Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Silver Diamine Fluoride Protocol for Reducing Preventable Dental Hospitalisations in Victorian Children

IntroductionThis study was designed to assess whether a dental caries management protocol combining a single application of 38% silver diamine fluoride (SDF) with comprehensive oral health education will successfully divert high-risk children from dental treatment under dental general anaesthesia (DGA), arrest active caries in primary teeth, and improve parent-reported child oral health–related quality of life (OHRQoL).MethodsChildren aged 2 to 10 years, who attended two public dental agencies in Victoria, Australia, and were unable to tolerate restorative treatments in the clinic setting, elected to participate in either a 38% SDF intervention protocol or, alternatively, referral for DGA. Follow-up examinations were completed at 6 months to assess caries progression, decayed missing filled tooth index, PUFA index (pulpal involvement, ulceration, fistula, abscess), DGA referral rates, and OHRQoL (Early Childhood Oral Health Impact Scale [ECOHIS]).ResultsOf the total sample, 89.5% of children (n = 102) [mean (SD) age, 4.1 (1.0) years] with 401 active carious lesions elected to participate in the 38% SDF protocol; 10.5% (n = 12) of parents opted for referral for treatment under DGA. The proportion of active caries subsequently arrested at follow-up (number of arrested lesions/number of lesions treated) was 0.78 (95% CI, 0.69 to 0.87). There was an 88% reduction in referrals for DGA in eligible children over the 6-month period. The 38% SDF intervention group showed a significant improvement in ECOHIS scores at follow-up (P < .001).DiscussionAdoption of the 38% SDF intervention protocol resulted in a significant reduction in the rate of preventable dental hospitalisations. Most parents opted against referral for DGA. Parent-reported OHRQoL for children improved significantly.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Obstetric analgesia and anaesthesia in SARS-CoV-2-positive parturients across 10 maternity units in the north-west of England: a retrospective cohort study

Since the start of the COVID-19 pandemic, few studies have reported anaesthetic outcomes in parturients with SARS-CoV-2 infection. We reviewed the labour analgesic and anaesthetic interventions utilised in symptomatic and asymptomatic parturients who had a confirmed positive test for SARS-CoV-2 across 10 hospitals in the north-west of England between 1 April 2020 and 31 May 2021. Primary outcomes analysed included the analgesic/anaesthetic technique utilised for labour and caesarean birth. Secondary outcomes included a comparison of maternal characteristics, caesarean birth rate, maternal critical care admission rate along with adverse composite neonatal outcomes. A positive SARS-CoV-2 test was recorded in 836 parturients with 263 (31.4%) reported to have symptoms of COVID-19. Neuraxial labour analgesia was utilised in 104 (20.4%) of the 509 parturients who went on to have a vaginal birth. No differences in epidural analgesia rates were observed between symptomatic and asymptomatic parturients (OR 1.03, 95%CI 0.64–1.67; p = 0.90). The neuraxial anaesthesia rate in 310 parturients who underwent caesarean delivery was 94.2% (95%CI 90.6–96.0%). The rates of general anaesthesia were similar in symptomatic and asymptomatic parturients (6% vs. 5.7%; p = 0.52). Symptomatic parturients were more likely to be multiparous (OR 1.64, 95%CI 1.19–2.22; p = 0.002); of Asian ethnicity (OR 1.54, 1.04–2.28; p = 0.03); to deliver prematurely (OR 2.16, 95%CI 1.47–3.19; p = 0.001); have a higher caesarean birth rate (44.5% vs. 33.7%; OR 1.57, 95%CI 1.16–2.12; p = 0.008); and a higher critical care utilisation rate both pre- (8% vs. 0%, p = 0.001) and post-delivery (11% vs. 3.5%; OR 3.43, 95%CI 1.83–6.52; p = 0.001). Eight neonates tested positive for SARS-CoV-2 while no differences in adverse composite neonatal outcomes were observed between those born to symptomatic and asymptomatic mothers (25.8% vs. 23.8%; OR 1.11, 95%CI 0.78–1.57; p = 0.55). In women with COVID-19, non-neuraxial analgesic regimens were commonly utilised for labour while neuraxial anaesthesia was employed for the majority of caesarean births. Symptomatic women with COVID-19 are at increased risk of significant maternal morbidity including preterm birth, caesarean birth and peripartum critical care admission.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

Extubación ultra fast-track vs. convencional tras cirugía cardiaca en un centro de referencia cardiovascular en Colombia. Estudio longitudinal

Introduction

The fast track / ultra-fast-track protocols are techniques used to optimise the patient care process and a quick recovery after cardiac surgery. They are one of the mainstays of efficient practice. With their use, the length of hospital and intensive care unit (ICU) stays are reduced, with a direct impact on costs and the quality of the health service.

Multidimensional Ultrasound Doppler Signal Analysis for Fetal Activity Monitoring

Fetal activity parameters such as movements, heart rate and the related parameters are essential indicators of fetal wellbeing, and no device provides simultaneous access to and sufficient estimation of all of these parameters to evaluate fetal health. This work was aimed at collecting these parameters to automatically separate healthy from compromised fetuses. To achieve this goal, we first developed a multi-sensor–multi-gate Doppler system. Then we recorded multidimensional Doppler signals and estimated the fetal activity parameters via dedicated signal processing techniques. Finally, we combined these parameters into four sets of parameters (or four hyper-parameters) to determine the set of parameters that is able to separate healthy from other fetuses. To validate our system, a data set consisting of two groups of fetal signals (normal and compromised) was established and provided by physicians. From the estimated parameters, an instantaneous Manning-like score, referred to as the ultrasonic score, was calculated and was used together with movements, heart rate and the associated parameters in a classification process employing the support vector machine method. We investigated the influence of the sets of parameters and evaluated the performance of the support vector machine using the computation of sensibility, specificity, percentage of support vectors and total classification error. The sensitivity of the four sets ranged from 79% to 100%. Specificity was 100% for all sets. The total classification error ranged from 0% to 20%. The percentage of support vectors ranged from 33% to 49%. Overall, the best results were obtained with the set of parameters consisting of fetal movement, short-term variability, long-term variability, deceleration and ultrasound score. The sensitivity, specificity, percentage of support vectors and total classification error of this set were respectively 100%, 100%, 35% and 0%. This indicated our ability to separate the data into two sets (normal fetuses and pathologic fetuses), and the results highlight the excellent match with the clinical classification performed by the physicians. This work indicates the feasibility of detecting compromised fetuses and also represents an interesting method of close fetal monitoring during the entire pregnancy.     

Dexamethasone as an adjuvant for peripheral nerve blockade: a randomised,triple-blinded crossover study in volunteers

Background

The efficacy of dexamethasone in extending the duration of local anaesthetic block is uncertain. In a randomised controlled triple blind crossover study in volunteers, we tested the hypothesis that neither i.v. nor perineurally administered dexamethasone prolongs the sensory block achieved with ropivacaine.