Unusual clinical spectra of childhood severe malaria during malaria epidemic in eastern Uganda: a prospective study.

BACKGROUND: In sub-Saharan Africa (SSA), malaria remains a public health problem despite recent reports of declining incidence. Severe malaria is a multiorgan disease with wide-ranging clinical spectra and outcomes that have been reported to vary by age, geographical location, transmission intensity over time. There are reports of recent malaria epidemics or resurgences, but few data, if any, focus on the clinical spectrum of severe malaria during epidemics. This describes the clinical spectrum and outcomes of childhood severe malaria during the disease epidemic in Eastern Uganda. METHODS: This prospective cohort study from October 1, 2021, to September 7, 2022, was nested within the 'Malaria Epidemiological, Pathophysiological and Intervention studies in Highly Endemic Eastern Uganda' (TMA2016SF-1514-MEPIE Study) at Mbale Regional Referral Hospital, Uganda. Children aged 60 days to 12 years who at admission tested positive for malaria and fulfilled the clinical WHO criteria for surveillance of severe malaria were enrolled on the study. Follow-up was performed until day 28. Data were collected using a customized proforma on social demographic characteristics, clinical presentation, treatment, and outcomes. Laboratory analyses included complete blood counts, malaria RDT (SD BIOLINE Malaria Ag P.f/Pan, Ref. 05FK60-40-1) and blood slide, lactate, glucose, blood gases and electrolytes. In addition, urinalysis using dipsticks (Multistix® 10 SG, SIEMENS, Ref.2300) at the bedside was done. Data were analysed using STATA V15.0. The study had prior ethical approval. RESULTS: A total of 300 participants were recruited. The median age was 4.6 years, mean of 57.2 months and IQR of 44.5 months. Many children, 164/300 (54.7%) were under 5 years, and 171/300 (57.0%) were males. The common clinical features were prostration 236/300 (78.7%), jaundice in 205/300 (68.3%), severe malarial anaemia in 158/300 (52.7%), black water fever 158/300 (52.7%) and multiple convulsions 51/300 (17.0%), impaired consciousness 50/300(16.0%), acidosis 41/300(13.7%), respiratory distress 26/300(6.7%) and coma in 18/300(6.0%). Prolonged hospitalization was found in 56/251 (22.3%) and was associated with acidosis, P = 0.041. The overall mortality was 19/300 (6.3%). Day 28 follow-up was achieved in 247/300 (82.3%). CONCLUSION: During the malaria epidemic in Eastern Uganda, severe malaria affected much older children and the spectrum had more of prostration, jaundice severe malarial anaemia, black water fever and multiple convulsions with less of earlier reported respiratory distress and cerebral malaria.

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.

BACKGROUND: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. METHODS: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190-220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). RESULTS: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1-5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. CONCLUSION: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals.

BACKGROUND: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. METHODS: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. RESULTS: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. CONCLUSIONS: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

Use of menstruation and fertility app trackers: a scoping review of the evidence.

INTRODUCTION: There has been a phenomenal worldwide increase in the development and use of mobile health applications (mHealth apps) that monitor menstruation and fertility. Critics argue that many of the apps are inaccurate and lack evidence from either clinical trials or user experience. The aim of this scoping review is to provide an overview of the research literature on mHealth apps that track menstruation and fertility. METHODS: This project followed the PRISMA Extension for Scoping Reviews. The ACM, CINAHL, Google Scholar, PubMed and Scopus databases were searched for material published between 1 January 2010 and 30 April 2019. Data summary and synthesis were used to chart and analyse the data. RESULTS: In total 654 records were reviewed. Subsequently, 135 duplicate records and 501 records that did not meet the inclusion criteria were removed. Eighteen records from 13 countries form the basis of this review. The papers reviewed cover a variety of disciplinary and methodological frameworks. Three main themes were identified: fertility and reproductive health tracking, pregnancy planning, and pregnancy prevention. CONCLUSIONS: Motivations for fertility app use are varied, overlap and change over time, although women want apps that are accurate and evidence-based regardless of whether they are tracking their fertility, planning a pregnancy or using the app as a form of contraception. There is a lack of critical debate and engagement in the development, evaluation, usage and regulation of fertility and menstruation apps. The paucity of evidence-based research and absence of fertility, health professionals and users in studies is raised.

Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.

