5,050 research outputs found
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 Δ-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
Research progress on deep learning in magnetic resonance imagingâbased diagnosis and treatment of prostate cancer: a review on the current status and perspectives
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future
A view of colonial life in South Australia: An osteological investigation of the health status among 19th-century migrant settlers
Studies of human skeletal remains contribute to understanding the extent to which conditions
prevailing in various past communities were detrimental to health. Few of these studies have
evaluated the situation in which the first European colonists of South Australia lived.
Colonial Australian skeletal collections are scarce, especially for research purposes. This
makes the 19th-century skeletal remains of individuals, excavated from St Maryâs Cemetery,
South Australia, a rare and valuable collection.
The overarching aim of this thesis was to investigate the general and oral health of this
specific group of 19th-century settlers, through the examination of their skeletons and
dentitions. Four research papers in this thesis address this overarching aim. The first two
papers determine the general skeletal health of the settlers, with a focus on pathological
manifestations on bones associated with metabolic deficiencies and the demands of
establishing an industrial society. Paper 3 investigated whether Large Volume Micro-
Computed Tomography (LV Micro-CT) could be used as a single technique for the analysis
of the in situ dentoalveolar complex of individuals from St Maryâs. This led to a detailed
investigation of the dentitions of the St Maryâs sample, in paper 4, with the aims of
determining the oral health status of these individuals, and understanding how oral conditions
may have influenced their general health.
The skeletal remains of 65 individuals (20 adults and 45 subadults) from St Maryâs sample
were available for the four component investigations using non-destructive techniques -
macroscopic, radiographic and micro-CT methods.
Signs of nutritional deficiencies (vitamin C and iron) were identified in Paper 1. The findings
of paper 2 showed joint diseases and traumatic fractures were seen and that gastrointestinal and pulmonary conditions were the leading causes of death in subadults and adults
respectively. Paper 3 found that the LV Micro-CT technique was the only method able to
generate images that allowed the full range of detailed measurements across all the oral
health categories studied. A combination of macroscopic and radiographic techniques
covered a number of these categories, but was more time-consuming, and did not provide the
same level of accuracy or include all measurements. Results for paper 4 confirmed that
extensive carious lesions, antemortem tooth loss and evidence of periodontal disease were
present in the St Maryâs sample. Developmental defects of enamel (EH) and areas of
interglobular dentine (IGD) were identified. Many individuals with dental defects also had
skeletal signs of co-morbidities. St Maryâs individuals had a similar percentage of carious
lesions as the British sample, which was more than other historic Australian samples, but less
than a contemporary New Zealand sample.
The 19th-century migrants to the colony of South Australia were faced with multiple
challenges such as adapting to local environmental conditions as well as participating in the
development of settlements, infrastructure and new industries. Evidence of joint diseases,
traumatic injuries and health insults, seen as pathological changes and/ or abnormalities on
the bone and/or teeth, confirmed that the settlers' health had been affected. The number of
burials in the âfree groundâ area between the 1840s -1870s was greater than the number in the
leased plots, reflecting the economic problems of the colony during these early years.
Validation of the reliability and accuracy of the LV Micro-CT system for the analysis of the
dentoalveolar complex, in situ within archaeological human skull samples, provided a
microanalytical approach for the in-depth investigations of the St Maryâs dentition. Extensive
carious lesions, antemortem tooth loss and periodontal disease seen in this group would have
affected their general health status. The presence of developmental defects (EH and IGD)
indicated that many of the settlers had suffered health insults in childhood to young adulthood. Contemporaneous Australian, New Zealand and British samples had comparable
findings suggesting that little improvement had occurred in their oral health since arriving in
South Australia.
In conclusion, the findings of this investigation largely fulfilled the initial aims. Our
understanding of the extent to which conditions prevailing in the new colony were
detrimental to human health has increased, as has our knowledge of why pathological
manifestations and/or abnormalities were seen on the bones and teeth of individuals from the
St Maryâs sample. A multiple-method approach, to derive enhanced information has been
shown to be effective, whilst establishing a new methodology (LV Micro-CT) for the analysis
of dentition in situ in human archaeological skulls. Further, this investigation has digitally
preserved data relating to this historical group of individuals for future comparisons.Thesis (Ph.D.) -- University of Adelaide, School of Biomedicine, 202
The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts
Randomized clinical trials, while often viewed as the highest evidentiary bar
by which to judge the quality of a medical intervention, are far from perfect.
