Search Results (17)

Several studies have shown that microsatellite changes can be profiled in urine for the detection of bladder cancer. The use of microsatellite analysis (MSA) for bladder cancer detection requires a comprehensive analysis of as many as 15 to 20 markers, based on the amplification and interpretations of many individual MSA markers, and it can be technically challenging. Here, to develop fast, more efficient, standardized, and less costly MSA for the detection of bladder cancer, we developed three multiplex-polymerase-chain-reaction-(PCR)-based MSA assays, all of which were analyzed via a genetic analyzer. First, we selected 16 MSA markers based on 9 selected publications. Based on samples from Johns Hopkins University (the JHU sample, the first set sample), we developed an MSA based on triplet, three-tube-based multiplex PCR (a Triplet MSA assay). The discovery, validation, and translation of biomarkers for the early detection of cancer are the primary focuses of the Early Detection Research Network (EDRN), an initiative of the National Cancer Institute (NCI). A prospective study sponsored by the EDRN was undertaken to determine the efficacy of a novel set of MSA markers for the early detection of bladder cancer. This work and data analysis were performed through a collaboration between academics and industry partners. In the current study, we undertook a re-analysis of the primary data from the Compass study to enhance the predictive power of the dataset in bladder cancer diagnosis. Using a four-stage pipeline of modern machine learning techniques, including outlier removal with a nonlinear model, correcting for majority/minority class imbalance, feature engineering, and the use of a model-derived variable importance measure to select predictors, we were able to increase the utility of the original dataset to predict the occurrence of bladder cancer. The results of this analysis showed an increase in accuracy (85%), sensitivity (82%), and specificity (83%) compared to the original analysis. The re-analysis of the EDRN study results using machine learning statistical analysis proved to achieve an appropriate level of accuracy, sensitivity, and specificity to support the use of the MSA for bladder cancer detection and monitoring. This assay can be a significant addition to the tools urologists use to both detect primary bladder cancers and monitor recurrent bladder cancer. Full article

Social networks, particularly Twitter 9.0 (known as X as of 23 July 2023), have provided an avenue for prompt interactions and sharing public health-related concerns and emotions, especially during the COVID-19 pandemic when in-person communication became less feasible due to stay-at-home policies in the United States (U.S.). The study of public emotions extracted from social network data has garnered increasing attention among scholars due to its significant predictive value for public behaviors and opinions. However, few studies have explored the associations between public health policies, local political ideology, and the spatial-temporal trends of emotions extracted from social networks. This study aims to investigate (1) the spatial-temporal clustering trends (or spillover effects) of negative emotions related to COVID-19; and (2) the association relationships between public health policies such as stay-at-home policies, political ideology, and the negative emotions related to COVID-19. This study employs multiple statistical methods (zero-inflated Poisson (ZIP) regression, random-effects model, and spatial autoregression (SAR) model) to examine relationships at the county level by using the data merged from multiple sources, mainly including Twitter 9.0, Johns Hopkins, and the U.S. Census Bureau. We find that negative emotions related to COVID-19 extracted from Twitter 9.0 exhibit spillover effects, with counties implementing stay-at-home policies or leaning predominantly Democratic showing higher levels of observed negative emotions related to COVID-19. These findings highlight the impact of public health policies and political polarization on spatial-temporal public emotions exhibited in social media. Scholars and policymakers can benefit from understanding how public policies and political ideology impact public emotions to inform and enhance their communication strategies and intervention design during public health crises such as the COVID-19 pandemic. Full article

Recent advances allow the use of Augmented Reality (AR) for many medical procedures. AR via optical navigators to aid various knee surgery techniques (e.g., femoral and tibial osteotomies, ligament reconstructions or menisci transplants) is becoming increasingly frequent. Accuracy in these procedures is essential, but evaluations of this technology still need to be made. Our study aimed to evaluate the system’s accuracy using an in vitro protocol. We hypothesised that the system’s accuracy was equal to or less than 1 mm and 1° for distance and angular measurements, respectively. Our research was an in vitro laboratory with a 316 L steel model. Absolute reliability was assessed according to the Hopkins criteria by seven independent evaluators. Each observer measured the thirty palpation points and the trademarks to acquire direct angular measurements on three occasions separated by at least two weeks. The system’s accuracy in assessing distances had a mean error of 1.203 mm and an uncertainty of 2.062, and for the angular values, a mean error of 0.778° and an uncertainty of 1.438. The intraclass correlation coefficient was for all intra-observer and inter-observers, almost perfect or perfect. The mean error for the distance’s determination was statistically larger than 1 mm (1.203 mm) but with a trivial effect size. The mean error assessing angular values was statistically less than 1°. Our results are similar to those published by other authors in accuracy analyses of AR systems. Full article

