Tag: pubmed

PLoS One. 2022 Apr 25;17(4):e0267396. doi: 10.1371/journal.pone.0267396. eCollection 2022.

ABSTRACT

Informed antibiotic prescription offers a practical solution to antibiotic resistance problem. With the increasing affordability of different sequencing technologies, molecular-based resistance prediction would direct proper antibiotic selection and preserve available agents. Amikacin is a broad-spectrum aminoglycoside exhibiting higher clinical efficacy and less resistance rates in Ps. aeruginosa due to its structural nature and its ability to achieve higher serum concentrations at lower therapeutic doses. This study examines the predictive potential of molecular markers underlying amikacin susceptibility phenotypes in order to provide improved diagnostic panels. Using a predictive model, genes and variants underlying amikacin resistance have been statistically and functionally explored in a large comprehensive and diverse set of Ps. aeruginosa completely sequenced genomes. Different genes and variants have been examined for their predictive potential and functional correlation to amikacin susceptibility phenotypes. Three predictive sets of molecular markers have been identified and can be used in a complementary manner, offering promising molecular diagnostics. armR, nalC, nalD, mexR, mexZ, ampR, rmtD, nalDSer32Asn, fusA1Y552C, fusA1D588G, arnAA170T, and arnDG206C have been identified as the best amikacin resistance predictors in Ps. aeruginosa while faoAT385A, nuoGA890T, nuoGA574T, lptAT55A, lptAR62S, pstBR87C, gidBE126G, gidBQ28K, amgSE108Q, and rplYQ41L have been identified as the best amikacin susceptibility predictors. Combining different measures of predictive performance together with further functional analysis can help design new and more informative molecular diagnostic panels. This would greatly inform and direct point of care diagnosis and prescription, which would consequently preserve amikacin functionality and usefulness.

PMID:35468158 | DOI:10.1371/journal.pone.0267396

PLoS One. 2022 Apr 25;17(4):e0267218. doi: 10.1371/journal.pone.0267218. eCollection 2022.

ABSTRACT

PURPOSE: It is currently unclear how the shift towards virtual care during the 2019 novel coronavirus (COVID-19) pandemic may have impacted chronic disease management at a population level. The goals of our study were to provide a description of the levels of use of virtual care services relative to in-person care in patients with chronic disease across Ontario, Canada and to describe levels of healthcare utilization in low versus high virtual care users.

METHODS: We used linked health administrative data to conduct a population-based, repeated cross-sectional study of all ambulatory patient visits in Ontario, Canada (January 1, 2018 to January 16, 2021). Further stratifications were also completed to examine patients with COPD, heart failure, asthma, hypertension, diabetes, mental illness, and angina. Patients were classified as low (max 1 virtual care visit) vs. high virtual care users. A time-series analysis was done using interventional autoregressive integrated moving average (ARIMA) modelling on weekly hospitalizations, outpatient visits, and diagnostic tests.

RESULTS: The use of virtual care increased across all chronic disease patient populations. Virtual care constituted at least half of the total care in all conditions. Both low and high virtual care user groups experienced a statistically significant reduction in hospitalizations and laboratory testing at the start of the pandemic. Hospitalization volumes increased again only among the high users, while testing increased in both groups. Outpatient visits among high users remained unaffected by the pandemic but dropped in low users.

CONCLUSION: The decrease of in-person care during the pandemic was accompanied by an increase in virtual care, which ultimately allowed patients with chronic disease to return to the same visit rate as they had before the onset of the pandemic. Virtual care was adopted across various chronic conditions, but the relative adoption of virtual care varied by condition with highest rates seen in mental health.

PMID:35468168 | DOI:10.1371/journal.pone.0267218

PLoS One. 2022 Apr 25;17(4):e0267079. doi: 10.1371/journal.pone.0267079. eCollection 2022.

ABSTRACT

Floods are among the devastating types of disasters in terms of human life, social and financial losses. Authoritative data from flood gauges are scarce in arid regions because of the specific type of dry climate that dysfunctions these measuring devices. Hence, social media data could be a useful tool in this case, where a wealth of information is available online. This study investigates the reliability of flood related data quality collected from social media, particularly for an arid region where the usage of flow gauges is limited. The data (text, images and videos) of social media, related to a flood event, was analyzed using the Machine Learning approach. For this reason, digital data (758 images and 1413 video frames) was converted into numeric values through ResNet50 model using the VGG-16 architecture. Numeric data of images, videos and text was further classified using different Machine Learning algorithms. Receiver operating characteristics (ROC) curve and area under curve (AUC) methods were used to evaluate and compare the performance of the developed machine learning algorithms. This novel approach of studying the quality of social media data could be a reliable alternative in the absence of real-time flow gauges data. A flash flood that occurred in the United Arab Emirates (UAE) from March 7-11, 2016 was selected as the focus of this study. Random forest showed the highest accuracy of 80.18% among the five other classifiers for images and videos. Precipitation/rainfall data were used to validate social media data, which showed a significant relationship between rainfall and the number of posts. The validity of the machine learning models was assessed using the area under the curve, precision-recall curve, root mean square error, and kappa statistics to confirm the validity and accuracy of the model. The data quality of YouTube videos was found to have the highest accuracy followed by Facebook, Flickr, Twitter, and Instagram. These results showed that social media data could be used when gauge data is unavailable.

