Tag: nevin manimala

J Chem Theory Comput. 2023 Aug 8. doi: 10.1021/acs.jctc.3c00383. Online ahead of print.

ABSTRACT

Explainable and interpretable unsupervised machine learning helps one to understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restricted Boltzmann machines compress consistently into a few bits the information stored in a sequence of five amino acids at the start or end of α-helices or β-sheets. The weights learned by the machines reveal unexpected properties of the amino acids and the secondary structure of proteins: (i) His and Thr have a negligible contribution to the amphiphilic pattern of α-helices; (ii) there is a class of α-helices particularly rich in Ala at their end; (iii) Pro occupies most often slots otherwise occupied by polar or charged amino acids, and its presence at the start of helices is relevant; (iv) Glu and especially Asp on one side and Val, Leu, Iso, and Phe on the other display the strongest tendency to mark amphiphilic patterns, i.e., extreme values of an effective hydrophobicity, though they are not the most powerful (non)hydrophobic amino acids.

PMID:37552831 | DOI:10.1021/acs.jctc.3c00383

JCO Clin Cancer Inform. 2023 Aug;7:e2300082. doi: 10.1200/CCI.23.00082.

ABSTRACT

PURPOSE: Altmetric Attention Scores (Altmetrics) are real-time measures of scientific impact and attention through various public outlets, including news, blogs, and social media. Herein, we aimed to describe and characterize the relationship between Altmetrics, conventional impact metrics, and features of published cancer clinical trials.

METHODS: We identified two-arm phase III cancer randomized clinical trials with a superiority end point and publication date between 2015 and 2020 from HemOnc and tabulated the following data: Altmetric, study positivity, US Food and Drug Administration (FDA) registration trial status, cancer site/category, treatment context (curative or palliative), trial design, primary end point type, experimental/control arm modality, and journal tier. We further collected conventional bibliometrics including the number of citations and relative citation ratio (RCR) for all published studies. Multiple linear regression modeling identified clinical trial factors predictive of Altmetrics, with alpha = .05 defining statistical significance.

RESULTS: Altmetrics were found for 681 (98%) of 698 publications, with a median score of 38.5 (IQR, 13-132.8). FDA registration studies (β [95% CI], 84.7 [48.8 to 120.6]; P < .001), studies reporting on curative (as opposed to palliative) interventions (-29 [-53.7 to -4.4]; P = .02), genitourinary trials (73.2 [28.1 to 118.2]; P = .001), studies published in tier 1 journals (P < .001), and those with an increased number of citations per year (0.81 [0.66 to 0.95]; P < .001) were significantly associated with increased engagement as measured by Altmetrics. Furthermore, there was a strong correlation between all collected bibliometrics and Altmetrics (R² = 0.63, 0.68, and 0.67; P < .001 for citation count, citations per year, and RCR, respectively).

CONCLUSION: FDA registration trials describing curative interventions, studies published in traditionally defined high-impact journals, and genitourinary trial publications tend to have the greatest Altmetrics. We observed a strong relationship between Altmetrics and conventional bibliometrics. The significance and consequences of these relationships warrant further investigation.

PMID:37552823 | DOI:10.1200/CCI.23.00082

Eur J Neurol. 2023 Aug 8. doi: 10.1111/ene.16025. Online ahead of print.

ABSTRACT

BACKGROUND: Existing data regarding the link between COVID-19 vaccine and myasthenia gravis (MG) are scarce. We aimed to assess the association between Pfizer-BioNTech vaccine with both new-onset MG and MG exacerbation.

METHODS: For the first aim, we conducted a nested case-control study in a cohort of 3,052,467 adults, without a diagnosis of MG, from the largest healthcare provider in Israel. Subjects were followed from January 1, 2021, until June 30, 2022, for the occurrence of MG. Ten randomly selected controls were matched to each case of new-onset MG on age, and sex. For the second aim, a nested case-control study was conducted in a cohort of 1,446 MG patients. Four randomly selected MG patients (controls) were matched to each case of MG exacerbation. Exposure to COVID-19 vaccine in the prior four weeks was assessed in cases and controls.

RESULTS: Overall, 332 patients had new-onset MG and were matched with 3,320 controls. Multivariable conditional logistic regression models showed that the OR for new-onset MG, associated with COVID-19 vaccine, was 1.14 (95% CI, 0.73-1.78). The results were consistent in sensitivity analysis that used more stringent criteria to define MG. Overall, 62 patients with MG exacerbation were matched to 248 MG controls. The multivariable OR for MG exacerbation, associated with COVID-19 vaccine, was 1.35 (0.37-4.89). All results were similar when the prior exposure to COVID-19 vaccine was extended to 8 weeks.

CONCLUSIONS: This study suggests that Pfizer-BioNTech vaccine is not associated with increased risk of new-onset nor exacerbation of myasthenia gravis.

