Successful Outcome of Anterior Cruciate Ligament (ACL) Reconstruction by Hamstring Tendon for Anterior Cruciate Ligament Deficit Knee at a University Hospital: A Descriptive Cross-sectional Study.

INTRODUCTION: Anterior cruciate ligament is a commonly injured and reconstructed ligament in the knee. Unlike in urban areas where sports activities and road accidents are common mechanisms of injuries, the semi-urban and rural population has a different mode of injuries, needs, and expectations. This study explores the prevalence of successful outcome of anterior cruciate ligament reconstruction in by hamstring tendon for anterior cruciate ligament deficit knee at a university hospital. METHODS: A descriptive cross-sectional study was conducted at Dhulikhel Hospital, Kathmandu University Hospital from 2018 February to 2020 January among patients having anterior cruciate ligament injuries after ethical approval. Whole sampling technique was used. Functional outcome was assessed with Lysholm scale at the end of at least one year. Data was analysed using Statistical Package for Social Sciences version 11. Point estimate at 95% Confidence Interval was calculated, with frequency and percentage. RESULTS: Out of 66 anterior cruciate ligament reconstruction, 35 (59%) cases had successful outcomes. Excellent outcomes were seen in 9 (15%) cases and 26 (44%) had good outcomes. The mean Lysholm score was 84. CONCLUSIONS: Anterior cruciate ligament injuries were seen in heterogeneous populations during their activities of daily living or recreational sports activities. Anatomic anterior cruciate ligamentreconstruction with hamstring grafts provided good functional outcomes, especially among the young population. Our findings are similar to current studies on anterior cruciate ligament-reconstruction.

Quantitation of Risk Reduction of E. coli Transmission After Using Antimicrobial Hand Soap.

Handwashing with soap is an effective and economical means to reduce the likelihood of Escherichia coli infection from indirect contact with contaminated surfaces during food preparation. The purpose of this study was to conduct a quantitative microbial risk assessment (QMRA) to evaluate the risk of infection from indirect contact with fomites contaminated with E. coli after hand washing with antimicrobial hand soaps. A Monte Carlo simulation was done with a total of 10,000 simulations to compare the effectiveness of two antimicrobial and one control (non-antimicrobial) bar soaps in reducing the exposure and infection risk compared to no hand washing. The numbers of E. coli on several fomites commonly found in household kitchens, as well as the transfer rates between fomites and onto fingertips, were collected from the literature and experimental data. The sponsor company provided the E. coli survival on hands after washing with antimicrobial and control soaps. A number of scenarios were evaluated at two different exposure doses (high and low). Exposure scenarios included transfer of E. coli between meat-to-cutting board surface-to-hands, meat-to-knife surface-to-hands, and from a countertop surface-to-hands, kitchen sponge-to-hands, hand towel-to-hands, and dishcloth-to-hands. Results showed that the risks of illness after washing with the control soap was reduced approximately 5-fold compared to no handwashing. Washing with antimicrobial soap reduced the risk of E. coli infection by an average of about 40-fold compared with no handwashing. The antimicrobial soaps ranged from 3 to 32 times more effective than the non-antimicrobial soap, depending on the specific exposure scenario. Importance: The Centers for Disease Control and Prevention indicate the yearly incidence rate of Shiga Toxin producing E. coli infections is about 1.7/100,000, with about 10% of cases leading to life-threatening hemolytic uremic syndrome and 3-5% leading to death. Our findings confirm handwashing with soap reduces the risks associated with indirect transmission of E. coli infection from contact with fomites during food preparation. Further, in these exposure scenarios, antimicrobial soaps were more effective overall than the non-antimicrobial soap in reducing exposure to E. coli and risk of infection.

Construction of A New Dose-Response Model for Staphylococcus aureus Considering Growth and Decay Kinetics on Skin.

. In order to determine the relationship between an exposure dose of Staphylococcus aureus (S. aureus) on the skin and the risk of infection, an understanding of the bacterial growth and decay kinetics is very important. Models are essential tools for understanding and predicting bacterial kinetics and are necessary to predict the dose of organisms post-exposure that results in a skin infection. One of the challenges in modeling bacterial kinetics is the estimation of model parameters, which can be addressed using an inverse problem approach. The objective of this study is to construct a microbial kinetic model of S. aureus on human skin and use the model to predict concentrations of S. aureus that result in human infection. In order to model the growth and decay of S. aureus on skin, a Gompertz inactivation model was coupled with a Gompertz growth model. A series of analyses, including ordinary least squares regression, scaled sensitivity coefficient analysis, residual analysis, and parameter correlation analysis were conducted to estimate the parameters and to describe the model uncertainty. Based on these analyses, the proposed model parameters were estimated with high accuracy. The model was then used to develop a new dose-response model for S. aureus using the exponential dose-response model. The new S. aureus model has an optimized k parameter equivalent to 8.05 × 10-8 with 95th percentile confidence intervals between 6.46 × 10-8 and 1.00 × 10-7.

A Case Study Evaluating the Risk of Infection from Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV) in a Hospital Setting Through Bioaerosols.

