1,912 research outputs found
Molekulargenetische Charakterisierung von Sarkomen zur Identifizierung prognostischer Risikogruppen und potentieller therapeutischer Angriffspunkte
Sarkome sind seltene Tumore, die sich durch eine erhebliche Heterogenität auf
histologischer, molekularer und genetischer Ebene auszeichnen. Trotz aller Fortschritte
in der modernen Krebsbehandlung haben Sarkom-Patienten im fortgeschrittenen
Stadium weiterhin begrenzte therapeutische Möglichkeiten und eine
ungünstige Prognose. Da die Untersuchung des genetischen Profils nicht nur die
Identifizierung prognostischer, sondern auch therapierelevanter Veränderungen
bei heterogenen Erkrankungen ermöglicht, sind genetische Analysen ein unverzichtbarer
Bestandteil der modernen Krebsbehandlung geworden.
In dieser Studie analysierten wir retrospektiv das genetische Profil einer real-life
Kohorte von 53 Sarkom-Patienten anhand eines 720-Gen-Panels.
In Anbetracht der Heterogenität von Sarkomen wurden mehrere histopathologische
Subtypen analysiert, wobei das Leiomyosarkom (17 %) am häufigsten vorkam.
Das Durchschnittsalter der Patienten zum Zeitpunkt der Analyse betrug 49
Jahre. Die durchschnittliche Zeitspanne von der Erstdiagnose bis zur genetischen
Analyse betrug 46,8 Monate. Das Gesamtüberleben betrug im Durchschnitt
55,9 Monate.
Jeder Patient erhielt eine Tumorgenomsequenzierung mit einem 720-Gene-Panel.
Bei 76,9% der Patienten wurde ein niedriger TMB-Wert festgestellt. Keiner
der Patienten wurde als mikrosatelliteninstabil identifiziert. 25% der Patienten
wiesen einen Mangel an der Funktionalität der homologen Rekombination (HRD)
auf. Bei 30,8% wurde ein Fusionsgen nachgewiesen, wobei EWSR1-FLI1 und
EWSR1- WT1 am häufigsten waren. Insgesamt wurden 38 Kopienzahlveränderungen
(CNAs) gefunden, was auf eine erhebliche genomische Instabilität hinweist.
Bei 15 Patienten wurden Keimbahnmutationen gefunden, die alle behandlungsrelevant
sind, wobei die Mutation im MUTYH-Gen die häufigste ist. Therapierelevante
somatische Mutationen wurden bei 47 Patienten gefunden (3,2 Mutationen/
Patient). Die am häufigsten betroffenen Gene waren TP53, CDKN2A-C,
CDK4, RB1 und ATRX.
93
Auf der Grundlage der NGS-Ergebnisse erhielten 39,6 % der Patienten eine personalisierte
Antitumortherapie. Das mediane Gesamtüberleben (OS) der Patienten
mit einer gemäß den Daten der NGS-Analyse ausgerichteten Behandlung
betrug 43 gegenüber 33 Monaten bei Patienten ohne zielgerichtete Therapien.
Unsere NGS-Daten aus einer heterogenen Kohorte von 53 Sarkom-Patienten
deuten darauf hin, dass personalisierte Therapien, die auf den Ergebnissen einer
720 Gen-Panel-Sequenzierung basieren, zu verbesserten klinischen Ergebnissen
bei Sarkom-Patienten führen könnten
Identification of genes with oscillatory expression in glioblastoma: the paradigm of SOX2
Quiescence, a reversible state of cell-cycle arrest, is an important state during both normal development and cancer progression. For example, in glioblastoma (GBM) quiescent glioblastoma stem cells (GSCs) play an important role in re-establishing the tumour, leading to relapse. While most studies have focused on identifying differentially expressed genes between proliferative and quiescent cells as potential drivers of this transition, recent studies have shown the importance of protein oscillations in controlling the exit from quiescence of neural stem cells. Here, we have undertaken a genome-wide bioinformatic inference approach to identify genes whose expression oscillates and which may be good candidates for controlling the transition to and from the quiescent cell state in GBM. Our analysis identified, among others, a list of important transcription regulators as potential oscillators, including the stemness gene SOX2, which we verified to oscillate in quiescent GSCs. These findings expand on the way we think about gene regulation and introduce new candidate genes as key regulators of quiescence
Hippocampal sparing in whole-brain radiotherapy for brain metastases: controversy, technology and the future
Whole-brain radiotherapy (WBRT) plays an irreplaceable role in the treatment of brain metastases (BMs), but cognitive decline after WBRT seriously affects patients’ quality of life. The development of cognitive dysfunction is closely related to hippocampal injury, but standardized criteria for predicting hippocampal injury and dose limits for hippocampal protection have not yet been developed. This review systematically reviews the clinical efficacy of hippocampal avoidance - WBRT (HA-WBRT), the controversy over dose limits, common methods and characteristics of hippocampal imaging and segmentation, differences in hippocampal protection by common radiotherapy (RT) techniques, and the application of artificial intelligence (AI) and radiomic techniques for hippocampal protection. In the future, the application of new techniques and methods can improve the consistency of hippocampal dose limit determination and the prediction of the occurrence of cognitive dysfunction in WBRT patients, avoiding the occurrence of cognitive dysfunction in patients and thus benefiting more patients with BMs
CANCER TREATMENT BY TARGETING HDAC4 TRANSLOCATION INDUCED BY MICROSECOND PULSED ELECTRIC FIELD EXPOSURE: MECHANISTIC INSIGHTS THROUGH KINASES AND PHOSPHATASES
