Nevin Manimala

PLoS Genet. 2021 Mar 2;17(3):e1009221. doi: 10.1371/journal.pgen.1009221. Online ahead of print.

ABSTRACT

Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes’ genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons.

PMID:33651813 | DOI:10.1371/journal.pgen.1009221

PLoS Negl Trop Dis. 2021 Mar 2;15(3):e0008599. doi: 10.1371/journal.pntd.0008599. Online ahead of print.

ABSTRACT

BACKGROUND: Surveillance is an essential component of global programs to eliminate infectious diseases and avert epidemics of (re-)emerging diseases. As the numbers of cases decline, costs of treatment and control diminish but those for surveillance remain high even after the ‘last’ case. Reducing surveillance may risk missing persistent or (re-)emerging foci of disease. Here, we use a simulation-based approach to determine the minimal number of passive surveillance sites required to ensure maximum coverage of a population at-risk (PAR) of an infectious disease.

METHODOLOGY AND PRINCIPAL FINDINGS: For this study, we use Gambian human African trypanosomiasis (g-HAT) in north-western Uganda, a neglected tropical disease (NTD) which has been reduced to historically low levels (<1000 cases/year globally), as an example. To quantify travel time to diagnostic facilities, a proxy for surveillance coverage, we produced a high spatial-resolution resistance surface and performed cost-distance analyses. We simulated travel time for the PAR with different numbers (1-170) and locations (170,000 total placement combinations) of diagnostic facilities, quantifying the percentage of the PAR within 1h and 5h travel of the facilities, as per in-country targets. Our simulations indicate that a 70% reduction (51/170) in diagnostic centres still exceeded minimal targets of coverage even for remote populations, with >95% of a total PAR of ~3million individuals living ≤1h from a diagnostic centre, and we demonstrate an approach to best place these facilities, informing a minimal impact scale back.

CONCLUSIONS: Our results highlight that surveillance of g-HAT in north-western Uganda can be scaled back without substantially reducing coverage of the PAR. The methodology described can contribute to cost-effective and equable strategies for the surveillance of NTDs and other infectious diseases approaching elimination or (re-)emergence.

PMID:33651803 | DOI:10.1371/journal.pntd.0008599

PLoS One. 2021 Mar 2;16(3):e0247708. doi: 10.1371/journal.pone.0247708. eCollection 2021.

ABSTRACT

Chronic obstructive Eustachian tube dysfunction (ETD) is a common disorder of the middle ear. In recent years, two main diagnostic tools have become available: Eustachian tube score (ETS-7) and computed tomography (CT) combined with Valsalva maneuver. The aim of this study is to evaluate the outcomes of ETS-7 and CT in a group of patients affected by middle ear atelectasis with a strong suspicion of ETD. Three males and nine females, affected by middle ear atelectasis with retraction of the TM were enrolled. Each patient underwent to Eustachian tube dysfunction evaluation adopting the ETS-7 score and a temporal bone CT with Valsalva maneuver. The ears analyzed at steady state were divided into 2 groups: ETS<7 group and ETS≥ 7 group. The same division was applied for the ears analyzed after the Valsalva maneuver: ETS<7 group and ETS≥ 7 group. ETs were categorized as “well defined” (WD) and “not defined” (ND). The results of the analysis of the ETS-7 score in all 24 ears showed that 42% presented ETS ≥7, while 58% had ETS <7, indicating a diagnosis of ETD. In the ETS<7 group after Valsalva, ET was visualized in 33% of patients. In the ETS≥7 group it was WD in 29% after the Valsalva manoeuver. In both groups the comparison between the visualization of the ET before and after the Valsalva manoeuver did not present a statistical difference. No correlation emerged between ET evaluation with CT scan during Valsalva maneuver and ETS-7 score. It confirms that there is not a gold standard for the study of ET dysfunction.

PMID:33651800 | DOI:10.1371/journal.pone.0247708

PLoS One. 2021 Mar 2;16(3):e0247046. doi: 10.1371/journal.pone.0247046. eCollection 2021.

ABSTRACT

Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source programming language Julia. We also apply our algorithms to several complex spatial stochastic phenomena. including a rock-paper-scissors game, cancer growth in response to immunotherapy, and lipid oxidation dynamics. Our approach aids in standardizing mathematical models and in generating hypotheses based on concrete mechanistic behavior across a wide range of observed spatial phenomena.

PMID:33651796 | DOI:10.1371/journal.pone.0247046

N Z Med J. 2021 Feb 19;134(1530):12-20.

ABSTRACT

AIM: Burnout has a damaging effect on both the wellbeing of medical professionals and patients alike. Empathy is an important part of the therapeutic relationship and could be damaged by burnout. We aimed to describe the prevalence of burnout, assess levels of empathy and explore the relationship between burnout and empathy among senior medical officers (SMOs). We hypothesised that there would be a negative correlation between empathy and burnout.

