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To evaluate and compare dentinal tubule penetration and push-out bond strength of BIO-C ION+, AH Plus and NanoSeal-S using Confocal Laser Scanning Microscopy (CLSM) and Universal Testing Machine (UTM). Sixty human mandibular premolars were prepared using ProTaper Gold till F3. Samples were then divided into 3 groups: Group I (n = 20) BIO-C ION + sealer, Group II (n = 20) AH Plus and Group III (n = 20) NanoSeal-S sealer. Groups were then sub-divided into two sub groups: In Subgroup A (n = 10) samples were obturated using single-cone with 0.1 % Rhodamine B dye and in Subgroup B (n = 10) samples were obturated using single cone. The samples were then transversely sectioned into coronal, middle and apical segments, samples in subgroup A & B were then submitted to CLSM analysis and UTM respectively. The Bond Strength data showed following means (MPa): Group I Subgroup B: (BIO–C ION+) coronal (1.64), middle (1.25), apical (0.93); Group II Subgroup B: (AH Plus) coronal (2.20), middle (1.85) apical (1.38) and Group III Subgroup B: (NanoSeal-S) coronal (1.26), middle (0.94), apical (0.58). The dentinal tubule penetration data showed following means: (μm) Group I Subgroup A (BIO–C ION+) coronal (1184.69), middle (997.03), apical (637.26); Group II Subgroup-A AH Plus (864.14) and NanoSeal-S (495.64). Statistical analysis (two-way ANOVA, Tukey's Post Hoc Test) showed significant difference among sealers (p < 0.001) and root canal thirds (p < 0.001). The results of the study concluded that BIO-C ION + sealer showed maximum dentinal tubule penetration and AH Plus demonstrated maximum push-out bond strength. [ABSTRACT FROM AUTHOR]

This research focuses on the development of polyurethane (PU)/silica (SiO2) nanofibrous membranes for the filtration of PM2.5 and bacteria, which has been further incorporated in three-layered face mask comprising cotton fabric, nanofibre membrane and polyester fabric. The surface morphology, PM2.5 filtration efficiency and bacterial filtration efficiency of PU/SiO2 nanofibrous membranes have been investigated to find out applicability of nanofibrous membranes in the filtration application. PU/SiO2 nanofibrous membranes (0.5%) are found to have a pressure drop of 126 Pa with filtration efficiency of 95.37% and bacterial filtration efficiency of 99.3% against E. coli bacteria strain. Two- and three-layered fabric structures are developed comprising PU/SiO2 nanofibrous membranes and their contact angle, water vapor transmission rate and air permeability are studied to observe the breathability and waterproof behaviour of the fabric structure. Three-layered PU/SiO2 nanofibrous membranes have contact angle, water vapor transmission rate and air permeability of 145±1°, 109 g/m²/day and 8.56 cm³/s/cm² respectively. [ABSTRACT FROM AUTHOR]

Purpose: The medial patellofemoral ligament (MPFL) acts as primary restraint to lateral patellar dislocation and its rupture has been reported in almost all cases of acute patellar dislocation. Various surgical techniques have been described for MPFL reconstruction, using many femoral and patellar fixation techniques and different grafts. This article details our technique for MPFL reconstruction using semitendinosus graft which avoids the use of implant at patellar end.Methods: Twenty patients (8 males and 12 females) with complaints regarding acute and chronic lateral patellar instability were evaluated and treated by MPFL reconstruction procedure. The mean age of patients was 21 years (range 17-34 years). MPFL reconstruction was performed using semitendinosus graft passing through two parallel, obliquely directed tunnels created in patella. Fixation of graft was done with an interference screw only at the femoral end. Mean follow-up period after intervention was 26.4 months (range 23-30 months). Results were evaluated using Kujala score.Results: All patients gained adequate patellar stability and full arc of motion. No incidence of patella fracture was noted. There were no postoperative complications related to the procedure. There was no recurrence of instability in patella at final follow-up.Conclusion: Passing the graft through the tunnels in patella without use of any implant has given excellent functional outcome and moreover has the advantages of less implant-related complications and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Objective: To estimate outpatient visits and hospitalization rates due to cardiopulmonary illness attributable to influenza from 2008-09 to 2012-13 in a large cohort of pregnant women from Catalonia, Spain.Methods: We estimated the event rates occurring during influenza epidemic, influenza non-epidemic and non-influenza seasons, and by pregnancy status (one year before pregnancy, first, second and third trimester). We fitted quasi-Poisson models in order to identify the variables associated to higher event rates.Results: During influenza epidemic seasons, pregnant women in their second trimester had the highest rates of outpatient visits (153 per 10,000 women-months). An increased risk of outpatient visits was associated to first or second trimester (adjusted rate ratio (aRR) = 1.17; 95% CI, 1.10-1.23 and aRR, 1.36; 95% CI, 1.28-1.43, respectively) and having any comorbidity (aRR = 1.28; 95% CI, 1.21-1.36). Women during third trimester had the highest rates of hospitalizations (1.60 per 10,000 women-months), and an increased risk of hospitalization was significantly associated to third trimester (aRR, 1.85; 95% CI, 1.01-3.39), having any comorbidity (aRR, 1.93; 95% CI, 1.10-3.41) and the pandemic influenza season (aRR, 2.90 (1.81; 95% CI, 1.81-4.64).Conclusion: Our findings provide significant information regarding influenza burden of disease among pregnant women. [ABSTRACT FROM AUTHOR]