In silico imaging trials are computational studies that seek to ascertain the
performance of a medical device by collecting this information entirely via
computer simulations. The benefits of in silico trials for evaluating new
technology include significant resource and time savings, minimization of
subject risk, the ability to study devices that are not achievable in the
physical world, allow for the rapid and effective investigation of new
technologies and ensure representation from all relevant subgroups. To conduct
in silico trials, digital representations of humans are needed. We review the
latest developments in methods and tools for obtaining digital humans for in
silico imaging studies. First, we introduce terminology and a classification of
digital human models. Second, we survey available methodologies for generating
digital humans with healthy and diseased status and examine briefly the role of
augmentation methods. Finally, we discuss the trade-offs of four approaches for
sampling digital cohorts and the associated potential for study bias with
selecting specific patient distributions
Performance Analysis of Clustering Algorithms in Brain Tumor Detection from PET Images
Brain metastases remain fatal and challenging, and their early detection is imperative. With the advancement in non-invasive imaging techniques, positron emission tomography, as a functional imaging, has been widely employed in oncological studies, including pathophysiological mechanisms of the tumors. While manual analysis and integration of dynamic 4D PET images are challenging and inefficient. Therefore, automated segmentation is adopted to improve the efficiency and accuracy. In recent years, clustering-based image segmentation has been gaining popularity in detecting tumors. This thesis applies three clustering-based algorithms to automatically identify and segment metastatic brain tumors from dynamic 4D PET images of mice. The clustering algorithms used include K-means and Gaussian mixture model clustering in combination with pre-processing principal component analysis, independent component analysis and post-processing connected component analysis. The performances of three clustering algorithms in execution time and accuracy were evaluated by the Jaccard index and validated by time activity curve. The results indicate that K-means clustering is the best-performing among the three clustering methods when combined with independent component analysis, and the post-processing method connected component analysis has significantly improved the performance of K-means clustering
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Automatic rib labeling and anatomical centerline extraction are common
prerequisites for various clinical applications. Prior studies either use
in-house datasets that are inaccessible to communities, or focus on rib
segmentation that neglects the clinical significance of rib labeling. To
address these issues, we extend our prior dataset (RibSeg) on the binary rib
segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT
scans (15,466 individual ribs in total) and annotations manually inspected by
experts for rib labeling and anatomical centerline extraction. Based on the
RibSeg v2, we develop a pipeline including deep learning-based methods for rib
labeling, and a skeletonization-based method for centerline extraction. To
improve computational efficiency, we propose a sparse point cloud
representation of CT scans and compare it with standard dense voxel grids.
Moreover, we design and analyze evaluation metrics to address the key
challenges of each task. Our dataset, code, and model are available online to
facilitate open research at https://github.com/M3DV/RibSegComment: 10 pages, 6 figures, journa
Characterising the neck motor system of the blowfly
Flying insects use visual, mechanosensory, and proprioceptive information to control their
movements, both when on the ground and when airborne. Exploiting visual information for
motor control is significantly simplified if the eyes remain aligned with the external horizon.
In fast flying insects, head rotations relative to the body enable gaze stabilisation during highspeed
manoeuvres or externally caused attitude changes due to turbulent air.
Previous behavioural studies into gaze stabilisation suffered from the dynamic properties
of the supplying sensor systems and those of the neck motor system being convolved.
Specifically, stabilisation of the head in Dipteran flies responding to induced thorax roll
involves feed forward information from the mechanosensory halteres, as well as feedback
information from the visual systems. To fully understand the functional design of the blowfly
gaze stabilisation system as a whole, the neck motor system needs to be investigated
independently.
Through X-ray micro-computed tomography (ÎŒCT), high resolution 3D data has become
available, and using staining techniques developed in collaboration with the Natural History
Museum London, detailed anatomical data can be extracted. This resulted in a full 3-
dimensional anatomical representation of the 21 neck muscle pairs and neighbouring cuticula
structures which comprise the blowfly neck motor system.
Currently, on the work presented in my PhD thesis, ÎŒCT data are being used to infer
function from structure by creating a biomechanical model of the neck motor system. This
effort aims to determine the specific function of each muscle individually, and is likely to
inform the design of artificial gaze stabilisation systems. Any such design would incorporate
both sensory and motor systems as well as the control architecture converting sensor signals
into motor commands under the given physical constraints of the system as a whole.Open Acces
EMERGING APPLICATIONS IN THE MEASUREMENT OF BODY COMPOSITION AND THEIR RELATIONSHIPS TO DISEASE RISK
Ph.D
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