METHOD: This was a cross-sectional observational study involving SMOs from a variety of specialities. The focus is on SMOs with relatively prolonged contact times with patients. Email invitations were sent out requesting participation in an electronic survey on the QuestionPro platform. The survey comprised 42 questions enquiring about demographics, empathy (Jefferson Scale of Physician Empathy) and burnout (Copenhagen Burnout Inventory). Correlational analyses were performed.

RESULTS: Three hundred and fourteen invitations were sent out and 178 responses were received (56.7% response rate). Forty-five percent of SMOs surveyed were experiencing high levels of personal burnout. There was a statistically significant negative correlation between empathy and patient-related burnout (p=0.018).

CONCLUSIONS: The results show high levels of personal burnout among SMOs and suggest that empathy reduces as patient-related burnout increases. The nature of this relationship is a complex one, and other contributing variables should be considered.

PMID:33651773

Clin Orthop Relat Res. 2021 Feb 26. doi: 10.1097/CORR.0000000000001685. Online ahead of print.

ABSTRACT

BACKGROUND: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported.

QUESTIONS/PURPOSES: (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients’ clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation?

METHODS: Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model.

RESULTS: The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively.

CONCLUSION: The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model.

LEVEL OF EVIDENCE: Level II, diagnostic study.

PMID:33651768 | DOI:10.1097/CORR.0000000000001685

Menopause. 2021 Mar 1. doi: 10.1097/GME.0000000000001754. Online ahead of print.

ABSTRACT

OBJECTIVES: First, to establish the respective ability of body mass index (BMI), waist circumference (WC), and relative fat mass index (RFM), to estimate body fat (BF%) measured by DXA (DXA-BF%) and correctly identify postmenopausal women living with obesity (BF% > 35). Second, to identify the best indicator of successful weight-loss intervention in postmenopausal women living with obesity.

METHODS: A total of 277 women (age: 59.8 ± 5.3 y; BF%: 43.4 ± 5.3) from five weight-loss studies with complete data for anthropometric measurements [BMI = weight/height (kg/m2); WC (cm)] and BF% were pooled together. Statistical performance indicators were determined to assess ability of RFM [64-(20 × height/waist circumference) + (12 × sex)], BMI and WC to estimate BF% before and after weight-loss intervention and to correctly identify postmenopausal women living with obesity.

RESULTS: Compared with RFM (r = 0.51; r2 = 0.27; RMSE = 4.4%; Lin’s CCC = 0.46) and WC (r = 0.49; r2 = 0.25; RMSE = 4.8%; Lin’s CCC = 0.41), BMI (r = 0.73; r2 = 0.52; RMSE = 3.7%; Lin’s CCC = 0.71) was the best anthropometric index to estimate DXA-BF% and correctly identify postmenopausal women living with obesity (sensitivity + specificity: BMI = 193; RFM = 152; WC = 158), with lower misclassification error, before weight-loss intervention. After weight-loss, the change in BMI was strongly correlated with change in DXA-BF%, indicating that the BMI is the best indicator of success weight-loss intervention.

CONCLUSION: In the absence of more objective measures of adiposity, BMI is a suitable proxy measure for BF% in postmenopausal women, for whom a lifestyle intervention is relevant. Furthermore, BMI can be used as an indicator to assess success of weight-loss intervention in this subpopulation.

PMID:33651744 | DOI:10.1097/GME.0000000000001754

J Strength Cond Res. 2021 Mar 2. doi: 10.1519/JSC.0000000000004001. Online ahead of print.

ABSTRACT

Gannon, EA, Higham, DG, Gardner, BW, Nan, N, Zhao, J, and Bisson, LJ. Changes in neuromuscular status across a season of professional men’s ice hockey. J Strength Cond Res XX(X): 000-000, 2021-To quantify changes in neuromuscular function over a full professional men’s ice hockey season, 27 players (n = 18 forwards and 9 defensemen) performed 3 countermovement jumps (CMJ) each week over 30 sessions separated into 4 phases: preseason, early-season, midseason, and late-season. Outcome variables represented jump performance (jump height), kinematics (mean velocity and peak velocity), and movement strategy (countermovement depth). Mixed models characterized relationships between positional group, season phase, and CMJ outcomes. Statistical significance was set at p ≤ 0.05. Concentric peak velocity (p = 0.02), jump height (p = 0.001), and countermovement depth (p < 0.001) displayed a significant reduction across the season. Peak velocity was lower during the early-season than the preseason (-0.10 ± 0.06 m·s-1, mean change ± 95% confidence limit, p = 0.05). Countermovement depth was reduced during the early-season (-0.06 ± 0.03 m, p = 0.02), midseason (-0.10 ± 0.04 m, p = 0.002), and late-season (-0.15 ± 0.04 m, p < 0.001) relative to the preseason. Reductions in CMJ variables from preseason to in-season ranged from trivial to large. Changes in countermovement depth differed for forwards and defensemen by the season phase (p = 0.04). A professional ice hockey season decreases CMJ performance, with the effects of fatigue most prominent during the late-season phase. Countermovement depth was most sensitive to fatigue and differentiated positional-group responses. Frequent CMJ testing is useful for identifying the neuromuscular status of team-sport athletes relative to season-specific phases. Fatigue monitoring should incorporate movement-strategy variables alongside traditional measures of performance and kinematics.

