-resistant strains against the drugs currently used in highly active antiretroviral therapy (HAART). The ongoing search for new molecules with therapeutic potential remains crucial. In this study, a virtual screening approach was employed to identify novel candidates with therapeutic potential for HIV. High-throughput screening (HTS) data were used to train and validate quantitative structure-activity relationship (QSAR) models, which were subsequently applie

Advances in nanomaterial-mediated CRISPR/Cas delivery: from lipid nanoparticles to vesicle-derived systems.

Gene and genome editing therapies are increasingly connected with nanomaterials, which protect and transport fragile nucleic acids and CRISPR/Cas systems through biological barriers safely and accurately. This review discusses how different nanocarriers, including lipid-based, polymeric, inorganic, and vesicle-derived systems, can improve delivery efficiency, cell targeting, endosomal escape, and intracellular movement for gene and genome editing. It summarizes findings from early clinical and preclinical studies, comparing several carrier types such as ionizable lipid nanoparticles, polymeric

From Early Diagnoses to New Treatments for Liver, Pancreatic, Gastric, and Colorectal Cancers Using Carbon Nanotubes: New Chances Still Underexplored.

Pancreatic, liver, gastric, colorectal, and rectal cancers (PC, LC, GC, CRC, and RC) are highly lethal tumours, with a 5-year survival rate of 10.5% (PC) and <20% (LC), and of 5%, 12%, and 13% for IV-stage GC, CRC, and RC, respectively. Currently, PC and LC represent the third leading cause of cancer-related death, while GCs and CRCs account for 4.8 million cancer cases and 4.4 million cancer deaths worldwide. Poor prognoses are mainly due to late diagnosis, limited efficacy of available treatments, tumour recurrence, as well as therapy-induced secondary tumorigenesis and drug resistance. In r

Identification and verification of the key genes involved in gallbladder cancer.

BackgroundGallbladder cancer (GBC) is a highly aggressive malignancy of the biliary tract. It often lacks distinct symptoms in its early stages, and no specific biomarkers have yet been identified for its diagnosis.ObjectiveTo identify key genes involved in GBC pathogenesis using public databases and bioinformatics analysis and validate these findings experimentally, providing a foundation for developing potential GBC biomarkers.MethodsAnalysis of GBC-related data from the Gene Expression Omnibus database revealed that G protein-coupled receptor 64 (GPR64) was differentially expressed in GBC.

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… Then, the highest accuracy and the lowest error rate were determined by the Support Vector Machine (SVM) algorithm and further applied to identify the DEGs. The key genes were identified in the LASSO regression model and SVM analysis. …

Medicinal chemistry perspectives on anticancer drug design based on clinical applications (2015-2025).

Cancer therapy has undergone a remarkable evolution over the past few decades, driven largely by innovations in medicinal chemistry. This review explores the pivotal role of medicinal chemistry in designing, optimizing, and classifying anticancer agents, from traditional cytotoxics to modern targeted therapies, immunotherapies, and radiotheranostics. The article categorizes FDA-approved anticancer drugs (2015-2025), evaluates their mechanisms of action, structural features, and structure-activity relationships (SAR), and highlights both success stories and challenges in clinical translation. A