PMID:35468157 | DOI:10.1371/journal.pone.0267079

PLoS One. 2022 Apr 25;17(4):e0267306. doi: 10.1371/journal.pone.0267306. eCollection 2022.

ABSTRACT

The study aimed to fabricate and evaluate Meloxicam (MLX) loaded Hydroxypropyl Methylcellulose (HPMC) microparticles for colon targeting because MLX is a potent analgesic used in the treatment of pain and inflammation associated with colorectal cancer (CRC). Nevertheless, its efficiency is limited by poor solubility and gastrointestinal tracts (GIT) associated side effects. Seventeen formulations of MLX loaded HPMC microparticles were fabricated by the oil-in-oil (O/O)/ emulsion solvent evaporation (ESE) technique. A 3-factor, 3-level Box Behnken (BBD) statistical design was used to estimate the combined effects of the independent variables on the dependent variables (responses), such as the percent yield (R1), the entrapment efficiency (EE) (R2), mean particle size (R3) and in vitro percentage of cumulative drug release (R4). For physicochemical characterization FTIR, XRD, DSC, and SEM analyses were performed. Biocompatibility and non-toxicity were confirmed by in-vivo acute oral toxicity determination. The percentage yield and EE were 65.75-90.71%, and 70.62-88.37%, respectively. However, the mean particle size was 62.89-284.55 μm, and the in vitro cumulative drug release percentage was 74.25-92.64% for 24 hours. FTIR analysis showed that the composition of the particles was completely compatible, while XRD analysis confirmed the crystalline nature of the pure drug and its transition into an amorphous state after formulation. DSC analysis revealed the thermal stability of the formulations. The SEM analysis showed dense spherical particles. The toxicity study in albino rabbits showed no toxicity and was found biocompatible. The histopathological evaluation showed no signs of altered patterns. Results of this study highlighted a standard colonic drug delivery system with the ability to improve patient adherence and reduce GIT drug-associated side effects in CRC treatment.

PMID:35468155 | DOI:10.1371/journal.pone.0267306

PLoS One. 2022 Apr 25;17(4):e0265380. doi: 10.1371/journal.pone.0265380. eCollection 2022.

ABSTRACT

Blebs, pressure driven protrusions of the cell membrane, facilitate the movement of eukaryotic cells such as the soil amoeba Dictyostelium discoideum, white blood cells and cancer cells. Blebs initiate when the cell membrane separates from the underlying cortex. A local rupture of the cortex, has been suggested as a mechanism by which blebs are initiated. However, much clarity is still needed about how cells inherently regulate rupture of the cortex in locations where blebs are expected to form. In this work, we examine the role of membrane energy and the motor protein myosin II (myosin) in facilitating the cell driven rupture of the cortex. We perform under-agarose chemotaxis experiments, using Dictyostelium discoideum cells, to visualize the dynamics of myosin and calculate changes in membrane energy in the blebbing region. To facilitate a rapid detection of blebs and analysis of the energy and myosin distribution at the cell front, we introduce an autonomous bleb detection algorithm that takes in discrete cell boundaries and returns the coordinate location of blebs with its shape characteristics. We are able to identify by microscopy naturally occurring gaps in the cortex prior to membrane detachment at sites of bleb nucleation. These gaps form at positions calculated to have high membrane energy, and are associated with areas of myosin enrichment. Myosin is also shown to accumulate in the cortex prior to bleb initiation and just before the complete disassembly of the cortex. Together our findings provide direct spatial and temporal evidence to support cortex rupture as an intrinsic bleb initiation mechanism and suggests that myosin clusters are associated with regions of high membrane energy where its contractile activity leads to a rupture of the cortex at points of maximal energy.

PMID:35468148 | DOI:10.1371/journal.pone.0265380

PLoS One. 2022 Apr 25;17(4):e0267047. doi: 10.1371/journal.pone.0267047. eCollection 2022.

ABSTRACT

COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determined key molecules and biological pathways in disease severity, and disease status. In addition to the individual omics analyses, we perform the integrative method Sparse Multiple Canonical Correlation Analysis to analyse relationships of the different view of data. Our findings suggest that COVID-19 status is associated with the cell cycle and death, as well as the inflammatory response. This relationship is reflected in all four sets of molecules analyzed. We further observe that the metabolic processes, particularly processes to do with vitamin absorption and cholesterol are implicated in COVID-19 status and severity.

PMID:35468151 | DOI:10.1371/journal.pone.0267047

PLoS One. 2022 Apr 25;17(4):e0266185. doi: 10.1371/journal.pone.0266185. eCollection 2022.

