METHODS: This study was a prospective randomized controlled trial conducted from March 2008 to February 2009 in a tertiary referral hospital at Sydney. The primary end point was cecal intubation time and the secondary endpoint was polyp detection rate. Consecutive cases of total colonoscopy over a 1-year period were recruited. Randomization into either standard colonoscopy (SC) or cap-assisted colonoscopy (CAC) was performed after consent was obtained. For cases randomized to CAC, one of the three sizes of cap was used: D-201-15004 (with a diameter of 15.3 mm), D-201-14304 (14.6 mm) and D-201-12704 (13.0 mm). All of these caps were produced by Olympus Medical Systems, Japan. Independent predictors for faster cecal time and better polyp detection rate were also determined from this study.
RESULTS: There were 200 cases in each group. There was no significant difference in terms of demographic characteristics between the two groups. CAC, when compared to the SC group, had no significant difference in terms of cecal intubation rate (96.0% vs 97.0%, P = 0.40) and time (9.94 +/- 7.05 min vs 10.34 +/- 6.82 min, P = 0.21), or polyp detection rate (32.8% vs 31.3%, P = 0.75). On the subgroup analysis, there was no significant difference in terms of cecal intubation time by trainees (88.1% vs 84.8%, P = 0.40), ileal intubation rate (82.5% vs 79.0%, P = 0.38) or total colonoscopy time (23.24 +/- 13.95 min vs 22.56 +/- 9.94 min, P = 0.88). On multivariate analysis, the independent determinants of faster cecal time were consultant-performed procedures (P < 0.001), male patients (P < 0.001), non-usage of hyoscine (P < 0.001) and better bowel preparation (P = 0.01). The determinants of better polyp detection rate were older age (P < 0.001), no history of previous abdominal surgery (P = 0.04), patients not having esophagogastroduodenoscopy in the same setting (P = 0.003), trainee-performed procedures (P = 0.01), usage of hyoscine (P = 0.01) and procedures performed for polyp follow-up (P = 0.01). The limitations of the study were that it was a single-center experience, no blinding was possible, and there were a large number of endoscopists.
CONCLUSION: CAC did not significantly different from SC in term of cecal intubation time and polyp detection rate.
AIM: To assess the diabetes empowerment scores and its correlated factors among type 2 diabetes patients in a primary care clinic in Malaysia.
METHODS: This is a cross sectional study involving 322 patients with type 2 diabetes mellitus (DM) followed up in a primary care clinic. Systematic sampling method was used for patient recruitment. The Diabetes Empowerment Scale (DES) questionnaire was used to measure patient empowerment. It consists of three domains: (1) Managing the psychosocial aspect of diabetes (9 items); (2) Assessing dissatisfaction and readiness to change (9 items); and (3) Setting and achieving diabetes goal (10 items). A score was considered high if it ranged from 100 to 140. Data analysis was performed using SPSS version 25 and multiple linear regressions was used to identify the predictors of total diabetes empowerment scores.
RESULTS: The median age of the study population was 55 years old. 56% were male and the mean duration of diabetes was 4 years. The total median score of the DES was 110 [interquartile range (IQR) = 10]. The median scores of the three subscales were 40 with (IQR = 4) for "Managing the psychosocial aspect of diabetes"; 36 with (IQR = 3) for "Assessing dissatisfaction and readiness to change"; and 34 with (IQR = 5) for "Setting and achieving diabetes goal". According to multiple linear regressions, factors that had significant correlation with higher empowerment scores among type 2 diabetes patients included an above secondary education level (P < 0.001), diabetes education exposure (P = 0.003), lack of ischemic heart disease (P = 0.017), and lower glycated hemoglobin (HbA1c) levels (P < 0.001).
CONCLUSION: Diabetes empowerment scores were high among type 2 diabetes patients in this study population. Predictors for high empowerment scores included above secondary education level, diabetes education exposure, lack of ischemic heart disease status and lower HbA1c.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.
Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.
Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.
Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.
METHODS/DESIGN: This open-labelled, randomised controlled trial (RCT) will randomly allocate patients into intervention and control groups. Ambulated Malaysian aged over 18 years and scheduled for elective surgery for (suspected) GC, will be included in this study. The intervention group will be given whey-protein-infused carbohydrate-loading drinks on the evening before their operation and 3 h before their operation as well as started on early oral feeding 4 h post-operatively. The control group will be fasted overnight pre-operation and only allowed plain water, and return to a normal diet is allowed when bowel sounds return post-operatively. The primary outcomes of study are length of post-operative hospital stay, length of clear-fluid tolerance, solid-food tolerance and bowel function. Additional outcome measures are changes in nutritional status, biochemical profile and functional status. Data will be analysed on an intention-to-treat basis.
TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT03667755. Retrospectively registered on 12 September 2018; Protocol version: version 3 dated 27 September 2017.
METHOD: A generalized linear model (GLM) estimates the relationships between different travel mode indicators (e.g., length of motorway per inhabitants, number of motorcycles per inhabitant, percentage of daily trips on foot and by bicycle, percentage of daily trips by public transport) and the number of passenger transport fatalities. Because this city-level model is developed using data sets from different cities all over the world, the impacts of gross domestic product (GDP) are also included in the model.
CONCLUSIONS: Overall, the results imply that the percentage of daily trips by public transport, the percentage of daily trips on foot and by bicycle, and the GDP per inhabitant have negative relationships with the number of passenger transport fatalities, whereas motorway length and the number of motorcycles have positive relationships with the number of passenger transport fatalities.
METHODS: Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO+ and iCOTM Smokerlyzers®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.
RESULTS: Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO+ Smokerlyzer®, p<0.001; r=0.91 for the iCOTM Smokerlyzer®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCOTM Smokerlyzer® reading of 0.82 (95% CI: 0.69-0.94, p<0.001) compared to that of the piCO+ Smokerlyzer®, and a significant intercept of -0.34 (95% CI: -0.61 - -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCOTM Smokerlyzer® devices.
CONCLUSIONS: The iCOTM Smokerlyzer® readings are highly reproducible compared to those of the piCO+ Smokerlyzer®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.
Methods: The proposed study will be conducted in three phases: Phase I will involve the development of the item-pool to be included in the tool, followed by a face, content validity and construct validity. The tool reliability, readability and difficulty index will be determined. Phase II will involve the utilization of the tool to assess baseline SAV knowledge among the HCPs followed by an educational intervention. Multiple Linear Regression analysis will be used to determine the factors associated with SAV knowledge among the HCPs. Lastly, Phase III which will be a repeat of Phase II to assess and evaluate the knowledge after the intervention.
Discussion: The study design and findings may guide future implementation and streamline the intervention of improving SAV knowledge in HCPs training and practice.
Lay Summary: Knowledge assessment and educational intervention of snake antivenom among healthcare practitioners in northern Nigeria: a study protocol Snakebite envenoming (SBE) is an important occupational and public health hazard especially in sub-Saharan Africa. For optimum management of SBE, adequate knowledge of snake antivenom (SAV) is very critical among the healthcare practitioners. The baseline knowledge SAV dosage, mode of administration, availability, and logistics is very relevant among healthcare professionals, particularly those that are directly involved in its logistics. It is paramount that SAV is handled and used appropriately. The efforts and advocacy for the availability for more SAV will be in vain if not handled appropriately before they are used. This study protocol aims to develop a tool, to assess SAV knowledge and effects of educational interventions among healthcare professionals (HCPs) in northern Nigeria. This protocol suggests conducting studies in three phases: (a) Development and validation of SAV knowledge assessment tool, (b) Baseline assessment of SAV knowledge assessment tool among HCPs, and (c) Development, implementation and evaluation of an educational intervention to improve SAV knowledge among HCPs in northern Nigeria.
METHODS: A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.
RESULTS: The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.
CONCLUSION: The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.