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Radiomics in spinal research: a narrative review.

Radiomics has emerged as a transformative tool in medical imaging, offering the ability to extract complex quantitative features that are generally indiscernible to the human eye. These radiomic features can be interrogated non-invasively on medical imaging. Importantly, in contrast to traditional diagnostic methods reliant on a single, often scalar, measure, radiomics features can form a high-dimensional data space from imaging data suitable for machine learning. Within the framework of artificial intelligence, radiomic features can be harnessed for differentiating healthy from pathological t

Революция онкологии: роль искусственного интеллекта (AI) в качестве антителного дизайна и средств оптимизации.

Антитела играют решающую роль в защите человеческого тела от болезней, включая такие опасные для жизни заболевания, как рак. Они оказывают посреднические услуги в связи с иммунной реакцией на иностранные антигены и, в некоторых случаях, на самоантигены. С течением времени антителные технологии эволюционировали от моноклонных антител (mAbs) до химерика антигенных рецепторов Т (клеток CAR-T), оказывая значительное влияние на биотехнологию, диагностику и лечение. Хотя эти достижения способствовали активизации терапевтического вмешательства, интеграция искусственного интеллекта (АИ) революционизир

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… Additionally, DL models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) integrate sequence and structural data to predict how CDR modifications influence binding and stability, enabling more informed design decisions [ 88 ]. … Neural Networks (RNN) Ideal for sequential data like time series and natural language Sequence representation [ 88 ], peptide HLA class binding prediction [ 225 ], B cell epitope prediction [ 119 ] Long Short-Term Memory (LSTM) A type of RNN that can capture long-term dependencies Antibody design [ …

Insights From the AO Spine Knowledge Forum Tumor Registries: Advancing the Understanding and Management of Primary Spine Tumors Through International Multicentric Collaboration. A Narrative Review.

Study DesignNarrative Review.ObjectivesTo summarize the scientific contributions generated from the AO Spine Knowledge Forum Tumor (AOSKFT) databases, focusing on primary spine tumors, and highlight key findings, research trends, and future directions.MethodsData from the Primary Tumor Retrospective (PT-Retro) and Primary Tumor Research Outcome Network (PTRON) registries were analyzed. The nineteen studies included were peer-reviewed manuscripts focused on primary spine tumors, excluding abstracts, book chapters, systematic reviews, and metastatic studies.ResultsThe PT-Retro registry compiled

Translating Data Science Results into Precision Oncology Decisions: A Mini Review.

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Not so Fast: Co-Requirements for Sonic Hedgehog Induced Brain Tumorigenesis.

The Sonic hedgehog (Shh) pathway plays an integral role in cellular proliferation during normal brain development and also drives growth in a variety of cancers including brain cancer. Clinical trials of Shh pathway inhibitors for brain tumors have yielded disappointing results, indicating a more nuanced role for Shh signaling. We postulate that Shh signaling does not work alone but requires co-activation of other signaling pathways for tumorigenesis and stem cell maintenance. This review will focus on the interplay between the Shh pathway and these pathways to promote tumor growth in brain tu

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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… Tomašev et al. trained recurrent neural networks on more than 700,000 adults and predicted AKI up to 48 h before clinical recognition, demonstrating that temporal trajectories contain information unavailable from isolated measurements [ 22 ]. … Recurrent Neural Networks for Multivariate Time Series with Missing Values Sci. Rep. 2018 8 6085 10.1038/s41598-018-24271-9 PMC5904216 29666385 81. Shukla S.N. Marlin B.M. …

The Power of Movement: How Exercise Influences Chemotherapy-Induced Peripheral Neuropathy.

As the number of cancer patients and survivors increases, we face a rising challenge: the long-term impact of the adverse effects of cancer treatment. One of the known adverse effects is chemotherapy-induced peripheral neuropathy (CIPN), which courses with pain complaints. The treatments of CIPN have reduced efficacy. The neurobiological causes of CIPN have been mainly ascribed to peripheral nerve damage, but recent studies show effects in the brain, namely in the descending pain modulatory systems. Physical exercise seems to be associated with better outcomes in CIPN patients, but the mechani

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset.

