bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lacrimal Gland Ductal Cyst Infection Presenting as Acute Orbital Cellulitis With Abscess
Authors: Park Hyung Jun , Park Su Jin , Chi Mijung 
Published: November 2020

Abstract
Herein, the authors report a case of lacrimal gland ductal cyst infection presenting as acute orbital cellulitis with an abscess. A 45-year-old man without any history of trauma and a specific medical history presented with progressive painful periorbital swelling in the left eye and diplopia. At the first visit, the patient showed a best-corrected visual acuity of 0.2; erythematous swelling of the upper eyelid; superficial punctate keratitis, severe conjunctival chemosis in the left eye; and pus in the lateral canthal area. The patient had binocular diplopia and left eyeball movement limitation. Orbital computed tomography showed a peripherally enhanced periorbital soft tissue mass along the superotemporal aspect of the left globe, which was suggestive of an abscess. The authors diagnosed the patient with acute orbital cellulitis with an abscess. Intravenous antibiotics and steroids and topical antibiotics and steroids were administered. On the 4th day of the treatment, visual acuity and eyeball movement limitation had improved, and a small opening of the lacrimal gland duct was observed in the left upper conjunctival fornix. Methicillin-resistant Staphylococcus aureus was identified on culture. Finally, the patient was diagnosed with lacrimal gland ductal cyst infection presenting as acute orbital cellulitis. Lacrimal duct cyst infection should be considered as a cause of orbital cellulitis.
Hyperbaric CO2 Cryotherapy for Managing Swelling After Mandibular Angle Ostectomy
Authors: Chen Xiaojun , Ma Liping , Aung Zin Mar , Sun Mengzhe , Yang Xianxian , Zhang Yan , Xu Haisong , Chai Gang 
Published: November 2020

Abstract
Postoperative facial swelling after mandibular angle ostectomy is a concern for patients as it affects their quality of life. This study aimed to evaluate the effect of hyperbaric CO2 cryotherapy in relieving postoperative swelling. Thirty-seven patients (mean age: 22.95 ± 3.49 years) who underwent bilateral mandibular angle ostectomy from April to October 2019, were included in this study. A split-mouth design was adopted and through a random sequence, either side of the face was designated as the experimental side. Both received routine nursing care, while the experimental side underwent an additional hyperbaric CO2 cryotherapy treatment. The facial structure was recorded by a 3-dimensional laser scanner pre- and postoperatively. Geomagic Studio was used for alignment, visualization, and quantification of the swelling. The largest deviation value on each side was adopted to assess the overall swelling. Parameters were compared using the paired t-test, and P<0.05 was considered statistically significant. No necrosis of the skin and adjacent structures or other complications occurred in these patients. After the first day of cryotherapy, the deviation in the experimental and the control groups was (8.40 ± 1.95) mm and (10.42 ± 2.03) mm, respectively. The next day, after cryotherapy, the value further reduced to (5.42 ± 1.36) mm and (8.24 ± 2.22) mm for the experimental and control groups, respectively. And the effect remained till the seventh day. No difference was observed in terms of volume of drainage. Hyperbaric CO2 cryotherapy is safe and effective in relieving postoperative swelling and seems to be more effective than the traditional cold-pack treatment after mandibular angle ostectomy.
Artificial Intelligence for Surgical Safety
Authors: Mascagni Pietro , Vardazaryan Armine , Alapatt Deepak , Urade Takeshi , Emre Taha , Fiorillo Claudio , Pessaux Patrick , Mutter Didier , Marescaux Jacques , Costamagna Guido , Dallemagne Bernard , Padoy Nicolas 
Published: November 2020

Objective:
To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).

Background:
Poor implementation and subjective interpretation of CVS contributes to the stable rates of bile duct injuries in LC. As CVS is assessed visually, this task can be automated by using computer vision, an area of artificial intelligence aimed at interpreting images.

Methods:
Still images from LC videos were annotated with CVS criteria and hepatocystic anatomy segmentation. A deep neural network comprising a segmentation model to highlight hepatocystic anatomy and a classification model to predict CVS criteria achievement was trained and tested using 5-fold cross validation. Intersection over union, average precision, and balanced accuracy were computed to evaluate the model performance versus the annotated ground truth.

Results:
A total of 2854 images from 201 LC videos were annotated and 402 images were further segmented. Mean intersection over union for segmentation was 66.6%. The model assessed the achievement of CVS criteria with a mean average precision and balanced accuracy of 71.9% and 71.4%, respectively.

