MICROORGANISMS ON HUMAN-COMPUTER INTERFACES: AN EXAMINATION OF AUTOMATED TELLER MACHINES AND CYBERCAFÉS IN ILESA AND IJEBU-JESA, NIGERIA.

This study examined ATMs and keyboards in public cybercafés of Ilesa & Ijebu-jesa, Nigeria for the presence of bacteria of health importance.Ten ATMs in Ilesa and 10 cybercafés each in Ilesa & Ijebu-jesa were sampled at random and cultured by standard procedures. Total bacterial counts and coliform counts were recorded. The susceptibility of isolates to commercially available antibiotics was assayed by the disk diffusion method.Total bacterial counts from ATM surfaces in Ilesa ranged from 2.80 x 105 CFU/Ml to 1.90 x 105 CFU/Ml and the mean count was 2.08 x 105 CFU/Ml. The mean heterotrophic count of keyboards from Ilesa was 2.01 x 105 CFU/Ml while the mean coliform count was 1.50 x  103 CFU/Ml. In Ijebu-jesa, the maximum bacterial count was observed on the 4th keyboard sampled; also the only keyboard from which coliform bacteria was isolated (1.00 x 103 CFU/Ml). The mean heterotrophic count of keyboards in this area was 2.42 x 105 CFU/Ml. Staphylococcus aureus made up 50 % of isolates from ATMs in Ilesa, 90 % of those from cybercafés in Ilesa and 20 % of cybercafés in Ijebu-jesa. Pseudomonas sp. was 10 % of isolates from ATMs surfaces in Ilesa and keyboards in Ijebu-jesa cybercafés and 30 % of those from keyboards in Ilesa. Isolates were 53 % susceptible to common antibiotics. The study concluded there was high presence of bacteria on the surfaces of ATMs and keyboards in Ilesa and Ijebu-jesa, some of which were potentially pathogenic and drug resistant

Relationship between bacterial density and chemical composition of a tropical sewage oxidation pond

Studies were carried out to examine the performance of the sewage oxidation pond situated in and serving the community of the Obafemi Awolowo University, Ile-Ife, Nigeria. A survey of the coliform and total bacterial populations was carried out. The sewage was also examined for biochemical oxygen demand, dissolved oxygen content as well as for nitrate, phosphate, silica and chloride contents. The mean coliform bacteria counts decreased gradually from 69.1×105 per 100 ml to about 10.1×105 per 100 ml as the sewage moved through the oxidation pond into the receiving stream. A similar decrease in mean biochemical oxygen demand of the sewage from 397.8 Ib/acre/day to 64.2 Ib/acre/day was also observed. The concentrations of nitrate, phosphate and chloride decreased from the pond influent to the pond effluent. On the other hand, both the silica and dissolved oxygen content of the sewage gradually increased from 14.1 to 19.0 mg/l and 8.1 to 13.9 mg/l respectively, across the pond to the effluent. The coliform and total bacterial counts as well as the concentrations of most of the chemicals in the receiving stream increased after being joined by the sewage oxidation pond effluent. It is therefore concluded that the receiving stream was subject to both bacteriological and chemical pollution. Building of additional oxidation ponds or addition of a primary sewage treatment to the existing system is recommended for more efficient wastewater treatment.Key words: Bacterial density, chemical composition, oxidation pond, sewage, tropics

Morbidity of Open Tibia Fractures in Lagos, Nigeria

No Abstract

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Percentile reference values for anthropometric body composition indices in European children from the IDEFICS study

INTRODUCTION: To characterise the nutritional status in children with obesity or wasting conditions, European anthropometric reference values for body composition measures beyond the body mass index (BMI) are needed. Differentiated assessment of body composition in children has long been hampered by the lack of appropriate references. OBJECTIVES: The aim of our study is to provide percentiles for body composition indices in normal weight European children, based on the IDEFICS cohort (Identification and prevention of Dietary-and lifestyle-induced health Effects in Children and infantS). METHODS: Overall 18 745 2.0-10.9-year-old children from eight countries participated in the study. Children classified as overweight/obese or underweight according to IOTF (N = 5915) were excluded from the analysis. Anthropometric measurements (BMI (N = 12 830); triceps, subscapular, fat mass and fat mass index (N = 11 845-11 901); biceps, suprailiac skinfolds, sum of skinfolds calculated from skinfold thicknesses (N = 8129-8205), neck circumference (N = 12 241); waist circumference and waist-to-height ratio (N = 12 381)) were analysed stratified by sex and smoothed 1st, 3rd, 10th, 25th, 50th, 75th, 90th, 97th and 99th percentile curves were calculated using GAMLSS. RESULTS: Percentile values of the most important anthropometric measures related to the degree of adiposity are depicted for European girls and boys. Age-and sex-specific differences were investigated for all measures. As an example, the 50th and 99th percentile values of waist circumference ranged from 50.7-59.2 cm and from 51.3-58.7 cm in 4.5-to < 5.0-year-old girls and boys, respectively, to 60.6-74.5 cm in girls and to 59.9-76.7 cm in boys at the age of 10.5-10.9 years. CONCLUSION: The presented percentile curves may aid a differentiated assessment of total and abdominal adiposity in European children