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of interpretability as w

Advances in Neuroimaging of Breast Cancer Pain: An Overview.

Breast cancer is currently the most common malignant tumor, primarily affecting women, and it frequently leads to chronic pain that significantly impairs physical and mental health. Neuroimaging studies have demonstrated that breast cancer-related pain is associated with specific brain alterations, including changes in activation, connectivity, and structure of pain-processing regions. This review synthesizes findings on functional and structural brain changes related to chronic pain in breast cancer and compares them with non-cancer chronic pain patterns. By integrating recent evidence, it pr

Breaking the Feedback Loop: A Model of Vicious Cycle for CIPN and Acupuncture-Medication Combination.

Chemotherapy-induced peripheral neuropathy (CIPN) is a prevalent and often debilitating complication of cancer therapy, whose chronicity and refractoriness cannot be adequately explained by traditional linear etiological models. This review proposes and organizes a "vicious cycle" model as a hypothesis: initial neuronal injury caused by chemotherapeutic agents initiates a self-sustaining positive feedback loop in which mitochondrial dysfunction and oxidative stress reciprocally amplify each other, thereby activating neuroinflammation and ion channel remodeling. These processes collectively dri

Искусственный интеллект, видоизменяющий парадигму гематологического диагноза и лечения злокачественных заболеваний: от статической оценки до динамического управления точностью.

Искусственный интеллект (ИИ) все чаще изучается в качестве инструмента содействия более точному и динамичному управлению диагностикой и лечением гематологов. В отличие от предыдущих обзоров, в которых основное внимание уделялось отдельным типам заболеваний или изолированным технологическим путям, в настоящем документе содержится всеобъемлющий обзор текущих прикладных программ АИШ и последних достижений в области диагностики, классификации, оценки прогнозов и принятия решений по лечению лейкемии, лимфомы, множественной миэломы и миэлодиспластических синдромов. Она охватывает ключевые технически

Фармакологическая модуляция CXCL12/CXCR4/ACKR3 для нейродисфункций — обзор.

Аксис CXCL12/CXCR4/ACKR3 является важным для развития и гомеостаза мозга. Нарушение регуляции CXCL12 или его рецепторов связано с различными расстройствами ЦНС, связанными с нарушениями нейропластичности, нейроинфляцией, нейродегенерацией и опухолями мозга. Эта сигнальная система представляет собой привлекательную целевую точку для терапии, и значительные усилия привели к разработке и применению специфических фармацевтических модуляторов для нейропатологий. Хотя фармацевтическая модуляция CXCR4 в последние десятилетия была активно изучена, целевой ACKR3 только недавно выделилась как критически

Революционизация медицины сна: влияние машинного обучения на диагностику, лечение и индивидуальное лечение.

Преобразовательным инструментом в медицине сна стало обучение основам махинации (ML), предлагающее новаторские решения для диагностики, лечения и понимания расстройств сна. В настоящем обзоре рассматривается нынешнее состояние и будущий потенциал применения ML в этой области. Methods Мы провели всеобъемлющий обзор литературы с уделением особого внимания ключевым достижениям в области технологий ML, применяемых для лечения сна. Были изучены, в частности, такие конкретные области, как классификация стадий сна, оптимизация лечения апноэ и бессонницы сна, а также внедрение износостойкой технологии

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… The methodologies employed across these studies varied significantly, with a prominent use of deep learning techniques, notably Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), alongside traditional machine learning algorithms such as Support Vector Machines (SVMs) and Random … These models excel in analyzing polysomnography (PSG) data to classify sleep stages with remarkable accuracy, using advanced algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). …

A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

BackgroundThe identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs).ObjectiveThis study aimed to develop and evaluate a scalable natural language processing (NLP)- and radiomics-based machine learning pipeline to differentiate between painless and painful BM lesions in simulation computed tomo

HDACs in the Brain: From Chromatin Remodeling to Neurodegenerative Disease.

