Sonography of Common Peripheral Nerve Disorders With Clinical Correlation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135210/1/jum2016354683.pd

Carbohydrate structure: : the rocky road to automation

With the introduction of intuitive graphical software, structural biologists who are not experts in crystallography are now able to build complete protein or nucleic acid models rapidly. In contrast, carbohydrates are in a wholly different situation: scant automation exists, with manual building attempts being sometimes toppled by incorrect dictionaries or refinement problems. Sugars are the most stereochemically complex family of biomolecules and, as pyranose rings, have clear conformational preferences. Despite this, all refinement programs may produce high-energy conformations at medium to low resolution, without any support from the electron density. This problem renders the affected structures unusable in glyco-chemical terms. Bringing structural glycobiology up to ‘protein standards’ will require a total overhaul of the methodology. Time is of the essence, as the community is steadily increasing the production rate of glycoproteins, and electron cryo-microscopy has just started to image them in precisely that resolution range where crystallographic methods falter most

Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists\u27 estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard

Delayed Sciatic Nerve Injury Resulting From Myositis Ossificans Traumatica

A motorcyclist sustained multipleâ system trauma, including a left buttock hematoma requiring decompression and evacuation. Presentation for severe hip pain and lower extremity weakness was delayed. Imaging revealed myositis ossificans traumatica compressing the sciatic nerve in the buttock. The patient underwent sciatic nerve decompression with resection of heterotopic calcification, resulting in improvement in pain and left lower extremity function. This case illustrates the contrast in differential diagnosis of peripheral nerve injury immediately posttrauma and that occurring in a slow, delayed fashion posttrauma. Myositis ossificans may be an underrecognized complication of trauma but should be considered in cases of delayed peripheral nerve injury after trauma.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147088/1/pmrj484.pd

NGC 7582: The Prototype Narrow-Line X-ray Galaxy

NGC 7582 is a candidate prototype of the Narrow Line X-ray Galaxies (NLXGs) found in deep X-ray surveys. An ASCA observation shows the hard (> 3 keV) X-ray continuum of NGC 7582 drops 40% in ~6 ks, implying an AGN, while the soft band (< 3 keV) does not drop in concert with the hard continuum, requiring a separate component. The X-ray spectrum of NGC 7582 also shows a clear 0.5-2 keV soft (kT = 0.8 (+0.9,-0.3) keV or Gamma = 2.4 +/- 0.6; L(X) = 6 x 10**40 ergs s**-1) low--energy component, in addition to a heavily absorbed [N(H) = (6 +/- 2)\times 10**22 cm**-2 ] and variable 2-10 keV power law [Gamma = 0.7 (+0.3,-0.4); L(X) = (1.7-2.3) x 10**42 ergs s**-1]. This is one of the flattest 2-10 keV slopes in any AGN observed with ASCA. (The ROSAT HRI image of NGC 7582 further suggests extent to the SE.) These observations make it clear that the hard X-ray emission of NGC 7582, the most "narrow-line" of the NLXGs, is associated with an AGN. The strong suggestion is that all NLXGs are obscured AGNs, as hypothesized to explain the X-ray background spectral paradox. The separate soft X-ray component makes NGC 7582 (and by extension other NLXGs) detectable as a ROSAT source.Comment: text: Latex2e 10 pages, including 1 table, and 2 postscript figures via psfi

In vivo killing of Staphylococcus aureus using a light-activated antimicrobial agent

Background: The widespread problem of antibiotic resistance in pathogens such as Staphylococcus aureus has prompted the search for new antimicrobial approaches. In this study we report for the first time the use of a light-activated antimicrobial agent, methylene blue, to kill an epidemic methicillin-resistant Staphylococcus aureus (EMRSA-16) strain in two mouse wound models.Results: Following irradiation of wounds with 360 J/cm(2) of laser light (670 nm) in the presence of 100 mu g/ml of methylene blue, a 25-fold reduction in the number of viable EMRSA was seen. This was independent of the increase in temperature of the wounds associated with the treatment. Histological examination of the wounds revealed no difference between the photodynamic therapy (PDT)-treated wounds and the untreated wounds, all of which showed the same degree of inflammatory infiltration at 24 hours.Conclusion: The results of this study demonstrate that PDT is effective at reducing the total number of viable EMRSA in a wound. This approach has promise as a means of treating wound infections caused by antibiotic-resistant microbes as well as for the elimination of such organisms from carriage sites

From “where” and “when” to “what” and “why”: archival tags for monitoring “complex” behaviours in fish.