Epigenetic modifications, arising from sub-cellular shifts in histone deacetylase (HDAC) activity and localization, present promising strategies for diverse cancer treatments. HDACs, enzymes responsible for post-translational histone modifications, induce these epigenetic changes by removing acetyl groups from ε-N-acetyl-lysine residues on histones, thereby suppressing gene transcription. Within the HDAC group, class IIa HDACs are notable for their responsiveness to extracellular signals, bridging the gap between external stimuli, plasma membrane, and genome through nuclear-cytoplasmic translocation. This localization offers two significant mechanisms for cancer treatment: nuclear accumulation of HDACs represses oncogenic transcription factors, such as myocyte-specific enhancer factor 2C (MEF2C), triggering various cell death pathways. Conversely, cytoplasmic HDAC accumulation acts similarly to HDAC inhibitors by silencing genes. My dissertation introduces an innovative approach for glioblastoma and breast cancer treatment by investigating the application of microsecond pulsed electric fields. It particularly focuses on HDAC4, a class IIa HDAC overexpressed in these cancers. Beyond demonstrating HDAC4 translocation, my research delves into the intricate roles of kinases and phosphatases, shedding light on the underlying factors governing HDAC4 translocation
Current status and future application of electrically controlled micro/nanorobots in biomedicine
Using micro/nanorobots (MNRs) for targeted therapy within the human body is an emerging research direction in biomedical science. These nanoscale to microscale miniature robots possess specificity and precision that are lacking in most traditional treatment modalities. Currently, research on electrically controlled micro/nanorobots is still in its early stages, with researchers primarily focusing on the fabrication and manipulation of these robots to meet complex clinical demands. This review aims to compare the fabrication, powering, and locomotion of various electrically controlled micro/nanorobots, and explore their advantages, disadvantages, and potential applications
Functional Nanomaterials and Polymer Nanocomposites: Current Uses and Potential Applications
This book covers a broad range of subjects, from smart nanoparticles and polymer nanocomposite synthesis and the study of their fundamental properties to the fabrication and characterization of devices and emerging technologies with smart nanoparticles and polymer integration
Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review
Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions
Medical Image Analysis using Deep Relational Learning
In the past ten years, with the help of deep learning, especially the rapid
development of deep neural networks, medical image analysis has made remarkable
progress. However, how to effectively use the relational information between
various tissues or organs in medical images is still a very challenging
problem, and it has not been fully studied. In this thesis, we propose two
novel solutions to this problem based on deep relational learning. First, we
propose a context-aware fully convolutional network that effectively models
implicit relation information between features to perform medical image
segmentation. The network achieves the state-of-the-art segmentation results on
the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain
Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new
hierarchical homography estimation network to achieve accurate medical image
mosaicing by learning the explicit spatial relationship between adjacent
frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and
our hierarchical homography estimation network outperforms the other
state-of-the-art mosaicing methods while generating robust and meaningful
mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism
to comprehend the contextual relationships within sequential data. Unlike
conventional neural networks or updated versions of Recurrent Neural Networks
(RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in
handling long dependencies between input sequence elements and enable parallel
processing. As a result, transformer-based models have attracted substantial
interest among researchers in the field of artificial intelligence. This can be
attributed to their immense potential and remarkable achievements, not only in
Natural Language Processing (NLP) tasks but also in a wide range of domains,
including computer vision, audio and speech processing, healthcare, and the
Internet of Things (IoT). Although several survey papers have been published
highlighting the transformer's contributions in specific fields, architectural
differences, or performance evaluations, there is still a significant absence
of a comprehensive survey paper encompassing its major applications across
various domains. Therefore, we undertook the task of filling this gap by
conducting an extensive survey of proposed transformer models from 2017 to
2022. Our survey encompasses the identification of the top five application
domains for transformer-based models, namely: NLP, Computer Vision,
Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze
the impact of highly influential transformer-based models in these domains and
subsequently classify them based on their respective tasks using a proposed
taxonomy. Our aim is to shed light on the existing potential and future
possibilities of transformers for enthusiastic researchers, thus contributing
to the broader understanding of this groundbreaking technology
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