During the first year of the COVID-19 pandemic, governments only had access to non-pharmaceutical interventions (NPIs) to mitigate the spread of the disease. Various methods have been discussed in the literature for calculating the effectiveness of NPIs. Among these methods, the interrupted time series analysis method is the area of our interest. To study the second wave, we clustered countries based on levels of implemented NPIs, except for the target NPI (X) whose effectiveness wanted to be evaluated. To do so, the COVID-19 Policy Response Tracker data-set gathered by the “Our World in Data” team of Oxford University, and COVID-19 statistical data gathered by the John Hopkins Hospital were used. After clustering, we selected a counterfactual country from the countries that were in the same cluster as the target country, and implemented NPI (X) at its lowest level. Thus, the target country and the counterfactual country were similar in implementation level of other NPIs and only differed in the implementation level of the target NPI (X). Therefore, we can calculate the effectiveness of NPI (X) without being concerned about the impurity of the effectiveness values that might be caused by other NPIs. This allowed us to calculate the effectiveness of NPI (X) using the interrupted time series analysis with the control group. Interrupted time series analysis assesses the effect of different policy-implementation levels by evaluating interruptions caused by policies in trend and level after the policy-implementation date. Before the NPI-implementation date, the implementation levels of NPIs were similar in both selected countries. After this date, the counterfactual country could be treated as a baseline for calculating changes in the trends and levels of COVID-19 cases in the target country. To demonstrate this approach, we used the generalized least square (GLS) method to estimate interrupted time series parameters related to the effectiveness of school closure (the target NPI) in Spain (the target country). The results show that increasing the implementation level of school closure caused a 34% decrease in COVID-19 prevalence in Spain after only 10 days compared to the counterfactual country. Full article

Low-density lipoprotein cholesterol (LDL-C) is a well-established biomarker in the management of dyslipidemia. Therefore, we aimed to evaluate the concordance of LDL-C-estimating equations with direct enzymatic measurement in diabetic and prediabetic populations. The data of 31,031 subjects included in the study were divided into prediabetic, diabetic, and control groups according to HbA1c values. LDL-C was measured by direct homogenous enzymatic assay and calculated by Martin–Hopkins, Martin–Hopkins extended, Friedewald, and Sampson equations. The concordance statistics between the direct measurements and estimations obtained by the equations were evaluated. All equations evaluated in the study had lower concordance with direct enzymatic measurement in diabetic and prediabetic groups compared to the non-diabetic group. Even so, the Martin–Hopkins extended approach demonstrated the highest concordance statistic in diabetic and prediabetic patients. Further, Martin–Hopkins extended was found to have the highest correlation with direct measurement compared with other equations. Over the 190 mg/dL LDL-C concentrations, the equation with the highest concordance was again Martin–Hopkins extended. In most scenarios, the Martin–Hopkins extended performed best in prediabetic and diabetic groups. Additionally, direct assay methods can be used at low values of the non-HDL-C/TG ratio (<2.4), as the performance of the equations in LDL-C estimation decreases as non-HDL-C/TG decreases. Full article

Weather and climate extremes such as heat waves, droughts and floods are projected to become more frequent and intense in several regions. There is compelling evidence indicating that changes in climate and its extremes over time influence the living conditions of society and the surrounding environment across the globe. This study applies max-stable models to capture the spatio–temporal extremes with dependence. The objective was to analyse the risk of drought caused by extremely high temperatures and deficient rainfall. Hopkin’s statistic was used to assess the clustering tendency before using the agglomerative method of hierarchical clustering to cluster the study area into $n=3$ temperature clusters and $n=3$ precipitation clusters. For the precipitation and temperature data, the values of Hopkin’s statistic were 0.7317 and 0.8446, respectively, which shows that both are significantly clusterable. Various max-stable process models were then fitted to each cluster of each variable, and the Schlather model with several covariance functions was found to be a good fit on both datasets compared to the Smith model with the Gaussian covariance function. The modelling approach presented in this paper could be useful to hydrologists, meteorologists and climatologists, including decision-makers in the agricultural sector, in enhancing their understanding of the behaviour of drought caused by extremely high temperatures and low rainfall. The modelling of these compound extremes could also assist in assessing the impact of climate change. It can be seen from this study that the size, including the topography of the location (cluster/region), provides important information about the strength of the extremal dependence. Full article