Histone deacetylases (HDACs) are key epigenetic regulators that influence chromatin remodeling, gene expression, and cellular plasticity in the central nervous system (CNS). This review provides a comprehensive overview of the classification and functional diversity of HDACs, with particular emphasis on their roles in neural progenitor cells, mature neurons, and glial populations. In neural stem and progenitor cells, HDACs modulate neurogenesis, fate specification, and lineage commitment. In differentiated neurons, HDACs govern synaptic plasticity, memory formation, and survival. In glial cell

Распространение эпсилонного токсина Clostridium Perfringens изолируется среди пациентов с множественным склерозом и расстройством спектра нейромиелита в Иране.

Клостридий перфрингенс эпсилон токсин (ETX) является мощным нейротоксином, который пересекает мозговой барьер (BBB) и который, как было предложено, связан с демиелинизирующими расстройствами CNS. Здесь мы изучили распространенность C. перфрингенов и антиETX иммуноглобулина G (IgG) и иммуноглобулина M (IgM) в стуле и сере пациентов с множественным склерозом (MS) и спектральным расстройством нейромиелита (NMOSD) по сравнению со здоровым контролем. МЕТОДЫ: В период с сентября 2019 года по июнь 2020 года было собрано 43 образца стула и 43 образца серы из MS (n = 33) и NMOSD (n = 10) пациентов, а т

MicroRNA-7: a versatile player and core target in brain disorders.

OBJECTIVE: This article provides a systematic review of the biological characteristics, regulatory mechanisms, and roles of MicroRNA-7 (miR-7) in the nervous system, as well as explores its clinical translation potential and challenges. METHODS: A review analysis integrating the progress of basic and clinical research related to miR-7. RESULTS: Characteristics and Regulation: miR-7 is highly expressed in mammalian brain tissues, with its function finely regulated by ceRNA networks (such as ciRS-7 and lncRNA SNHG1) and epigenetic modifications, forming a multi-layer dynamic regulatory system.

Полиморфизм HLA в нейроиммунных болезнях: увязка представления антигенов с нейроинфламматическими программами.

Полиморфизм человека антигена лейкоцита (HLA) формирует антигенное представление и подверженность иммунизированным заболеваниям. В нейроиммунных расстройствах главный вопрос заключается в том, как зависимость от HLA антигенная видимость превращается в специфический иммунитет от болезней и повреждение тканей. В этом Обзоре мы интегрируем генетические, молекулярные, клеточные и клинические данные по множеству склерозов (MS), расстройству спектра нейромиелитов, синдрому Гильен-Барре, нарколепсии, аутоиммунальному энцефалиту и миастении гравис. :: HLA-DR15-MS Ось является наиболее механистически р

Surgery of the tumors of the spine.

Spinal tumors encompass a heterogeneous group of primary and metastatic lesions requiring individualized management strategies based on tumor biology, patient prognosis, and mechanical stability. Decision-making frameworks, such as NOMS, SINS, and Tokuhashi score, provide structured guidance for selecting metastatic patients for surgery and defining individualized treatment strategies. En bloc resection remains the gold standard for selected primary malignant tumors, whereas separation surgery combined with stereotactic radiosurgery represents the cornerstone of modern metastatic spine disease

Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.

Metastatic castration-resistant prostate cancer (mCRPC) remains a formidable clinical challenge despite advancements in therapy. This narrative review explores the role of artificial intelligence (AI), machine learning and deep learning in addressing therapeutic resistance in mCRPC. AI-driven approaches leverage integrated datasets encompassing genomics, proteomics and clinical parameters to uncover molecular mechanisms, predict treatment responses and identify biomarkers of resistance. These methodologies promise personalised treatment strategies tailored to individual patient profiles. Howev