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Warming Lorentzen posted an update 6 months ago
Recent advances in comprehensive genomic profiling by next-generation sequencing have uncovered the genomic alterations at the molecular level for many types of tumors; as such, numerous small specific molecules that target these alterations have been developed and widely used in the management of these cancers.
To provide a concise molecular genomic update in solid, bone and soft tissue tumors, hematopoietic as well as lymphoid malignancies; discuss its clinical applications; and familiarize practicing pathologists with the emerging cancer biomarkers and their diagnostic utilities.
This review is based on the National Comprehensive Cancer Network guidelines and peer-reviewed English literature.
Tumor-specific biomarkers and molecular/genomic alterations, including pan-cancer markers, have been significantly expanded in the past decade thanks to large-scale high-throughput technologies and will continue to emerge in the future. These biomarkers can be of great value in diagnosis, prognosis, and/or targeted therapy/treatment. Familiarization with these emerging and ever-changing tumor biomarkers will undoubtedly aid pathologists in making accurate and state-of-the-art diagnoses and enable them to be more actively involved in the care of cancer patients.
Tumor-specific biomarkers and molecular/genomic alterations, including pan-cancer markers, have been significantly expanded in the past decade thanks to large-scale high-throughput technologies and will continue to emerge in the future. These biomarkers can be of great value in diagnosis, prognosis, and/or targeted therapy/treatment. Familiarization with these emerging and ever-changing tumor biomarkers will undoubtedly aid pathologists in making accurate and state-of-the-art diagnoses and enable them to be more actively involved in the care of cancer patients.
Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs.
We present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average ‘natural balance’ with ADEs present in about 7% of the tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all 3 tasks.
The system presented achieved state-of-the-art performance on comparable datasets and scored a classification performance of F1 = 0.63, span extraction performance of F1 = 0.44 and an end-to-end entity resolution performance of F1 = 0.34 on the presented dataset.
The performance of the models continues to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. Dactolisib We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements.
Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task.
Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task.
To determine whether sociodemographic factors are associated with heterogeneity in pain evolution in inflammatory rheumatic diseases (IRDs) after accounting for disease-specific characteristics in a system with universal health care.
This analysis included the data from two prospective observational cohorts of early IRDs (ESPOIR for early rheumatoid arthritis (RA) and DESIR for early spondyloarthritis (SpA)). Data on pain was measured respectively at 13 and 9 occasions spanning 10 and 6 years of follow-up using Short-Form 36 bodily pain amongst 810 participants of ESPOIR, and 679 participants of DESIR. Linear mixed models were used to characterise differences in pain evolution as a function of age (tertiles), sex, ethnicity, education, marital, and professional status after accounting for disease-related, treatment, lifestyle, and health factors.
While transitioning from early (disease duration ≤6 months for RA and ≤3 years for SpA) to long-standing disease, differences in pain evolution emerged as a fusparities.
Knowledge curation from the biomedical literature is very valuable but can be a repetitive and laborious process. The paucity of user-friendly tools is one of the reasons for the lack of widespread adoption of good biomedical knowledge curation practices.
Here we present Ontoclick, a web browser extension that streamlines the process of annotating a text span with a relevant ontology term. We hope this tool will make biocuration more accessible to a wider audience of biomedical researchers.
Ontoclick is freely available under the GPL-3.0 license on the Chrome Web Store and on the Mozilla Add-Ons for Firefox Store. Source code and documentation are available at https//github.com/azankl/Ontoclick.
Ontoclick is freely available under the GPL-3.0 license on the Chrome Web Store and on the Mozilla Add-Ons for Firefox Store. Source code and documentation are available at https//github.com/azankl/Ontoclick.
Autologous lipofilling is emerging procedure to augment and possibly reverse dermal scars and to reduce scar-related pain, but its efficacy and mechanisms are poorly understood.
To test the hypothesis that repeated lipografts reverse dermal scars by re-initiation of wound healing.
In a prospective, non-placebo controlled clinical study, 27 adult patients with symptomatic scars were given two lipofilling treatments at 3-month intervals. As primary outcome, clinical effects were measured using the patient and observer scar assessment scale (POSAS). Scar biopsies were taken before and after treatments to assess scar remodeling at a cellular level.
Twenty patients completed the study. Patients’ scars improved after lipofilling. The total POSAS scores (combined patient and observer scores) decreased from 73.2±14.7 points pre-treatment to 46.1±14.0 and 32.3±13.2 after the first and second treatment, respectively. Patient POSAS scores decreased from 37.3±8.8 points to 27.2±11.3 and 21.1±11.4 points, whereas Observer POSAS scores decreased from 35.
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