Biodiversity of Prokaryotic Communities Associated with the Ectoderm of Ectopleura crocea (Cnidaria, Hydrozoa)

The surface of many marine organisms is colonized by complex communities of microbes, yet our understanding of the diversity and role of host-associated microbes is still limited. We investigated the association between Ectopleura crocea (a colonial hydroid distributed worldwide in temperate waters) and prokaryotic assemblages colonizing the hydranth surface. We used, for the first time on a marine hydroid, a combination of electron and epifluorescence microscopy and 16S rDNA tag pyrosequencing to investigate the associated prokaryotic diversity. Dense assemblages of prokaryotes were associated with the hydrant surface. Two microbial morphotypes were observed: one horseshoe-shaped and one fusiform, worm-like. These prokaryotes were observed on the hydrozoan epidermis, but not in the portions covered by the perisarcal exoskeleton, and their abundance was higher in March while decreased in late spring. Molecular analyses showed that assemblages were dominated by Bacteria rather than Archaea. Bacterial assemblages were highly diversified, with up to 113 genera and 570 Operational Taxonomic Units (OTUs), many of which were rare and contributed to <0.4%. The two most abundant OTUs, likely corresponding to the two morphotypes present on the epidermis, were distantly related to Comamonadaceae (genus Delftia) and to Flavobacteriaceae (genus Polaribacter). Epibiontic bacteria were found on E. crocea from different geographic areas but not in other hydroid species in the same areas, suggesting that the host-microbe association is species-specific. This is the first detailed report of bacteria living on the hydrozoan epidermis, and indeed the first study reporting bacteria associated with the epithelium of E. crocea. Our results provide a starting point for future studies aiming at clarifying the role of this peculiar hydrozoan-bacterial association

Evaluation of energy and dietary intake estimates from a food frequency questionnaire using independent energy expenditure measurement and weighed food records

<p>Abstract</p> <p>Background</p> <p>We have developed a food frequency questionnaire (FFQ) for the assessment of habitual diet, with special focus on the intake of fruit, vegetables and other antioxidant-rich foods and beverages. The aim of the present study was to evaluate the relative validity of the intakes of energy, food and nutrients from the FFQ.</p> <p>Methods</p> <p>Energy intake was evaluated against independent measures of energy expenditure using the ActiReg^®system (motion detection), whereas 7-days weighed food records were used to study the relative validity of food and nutrient intake. The relationship between methods was investigated using correlation analyses and cross-classification of participants. The visual agreement between the methods was evaluated using Bland-Altman plots.</p> <p>Results</p> <p>We observed that the FFQ underestimated the energy intake by approximately 11% compared to the energy expenditure measured by the ActiReg^®. The correlation coefficient between energy intake and energy expenditure was 0.54 and 32% of the participants were defined as under-reporters. Compared to the weighed food records the percentages of energy from fat and added sugar from the FFQ were underestimated, whereas the percentage of energy from total carbohydrates and protein were slightly overestimated. The intake of foods rich in antioxidants did not vary significantly between the FFQ and weighed food records, with the exceptions of berries, coffee, tea and vegetables which were overestimated. Spearman's Rank Order Correlations between FFQ and weighed food records were 0.41 for berries, 0.58 for chocolate, 0.78 for coffee, 0.61 for fruit, 0.57 for fruit and berry juices, 0.40 for nuts, 0.74 for tea, 0.38 for vegetables and 0.70 for the intake of wine.</p> <p>Conclusions</p> <p>Our new FFQ provides a good estimate of the average energy intake and it obtains valid data on average intake of most antioxidant-rich foods and beverages. Our study also showed that the FFQs ability to rank participants according to intake of total antioxidants and most of the antioxidant-rich foods was good.</p

A copula model for marked point processes

The final publication (Diao, Liqun, Richard J. Cook, and Ker-Ai Lee. (2013) A copula model for marked point processes. Lifetime Data Analysis, 19(4): 463-489) is available at Springer via http://dx.doi.org/10.1007/s10985-013-9259-3Many chronic diseases feature recurring clinically important events. In addition, however, there often exists a random variable which is realized upon the occurrence of each event reflecting the severity of the event, a cost associated with it, or possibly a short term response indicating the effect of a therapeutic intervention. We describe a novel model for a marked point process which incorporates a dependence between continuous marks and the event process through the use of a copula function. The copula formulation ensures that event times can be modeled by any intensity function for point processes, and any multivariate model can be specified for the continuous marks. The relative efficiency of joint versus separate analyses of the event times and the marks is examined through simulation under random censoring. An application to data from a recent trial in transfusion medicine is given for illustration.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626