ABSTRACT

BACKGROUND: Maternal mortality is a persistent public health problem worldwide. The maternal mortality ratio of Myanmar was 250 deaths per 100,000 live births in 2017 which was the second-highest among ASEAN member countries in that year. Myanmar’s infant mortality rate was twice the average of ASEAN member countries in 2020. This study examined factors influencing institutional maternity service utilization and identified the need for improved maternal health outcomes.

METHODS: A cross-sectional study design was used to examine the experience of 3,642 women from the 2015-16 Myanmar Demographic and Health Survey by adapting Andersen’s Behavioral Model. Both descriptive and inferential statistics were applied. Adjusted odds ratios and 95% confidence interval were reported in the logistic regression results.

RESULTS: The findings illustrate that the proportion of women who delivered their last child in a health/clinical care facility was 39.7%. Women live in rural areas, states/regions with a high levels of poverty, poor households, experience with financial burden and the husband’s occupation in agriculture or unskilled labor were negatively associated with institutional delivery. While a greater number of ANC visits and level of the couple’s education had a positive association with institutional delivery.

CONCLUSION: The determinants of institutional delivery utilization in this study related to the institutional facilities environment imply an improvement of the institutional availability and accessibility in rural areas, and different states/regions, particularly Chin, Kayah and Kachin States- the poorest states in Myanmar. The poverty reduction strategies are urgently implemented because problems on health care costs and household economic status played important roles in institutional delivery utilization. The ANC visits indicated a significant increase in institutional delivery. The government needs to motivate vulnerable population groups to seek ANC and institutional delivery. Moreover, education is crucial in increasing health knowledge, skills, and capabilities. Thus, improving access to quality, formal, and informal education is necessary.

PMID:35468140 | DOI:10.1371/journal.pone.0266185

PLoS Negl Trop Dis. 2022 Apr 25;16(4):e0010394. doi: 10.1371/journal.pntd.0010394. Online ahead of print.

ABSTRACT

Murine typhus is a flea-borne zoonotic disease with acute febrile illness caused by Rickettsia typhi and is distributed widely throughout the world, particularly in port cities and coastal regions. We observed that murine typhus was an endemic disease (number of annual indigenous cases = 29.23±8.76) with a low incidence rate (0.13±2.03*10-4 per 100,000 person-years) in Taiwan from 2007-2019. Most (45.79%, 174/380) indigenous infections were reported in May, June, and July. The incidence rates in both May and June were statistically higher than those in other months (p<0.05). Correspondingly, sera collected from small mammals (rodents and shrews) trapped in airports and harbors demonstrated anti-R. typhi antibody responses (seropositive rate = 8.24±0.33%). Interestingly, the ports with the highest seropositivity rates in small mammals are all inside/near the areas with the highest incidence rates of indigenous murine typhus. In addition, incidence rates in humans were positively correlated with the 1-month and 2-month prior seropositive rates in small mammals (R = 0.31 and 0.37, respectively). As early treatment with appropriate antibiotics for murine typhus could effectively shorten the duration of illness and reduce the risk of hospitalization and fatality, flea-related exposure experience should be considered in clinics during peak seasons and the months after a rise in seropositivity rates in small mammals. Surveillance in small mammals might be helpful for the development of real-time reporting or even early reminders for physicians of sporadic murine typhus cases based on the delayed correlation observed in this study.

PMID:35468137 | DOI:10.1371/journal.pntd.0010394

PLoS Comput Biol. 2022 Apr 25;18(4):e1009885. doi: 10.1371/journal.pcbi.1009885. eCollection 2022 Apr.

ABSTRACT

B cells undergo rapid mutation and selection for antibody binding affinity when producing antibodies capable of neutralizing pathogens. This evolutionary process can be intermixed with migration between tissues, differentiation between cellular subsets, and switching between functional isotypes. B cell receptor (BCR) sequence data has the potential to elucidate important information about these processes. However, there is currently no robust, generalizable framework for making such inferences from BCR sequence data. To address this, we develop three parsimony-based summary statistics to characterize migration, differentiation, and isotype switching along B cell phylogenetic trees. We use simulations to demonstrate the effectiveness of this approach. We then use this framework to infer patterns of cellular differentiation and isotype switching from high throughput BCR sequence datasets obtained from patients in a study of HIV infection and a study of food allergy. These methods are implemented in the R package dowser, available at https://dowser.readthedocs.io.

PMID:35468128 | DOI:10.1371/journal.pcbi.1009885

PLoS Comput Biol. 2022 Apr 25;18(4):e1010048. doi: 10.1371/journal.pcbi.1010048. Online ahead of print.

ABSTRACT

Tumors often harbor orders of magnitude more mutations than healthy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover, or a combination of the two. It is difficult to disentangle these two mechanisms based on widely available bulk sequencing data, where sequences from individual cells are intermixed and, thus, the cell lineage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data. Our method works by simulating tumor growth and finding the parameters with which the observed data can be reproduced with maximum likelihood. Applying this method to a real tumor sample, we find that both the mutation rate and the frequency of death may be high.

PMID:35468135 | DOI:10.1371/journal.pcbi.1010048