PMID:37552795 | DOI:10.1111/ene.16025

Clin Infect Dis. 2023 Aug 8:ciad464. doi: 10.1093/cid/ciad464. Online ahead of print.

ABSTRACT

BACKGROUND: Scarce data are available comparing infective endocarditis (IE) following surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). This study aimed to compare the clinical presentation, microbiological profile, management, and outcomes of IE after SAVR vs. TAVR.

METHODS: Data were collected from the “Infectious Endocarditis after TAVR International” (enrollment from 2005 to 2020) and the “International Collaboration on Endocarditis” enrollment from 2000 to 2012) registries. Only patients with an IE affecting the aortic valve prosthesis were included. A 1:1 paired matching approach was used to compare patients with TAVR and SAVR.

RESULTS: A total of 1688 patients were included. Of them, 602 (35.7%) had a surgical bioprosthesis (SB), 666 (39.5%) a mechanical prosthesis, 70 (4.2%) a homograft, and 350 (20.7%) a transcatheter heart valve. In the SAVR vs. TAVR matched population, the rate of new moderate or severe aortic regurgitation was higher in the SB group (43.4% vs. 13.4%, p < 0.001), and fewer vegetations were diagnosed in the SB group (62.5% vs. 82%, p < 0.001). Patients with a SB had a higher rate of perivalvular extension (47.9% vs.27%, p < 0.001) and Staphylococcus Aureus was less common in this group (13.4% vs. 22%, p = 0.033). Despite a higher rate of surgery in patients with SB (44.4% vs. 26.8%, p < 0.001), 1-year mortality was similar (SB: 46.5%, TAVR: 44.8%, log-rank p = 0.697).

CONCLUSIONS: Clinical presentation, type of causative microorganism and treatment differed between patients with an IE located on SB compared to TAVR. Despite these differences, both groups exhibited high and similar mortality at 1-year follow-up.

PMID:37552784 | DOI:10.1093/cid/ciad464

ACS ES T Water. 2022 May 12:acsestwater.2c00060. doi: 10.1021/acsestwater.2c00060. Online ahead of print.

ABSTRACT

Because of the increased population density, high-risk behavior of young students, and lower vaccination rates, university campuses are considered hot spots for COVID-19 transmission. This study monitored the SARS-CoV-2 RNA levels in the wastewater of a Canadian university campus for a year to provide actionable information to safely manage COVID-19 on campus. Wastewater samples were collected from the campus sewer and residence buildings to identify changes, peaks, and hotspots and search for associations with campus events, social gatherings, long weekends, and holidays. Furthermore, the impact of wastewater parameters (total solids, volatile solids, temperature, pH, turbidity, and UV absorbance) on SARS-CoV-2 detection was investigated, and the efficiency of ultrafiltration and centrifugation concentration methods were compared. RT-qPCR was used for detecting SARS-CoV-2 RNA. Wastewater signals largely correlated positively with the clinically confirmed COVID-19 cases on campus. Long weekends and holidays were often followed by increased viral signals, and the implementation of lockdowns quickly decreased the case numbers. In spite of online teaching and restricted access to campus, the university represented a microcosm of the city and mirrored the same trends. Results indicated that the centrifugation concentration method was more sensitive for wastewater with high solids content and that the ultrafiltration concentration method was more sensitive for wastewater with low solids content. Wastewater characteristics collected from the buildings and the campus sewer were different. Statistical analysis was performed to manifest the observations. Overall, wastewater surveillance provided actionable information and was also able to bring high-risk factors and events to the attention of decision-makers, enabling timely corrective measures.

PMID:37552746 | PMC:PMC9128010 | DOI:10.1021/acsestwater.2c00060

ACS ES T Water. 2022 Jul 29:acsestwater.2c00053. doi: 10.1021/acsestwater.2c00053. Online ahead of print.

ABSTRACT

Wastewater-based epidemiology (WBE) is a useful complement to clinical testing for managing COVID-19. While community-scale wastewater and clinical data frequently correlate, less is known about subcommunity relationships between the two data types. Moreover, nondetects in qPCR wastewater data are typically handled through methods known to bias results, overlooking perhaps better alternatives. We address these knowledge gaps using data collected from September 2020-June 2021 in Davis, California (USA). We hypothesize that coupling the expectation maximization (EM) algorithm with the Markov Chain Monte Carlo (MCMC) method could improve estimation of “missing” values in wastewater qPCR data. We test this hypothesis by applying EM-MCMC to city wastewater treatment plant data and comparing output to more conventional nondetect handling methods. Dissimilarities in results (i) underscore the importance of specifying nondetect handling method in reporting and (ii) suggest that using EM-MCMC may yield better agreement between community-scale clinical and wastewater data. We also present a novel framework for spatially aligning clinical data with wastewater data collected upstream of a treatment plant (i.e., distributed across a sewershed). Applying the framework to data from Davis reveals reasonable agreement between wastewater and clinical data at highly granular spatial scales-further underscoring the public-health value of WBE.