Conclusions:
Deep learning algorithms can be trained to reliably segment hepatocystic anatomy and assess CVS criteria in still laparoscopic images. Surgical-technical partnerships should be encouraged to develop and evaluate deep learning models to improve surgical safety.
An Implementation Assessment of the Virtual Acute Care for Elders Program From the Perspective of Key Stakeholders
Authors: Balentine Courtney J. , Morris Melanie , Knight Sara J. , Turan Janet M. , Flood Kellie L. , Gutierrez-Meza Diana , Macdonald Cameron L. , Bhatia Smita , Brown Cynthia J. 
Published: November 2020

Objective:
The aim of this study was to obtain feedback from key stakeholders and end users to identify program strengths and weaknesses to plan for wider dissemination and implementation of the Virtual Acute Care for Elders (Virtual ACE) program, a novel intervention that improves outcomes for older surgical patients.

Background:
Virtual ACE was developed to deliver evidence-based geriatric care without requiring daily presence of a geriatrician. Previous work demonstrated that Virtual ACE increased mobility and decreased delirium rates for surgical patients.

Methods:
We conducted semi-structured interviews with 30 key stakeholders (physicians, nurses, hospital leadership, nurse managers, information technology staff, and physical/occupational therapists) involved in the implementation and use of the program.

Results:
Our stakeholders indicated that Virtual ACE was extremely empowering for bedside nurses. The program helped nurses identify older patients who were at risk for a difficult postoperative recovery. Virtual ACE also gave them skills to manage complex older patients and more effectively communicate their needs to surgeons and other providers. Nurse managers felt that Virtual ACE helped them allocate limited resources and plan their unit staffing assignments to better manage the needs of older patients. The main criticism was that the Virtual ACE Tracker that displayed patient status was difficult to interpret and could be improved by a better design interface. Stakeholders also felt that program training needed to be improved to accommodate staff turnover.

Conclusions:
Although respondents identified areas for improvement, our stakeholders felt that Virtual ACE empowered them and provided effective tools to improve outcomes for older surgical patients.
Improved Detection of Air Trapping on Expiratory Computed Tomography Using Deep Learning
Authors: Ram Sundaresh , Hoff Benjamin A. , Bell Alexander J. , Galban Stefanie , Fortuna Aleksa B. , Weinheimer Oliver , Wielpütz Mark O. , Robinson Terry E. , Newman Beverley , Vummidi Dharshan , Chughtai Aamer , Kazerooni Ella A. , Johnson Timothy D. , Han MeiLan K. , Hatt Charles R. , Galban Craig J. 
Published: November 2020

ABSTRACTBackgroundRadiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.ObjectiveTo investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques.Materials and MethodsPaired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.ResultsQAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.ConclusionThe CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.
Impact of a public policy restricting staff mobility between long-term care homes in Ontario, Canada during the COVID-19 pandemic
Authors: Jones Aaron , Watts Alexander G. , Khan Salah Uddin , Forsyth Jack , Brown Kevin A. , Costa Andrew P. , Bogoch Isaac I. , Stall Nathan M. 
Published: November 2020

AbstractObjectivesTo assess changes in the mobility of staff between long-term care homes in Ontario, Canada before and after enactment of public policy restricting staff from working at multiple homes.DesignPre-post observational study.Setting and Participants623 long-term cares homes in Ontario, Canada between March 2020 and June 2020.MethodsWe used anonymized mobile device location data to approximate connectivity between all 623 long-term care homes in Ontario during the 7 weeks before (March 1 – April 21) and after (April 22 – June 13) the policy restricting staff movement was implemented. We visualized connectivity between long-term care homes in Ontario using an undirected network and calculated the number of homes that had a connection with another long-term care home and the average number of connections per home in each period. We calculated the relative difference in these mobility metrics between the two time periods and compared within-home changes using McNemar’s test and the Wilcoxon rank-sum test.ResultsIn the period preceding restrictions, 266 (42.7%) long-term care homes had a connection with at least one other home, compared to 79 (12.7%) homes during the period after restrictions, a drop of 70.3% (p <0.001). The average number of connections in the before period was 3.90 compared to 0.77 in after period, a drop of 80.3% (p < 0.001). In both periods, mobility between long-term care homes was higher in homes located in larger communities, those with higher bed counts, and those part of a large chain.Conclusions and ImplicationsMobility between long-term care homes in Ontario fell sharply after an emergency order by the Ontario government limiting long-term care staff to a single home, though some mobility persisted. Reducing this residual mobility should be a focus of efforts to reduce risk within the long-term care sector during the COVID-19 pandemic.
Dissociation of motor control from motor awareness in awake sleepwalkers: An EEG study in virtual reality
Authors: Rothacher Yannick , Nguyen Anh , Efthymiou Evdokia , Werth Esther , Baumann Christian R. , Lenggenhager Bigna , Brugger Peter , Kunz Andreas , Imbach Lukas L. 
Published: November 2020