Fuzzy c-means (FCM), the fuzzy variant of the popular k-means, has been used for data clustering when cluster boundaries are not well defined. The choice of initial cluster prototypes (or the initialization of cluster memberships), and the fact that the number of clusters needs to be defined a priori are two major factors that can affect the performance of FCM. In this paper, we review algorithms and methods used to overcome these two specific drawbacks. We propose a new cooperative multi-population differential evolution method with elitism to identify near-optimal initial cluster prototypes and also determine the most optimal number of clusters in the data. The differential evolution populations use a smaller subset of the dataset, one that captures the same structure of the dataset. We compare the proposed methodology to newer methods proposed in the literature, with simulations performed on standard benchmark data from the UCI machine learning repository. Finally, we present a case study for clustering time-series patterns from sensor data related to real-time machine health monitoring using the proposed method. Simulation results are promising and show that the proposed methodology can be effective in clustering a wide range of datasets. Full article

This research argues that national governance quality may moderate the relationship between COVID-19 and stock returns across markets. Building on the well-established relationship between COVID-19 shock and stock returns, we focus on how the quality of a country’s governance system affects the relationship between the COVID-19 crisis and stock returns. Using data from the World Governance Indicators, the World Bank, and the John Hopkins University Coronavirus Resource Centre (JHU-CRC) for 29 OECD markets from 23 January to 31 December 2020, our findings confirm this hypothesis. Our results suggest that, the estimated coefficient on the interaction term is negative (−0.004) and statistically different from zero at the 5% level of significance. This result can be inferred that the higher the national governance quality is, the weaker the effect of COVID-19 on stock returns will be. Specifically, the negative impact of COVID-19 on stock market returns was more pronounced in countries where the national governance quality index is lower. Our results also show a strong negative association between COVID-19 and stock market returns across the sample. The results are robust to changes in governance quality measures, estimation methods, and explanatory variables. The results have several policy implications such that better institutions may partially offset the adverse impact of the COVID-19 shock on stock market returns. Full article

We present a new subgrid stress model for the large eddy simulation of turbulent flows based on the solution of transport equations for stress tensor components. The model was a priori term-by-term calibrated against an open DNS database on forced isotropic turbulence (Johns Hopkins University database). After that, it was applied in a large eddy simulation of non-premixed turbulent combustion. To demonstrate the impact of the new subgrid stress model on scalar fields, we excluded the backward effect of heat release on the subgrid stresses, considering an isothermal reaction (i.e., diluted mixture; the density variations associated with chemical heat release can be neglected) and a Burke–Schumann reaction sheet approximation. A periodic box filled with a homogeneous turbulent velocity field and a three-layer top-hat mixture fraction field was studied. Four simulations were performed in which a fixed model for mixture fraction and its variance was combined with either the proposed subgrid stress model or one of the standard models, including Smagorinsky, dynamic Smagorinsky and WALE. Qualitatively correct backscatter was observed in a simulation with the new model. The differences in the statistics of the mixture fraction and reactive component fields caused by the new subgrid stress model were analyzed and discussed. The importance of using an advanced subgrid stress model was highlighted. Full article

The appearance of SARS-CoV-2 represented a new health threat to humanity and affected millions of people; the transmission of this virus occurs through different routes, and one of them recently under debate in the international community is its possible incorporation and spread by sewage. Therefore, the present work’s research objectives are to review the presence of SARS-CoV-2 in wastewater throughout the world and to analyze the coverage of wastewater treatment in Mexico to determine if there is a correlation between the positive cases of COVID-19 and the percentages of treated wastewater in Mexico as well as to investigate the evidence of possible transmission by aerosol sand untreated wastewater. Methodologically, a quick search of scientific literature was performed to identify evidence the presence of SARS-CoV-2 RNA (ribonucleic acid) in wastewater in four international databases. The statistical information of the positive cases of COVID-19 was obtained from data from the Health Secretary of the Mexican Government and the Johns Hopkins Coronavirus Resource Center. The information from the wastewater treatment plants in Mexico was obtained from official information of the National Water Commission of Mexico. The results showed sufficient evidence that SARS-CoV-2 remains alive in municipal wastewater in Mexico. Our analysis indicates that there is a low but significant correlation between the percentage of treated water and positive cases of coronavirus r = −0.385, with IC (95%) = (−0.647, −0.042) and p = 0.030; this result should be taken with caution because wastewater is not a transmission mechanism, but this finding is useful to highlight the need to increase the percentage of treated wastewater and to do it efficiently. In conclusions, the virus is present in untreated wastewater, and the early detection of SAR-CoV-2 could serve as a bioindicator method of the presence of the virus. This could be of great help to establish surveillance measures by zones to take preventive actions, which to date have not been considered by the Mexican health authorities. Unfortunately, wastewater treatment systems in Mexico are very fragile, and coverage is limited to urban areas and non-existent in rural areas. Furthermore, although the probability of contagion is relatively low, it can be a risk for wastewater treatment plant workers and people who are close to them. Full article