Jamun (Syzygium cumini Skeels) is a nutritious fruit and has historically been used as both as an edible and a traditional medicine. The jamun seed is an inedible by-product of the fruit. However, their high concentration of phytochemicals makes them valuable components of nutraceuticals. Various studies on diverse biological activities of jamun seeds have warranted their application in human health and biomedical fields. This review paper discusses critically the phytochemical composition of jamun seeds and the bioactivities, including their antioxidant, anti-inflammatory, anti-cancer, antidiabetic, anti-microbial, anti-obesity, hepato-protective, cardio and gastro-protective properties. The bioactivity of jamun seed is related to the presence of phenols, flavonoids, steroids, alkaloids, triterpenoids, tannins, and saponins. Animal research in vivo, and in vitro experiments with human and animal cell models support the idea that jamun seed extract can be valuable additions in food and biomedical fields due to the diverse bio-functional properties. However, in-depth and systematic in vivo clinical trials using human subjects must be conducted to confirm the safe consumption limit and establish other therapeutic roles of jamun seeds for their abundant utilization as a nutraceutical or pharmacological component. Moreover, research is required to understand the exact mechanisms of the bioactivities exhibited by jamun seeds. [ABSTRACT FROM AUTHOR]

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data. [Display omitted] • MixEHR-Guided infers 1500 identifiable phenotypic topics from multi-modal EHR data. • The inferred 1500 topics exhibit meaningful connections among ICD and ATC codes. • The inferred phenotypic topics accurately recovered 9 out of 12 rule-based labels. • MixEHR-Guided identified meaningful age and sex-dependent phenotypic topics. • Predicted age prevalence of phenotypic topics is consistent with existing surveys. [ABSTRACT FROM AUTHOR]

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data. [ABSTRACT FROM AUTHOR]

Objective Investigating the contacts of a newly diagnosed tuberculosis (TB) case to prevent TB transmission is a core public health activity. In the context of limited resources, it is often necessary to prioritize investigation when multiple cases are reported. Public health personnel currently prioritize contact investigation intuitively based on past experience. Decision-support software using patient attributes to predict the probability of a TB case being involved in recent transmission could aid in this prioritization, but a prediction model is needed to drive such software. Methods We developed a logistic regression model using the clinical and demographic information of TB cases reported to Montreal Public Health between 1997 and 2007. The reference standard for transmission was DNA fingerprint analysis. We measured the predictive performance, in terms of sensitivity, specificity, negative predictive value, positive predictive value, the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC (AUC). Results Among 1552 TB cases enrolled in the study, 314 (20.2%) were involved in recent transmission. The AUC of the model was 0.65 (95% confidence interval: 0.61–0.68), which is significantly better than random prediction. The maximized values of sensitivity and specificity on the ROC were 0.53 and 0.67, respectively. Conclusions The characteristics of a TB patient reported to public health can be used to predict whether the newly diagnosed case is associated with recent transmission as opposed to reactivation of latent infection. [ABSTRACT FROM AUTHOR]

Highlights: [•] We used prospective space–time surveillance to detect tuberculosis (TB) transmission. [•] We compared TB genotypes in the space–time clusters to verify recent transmission. [•] Many space–time clusters were not due to recent transmission, causing false alarms. [•] Prospective space–time surveillance for TB is not useful in public health practice. [Copyright &y& Elsevier]

Abstract: The residential addresses of persons with reportable communicable diseases are used increasingly for spatial monitoring and cluster detection, and public health may direct interventions based upon the results of routine spatial surveillance. There has been little assessment, however, of the quality of address data in reportable disease notifications and of the corresponding impact of these errors on geocoding and routine public health practices. The objectives of this study were to examine address errors for a selected reportable disease in a large urban center in Canada and to assess the impact of identified errors on geocoding and the estimated spatial distribution of the disease. We extracted data for all notifications of campylobacteriosis from the Montreal public health department from 1995 to 2008 and used an address verification algorithm to determine the validity of the residential address for each case and to suggest corrections for invalid addresses. We assessed the types of address errors as well as the resulting positional errors, calculating the distance between the original address and the correct address as well as changes in disease density. Address errors and missing addresses were prevalent in the public health records (10% and 5%, respectively) and they influenced the observed distribution of campylobacteriosis in Montreal, with address correction changing case location by a median of 1.1km. Further examination of the extent of address errors in public health data is essential, as is the investigation of how these errors impact routine public health functions. [Copyright &y& Elsevier]

The bone bank unit of interest in this article was established in January 2018, in a tertiary care teaching institute of north India. Aim of this article is to describe the sources of allografts obtained, discard rates of allografts and infection rates in the recipients after use. All the relevant details of donors and recipients were maintained, and donors were screened for standard inclusion and exclusion criteria before obtaining the grafts. Aerobic culture was performed before storage and just prior to use. Samples with incomplete documentation, incomplete donor screening or positive cultures were discarded. Data on surgical site infection in recipients was collected from hospital records retrospectively. Initially ELISA based serological tests were used for screening. Donor has to undergo these tests again after 6 months to account for the window period of proliferation of viruses. Nucleic acid amplification tests (NAAT) for these viral agents were introduced in the hospital in May 2018. Allografts from a total of 196 donors were obtained in the bone bank over 2 years. Major source of bone was femoral heads harvested during total hip arthroplasty or hemi-arthroplasty. 44(22.4%) grafts had to be discarded. 95 allografts were used in 88 patients during this time. Most common indication for use was surgery for bone tumors (40%), followed by complex primary or revision arthroplasty (30.5%). Three (3.4%) recipients developed deep infection postoperatively. Frozen allograft bone from hospital based bone banks is a reliable source of allografts. When meticulous precautions for sterility are followed, risk of infection is low. Monitoring of such bone banks should fall within a framework of the local legislature. Incomplete documentation is the major reason for wastage of the samples obtained. NAAT may be useful in screening of donors, as it reduces the wastage and the holding time of the allografts. [ABSTRACT FROM AUTHOR]

The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice. [ABSTRACT FROM AUTHOR]