Middle Eastern respiratory syndrome, an emerging viral infection with a global case fatality rate of 35.5%, caused major outbreaks first in 2012 and 2015, though new cases are continuously reported around the world. Transmission is believed to mainly occur in healthcare settings through aerosolized particles. This study uses Quantitative Microbial Risk Assessment to develop a generalizable model that can assist with interpreting reported outbreak data or predict risk of infection with or without the recommended strategies. The exposure scenario includes a single index patient emitting virus-containing aerosols into the air by coughing, leading to short- and long-range airborne exposures for other patients in the same room, nurses, healthcare workers, and family visitors. Aerosol transport modeling was coupled with Monte Carlo simulation to evaluate the risk of MERS illness for the exposed population. Results from a typical scenario show the daily mean risk of infection to be the highest for the nurses and healthcare workers (8.49 × 10-4 and 7.91 × 10-4 , respectively), and the lowest for family visitors and patients staying in the same room (3.12 × 10-4 and 1.29 × 10-4 , respectively). Sensitivity analysis indicates that more than 90% of the uncertainty in the risk characterization is due to the viral concentration in saliva. Assessment of risk interventions showed that respiratory masks were found to have a greater effect in reducing the risks for all the groups evaluated (>90% risk reduction), while increasing the air exchange was effective for the other patients in the same room only (up to 58% risk reduction).

Evaluation of the effectiveness of conservation practices under implementation site uncertainty.

Agricultural nonpoint source pollution is the leading source of water quality degradation in United States, which has led to the development of programs that aim to mitigate this pollution. One common approach to mitigating nonpoint source pollution is the use of best management practices (BMPs). However, it can be challenging to evaluate the effectiveness of implemented BMPs due to polices that limit data sharing. In this study, the uncertainty introduced by data sharing limitations is quantified through the use of a watershed model. Results indicated that BMP implementation improved the overall water quality in the region (up to ∼15% pollution reduction) and that increasing the area of BMP implementation resulted in higher pollution reduction. However, the model outputs also indicated that uncertainty caused by data sharing limitations resulted in variabilities ranging from -160% to 140%. This shows the importance of data sharing among agencies to better guide current and future conservation programs.

Evaluating the significance of wetland restoration scenarios on phosphorus removal.

Freshwater resources are vital for human and natural systems. However, anthropogenic activities, such as agricultural practices, have led to the degradation of the quality of these limited resources through pollutant loading. Agricultural Best Management Practices (BMPs), such as wetlands, are recommended as a valuable solution for pollutant removal. However, evaluation of their long-term impacts is difficult and requires modeling since performing in-situ monitoring is expensive and not feasible at the watershed scale. In this study, the impact of natural wetland implementation on total phosphorus reduction was evaluated both at the subwatershed and watershed levels. The study area is the Saginaw River Watershed, which is largest watershed in Michigan. The phosphorus reduction performances of four different wetland sizes (2, 4, 6, and 8 ha) were evaluated within this study area by implementing one wetland at a time in areas identified to have the highest potential for wetland restoration. The subwatershed level phosphorus loads were obtained from a calibrated Soil and Water Assessment Tool (SWAT) model. These loads were then incorporated into a wetland model (System for Urban Stormwater Treatment and Analysis IntegratioN-SUSTAIN) to evaluate phosphorus reduction at the subwatershed level and then the SWAT model was again used to route phosphorus transport to the watershed outlet. Statistical analyses were performed to evaluate the spatial impact of wetland size and placement on phosphorus reduction. Overall, the performance of 2 ha wetlands in total phosphorus reduction was significantly lower than the larger sizes at both the subwatershed and watershed levels. Regarding wetland implementation sites, wetlands located in headwaters and downstream had significantly higher phosphorus reduction than the ones located in the middle of the watershed. More specifically, wetlands implemented at distances ranging from 200 to 250 km and 50-100 km from the outlet had the highest impact on phosphorus reduction at the subwatershed and watershed levels, respectively. A multi criteria decision making (MCDM) method named VIKOR was successfully executed to identify the most suitable wetland size and location for each subwatershed considering the phosphorus reduction and economic cost associated with wetland implementation. The methods introduced in this study can be easily applied to other watersheds for selection and placement of wetlands while considering environmental benefits and economic costs.

Development and evaluation of a comprehensive drought index.

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R2 = 0.75 for MASH to R2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors.

Modeling Escherichia coli removal in constructed wetlands under pulse loading.

Manure-borne pathogens are a threat to water quality and have resulted in disease outbreaks globally. Land application of livestock manure to croplands may result in pathogen transport through surface runoff and tile drains, eventually entering water bodies such as rivers and wetlands. The goal of this study was to develop a robust model for estimating the pathogen removal in surface flow wetlands under pulse loading conditions. A new modeling approach was used to describe Escherichia coli removal in pulse-loaded constructed wetlands using adaptive neuro-fuzzy inference systems (ANFIS). Several ANFIS models were developed and validated using experimental data under pulse loading over two seasons (winter and summer). In addition to ANFIS, a mechanistic fecal coliform removal model was validated using the same sets of experimental data. The results showed that the ANFIS model significantly improved the ability to describe the dynamics of E. coli removal under pulse loading. The mechanistic model performed poorly as demonstrated by lower coefficient of determination and higher root mean squared error compared to the ANFIS models. The E. coli concentrations corresponding to the inflection points on the tracer study were keys to improving the predictability of the E. coli removal model.