PMID:37552742 | PMC:PMC9397567 | DOI:10.1021/acsestwater.2c00053

ACS ES T Water. 2022 Jul 1:acsestwater.2c00094. doi: 10.1021/acsestwater.2c00094. Online ahead of print.

ABSTRACT

There have been over 507 million cases of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in 6 million deaths globally. Wastewater surveillance has emerged as a valuable tool in understanding SARS-CoV-2 burden in communities. The National Wastewater Surveillance System (NWSS) partnered with the United States Geological Survey (USGS) to implement a high-frequency sampling program. This report describes basic surveillance and sampling statistics as well as a comparison of SARS-CoV-2 trends between high-frequency sampling 3-5 times per week, referred to as USGS samples, and routine sampling 1-2 times per week, referred to as NWSS samples. USGS samples provided a more nuanced impression of the changes in wastewater trends, which could be important in emergency response situations. Despite the rapid implementation time frame, USGS samples had similar data quality and testing turnaround times as NWSS samples. Ensuring there is a reliable sample collection and testing plan before an emergency arises will aid in the rapid implementation of a high-frequency sampling approach. High-frequency sampling requires a constant flow of information and supplies throughout sample collection, testing, analysis, and data sharing. High-frequency sampling may be a useful approach for increased resolution of disease trends in emergency response.

PMID:37552727 | PMC:PMC9291391 | DOI:10.1021/acsestwater.2c00094

ACS ES T Water. 2022 May 13:acsestwater.2c00059. doi: 10.1021/acsestwater.2c00059. Online ahead of print.

ABSTRACT

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020-2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident-rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.

PMID:37552724 | PMC:PMC9128018 | DOI:10.1021/acsestwater.2c00059

ACS ES T Eng. 2022 Feb 17:acsestengg.1c00321. doi: 10.1021/acsestengg.1c00321. Online ahead of print.

ABSTRACT

In indoor environments with limited ventilation, recirculating portable air filtration (PAF) units may reduce COVID-19 infection risk via not only the direct aerosol route (i.e., inhalation) but also via an indirect aerosol route (i.e., contact with the surface where aerosol particles deposited). We systematically investigated the impact of PAF units in a mock classroom, as a supplement to background ventilation, on localized and whole-room surface deposition and particle concentration. Fluorescently tagged particles with a volumetric mean diameter near 2 μm were continuously introduced into the classroom environment via a breathing simulator with a prescribed inhalation-exhalation waveform. Deposition velocities were inferred on >50 horizontal and vertical surfaces throughout the classroom, while aerosol concentrations were spatially monitored via optical particle spectrometry. Results revealed a particle decay rate consistent with expectations based upon the reported clean air delivery rates of the PAF units. Additionally, the PAF units reduced peak concentrations by a factor of around 2.5 compared to the highest concentrations observed and led to a statistically significant reduction in deposition velocities for horizontal surfaces >2.5 m from the aerosol source. Our results not only confirm that PAF units can reduce particle concentrations but also demonstrate that they may lead to reduced particle deposition throughout an indoor environment when properly positioned with respect to the location of the particle source(s) within the room (e.g., where the largest group of students sit) and the predominant air distribution profile of the room.

PMID:37552723 | PMC:PMC8864773 | DOI:10.1021/acsestengg.1c00321

ACS ES T Water. 2022 Jul 6:acsestwater.2c00103. doi: 10.1021/acsestwater.2c00103. Online ahead of print.

ABSTRACT

After its emergence in late November/December 2019, the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) rapidly spread globally. Recognizing that this virus is shed in feces of individuals and that viral RNA is detectable in wastewater, testing for SARS-CoV-2 in sewage collections systems has allowed for the monitoring of a community’s viral burden. Over a 9 month period, the influents of two regional wastewater treatment facilities were concurrently examined for wild-type SARS-CoV-2 along with variants B.1.1.7 and B.1.617.2 incorporated as they emerged. Epidemiological data including new confirmed COVID-19 cases and associated hospitalizations and fatalities were tabulated within each location. RNA from SARS-CoV-2 was detectable in 100% of the wastewater samples, while variant detection was more variable. Quantitative reverse transcription PCR (RT-qPCR) results align with clinical trends for COVID-19 cases, and increases in COVID-19 cases were positively related with increases in SARS-CoV-2 RNA load in wastewater, although the strength of this relationship was location specific. Our observations demonstrate that clinical and wastewater surveillance of SARS-CoV-2 wild type and constantly emerging variants of concern can be combined using RT-qPCR to characterize population infection dynamics. This may provide an early warning for at-risk communities and increases in COVID-19 related hospitalizations.

PMID:37552718 | PMC:PMC9291321 | DOI:10.1021/acsestwater.2c00103