17 research outputs found
Local control of brain metastasis with post-surgical cavity-directed adjuvant radiation utilizing stereotactic radiosurgery: a scoping literature review and institutional retrospective study
No full textObjective: To investigate potential factors influencing local control in metastatic brain disease
patients undergoing adjuvant stereotactic radiosurgery (SRS) to surgical cavities.
Methods: A scoping review of the literature and retrospective analysis were conducted. The
scoping review encompassed multiple databases, to determine relevant studies that identified
factors impacting local control in metastatic tumor surgical cavities. The retrospective analysis
involved a 17-year cohort of patients who underwent adjuvant SRS for metastatic surgical cavities.
Data regarding factors influencing control rates were collected and analyzed.
Results: The scoping review yielded 10,633 articles, with 22 included in the final analysis. Factors
such as histology, radiation dose, tumor size, extent of resection, treatment timing, tumor depth,
and dural or pial attachment demonstrated impacts on local control. However, primary disease
status, surgical corridor coverage, and tumor location did not significantly affect control. In the
retrospective study of 63 patients with 63 surgical cavities, the 12-month local control rate was
66.7%, and the 24-month rate was 57.1%. Distant progression occurred in 58.7% of cases. Overall
development of leptomeningeal disease, and treated cavity adverse radiation effects were observed
in 15.9% and 20.6% of cases, respectively. None of the examined factors significantly influenced
local control. Local progression within the first year of treatment was associated with a 5.0-fold
increased risk of death at 24 months, while distant intracranial progression showed a 6.0-fold
increased risk at 12 months and an 8.2-fold increased risk at 24 months.
Conclusion: Prospective studies are necessary to identify predictive factors for achieving local
control following cavity-directed SRS. These findings have implications for developing future
treatment guidelines and optimizing outcomes in the management of metastatic brain disease.NAFebruary 202
Describing phenotypic subtypes of GBM in DWI imaging in relation to its genotypic subtypes
No full textGlioblastoma (GBM) is the most lethal primary brain tumour of the central nervous system, and has an unpredictable response to treatment with wide range of survival. There have been many attempts to identify factors that influence survival. We investigated the possible association between diffusion MRI, molecular signature, and survival of patients with GBM. This is a retrospective study conducted in Winnipeg (Health Sciences Center) for patients with GBMs from January 2015 to January 2018. In 93 patients, correlating normalized apparent diffusion coefficient (nADC) to time to death in days showed a Spearman’s rho correlation value of 0.244, indicating a weakly positive linear correlation. IDH mutation status in relation to nADC was found to be significant (mean difference of 0.38 and p-value of 0.015). The log-rank (Mantel–Cox) of nADC with cut-off point of 1.1725 was found to be significant (p-value of 0.046). The median survival was 11.5 months for nADC>1.1725 .vs 7.5 months for nADC1.1725 after adjusting for covariates of age, gender, and IDH mutation status. Individuals 70-year-old old after adjusting for covariates of nADC, IDH mutation status, and extent of resection. In conclusion, nADC might have some value in identifying GBM patients with worse survival via IDH-mutation status.February 202
Self-performed glansectomy and surgical repair by a nonpsychotic patient on androgen replacement therapy
No full textEfficient learning of microbial genotype-phenotype association rules
No full textMotivation: Finding biologically causative genotype-phenotype associations from whole-genome data is difficult due to the large gene feature space to mine, the potential for interactions among genes and phylogenetic correlations between genomes. Associations within phylogentically distinct organisms with unusual molecular mechanisms underlying their phenotype may be particularly difficult to assess. Results: We have developed a new genotype-phenotype association approach that uses Classification based on Predictive Association Rules (CPAR), and compare it with NETCAR, a recently published association algorithm. Our implementation of CPAR gave on average slightly higher classification accuracy, with approximately 100x faster running times. Given the influence of phylogenetic correlations in the extraction of genotype-phenotype association rules, we furthermore propose a novel measure for down-weighting the dependence among samples by modeling shared ancestry using conditional mutual information, and demonstrate its complementary nature to traditional mining approaches. Availability: Software implemented for this study is available under the Creative Commons Attribution 3.0 license from the author a
