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Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed

Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.

Background/Objectives: Acute kidney injury (AKI) is a common complication across the cancer care continuum and can compromise kidney function, delay or interrupt anticancer therapy, and worsen renal and oncologic outcomes. This state-of-the-art narrative review examines conventional clinical risk scores and artificial intelligence (AI)- and machine learning (ML)-based approaches to AKI prediction in oncology and identifies priorities for clinical translation. Methods: We conducted iterative searches of PubMed and Google Scholar through July 2026 and screened reference lists of relevant primary

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… The expansion of structured EHR data enabled random forests, gradient-boosted trees, support vector machines, and neural networks to model nonlinear relationships and interactions across laboratory, medication, physiologic, and demographic variables. …

Checkpoint inhibition and beyond: Precision immune engineering for the immune-privileged landscape of ocular malignancies.

Ocular malignancies, particularly uveal and conjunctival melanoma, exemplify tumors that evolve within one of the body's most immunologically constrained ecosystems, the eye's immune-privileged microenvironment. The limited success of PD-1/PD-L1 and CTLA-4 blockade in these cancers underscores the need to move beyond linear checkpoint inhibition toward multidimensional immune engineering. Through the confluence of synthetic bio-nanotechnology, AI-guided immunogenomics, and spatial immunomics, this review reframes ocular immunotherapy and redefines how tolerance and immunity might be programmat

Cancer vaccines: an update on recent achievements and prospects for cancer therapy.

Decades of basic and translational research have led to a momentum shift in dissecting the relationship between immune cells and cancer. This culminated in the emergence of breakthrough immunotherapies that paved the way for oncologists to manage certain hard-to-treat cancers. The application of high-throughput techniques of genomics, transcriptomics, and proteomics was conclusive in making and expediting the manufacturing process of cancer vaccines. Using the latest research technologies has also enabled scientists to interpret complex and multiomics data of the tumour mutanome, thus identify

Chimeric antigen receptor T cell: A cancer immunotherapy.

During present decade, targeted drug therapy has been the epitome for treatment of cancer. Drugs like Imatinib, a tyrosine kinase receptor inhibitor and Trastuzumab, an human epidermal growth factor receptor-2/neu inhibitor, has been developed and accepted widely for management of chronic myeloid leukaemia and breast cancer respectively. Recent development among the various immunotherapies is adoptive cell transfer (ACT). Research on development of various types of ACT immunotherapy is going on, but so far, Chimeric antigen receptors T cell therapy (CAR-T) has achieved the maximum advancement

Unlocking the potential: advancements and applications of gene therapy in severe disorders.

INTRODUCTION: Several severe disorders, such as inherited diseases (e.g. cystic fibrosis and beta thalassemia), genetic diseases (e.g. malignant tumors and diabetes), and infectious diseases (e.g. HIV) are pose significant challenges to human health. BACKGROUND: Over the past few decades, researchers have been working on gene therapies, and currently, terrible dreams have come true. To date, the Food and Drug Administration (FDA) has approved multiple gene therapies such as Kynamro for familial hypercholesterolaemia, Exondys51 for duchenne muscular dystrophy, Spinraza for spinal muscular atro

Engineering with Biomedical Sciences Changing the Horizon of Healthcare-A Review.

In the dynamic realm of healthcare, the convergence of engineering and biomedical sciences has emerged as a pivotal frontier. In this review we go into specific areas of innovation, including medical imaging and diagnosis, developments in biomedical sensors, and drug delivery systems. Wearable biosensors, non-wearable biosensors, and biochips, which include gene chips, protein chips, and cell chips, are all included in the scope of the topic that pertains to biomedical sensors. Extensive research is conducted on drug delivery systems, spanning topics such as the integration of computer modelin

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… Principal component analysis, support vector machines, convolutional neural networks, and other techniques are a few examples. The trained algorithm is then expected to identify the characteristics and classify the testing image. …

Advances and development of prostate cancer, treatment, and strategies: A systemic review.

The most common type of cancer in the present-day world affecting modern-day men after lung cancer is prostate cancer. Prostate cancer remains on the list of top three cancer types claiming the highest number of male lives. An estimated 1.4 million new cases were reported worldwide in 2020. The incidence of prostate cancer is found predominantly in the regions having a high human development index. Despite the fact that considerable success has been achieved in the treatment and management of prostate cancer, it remains a challenge for scientists and clinicians to curve the speedy advancement

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… Traditional techniques such as support vector machine (SVM), decision tree (DT), k-nearest neighbor-cosine (KNN-Cosine), RUSBoost tree, and kernel naive Bayes are unable to extricate the complicated characteristics of cancer. …

Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review.

PurposePatients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis.MethodsThis scoping review followed the five-stage framework of Arksey and O'Malley. The literature search was condu

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy.

