Does dental students' attendance in classroom lectures depend on the mode of attendance tracking?

Purpose: The necessity to attend classroom lectures is a disputable topic among dental schools globally. Since there is an ongoing debate on different aspects of this problem in literature, the purpose of this study was to compare students’ attitudes toward classroom attendance and investigate if stricter attendance tracking methods could lead to better classroom attendance at two dental schools utilising different modes of tracking students’ attendance. Method: This was an observational, cross-sectional survey distributed among dental students enrolled at King Abdul-Aziz University (KAU) and King Saud University (KSU) in Saudi Arabia. The survey included questions on demographics, average travel time, student's attitudes toward classroom lectures and common reasons for absenteeism. Collected data were analysed and summarised as frequencies and percentages and then compared using the Chi-square test for statistical significance. Findings: The study involved 678 participants from KAU and 475 participants from KSU. In general, there was a significant difference in students’ attendance between both schools in which 26.8% of KAU dental students skipped 5 or more lectures/month compared to 11.5% of students at KSU. Among the factors affecting classroom lecture attendance, commuting time was a major one reported by students (44.8% of students at KSU and 51.4% at KAU needed 30-60 min to reach their schools). The availability of lectures through online resources and the necessity to study for exams were additional factors reported by students of both schools. Implications for research and practices: Based on the current data, the school’s method to track students’ attendance may have a role in the pattern of classroom absenteeism

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

ICB-DCM/pyABC: Release 0.12.14

&lt;p&gt;Visualization:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Selected plotly versions of matplotlib visualizations&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;General:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Added functionality to evaluate the model using boundary values of parameter&lt;/li&gt; &lt;/ul&gt

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

&lt;p&gt;Supplementary Data to the publication &quot;FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes&quot;, Alamoudi&nbsp;et al. 2023.&lt;/p&gt

Run-time and posterior approximation for 5 different runs of model T1 with STAT, DYN, LA Prel and LA Past, with population size <i>N</i> = 100 on <i>W</i> = 144, 240, 432 workers (top to bottom).

Run-time and posterior approximation for 5 different runs of model T1 with STAT, DYN, LA Prel and LA Past, with population size N = 100 on W = 144, 240, 432 workers (top to bottom).</p

Concept visualization of look-ahead scheduling (LA).

As soon as no more simulations are required for generation t − 1 (green), a preliminary simulation task for generation t is formulated either based on population Pt−2 (grey, LA Past) or the preliminary population Pt−1 (purple, LA Prel). Resulting simulations are considered when evaluating the next generation, and suitable weight normalization is applied to all samples (top right). Over time, the number of workers dedicated to generation t − 1 decreases, while that for generation t increases (bottom).</p

Speed-up (1 − {Wall- time LA}/{Wall- time DYN}) of LA Prel (left) and LA Past (right) over DYN for various population sizes and numbers of workers, for a model sleep time variance of <i>σ</i>² = 1.

Speed-up (1 − {Wall- time LA}/{Wall- time DYN}) of LA Prel (left) and LA Past (right) over DYN for various population sizes and numbers of workers, for a model sleep time variance of σ2 = 1.</p