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Interventions for central serous chorioretinopathy: a network meta-analysis.

BACKGROUND: Central serous chorioretinopathy (CSC) is characterized by serous detachment of the neural retina with dysfunction of the choroid and retinal pigment epithelium (RPE). The effects on the retina are usually self limited, although some people are left with irreversible vision loss due to progressive and permanent photoreceptor damage or RPE atrophy. There have been a variety of interventions used in CSC, including, but not limited to, laser treatment, photodynamic therapy (PDT), and intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) agents. However, it is n

Predicting unfavorable response to initial radioactive iodine therapy in differentiated thyroid cancer: an explainable machine learning and survival analysis approach.

BACKGROUND: Early post-treatment prognostic assessment may facilitate individualized follow-up in patients with differentiated thyroid cancer (DTC) after initial radioactive iodine (RAI) therapy (RAIT).This study aimed to develop and internally validate a dual-mode machine-learning framework integrating clinical information available from the pre-RAI baseline through the early post-RAI assessment period to predict subsequent unfavorable treatment response and persistence/recurrence-free survival (PRFS). METHODS: A retrospective cohort of 550 DTC patients who underwent total thyroidectomy foll

Возможности и препятствия для глубокого изучения биологии и медицины.

Глубокое обучение описывает класс алгоритмов машинного обучения, которые способны объединять исходные ресурсы в слои промежуточных характеристик. Эти алгоритмы недавно продемонстрировали впечатляющие результаты в различных областях. Биология и медицина являются богатыми данными дисциплинами, однако данные являются сложными и зачастую недопонятными. Поэтому методы глубокого обучения могут быть особенно подходящими для решения проблем в этих областях. Мы изучаем возможности углубленного изучения различных биомедицинских проблем - стационарная классификация, фундаментальные биологические процессы

Эффективность моделей машинного обучения для прогнозирования симптомов высокой чувствительности при множественном склерозе.

Текущий уход за многочисленными склерозами (МС) в первую очередь опирается на редко получаемые данные, такие, как магнитно-резонансная съемка, клинические лабораторные тесты или клиническая история, что приводит к незначительным изменениям, которые могут произойти в период между посещениями. Мобильные технологии позволяют осуществлять постоянный сбор данных и прокладывают путь к прогнозированию сложных аспектов НР, таких, как симптомы и заболевания. С этой целью мы провели первое в своем роде исследование по наблюдению под названием MS Mosail. Во-первых, мы разработали и публично запустили моб

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… Three classical machine learning (logistic regression, multi-layer perceptron, and gradient boosted classifier) and two deep learning methods (recurrent neural network and temporal convolutional network) were separately trained to find the best performing model. … We compared five modeling strategies: 3 classical ML algorithms (logistic regression (LR) , gradient boosting classifier (GBC) 26 , multilayer perceptron (MLP) and two sequential deep neural networks (recurrent neural networks (RNN) 27 and temporal convolutional neural (TCN) networks 28 ). …

Artificial Neural Network Prediction of Mortality in Cancer Patients Presenting for Radiation Therapy at a Multisite Institution.

IntroductionFor many decades, the management of cancer has utilized radiation therapy, which continues to evolve with technology to improve patient outcomes. However, despite the standardization of treatment plans and the establishment of best clinical practices based on prospective, randomized trials and adherence to National Comprehensive Cancer Network (NCCN) guidelines, the outcomes from radiation therapy are highly variable and dependent on a number of factors, including patient demographics, tumor characteristics/histology, and treatment parameters. In this study, we attempt to use avail

Применение искусственного интеллекта в перипостетической суставной инфекции.

Перипростетическая суставная инфекция (ПСО) представляет собой one Из наиболее разрушительных осложнений, вызванных общей совместной артропластикой, часто требующих дополнительных операций и антимикробной терапии, и потенциально ведущих к инвалидности. Это значительно увеличивает нагрузку как на пациентов, так и на систему здравоохранения. С учетом значительных страданий, причиняемых ПСО, его профилактика и лечение уже давно являются ключевыми факторами, вызывающими озабоченность. Вместе с тем сохраняются проблемы, связанные с точной оценкой индивидуального риска, предотвращением инфекции, сов

Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016.

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has bee

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… ANNs comprise many varieties, like multilayer perceptron (MLP) networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), engaging either supervised or unsupervised training operations [ 21 , 22 ]. … The new molecular libraries engendered by Recurrent Neural Networks (RNNs) can be probed with ML algorithms like Deep Neural Networks (DNNs) and Gradient Boosting Trees (GBTs). …

Emerging mechanism and therapeutic potential of neurofibromatosis type 1-related nerve system tumor: Advancing insights into tumor development.

Neurofibromatosis Type 1 (NF1) is a genetic disorder resulting from mutations in the NF1 gene, which increases susceptibility to various nervous system tumors, including plexiform neurofibromas, malignant peripheral nerve sheath tumors, and optic pathway gliomas. Recent research has shown that these tumors are intricately connected to the complex, dynamic interactions within neurons, culminating in neuronal signaling that fosters tumor growth. These interactions offer crucial insights into the molecular mechanisms underpinning tumor development, as well as broader implications for therapeutic

Peripheral Nerves in Cancer: Regulatory Roles and Therapeutic Strategies.

Cancer neuroscience has emerged as a transformative frontier in oncology research, focusing on the interplay between cancer cells and the nervous system. Cancer cells establish tumorspecific neural networks within tumor tissues via neurotrophic hijacking. The nervous system regulates tumor initiation, progression, and metastasis either directly by regulating signal transduction in tumor cells or indirectly by modulating the tumor microenvironment (TME). The positive feedback loop between cancer cells and nerves promotes tumor progression. Deciphering the regulatory role of nerves in tumor prog

Глубокое изучение анализа изображений для множественных склерозов: диагностика и мониторинг.