BACKGROUND: Stress echocardiography is a well-established diagnostic tool for suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of stress echocardiography and patients' variables including risk factors, current medication, and anthropometric variables has not been widely investigated. OBJECTIVE: This study aimed to use machine learning to predict significant CAD defined by positive stress echocardiography results in patients with chest pain based on anthropometrics, cardiovascular risk factors, and medication as variables. This could allow clinical prioritization of patients with likely prediction of CAD, thus saving clinician time and improving outcomes. METHODS: A machine learning framework was proposed to automate the prediction of stress echocardiography results. The framework consisted of four stages: feature extraction, preprocessing, feature selection, and classification stage. A mutual information-based feature selection method was used to investigate the amount of information that each feature carried to define the positive outcome of stress echocardiography. Two classification algorithms, support vector machine (SVM) and random forest classifiers, have been deployed. Data from 529 patients were used to train and validate the framework. Patient mean age was 61 (SD 12) years. The data consists of anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolemia, prior diagnosis of CAD, and prescribed medications at the time of the test. There were 82 positive (abnormal) and 447 negative (normal) stress echocardiography results. The framework was evaluated using the whole dataset including cases with prior diagnosis of CAD. Five-fold cross-validation was used to validate the performance of the framework. We also investigated the model in the subset of patients with no prior CAD. RESULTS: The feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin-converting enzyme inhibitor/angiotensin receptor blocker were the features that shared the most information about the outcome of stress echocardiography. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. Using only these three features, we achieved an accuracy of 67.63% with sensitivity and specificity 72.87% and 66.67% respectively. However, for patients with no prior diagnosis of CAD, only two features (sex and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%. CONCLUSIONS: This study shows that machine learning can predict the outcome of stress echocardiography based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent stress echocardiography could further improve the performance of the proposed algorithm with the potential of facilitating patient selection for early treatment/intervention avoiding unnecessary downstream testing.

Mobile Self-Monitoring ECG Devices to Diagnose Arrhythmia that Coincide with Palpitations: A Scoping Review.

The use and deployment of mobile devices across society is phenomenal with an increasing number of individuals using mobile devices to track their everyday health. However, there is a paucity of academic material examining this recent trend. Specifically, little is known about the use and deployment of mobile heart monitoring devices for measuring palpitations and arrhythmia. In this scoping literature review, we identify the contemporary evidence that reports the use of mobile heart monitoring to assess palpitations and arrhythmia across populations. The review was conducted between February and March 2018. Five electronic databases were searched: Association for Computing Machinery (ACM), CINHAL, Google Scholar, PubMed, and Scopus. A total of 981 records were identified and, following the inclusion and exclusion criteria, nine papers formed the final stage of the review. The results identified a total of six primary themes: purpose, environment, population, wearable devices, assessment, and study design. A further 24 secondary themes were identified across the primary themes. These included detection, cost effectiveness, recruitment, type of setting, type of assessment, and commercial or purpose-built mobile device. This scoping review highlights that further work is required to understand the impact of mobile heart monitoring devices on how arrhythmias and palpitations are assessed and measured across all populations and ages of society. A positive trend revealed by this review demonstrates how mobile heart monitoring devices can support primary care providers to deliver high levels of care at a low cost to the service provider. This has several benefits: alleviation of patient anxiety, lowering the risk of morbidity and mortality, while progressively influencing national and international care pathway guidelines. Limitations of this work include the paucity of knowledge and insight from primary care providers and lack of qualitative material. We argue that future studies consider qualitative and mixed methods approaches to complement quantitative methodologies and to ensure all actors' experiences are recorded.

Colostrinin alleviates amyloid-beta induced toxicity in rat primary hippocampal cultures.

Colostrinin (CLN), a complex mixture of proline-rich polypeptides derived from colostrums, can alleviate cognitive decline in early Alzheimer's disease patients. The molecular basis of the action of CLN has been studied in vitro using human neuroblastoma cell lines. The aim of the present study was to use quantitative immunocytochemistry and immunoblotting to investigate the ability of CLN to relieve amyloid-beta (Abeta)-induced cytotoxicity in rat primary hippocampal neuronal cells. Our data confirm that CLN alleviates the effect of Abeta-induced cytotoxicity and causes a significant reduction in the elevated levels of the antioxidant enzyme SOD1.

Enhancement of long-term memory retention by Colostrinin in one-day-old chicks trained on a weak passive avoidance learning paradigm.

Colostrinin (CLN) is a biologically active proline-rich polypeptide which has therapeutic potential for the alleviation of memory deficits in age-related dementias in a number of human conditions, particularly Alzheimer's disease. To examine the efficacy of CLN in other species, day-old domestic chicks were used as a model system to study its effects on retention of memory for a single one-trial learning paradigm--avoidance of a bitter-tasting substance (methylanthranilate, MeA). Birds were presented with a bead coated with either a dilute (10%) solution of MeA or a bead coated with 100% MeA. Those trained on 100% MeA avoided pecking at a similar but dry bead 24 h later, thereby demonstrating long-term memory whereas chicks trained on the 10% solution pecked the bead at 24 h, indicating lack of long term memory for the task. However, when CLN was injected (i.c.) into a region known to be important in memory formation, the mesopallium intermediomediale (IMM), prior to training with 10% MeA, chicks exhibited strong memory retention at 24 h, similar to those trained on 100% MeA. Control chicks trained on 10% MeA but injected i.c. with a 10% saline solution did not show improvement in memory retention. Intraperitoneal (i.p.) injections of CLN were as effective as the i.c. route. These data extend the known efficacy of CLN from mammals demonstrating its widespread efficacy as a cognitive enhancer.