METHODS: This observational research used a spatiotemporal approach. We obtained CRS and rubella surveillance data from Dr. Sardjito Hospital, Provincial, and District Health Offices in Yogyakarta, Indonesia during January-April 2019. The home addresses of rubella and CRS cases were geocoded using the Global Positioning System. Average of the nearest neighbour and space-time permutation analyses were conducted to discover the spatiotemporal patterns and clusters of rubella and CRS cases.
RESULTS: The peak of rubella cases occurred in 2017 (IR: 22.3 per 100,000 population). Twelve confirmed cases of CRS were found in the 2016-2018 period (IR: 0.05 per 1,000 live births). The occurrence of CRS in Yogyakarta was detected 6-8 months after the increase and peak of rubella cases. The spatiotemporal analysis showed that rubella cases were mostly clustered, while CRS cases were distributed in a dispersed pattern. Rubella cases were found within a buffer zone of 2.5 km from any CRS case.
CONCLUSIONS: Rubella cases were spatiotemporally associated with the occurrence of CRS in Yogyakarta. We recommend strengthening the surveillance system of CRS and rubella cases in order to contain any further spreading of the disease.
METHODS: This pre-post, single-arm, quasi-experimental study randomly sampled 140 healthcare providers working in the Emergency Department of Hospital Ampang, Malaysia. Parameters of CPR quality, namely chest compression rate and depth were compared among participants when they performed CPR with and without an AV CPR feedback device. The efficacy of the AV CPR feedback device was assessed using the Chi-square test and Generalised Estimating Equations (GEE) models.
RESULTS: The use of an AV CPR feedback device increased the proportion of healthcare providers achieving recommended depth of chest compressions from 38.6% (95% Confidence Interval, 95%CI: 30.5, 47.2) to 85.0% (95%CI: 78.0, 90.5). A similar significant improvement from 39.3% (95%CI: 31.1, 47.9) to 86.4% (95%CI: 79.6, 91.6) in the recommended rate of chest compressions was also observed. Use of the AV CPR device significantly increased the likelihood of a CPR provider achieving recommended depth of chest compressions (Odds Ratio, OR=13.01; 95%CI: 7.12, 24.01) and rate of chest compressions (OR=13.00; 95%CI: 7.21, 23.44).
CONCLUSION: The use of an AV CPR feedback device significantly improved the delivered rate and depth of chest compressions closer to American Heart Association (AHA) recommendations. Usage of such devices within real-life settings may help in improving the quality of CPR for patients receiving CPR.
METHODS: Observing anti-urolithiathic activity via in vitro nucleation and aggregation assay using a spectrophotometer followed by microscopic observation. A total of 12 methanolic extracts were tested to determine the potential extracts in anti-urolithiasis activities. Cystone was used as a positive control.
RESULTS: The results manifested an inhibition of nucleation activity (0.11 ± 2.32% to 55.39 ± 1.01%) and an aggregation activity (4.34 ± 0.68% to 58.78 ± 1.81%) at 360 min of incubation time. The highest inhibition percentage in nucleation assay was obtained by the Musa acuminate x balbiciana Colla cv "Awak Legor" methanolic pseudo-stem extract (2D) which was 55.39 ± 1.01%at 60 min of incubation time compared to the cystone at 30.87 ± 0.74%. On the other hand,the Musa acuminate x balbiciana Colla cv "Awak Legor" methanolic bagasse extract (3D) had the highest inhibition percentage in the aggregation assay incubated at 360 min which was obtained at 58.78 ± 1.8%; 5.53% higher than the cystone (53.25%).The microscopic image showed a great reduction in the calcium oxalate (CaOx) crystals formation and the size of crystals in 2D and 3D extracts, respectively, as compared to negative control.
CONCLUSIONS: The results obtained from this study suggest that the extracts are potential sources of alternative medicine for kidney stones disease.