AbstractRecent behavioral evidence indicates that awake sleepwalkers show dissociation of motor control and motor awareness in a virtual reality (VR) paradigm. Intriguingly, this dissociation resembles the nocturnal disintegration of motor awareness and movement during episodes of sleepwalking. Here, we set out to examine the neural underpinnings of altered motor agency in sleepwalkers by measuring EEG modulation during redirected walking in VR. Using this approach, we discovered distinct EEG patterns in awake sleepwalkers indicating facilitated dual tasking and salient habitual motor control as compared to healthy subjects. These observations provide electrophysiological evidence for the proposed brain-body dissociation in awake sleepwalkers. In conclusion, this study shows proof-of-principle that EEG biomarkers of movement in a VR setting might add to the diagnostic work-up of awake sleepwalkers.
Detection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India
Authors: Mandal Manisha , Mandal Shyamapada 
Published: November 2020

ABSTRACTDocumentation in scientific literature is not available on prospective evaluation of the efficiency of the unlock measure related to COVID-19 transmission change points in India, projecting the infected population, planning suitable measures related to future interventions and lifting of restrictions so that the economic settings are not damaged beyond repair. We have applied SIR model and Bayesian approach combined with Monte Carlo Markov algorithms on the Indian COVID-19 daily new infected cases from 1 August 2020 to 30 September 2020. We showed that the COVID-19 epidemic declined after implementing unlock-4 measure and the identified change-points were consistent with the timelines of announced unlock-3 and unlock-4 measure, on 1 August 2020 and 1 September 2020, respectively, effectiveness of which were quantified as the change in both effective transmission rates (100% reduction) and the basic reproduction number attaining 1, implying measures taken to control and mitigate the COVID-19 epidemic in India managed to flatten and recede the epidemic curve.
Patterns and persistence of SARS-CoV-2 IgG antibodies in Chicago to monitor COVID-19 exposure
Authors: Demonbreun Alexis R. , McDade Thomas W. , Pesce Lorenzo , Vaught Lauren A. , Reiser Nina L. , Bogdanovic Elena , Velez Matthew P. , Hsieh Ryan R. , Simons Lacy M. , Saber Rana , Ryan Daniel T. , Ison Michael G. , Hultquist Judd F. , Wilkins John T. , D’Aquila Richard T. , Mustanski Brian , McNally Elizabeth M. 
Published: November 2020

AbstractBackgroundEstimates of seroprevalence to SARS-CoV-2 vary widely and may influence vaccination response. We ascertained IgG levels across a single US metropolitan site, Chicago, from June 2020 through December 2020.MethodsParticipants (n=7935) were recruited through electronic advertising and received materials for a self-sampled dried blood spot assay through the mail or a minimal contact in person method. IgG to the receptor binding domain of SARS-CoV-2 was measured using an established highly sensitive and highly specific assay.ResultsOverall seroprevalence was 17.9%, with no significant difference between method of contact. Only 2.5% of participants reported having had a diagnosis of COVID-19 based on virus detection, consistent with a 7-fold greater exposure to SARS-CoV-2 measured by serology than detected by viral testing. The range of IgG level observed in seropositive participants from this community survey overlapped with the range of IgG levels associated with COVID-19 cases having a documented positive PCR positive test. From a subset of those who participated in repeat testing, half of seropositive individuals retained detectable antibodies for 3-4 months.ConclusionsQuantitative IgG measurements with a highly specific and sensitive assay indicate more widespread exposure to SARS-CoV-2 than observed by viral testing. The range of IgG concentration produced from these asymptomatic exposures is similar to IgG levels occurring after documented non-hospitalized COVID-19, which is considerably lower than that produced from hospitalized COVID-19 cases. The differing ranges of IgG response, coupled with the rate of decay of antibodies, may influence response to subsequent viral exposure and vaccine.Graphical Abstract
METABOLIC PROFILE IN PLASMA AND CSF OF LEVODOPA-INDUCED DYSKINESIA OF PARKINSON’S DISEASE
Authors: Santos-Lobato Bruno L. , Gardinassi Luiz Gustavo , Bortolanza Mariza , Ferranti Peti Ana Paula , Pimentel Ângela V. , Faccioli Lúcia Helena , Del-Bel Elaine A. , Tumas Vitor 
Published: November 2020

