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Novel Insights in Venous Thromboembolism Risk Assessment Methods in Ambulatory Cancer Patients: From the Guidelines to Clinical Practice.

Many cancer patients will experience venous thromboembolism (VTE) at some stage, with the highest rate in the initial period following diagnosis. Novel cancer therapies may further enhance the risk. VTE in a cancer setting is associated with poor prognostic, a decreased quality of life, and high healthcare costs. If thromboprophylaxis in hospitalized cancer patients and perioperative settings is widely accepted in clinical practice and supported by the guidelines, it is not the same situation in ambulatory cancer patient settings. The guidelines do not recommend primary thromboprophylaxis, exc

Outcome Prediction of Hematologic Malignancy in Critically Sick People.

People with hematologic malignancies (HM) frequently postulate intensive care unit (ICU) hospitalization due to organ damage caused by the disease process or treatment-related consequences. This study is aimed at looking at mortality and sign factors in adult patients with hematologic malignancy (HM) who have been hospitalized in the ICU. Death was one quality indicator; researchers used a machine learning approach to find determinants of death. As per the study, there have been 206 patients hospitalized in the ICU (mean age: 51.3 ± 13.6 years; 60% male). The average length of stay was three d

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… The forecast design is based on a supervised machine learning approach and various input variables and datasets. …

Cuproptosis related genes in immune infiltration and treatment of osteoporosis by bioinformatic analysis and machine learning methods.

Cuproptosis, a copper-dependent form of cell death, has been implicated in immune function and osteoporosis. However, the specific roles of cuproptosis-related genes (CRGs) in osteoporosis remain unclear. The differentially expressed CRGs from the Gene Expression Omnibus datasets of persons with osteoporosis and healthy individuals were categorized using R software tools in this study. Following that, the CIBERSORT algorithm and the GSVA technique were used to investigate the relationships between the different clusters and immune infiltration characteristics. Based on four machine learning te

Editorial: Digital health and real-world evidence in onco-hematological patients.

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Integrating Artificial Intelligence, Circulating Tumor DNA, and Real-World Evidence to Optimize Hematologic Clinical Trials: Toward Adaptive and Learning Trial Designs.

The integration of emerging technologies and real-world data is transforming the landscape of hematologic clinical trials. Artificial intelligence (AI) offers remarkable capabilities for predictive modeling, patient stratification, and adaptive trial design, while circulating tumor DNA (ctDNA) provides a minimally invasive biomarker for disease monitoring, the early detection of relapse, and treatment response assessment. Concurrently, real-world evidence (RWE) complements traditional clinical trial data by capturing treatment effectiveness, safety, and patient outcomes in broader, heterogeneo

Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.

Mucosal melanoma (MM) is a rare and lethal subtype of melanoma, disproportionately affecting Asian populations and exhibiting distinct clinicopathological and genetic features compared to cutaneous melanoma (CM). Often diagnosed at advanced stages, MM shows poor responses to conventional therapies, and no standardized treatment regimen currently exists. Progress in preclinical modeling, including cell lines, patient-derived xenografts (PDXs), organoids (PDOs), and comparative animal models-has provided valuable tools for studying MM pathogenesis and therapeutic resistance. Yet these models rem

Autoimmune Neuromuscular Disorders at a Molecular Crossroad: Linking Pathogenesis to Targeted Immunotherapy.

Myasthenia gravis, chronic inflammatory demyelinating polyneuropathy, and idiopathic inflammatory myopathies are among the most widely recognized autoimmune neuromuscular disorders. Although they differ in clinical presentation, shared immunopathogenic mechanisms place them at a molecular crossroads. Evidence of overlapping pathways has led to the development of targeted strategies including complement inhibition, FcRn antagonism, B-cell depletion, and the CAR-T cell approach. In this review, we analyze current knowledge regarding pathogenic mechanisms and their link to immunotherapy, extensiv

Mapping the research landscape of myelodysplastic syndromes: a scientometric analysis (2014-2025).

This scientometric study critically evaluates the global evolution of myelodysplastic syndromes (MDS) research from 2014 to 2025, aiming to integrate fragmented knowledge and identify emerging trends. A scientometric analysis was conducted using data retrieved from the Web of Science Core Collection on August 2, 2025. The search was restricted to "article" and "review" in English containing "Myelodysplastic syndrome" or "Myelodysplastic syndromes" in the abstract, covering the period from January 1, 2014 onward. Scientometric tools (VOSviewer, CiteSpace, R-bibliometrix) were employed to analyz

Developing and validating a nomogram prediction model for osteoporosis risk in the UK biobank: a national prospective cohort.

BACKGROUND: Osteoporosis is a prevalent bone disease that increases frailty. Developing a nomogram prediction model to predict osteoporosis risk at multiple time points using bone mineral densities, behavioral habits, and clinical risk factors would be essential to identify individual risk and guide prevention. METHODS: The study population from the UK Biobank was followed from 2014 to December 31st, 2022. The study outcome was identified as the first occurrence of osteoporosis in the UK Biobank during the follow-up period. After rebalancing with the synthetic minority over-sampling technique

New genomic and proteomic biomarker discovery in cancer: revolutionizing diagnosis and prognostication.