PMID:33651739 | DOI:10.1519/JSC.0000000000004001

Shock. 2021 Feb 25. doi: 10.1097/SHK.0000000000001766. Online ahead of print.

ABSTRACT

BACKGROUND: The weekend effect is the increased mortality in hospitalized patients admitted on the weekend. The aim of this study was to examine the effect of weekend admissions on septic shock patients.

METHODS: This is a retrospective observational study of the 2014 Nationwide Emergency Department Sample Database (NEDS). Septic shock patients were included in this study using ICD-9-CM codes. Descriptive analysis was done, in addition to bivariate analysis to compare variables based on admission day. Multivariate analysis was conducted to examine the association between admission day and mortality in septic shock patients after adjusting for potential confounding factors.

RESULTS: A total of 364,604 septic shock patients were included in this study. The average age was 67.19 years, and 51.1% were males. 73.0% of patients presented on weekdays. 32.3% of septic shock patients died during their hospital stay. After adjusting for confounders, there was no significant difference in the ED or in-hospital mortality of septic shock patients admitted on the weekend compared to those admitted during weekdays, (OR = 1.00 [95%CI: 0.97 – 1.03], p-value = 0.985).

CONCLUSION: There was no statistically significant difference in overall mortality between septic shock patients admitted on the weekend or weekday. Our results are contradictory to previous studies showing an increased mortality with the weekend effect. The previous observations which have been made may not stand up with current treatment protocols.

PMID:33651724 | DOI:10.1097/SHK.0000000000001766

J Med Internet Res. 2021 Feb 17. doi: 10.2196/24883. Online ahead of print.

ABSTRACT

BACKGROUND: Effective communication during a health crisis can ease public concerns and promote the adoption of important risk-mitigating behaviors. Public health agencies and leaders have served as the primary communicators of information related to COVID-19 and a key part of their public outreach has taken place on social media platforms.

OBJECTIVE: This study examined the content and engagement of COVID-19 Tweets authored by Canadian public health agencies and decision-makers. We propose ways that public health accounts can adjust their Tweeting practices during public health crises to improve risk communication and maximize engagement.

METHODS: We retrieved data from Tweets by Canadian public health agencies and decision-makers from January 1st, 2020 to June 30th, 2020. The Twitter accounts were categorized as either a public health agency, regional/local health department, provincial health authority, medical health officer, or minister of health. We analyzed trends in COVID-19 Tweet engagement and conducted a content analysis on a stratified random sample of 485 Tweets to examine the message functions and risk communication strategies used by each account type.

RESULTS: We analyzed 32,737 Tweets authored by 111 Canadian public health Twitter accounts, of which 6,982 Tweets were about COVID-19. Medical health officers authored the largest percentage of COVID-related Tweets (35%) relative to total Tweets, and averaged the highest number of retweets per COVID-19 Tweet (60 retweets per Tweet). Public health agencies had the highest frequency of daily Tweets about COVID-19 throughout the study period. Compared to Tweets containing media and user-mentions, hashtags and URLs were used in Tweets more frequently by all account types, appearing in 69% and 68% of COVID-related Tweets, respectively. Tweets containing hashtags also received the highest average retweets (47 retweets per Tweet). Our content analysis revealed that of the three Tweet message functions analyzed (information, action, community), Tweets providing information constituted the largest share (42%); however, Tweets promoting actions from users received higher average retweets (55 retweets per Tweet). When examining Tweets that received one or more retweet (n=359), the difference between mean retweets across the message functions was statistically significant (P<.001). Risk communication strategies that we examined were not widely used by any account type, only appearing in 262 out of 485 Tweets. However, when these strategies were used, these Tweets received more retweets compared to Tweets that did not use any risk communication strategies (P<.001) (61 retweets versus 13 retweets on average).

CONCLUSIONS: Public health agencies and decision-makers ought to examine what messaging best meets the needs of their Twitter audiences to maximize shares of their communications. Public health accounts that do not currently employ risk communication strategies in their Tweets may be missing an important opportunity to engage with information users about the mitigation of health risks related to COVID-19.

PMID:33651705 | DOI:10.2196/24883