METHODS AND RESULTS: Oral health assessment included dental caries and dental plaque maturity scores (DPMS) while the nutritional assessment included children's height-for-age Z-score (HAZ), body mass index-for-age Z-score (BAZ), mid-upper-arm circumference (MUAC), nutrient intake, cariogenic food frequency (CFF) and daily sugar exposure (DSE). Ninety-three CP children were recruited. The prevalence of caries was 81.7% (95% CI: 72.7%-88.3%). The median (IQR) of the DMFT and dft scores were 0.5(4.0) and 3.0(8.0), respectively. Most of the participants had acid-producing plaque (90.3%), severely stunted (81.4%), and 45% were severely thin with acute malnutrition. Intakes of calcium, iron, zinc, vitamin A, vitamin D and total fat were below 77% of the Recommended Nutrient Intakes for Malaysian children (RNI 2017). Nine types of cariogenic foods/drinks were consumed moderately, and DSE indicated that 45% of the children were at moderate risk of dental caries.
CONCLUSION: Untreated dental caries, severe stunting and thinness were prevalent, and cariogenic foods/drinks were consumed moderately suggesting a moderate risk of caries. Therefore, controlling cariogenic food intake is crucial, but monitoring daily nutrient intake is needed for the optimum growth of children with CP.
OBJECTIVE: This article aims to evaluate current artificial intelligence applications and discuss their performance concerning the algorithm architecture used in forensic odontology.
METHODS: This study summarizes the findings of 28 research papers published between 2010 and June 2022 using the Arksey and O'Malley framework, updated by the Joanna Briggs Institute Framework for Scoping Reviews methodology, highlighting the research trend of artificial intelligence technology in forensic odontology. In addition, a literature search was conducted on Web of Science (WoS), Scopus, Google Scholar, and PubMed, and the results were evaluated based on their content and significance.
RESULTS: The potential application of artificial intelligence technology in forensic odontology can be categorized into four: (1) human bite marks, (2) sex determination, (3) age estimation, and (4) dental comparison. This powerful tool can solve humanity's problems by giving an adequate number of datasets, the appropriate implementation of algorithm architecture, and the proper assignment of hyperparameters that enable the model to perform the prediction at a very high level of performance.
CONCLUSION: The reviewed articles demonstrate that machine learning techniques are reliable for studies involving continuous features such as morphometric parameters. However, machine learning models do not strictly require large training datasets to produce promising results. In contrast, deep learning enables the processing of unstructured data, such as medical images, which require large volumes of data. Occasionally, transfer learning was used to overcome the limitation of data. In the meantime, this method's capacity to automatically learn task-specific feature representations has made it a significant success in forensic odontology.
METHODS: This narrative review examines various septic markers to identify the appropriate tools for diagnosis and to distinguish between diabetic ketoacidosis with and without infection. Electronic databases were searched using the Google engine with the keywords "Diabetes Mellitus", "Diabetic Ketoacidosis", "Infection with Diabetic Ketoacidosis", "biomarkers for infection in Diabetic Ketoacidosis", "Procalcitonin", "Inflammatory cytokines in DKA", "Lactic acidosis in DKA", and "White blood cell in infection in DKA".
RESULTS: This narrative review article presents the options for diagnosis and also aims to create awareness regarding the gravity of diabetic ketoacidosis with infection and emphasizes the importance of early diagnosis for appropriate management. Diabetes mellitus is a clinical condition that may lead to several acute and chronic complications. Acute diabetic ketoacidosis is a life-threatening condition in which an excess production of ketone bodies results in acidosis and hypovolemia. Infection is one of the most common triggers of diabetic ketoacidosis. When bacterial infection is present along with diabetic ketoacidosis, the mortality rate is even higher than for patients with diabetic ketoacidosis without infection. The symptoms and biomarkers of diabetic ketoacidosis are similar to that of infection, like fever, C reactive protein, and white blood cell count, since both create an environment of systemic inflammation. It is also essential to distinguish between the presence and absence of bacterial infection to ensure the appropriate use of antibiotics and prevent antimicrobial resistance. A bacterial culture report is confirmatory for the existence of bacterial infection, but this may take up to 24 h. Diagnosis needs to be performed approximately in the emergency room upon admission since there is a need for immediate management. Therefore, researching the possible diagnostic tools for the presence of infection in diabetic ketoacidosis patients is of great importance. Several of such biomarkers have been discussed in this research work.