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation betw

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… Chen et al. showed that SERS serum c in combination with generic algorithm-LDA and support vector machine (SVM) can achieve diagnosis and staging of diffuse large B-cell lymphoma (accuracy for LDA = 71.1% and SNV = 78.4%) [ 53 ] and discrimination of multiple myeloma (accuracy for LDA = 75.5% and SNV … These results are in line with other studies; Sikirzhytskaya et al. demonstrated that Raman spectroscopy combined with other statistic methods, such as support vector machine (SVM), might be useful in determining the sex of a blood sample [ 69 ]. …

Catching Them Early: Framework Parameters and Progress for Prenatal and Childhood Application of Advanced Therapies.

Advanced therapy medicinal products (ATMPs) are medicines for human use based on genes, cells or tissue engineering. After clear successes in adults, the nascent technology now sees increasing pediatric application. For many still untreatable disorders with pre- or perinatal onset, timely intervention is simply indispensable; thus, prenatal and pediatric applications of ATMPs hold great promise for curative treatments. Moreover, for most inherited disorders, early ATMP application may substantially improve efficiency, economy and accessibility compared with application in adults. Vindicating t

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30

Тенсорная диффузия МРТ и NODDI в классификации множественных склерозов.

СПРАВОЧНАЯ ИНФОРМАЦИЯ: Многокамерные модели диффузии и многокамерные приобретения все чаще используются для преодоления ограничений, связанных с обычной диффузионной тенсорной съемкой, но требуют более длительных сканирования и более сложной обработки. В то же время исследования по диффузионной классификации МРТ часто основываются на узком наборе тенсорных метрик, особенно на фракционной анизотропии. Мы изучили вопрос о том, может ли более широкое использование настольных элементов, включая дескрипторы формы, улучшить классификацию без повышения сложности приобретения.МЕТОДЫ: МРИ с несколькими

Computed tomography with low-dose radiation versus standard-dose radiation for diagnosing fractures: systematic review and meta-analysis.

BackgroundComputed tomography (CT) accounts for 13% of all radiological examinations in the United States and 40-70% of the radiation that patients receive. Even with the advent of magnetic resonance imaging (MRI), CT continues to be the gold standard for diagnosing bone fractures. There is uncertainty as to whether CT with a low radiation dose has a fracture detection rate similar to that of standard-dose CT.ObjectiveTo determine the detection rate of low-dose radiation CT and standard-dose radiation CT for fractures, in patients with suspected fractures.Design and settingSystematic review of

A study on a real-world data-based VTE risk prediction model for lymphoma patients.

BackgroundPatients diagnosed with malignant tumors exhibit a markedly elevated risk of venous thromboembolism (VTE), which has a negative impact on their prognosis. Currently, there is no reliable predictive model specifically for thrombosis risk in lymphoma patients. This study aims to develop and validate a machine learning model leveraging real-world data, offering a dependable risk assessment tool for the early identification of VTE in lymphoma patients.MethodsWe retrospectively analyzed 605 hospitalized patients with lymphoma between January 2019 and June 2024. Candidate predictors includ

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… The ML algorithms include logistic regression (LR), decision trees (DT), random forests (RF), support vector machines (SVM), naive Bayes (NB), KNN, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). …

Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs.

In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs pathways. Two cancer drugs pathway schemas were constructed using these relationships extracted from SemMedDB. Through both pathway schemas, we found drugs already used for prostate cancer therapy and drugs not currently listed as the prostate cancer

Chimeric Antigen Receptor T Cells as Living Therapeutics Targeting Senescence and Age-Related Diseases.

The aging of the global population exacerbates the burden of age-related diseases; however, therapies that can intervene in fundamental aging processes are lacking. Senescent cells drive chronic inflammation and multitissue dysfunction through the secretion of proinflammatory and profibrotic senescence-associated secretory phenotype cells, making them emerging therapeutic targets. Although first-generation senolytic drugs have entered clinical trials, they face limitations such as insufficient targeting specificity and transient efficacy. The success of chimeric antigen receptor T cell (CAR T

Fracture Risk of Long Bone Metastases: A Review of Current and New Decision-Making Tools for Prophylactic Surgery.

Long bone pathological fractures very much reflect bone metastases morbidity in many types of cancer. Bearing in mind that they not only compromise patient function but also survival, identifying impending fractures before the actual event is one of the main concerns for tumor boards. Indeed, timely prophylactic surgery has been demonstrated to increase patient quality of life as well as survival. However, early surgery for long bone metastases remains controversial as the current fracture risk assessment tools lack accuracy. This review first focuses on the gold standard Mirels rating system.

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certa

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… Wang M Liang Y Hu Z et al Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines Comput Biol Med Jun 2022 145 105435 doi 10.1016/j.compbiomed.2022.105435 Medline 35397339 33. …

Development and early feasibility testing of machine-learning algorithms to non-invasively assess hemoglobin levels.

The HeMonitor study evaluated the feasibility and accuracy of non-invasive hemoglobin (Hb) assessment using image-based techniques and machine learning in patients with hematologic malignancies. A total of 367 patients with hematologic malignancies and 184 healśśthy donors were enrolled, with fingernail and eyelid photographs collected and analyzed using Light Gradient-Boosting Machine (LightGBM) regression models. The best-performing model achieved a residual standard deviation of ±1.02 mmol/L for Hb prediction. Our framework further explored a two-stage concept combining (i) a non-invasive i