Hemorrhagic stroke after Epley maneuver: a case report
No full textAbstract Background This is the first case to our knowledge of a serious adverse event following the Epley maneuver, which is the treatment of choice for benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder in adults. Case presentation A 77 year old female presented for outpatient evaluation of vertigo at a tertiary otolaryngology clinic. She was found to have BPPV clinically, and elected to have a particle repositioning maneuver (Epley maneuver) performed in clinic. Immediately following Epley maneuver, she had severe nausea and vomiting, with evolving visual changes. A CT angiogram of the brain was performed urgently through the emergency department and demonstrated an acute intraparenchymal hemorrhage in the occipital lobe. After medical stabilization and rehabilitation, the patient continues to have a permanent visual field deficit. Conclusion The Epley maneuver is safe and effective, and there are no prior reports of serious adverse events associated with its use. This case, in which a patient experienced a hemorrhagic stroke after undergoing the Epley maneuver, is the first and sole case in the medical literature of an Epley-associated serious adverse event. The indirect causation and extreme rarity of this event do not warrant any change to patterns of practice
Complex Behavioral Strategy and Reversal Learning in the Water Maze without NMDA Receptor-Dependent Long-Term Potentiation
No full textNovel CTRP8‐RXFP1‐JAK3‐STAT3 axis promotes Cdc42‐dependent actin remodeling for enhanced filopodia formation and motility in human glioblastoma cells
No full textUNSUPERVISED PARAPHRASE GENERATION FROM HIERARCHICAL LANGUAGE MODELS
No full textParaphrase generation is a challenging problem that requires a semantic
representation of language. Language models implemented with deep
neural networks (DNN) have the ability to transform text to a real
valued vector space that can capture useful semantic information.
In light of this, this work employs hierarchical language modeling
to produce semantic representations of sentences. An encoder-decoder
model is employed that uses four components: a word encoder, sentence
encoder, sentence decoder, and word decoder. These components hierarchically
convert a sentence from characters through word representations to a fixed-size sentence representation, then back down through words to characters.
Many types of neural network are suitable for each component, and a
number of them are compared in this work, including a novel architecture,
the Self Attentive Recurrent Array (SARAh). The SARAh is shown to perform at least
as well as Gated Recurrent Units (GRU) and Transformers on language modeling
tasks, and requires fewer parameters. These language models are trained
on a large and diverse dataset, but this work also shows that it is possible
to fine tune such models to a particular domain, such as the works of a
single author. These fine tuned models are able to leverage information
learned on the larger dataset in order to perform better on the target domain.
Finally, a language model is trained to produce semantic representations of
sentences that are subsequently used to produce paraphrases in a completely
unsupervised setting. The language model, which is trained to predict
the sentence most likely to follow the input sentence, is fine tuned to
instead autoencode the input sentence. Given that the sentence encoder
produces a semantic representation, it is possible to use a number of
techniques to encourage the decoder to generate a paraphrase rather
than reconstruct the exact input sentence. These techniques include
adding noise to the sentence representation, and sampling characters from
the model's output layer
Patterns, sources, and consequences of intraspecific variation in responses of marine fauna to environmental stressors