Understanding the movements (“where” and “when”) and behaviour (“what” and, hopefully, “why”) of individuals and populations is key to answering fundamental questions in fish ecology. The use of archival tags in telemetry studies of marine fish have, by and large, involved recording “simple” measurements of variables such as pressure (giving depth), temperature and light over extended timescales. These have then been used to provide information about location and movement of individuals. However, our understanding of more complex behaviours (i.e. what fish are doing as different from spatial movements) has usually been inferred from movement data because it has not been possible to record directly specific behavioural events such as feeding or spawning. This is because the events are usually infrequent, irregular and often quite brief and so not amenable to a technology based on taking regular but infrequent records of continuously available variables. The recent implementation of new sensors (e.g. physical movement, tri-axial accelerometers), rapid (< 30 Hz) sampling capabilities, enhanced memory and more complex data capture protocols has lead to the development of archival tags that can be used to detect and record complex behaviours such as feeding and spawning. We describe recent developments with archival tags and their use to monitor feeding and spawning in fish together with the application of tri-axial accelerometry that can be used to quantify behaviour and metabolic rate. These can then be used to assess the cost of behaviours with a view to understanding how appropriate they are as responses to environmental variability. Keywords: telemetry, behaviour, data storage ta

Implicit motor learning promotes neural efficiency during laparoscopy

Background An understanding of differences in expert and novice neural behavior can inform surgical skills training. Outside the surgical domain, electroencephalographic (EEG) coherence analyses have shown that during motor performance, experts display less coactivation between the verbal-analytic and motor planning regions than their less skilled counterparts. Reduced involvement of verbal-analytic processes suggests greater neural efficiency. The authors tested the utility of an implicit motor learning intervention specifically devised to promote neural efficiency by reducing verbal-analytic involvement in laparoscopic performance. Methods In this study, 18 novices practiced a movement pattern on a laparoscopic trainer with either conscious awareness of the movement pattern (explicit motor learning) or suppressed awareness of the movement pattern (implicit motor learning). In a retention test, movement accuracy was compared between the conditions, and coactivation (EEG coherence) was assessed between the motor planning (Fz) region and both the verbal-analytic (T3) and the visuospatial (T4) cortical regions (T3-Fz and T4-Fz, respectively). Results Movement accuracy in the conditions was not different in a retention test (P = 0.231). Findings showed that the EEG coherence scores for the T3-Fz regions were lower for the implicit learners than for the explicit learners (P = 0.027), but no differences were apparent for the T4-Fz regions (P = 0.882). Conclusions Implicit motor learning reduced EEG coactivation between verbal-analytic and motor planning regions, suggesting that verbal-analytic processes were less involved in laparoscopic performance. The findings imply that training techniques that discourage nonessential coactivation during motor performance may provide surgeons with more neural resources with which to manage other aspects of surgery. © 2011 The Author(s).published_or_final_versio

Verbal De-escalation of the Agitated Patient: Consensus Statement of the American Association for Emergency Psychiatry Project BETA De-escalation Workgroup

Agitation is an acute behavioral emergency requiring immediate intervention. Traditional methods of treating agitated patients, ie, routine restraints and involuntary medication, have been replaced with a much greater emphasis on a noncoercive approach. Experienced practitioners have found that if such interventions are undertaken with genuine commitment, successful outcomes can occur far more often than previously thought possible. In the new paradigm, a 3-step approach is used. First, the patient is verbally engaged; then a collaborative relationship is established; and, finally, the patient is verbally de-escalated out of the agitated state. Verbal de-escalation is usually the key to engaging the patient and helping him become an active partner in his evaluation and treatment; although, we also recognize that in some cases nonverbal approaches, such as voluntary medication and environment planning, are also important. When working with an agitated patient, there are 4 main objectives: (1) ensure the safety of the patient, staff, and others in the area; (2) help the patient manage his emotions and distress and maintain or regain control of his behavior; (3) avoid the use of restraint when at all possible; and (4) avoid coercive interventions that escalate agitation. The authors detail the proper foundations for appropriate training for de-escalation and provide intervention guidelines, using the “10 domains of de-escalation.