Big 5 questions anticipating Mitalipov human embryo CRISPR paper

The human embryo CRISPR paper with Shoukhrat Mitalipov as senior author is coming soon. It will reportedly be focused on the use of CRISPR to genetically modify viable human embryos for reversal of a disease-associated mutation. While strangely press already [...]

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from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2uRl013

Preprocessing Prediction of Advanced Algorithms for Medical Imaging

Abstract

Advanced medical imaging algorithms (such as bone removal, vessel segmentation, or a lung nodule detection) can provide extremely valuable information to the radiologists, but they might sometimes be very time consuming. Being able to run the algorithms in advance can be a possible solution. However, we do not know which algorithm to run on a given dataset before it is actually used. It is possible to manually insert matching rules for preprocessing algorithms, but it requires high maintenance and does not work well in practice. This paper presents a dynamic machine learning solution for predicting which advanced visualization (AV) algorithm needs to be applied on a given series. The system gets a handful of free text DICOM tags as an input and builds a model in the clinical setting. It incorporates a Bag of Words (BOW) feature extractor and a Random Forest classifier. The approach was tested on two datasets from clinical sites which use different languages and varying scanner models. We show that even without feature extraction, sensitivity of above 90% can be reached on both of them. By using BOW feature extractor, precision and sensitivity can usually be further improved. Even on a noisy and highly unbalanced dataset, only around 100 samples were needed to reach sensitivity of above 80% and specificity of above 97%. We show how the solution can be part of a Smart Preprocessing mechanism in a viewing software. Using such a system will ultimately minimize the time to launch studies and improve radiologists reading time efficiency.

from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2hhhYA9

Ultrasound-Guided Regional Anesthesia Simulation Training: A Systematic Review.

Background and Objectives: Ultrasound-guided regional anesthesia (UGRA) has become the criterion standard of regional anesthesia practice. Ultrasound-guided regional anesthesia teaching programs often use simulation, and guidelines have been published to help guide URGA education. This systematic review aimed to examine the effectiveness of simulation-based education for the acquisition and maintenance of competence in UGRA. Methods: Studies identified in MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and ERIC were included if they assessed simulation-based UGRA teaching with outcomes measured at Kirkpatrick level 2 (knowledge and skills), 3 (transfer of learning to the workplace), or 4 (patient outcomes). Two authors independently reviewed all identified references for eligibility, abstracted data, and appraised quality. Results: After screening 176 citations and 45 full-text articles, 12 studies were included. Simulation-enhanced training improved knowledge acquisition (Kirkpatrick level 2) when compared with nonsimulation training. Seven studies measuring skill acquisition (Kirkpatrick level 2) found that simulation-enhanced UGRA training was significantly more effective than alternative teaching methods or no intervention. One study measuring transfer of learning into the clinical setting (Kirkpatrick level 3) found no difference between simulation-enhanced UGRA training and non-simulation-based training. However, this study was discontinued early because of technical challenges. Two studies examined patient outcomes (Kirkpatrick level 4), and one of these found that simulation-based UGRA training improved patient outcomes compared with didactic teaching. Conclusions: Ultrasound-guided regional anesthesia knowledge and skills significantly improved with simulation training. The acquired UGRA skills may be transferred to the clinical setting; however, further studies are required to confirm these changes translate to improved patient outcomes. Copyright (C) 2017 by American Society of Regional Anesthesia and Pain Medicine.

from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2vmqa87

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

This protocol was developed to quantitatively identify tumor microenvironment components in glioblastoma patient resections using chromogenic immunohistochemistry and ImageJ.

from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2vZ6FzO

All That Wheezes...

A 20-year-old woman with a history of allergic rhinitis presented to her physician in the autumn with persistent cough and shortness of breath. She had been seen 6 weeks earlier for nasal congestion, rhinorrhea, postnasal drip, and cough that had bothered her on and off throughout the summer. Her…

from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2tIbkVc

Editorial Board

Publication date: 1 August 2017
Source:American Journal of Infection Control, Volume 45, Issue 8





from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2vgcif9

APIC Masthead

Publication date: 1 August 2017
Source:American Journal of Infection Control, Volume 45, Issue 8





from # All Medicine by Alexandros G. Sfakianakis via alkiviadis.1961 on Inoreader http://ift.tt/2vfTGf1