Structured AbstractBackgroundThe existence of few biomarkers and the lack of a better understanding of the pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson’s disease (PD) require new approaches, as the metabolomic analysis, for discoveries.ObjectivesWe aimed to identify a metabolic profile associated with LID in patients with PD in an original cohort, and to confirm the results in an external cohort (BioFIND).MethodsIn the original cohort, plasma and CSF were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. LC-MS/MS and metabolomics data analysis were used to perform untargeted metabolomics. Untargeted metabolomics data from the BioFIND cohort were analyzed.ResultsWe identified a metabolic profile associated with LID in PD, composed of multiple metabolic pathways. In particular, the dysregulation of glycosphingolipids metabolic pathway was more related to LID and was strongly associated with the severity of dyskinetic movements. Further, bile acid biosynthesis and C21-steroid hormone biosynthesis metabolites simultaneously found in plasma and CSF have distinguished patients with LID from other participants. Levels of cortisol and cortisone were reduced in patients with PD and LID compared to patients with PD without LID. Data from the BioFIND cohort confirmed dysregulation in plasma metabolites from the bile acid biosynthesis and C21-steroid hormone biosynthesis pathways.ConclusionThere is a distinct metabolic profile associated with LID in PD, both in plasma and CSF, which may be associated with the dysregulation of lipid metabolism and neuroinflammation.
Negative attitudes about facemasks during the COVID-19 pandemic: The dual importance of perceived ineffectiveness and psychological reactance
Authors: Taylor Steven , Asmundson Gordon J. G. 
Published: November 2020

AbstractThis study reports a comprehensive empirical investigation of the nature and correlates of anti-mask attitudes during the COVID-19 pandemic. Accumulating evidence underscores the importance of facemasks, as worn by the general public, in limiting the spread of infection. Accordingly, mask-wearing has become increasingly mandatory in public places such as stores and on public transit. Although the public has been generally adherent to mask-wearing, a small but vocal group of individuals refuse to wear masks. Anti-mask protest rallies have occurred in many places throughout the world, sometimes erupting violently. Few empirical studies have examined the relationship between anti-mask attitudes and mask non-adherence and little is known about how such attitudes relate to one another or other factors (e.g., non-adherence to social distancing, anti-vaccination attitudes). To investigate these issues, the present study surveyed 2,078 adults from the US and Canada. Consistent with other surveys, we found that most (84%) people wore masks because of COVID-19. The 16% who did not wear masks scored higher on most measures of negative attitudes towards masks. Network analyses indicated that negative attitudes about masks formed an intercorrelated network, with the central nodes in the network being (a) beliefs that masks are ineffective in preventing COVID-19, and (b) psychological reactance (PR; i.e., an aversion to being forced to wear masks). These central nodes served as links, connecting the network of anti-masks attitudes to negative attitudes toward SARSCoV2 vaccination, beliefs that the threat of COVID-19 has been exaggerated, disregard for social distancing, and political conservatism. Findings regarding PR are important because, theoretically, PR is likely to strengthen other anti-masks attitudes (e.g., beliefs that masks are ineffective) because people with strong PR react with anger and counter-arguments when their beliefs are challenged, thereby leading to a strengthening of their anti-mask beliefs. Implications for improving mask adherence are discussed.
Valuation system connectivity is correlated with poly-drug use in young adults
Authors: Loganathan Kavinash , Lv Jinglei , Cropley Vanessa , Zalesky Andrew , Wei Ho Eric Tatt 
Published: November 2020

AbstractPoly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population sample (n=992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
Optimizing interneuron circuits for compartment-specific feedback inhibition
Authors: Keijser Joram , Sprekeler Henning 
Published: November 2020

AbstractCortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent advances in training spiking network models, we optimized the connectivity and short-term plasticity of interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons. Over the course of the optimization, the interneurons diversified into two classes that resembled parvalbumin (PV) and somatostatin (SST) expressing interneurons. The resulting circuit can be understood as a neural decoder that inverts the nonlinear biophysical computations performed within the pyramidal cells. Our model provides a proof of concept for studying structure-function relations in cortical circuits by a combination of gradient-based optimization and biologically plausible phenomenological models.