Cancer remains a major global health challenge, with substantial morbidity and mortality worldwide. Advances in genomic and proteomic biomarker discovery have transformed cancer diagnosis, prognosis, and treatment selection. The evolution from early serological markers such as carcinoembryonic antigen (CEA) and prostate-specific antigen (PSA) to modern multi-omics approaches enabled by next-generation sequencing (NGS), mass spectrometry (MS), and advanced bioinformatics has revolutionized personalized medicine and targeted therapy, leading to improved outcomes. This review highlights the histo

:: 8th Европейский конгресс фармацевтики: выдержки из выступлений

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Application of radiomics model based on FDG-PET/CT for the assessment of therapeutic effect in patients with newly-diagnosed multiple myeloma.

ObjectivesTo evaluate the prediction value of radiomics models based on FDG-PET/CT for the therapeutic effect in patients with newly-diagnosed multiple myeloma (MM).Materials and methodsWe retrospectively reviewed the clinical characteristics and 18F-FDG-PET/CT imaging data of 165 MM patients. Randomly divided into a training set (n=133) and a test set (n=32) at a ratio of 8:2. All patients underwent whole-body PET-CT scans within one month prior to the commencement of treatment. Overall response rate was the principal efficacy endpoint, including stringent complete response (sCR), complete re

Прогнозирование неблагоприятных явлений для стратификации риска химиотерапии на основе мобилизации стволовых клеток в множестве миеломы.

Автологическая трансплантация стволовых клеток является фундаментальной терапией для множественных миелом. Хотя стационарная химическая мобилизация стволовых клеток (КЦМ) является в Германии стандартной системой ухода, амбулаторные подходы могут облегчить проблемы, связанные с медицинским обслуживанием. Мы проанализировали 109 Пациенты миэломы проходят курс SCM и коллекционируются в медицинском центре Университета Геттингена на предмет безопасности. Затем мы подготовили модели машинного обучения для прогнозирования неблагоприятных явлений (АЭ), требующих госпитализации, и прогнозирования сроко

Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.

BACKGROUND: End-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, requiring dialysis treatment or kidney transplantation. The endothelial activation and stress index (EASIX), reflecting inflammatory status and endothelial dysfunction, has demonstrated predictive value for outcomes in multiple diseases. However, its association with in-hospital mortality (IHM) risk in ESRD patients remains unclear, and machine learning prediction models remain unestablished. METHODS: Clinical data for ESRD patients were extracted from the MIMIC-IV (3.1) database. The outcome wa

Immune mechanism of low bone mineral density caused by ankylosing spondylitis based on bioinformatics and machine learning.

Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The modu

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… The e1071 package was used to build the support vector machine (SVM) model, a typical supervised machine learning method. …

The Art of Bioimmunogenomics (BIGs) 5.0 in CAR-T Cell Therapy for Lymphoma Management.

PurposeLymphoma, the most predominant neoplastic disorder, is divided into Hodgkin and Non-Hodgkin Lymphoma classifications. Immunotherapeutic modalities have emerged as essential methodologies in combating lymphoid malignancies. Chimeric Antigen Receptor (CAR) T cells exhibit promising responses in chemotherapy-resistant B-cell non-Hodgkin lymphoma cases.MethodsThis comprehensive review delineates the advancement of CAR-T cell therapy as an immunotherapeutic instrument, the selection of lymphoma antigens for CAR-T cell targeting, and the conceptualization, synthesis, and deployment of CAR-T c

Predictors and Profile of Severe Infectious Complications in Multiple Myeloma Patients Treated with Daratumumab-Based Regimens: A Machine Learning Model for Pneumonia Risk.

BackgroundDaratumumab (Dara) is the first monoclonal antibody introduced into clinical practice to treat multiple myeloma (MM). It currently forms the backbone of therapy regimens in both newly diagnosed (ND) and relapsed/refractory (RR) patients. However, previous reports indicated an increased risk of infectious complications (ICs) during Dara-based treatment. In this study, we aimed to determine the profile of ICs in MM patients treated with Dara-based regimens and establish predictors of their occurrence.MethodsThis retrospective, real-life study included MM patients treated with Dara-base

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… Application of Supervised Machine Learning Algorithms for Classification and Prediction of Type-2 Diabetes Disease Status in Afar Regional State, Northeastern Ethiopia 2021 Sci. Rep. 2023 13 1 12 10.1038/s41598-023-34906-1 37179444 PMC10182985 53. Hassan M.M. Hassan M.M. Yasmin F. Khan M.A.R. …

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

IntroductionAs a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

Инструменты скрининга для раннего выявления потребностей в паллиативном уходе у пациентов с повышенными хроническими условиями: обновленный систематический обзор.

Предыстория/задачи: Более раннее начало паллиативного ухода способствует улучшению клинических результатов, включая повышение эффективности мер по борьбе с симптомами, повышение качества жизни и сокращение использования ресурсов здравоохранения для лечения передовых заболеваний. Цель этого систематического обзора заключалась в выявлении и критической оценке существующих инструментов, разработанных как в обычном порядке, так и на основе искусственного интеллекта, предназначенных для выявления пациентов, имеющих право на раннее паллиативное лечение. Методы: Six Были изучены электронные базы данн

Гематологические болезни у пожилых пациентов: уделение особого внимания потенциальной роли управления гериатрической оценкой.

Управление гериатрической оценкой представляет собой многомерный инструмент, используемый для оценки прогнозов в отношении клинических результатов и целевых показателей в отношении мероприятий для пожилых людей, в которых рак проходит химиотерапию. В ходе этого обзора мы провели оценку возможного применения методов управления гериатрической оценкой (GAM) в гематологических паразитах. У пожилых пациентов с диффузной клеточной лимфомой B GAM может быть полезным как в прогнозировании запланированных госпитализаций, так и в улучшении качества жизни. В хронической миелоидной лейкемии индекс коморби