No full text No abstracts are to be cited without prior reference to the author. Conveners: R. Christopher Chambers (USA), Hanner Baumann (USA), Gudrun Marteinsdottir (Iceland).CM 2017/O:599. How many old fish remain in the sea? Quantifying the extent of age truncation in fished stocks. Lewis Barnett, Lewis A.K. Barnett, Trevor A. Branch, R. Anthony Ranasinghe, Timothy E. EssingtonCM 2017/O:411. Changes in the size structure of spawning cod in the Baltic Sea may have led to depensation. Andrei Makarchouk, Ivo Šics, Tatjana BaranovaCM 2017/O:610. Is the range expansion of blue crabs (Callinectes sapidus) in the northeast US a function of winter mortality?. Adelle Molina, Janet NyeCM 2017/O:480. Black sea bass, Centropristis striata, spawning and first-year growth in New England: northward expansion of spawning and nursery grounds in a warming Gulf of Maine. Richard S. McBrideCM 2017/O:533. Distribution Shifts Associated with Changing Environmental Parameters in Two Demersal Species Summer Flounder (Paralichthys dentatus) and Black Sea Bass (Centropristis striata). Emily Markowitz, Michael Frisk, Skyler Sagarese, Janet Nye MarkowitzCM 2017/O:328. How cryptic intraspecific genomic diversity could mediate population, species and ecosystem responses to marine climate change in the Northwest Atlantic. Ryan RE Stanley, Claudio DiBacco, Ben Lowen, Robert G. Beiko, Mallory Van Wyngaarden, Nick W. Jeffery, Paul Bentzen, Louis Bernatchez, Catherine Johnson, Paul V.R. Snelgrove, Brendan F. Wringe, Ian R. BradburyCM 2017/O:451. Range wide climate-associated genomic clines in Atlantic salmon. Nicholas W. Jeffery, Ryan R.E. Stanley, Brendan F. Wringe, Javier Guijarro-Sabaniel, Vincent Bourret, Louis Bernatchez, Paul Bentzen, Robert Beiko, John Gilbey, Marie Clement, Ian R. BradburyCM 2017/O:609. Plasticity of Responses to Thermal and CO2 Variation in Early Life-Stages of Atlantic Silverside, Menidia menidia. R. Christopher Chambers, Delan J. Boyce, Ehren A. Habeck, Kristin M. Habeck, Megan M. Dotterweich, Melissa DrownCM 2017/O:686. Growth costs of high CO2 environments in a marine fish: importance of feeding methodology. H. Baumann, C. S. MurrayCM 2017/O:96. Characterization of the functional and anatomical differences in the atrial and ventricular myocardium from three species of elasmobranch fishes: smooth dogfish (Mustelus canis), sandbar shark (Carcharhinus plumbeus), and clearnose skate (Raja eglanteria). Julie Juanita Larsen, Peter Bushnell, John Steffensen, Morten Pedersen, Klaus Qvortrup, Richard BrillCM 2017/O:601. A preliminary study testing the effects of high CO2 on the early life stages of the northern sand lance Ammodytes dubius. Christopher S. Murray, David N. Wiley, Hannes BaumannCM 2017/O:651. Why were nearshore species assemblages so resilient to the Deepwater Horizon oil spill?. Kiva L. Oken, Olaf P. Jensen, Kenneth W. Able, Paola C. López-DuarteCM 2017/O:460. Assessment of potential sediment transport effect on the south eastern Baltic Sea coastal reef habitats in a changing climate. Maija Viska, Juris Aigars, Sandra Sprukta</p
Identification of key drivers of antimicrobial resistance in Enterococcus using machine learning
No full textWith antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML) models show promise for AMR prediction in diagnostics but require a deep understanding of internal processes to use effectively. Our study utilized AMR gene, pangenomic, and predicted plasmid features from 647 Enterococcus faecium and Enterococcus faecalis genomes across the One Health continuum, along with corresponding resistance phenotypes, to develop interpretive ML classifiers. Vancomycin resistance could be predicted with 99% accuracy with AMR gene features, 98% with pangenome features, and 96% with plasmid clusters. Top pangenome features overlapped with the resistance genes of the vanA operon, which are often laterally transmitted via plasmids. Doxycycline resistance prediction achieved approximately 92% accuracy with pangenome features, with the top feature being elements of Tn916 conjugative transposon, a tet(M) carrier. Erythromycin resistance prediction models achieved about 90% accuracy, but top features were negatively correlated with resistance due to the confounding effect of population structure. This work demonstrates the importance of reviewing ML models’ features to discern biological relevance even when achieving high-performance metrics. Our workflow offers the potential to propose hypotheses for experimental testing, enhancing the understanding of AMR mechanisms, which are crucial for combating the AMR crisis.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author