Множественный склероз (MS) — аутоиммунное заболевание, которое затрагивает различные части центральной нервной системы и часто происходит среди молодежи (между 20-40 возраст). С учетом того, что MS является заболеванием на протяжении всей жизни и что в настоящее время не существует окончательного лечения MS, ранняя диагностика, начало лечения наиболее подходящими лекарствами и наблюдение за пациентами являются: three :: сложные факторы при определении статуса пациентов с НР. Магнитная резонансная съемка (MRI) и оптическая последовательность (OCT) two важных и полезных методов визуализации thre

Genes predisposing tunneled catheter thrombosis in hemodialysis patients.

BackgroundThis study aims to investigate the association of genes predisposing thrombophilia with tunneled catheter thrombosis in hemodialysis patients.MethodsBetween October 2018 and December 2020, we compared the frequencies of genetic polymorphisms causing thrombophilia, including prothrombin G20210A, factor V Leiden, methylene tetrahydrofolate reductase (MTHFR) C677T, MTHFR A1298C, plasminogen activator inhibitor (PAI), factor XIII V34L and clinical characteristics of 52 patients with a history of 2 tunneled2 tunneled catheter thrombosis occlusions within a year (Group 1; 24 males, 28 fema

Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma.

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3)

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… Event-prediction heads In contrast to state space models such as recurrent neural networks, attention-based models like SCOPE do not readily provide a hidden state vector that acts as a sufficient representation for the entire trajectory. … Recurrent neural networks RNNs are a widely used class of neural networks for time series processing. They operate by maintaining an internal memory state that is updated as the network processes each input in the sequence. …

Repurposing of nervous system drugs for cancer treatment: recent advances, challenges, and future perspectives.

The nervous system plays a critical role in developmental biology and oncology, influencing processes from ontogeny to the complex dynamics of cancer progression. Interactions between the nervous system and cancer significantly affect oncogenesis, tumor growth, invasion, metastasis, treatment resistance, inflammation that promotes tumors, and the immune response. A comprehensive understanding of the signal transduction pathways involved in cancer biology is essential for devising effective anti-cancer strategies and overcoming resistance to existing therapies. Recent advances in cancer neurosc

Artificial intelligence for risk assessment and outcome prediction in malignant haematology.

Risk stratification is the backbone of clinical decision-making for patients with haematological malignancies. Models currently used in routine clinical practice often rely on static rules, conventional statistics and expert consensus. While pragmatic and accessible, these models fail to capture the complex biological interactions and patient heterogeneity, especially as data dimensionality grows given the availability of more comprehensive molecular analysis. Artificial intelligence (AI), particularly machine learning (ML), offers the potential to process such high-dimensional data, uncover l

Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.

BACKGROUND: Leukemia, a heterogeneous group of blood cancers, poses significant challenges to global health due to its complexity, diverse risk factors, and variable outcomes. Accurate and early diagnosis is critical but remains a significant hurdle, particularly in low-resource settings. Recent advancements in artificial intelligence (AI) and image processing offer transformative solutions to improve leukemia detection and classification, addressing limitations in traditional diagnostic methods. METHODS: This study systematically reviewed over 25,000 scientific articles sourced from Scopus,

Методы машинного обучения для классификации множественного склероза и болезни Альцгеймера с использованием геномных данных.

Сложные заболевания создают трудности в прогнозировании из-за их многофакторного и полигенного характера. В этом исследовании используется машинное обучение (ML) для анализа геномных данных из британского биобанка с целью прогнозирования геномной предрасположенности к таким сложным болезням, как множественный склероз (MS) и болезнь Альцгеймера (AD). Мы проверили логистическую регрессию (LR), методы ансамбля деревьев и модели глубокого обучения с этой целью. LR продемонстрировал замечательную стабильность в различных поднаборах данных, превосходя глубинные подходы к обучению, которые продемонст

The mutual impacts of stem cells and sleep: opportunities for improved stem cell therapy.

Sleep is an indispensable physiological function regulated by circadian rhythms, which influence the biological pathways and overall health of the body. Sleep is crucial for the maintenance and restoration of bodily systems, and disturbances can lead to various sleep disorders, which can impair both mental and physical health. Treatment options for these disorders encompass lifestyle modifications, psychotherapy, medications, and therapies such as light therapy and surgery. Not only sleep deprivation has a significant impact on essential organs, but it also influences various types of stem cel

A malignant symbiosis: The neuro-metabolic symphony rewires the tumor microenvironment.

Emerging evidence in cancer neuroscience has established a reciprocal bidirectional loop between neural signaling and tumor metabolism as a central driver of malignant progression. This review critically evaluates bidirectional neuro-metabolic symbiosis and its profound implications for overcoming treatment refractoriness. We first analyzed the anterograde mechanism, in which peripheral nerve terminals and systemic endocrine factors impose distinct metabolic dependencies on malignant and stromal cell populations. By forcing alterations in nutrient utilization pathways, these neural cues create

Достижения в области диагностических средств и терапевтических подходов к глиомамам: всеобъемлющий обзор.

Глиомы, широко распространенная категория первичных злокачественных опухолей мозга, создают огромные клинические проблемы из-за их инвазивного характера и ограниченных возможностей лечения. Нынешний терапевтический ландшафт для глиомов сдерживается ".one-Эта парадигма подходит для всех, что существенно ограничивает эффективность лечения. Несмотря на осуществление стратегий мультимодального лечения, показатели выживаемости по-прежнему вызывают разочарование. Традиционный подход к лечению, предусматривающий хирургическое пересечение, излучение и химиотерапию, сталкивается с серьезными ограничени