Background: Malnutrition has been linked to adverse health economic outcomes. There is a paucity of data on malnutrition in patients admitted with COVID-19. Methods: This is a retrospective cohort study consisting of 4311 COVID-19 adult (18 years and older) inpatients at 5 Johns Hopkins-affiliated hospitals between 1 March and 3 December 2020. Malnourishment was identified using the malnutrition universal screening tool (MUST), then confirmed by registered dietitians. Statistics were conducted with SAS v9.4 (Cary, NC, USA) software to examine the effect of malnutrition on mortality and hospital length of stay among COVID-19 inpatient encounters, while accounting for possible covariates in regression analysis predicting mortality or the log-transformed length of stay. Results: COVID-19 patients who were older, male, or had lower BMIs had a higher likelihood of mortality. Patients with malnutrition were 76% more likely to have mortality (p < 0.001) and to have a 105% longer hospital length of stay (p < 0.001). Overall, 12.9% (555/4311) of adult COVID-19 patients were diagnosed with malnutrition and were associated with an 87.9% increase in hospital length of stay (p < 0.001). Conclusions: In a cohort of COVID-19 adult inpatients, malnutrition was associated with a higher likelihood of mortality and increased hospital length of stay. Full article

This work describes a non-invasive, automated software framework to discriminate between individuals with a genetic disorder, Pitt–Hopkins syndrome (PTHS), and healthy individuals through the identification of morphological facial features. The input data consist of frontal facial photographs in which faces are located using histograms of oriented gradients feature descriptors. Pre-processing steps include color normalization and enhancement, scaling down, rotation, and cropping of pictures to produce a series of images of faces with consistent dimensions. Sixty-eight facial landmarks are automatically located on each face through a cascade of regression functions learnt via gradient boosting to estimate the shape from an initial approximation. The intensities of a sparse set of pixels indexed relative to this initial estimate are used to determine the landmarks. A set of carefully selected geometric features, for example, the relative width of the mouth or angle of the nose, is extracted from the landmarks. The features are used to investigate the statistical differences between the two populations of PTHS and healthy controls. The methodology was tested on 71 individuals with PTHS and 55 healthy controls. The software was able to classify individuals with an accuracy rate of 91%, while pediatricians achieved a recognition rate of 74%. Two geometric features related to the nose and mouth showed significant statistical difference between the two populations. Full article

Small intestinal bacterial overgrowth (SIBO) is characterized by abnormal and excessive amounts of bacteria in the small intestine. Since symptoms and lab tests are non-specific, the diagnosis of SIBO is highly dependent on breath testing. There is a lack of a universally accepted cut-off point for breath testing to diagnose SIBO, and the dilemma of defining “SIBO patients” has made it more difficult to explore the gold standard for SIBO diagnosis. How to validate the gold standard for breath testing without defining “SIBO patients” has become an imperious demand in clinic. Breath-testing datasets from 1071 patients were collected from Xiangya Hospital in the past 3 years and analyzed with an artificial intelligence method using cluster analysis. K-means and DBSCAN algorithms were applied to the dataset after the clustering tendency was confirmed with Hopkins Statistic. Satisfying the clustering effect was evaluated with a Silhouette score, and patterns of each group were described. Advantages of artificial intelligence application in adaptive breath-testing diagnosis criteria with SIBO were discussed from the aspects of high dimensional analysis, and data-driven and regional specific dietary influence. This research work implied a promising application of artificial intelligence for SIBO diagnosis, which would benefit clinical practice and scientific research. Full article

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions. Full article

This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities. Full article