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Background: Machine learning methods may complement traditional analytic methods for medical device surveillance. Methods and results Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; nonfatal ICD-related adverse events, 19.3%–26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%–37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=−0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=−0.042). Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance.

Acyl -CoA: diacylglycerol acyltransferase (DGAT) is the only key enzyme evolved in the Kennedy pathway and catalyzes the last step. This paper summrized the features of DGAT1 (subtype 1 of DGAT family) in eukaryote, including construction, substrate specificity, and spatio - temporal expressions. It especially reviewed DGAT1 function on plant growth and development. In discussion, some suggestions were made for further study of DGAT1 in plant. [ABSTRACT FROM AUTHOR]

We evaluated the immunogenicity and protective efficacy of a combined DNA vaccine containing six genes encoding immunodominant antigens from Mycobacterium bovis and Brucella abortus. The number of lymph node and spleen cultures positive for M. bovis and B. abortus from calves immunized with the combined DNA vaccine was significantly reduced ( p < 0.01) compared with unvaccinated calves after challenge with virulent M. bovis and B. abortus 544. The combined DNA vaccine group displayed stronger antigen-specific interferon-γ (IFN-γ) responses and antigen-specific IFN-γ ELISPOT activities 2 months after final immunization and after challenge. Antigen-specific CD4+ and CD8+ T cell responses in the combined DNA vaccine group were higher than either the Bacillus Calmette-Guerin (BCG)–positive or S19-positive control group. Likewise, more calves in the DNA vaccine group exhibited antigen-specific IgG titers and had higher IgG titers than those in the BCG- or S19-immunized groups 2 months after the final immunization. Moreover, two antigens in the combined DNA vaccine induced significant antigen-specific IFN-γ responses 6 months after challenge ( p < 0.05). Bacterial counts and pathological analyses of the challenged animals indicated that the combined DNA vaccine provided significantly better protection than the BCG vaccine against M. bovis, and the protection level induced by the combined DNA vaccine was comparable to S19 against B. abortus. This is the first report to demonstrate that a single combined DNA vaccine protects cattle against two infectious diseases. [ABSTRACT FROM AUTHOR]

Objective Data Sources and Study Setting Study Design Data Collection/Extraction Methods Principal Findings Conclusions To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter‐ and hospital‐submitted inpatient claims.Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim.We combined hospital‐ and MAO‐submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO‐submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures.After removing void and duplicate claims, identifying overlapped claims between the hospital‐ and MAO‐submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital‐submitted claims were submitted within 3 months after a one‐year performance period, versus 85.2% of MAO‐submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code.Inpatient MA data are feasible for use in CMS claims‐based hospital outcome measures. We recommend prioritizing hospital‐submitted over MAO‐submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors. [ABSTRACT FROM AUTHOR]

Background: Efforts to decrease hospitalization costs could increase post-acute care costs. This effect could undermine initiatives to reduce overall episode costs and have implications for the design of health care under alternative payment models.Methods: Among Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with acute myocardial infarction (AMI) between July 2010 and June 2013 in the Premier Healthcare Database, we studied the association of in-hospital and post-acute care resource utilization and outcomes by in-hospital cost tertiles.Results: Among patients with AMI at 326 hospitals, the median (range) of each hospital's mean per-patient in-hospital risk-standardized cost (RSC) for the low, medium, and high cost tertiles were $16,257 ($13,097-$17,648), $18,544 ($17,663-$19,875), and $21,831 ($19,923-$31,296), respectively. There was no difference in the median (IQR) of risk-standardized post-acute payments across cost-tertiles: $5014 (4295-6051), $4980 (4349-5931) and $4922 (4056-5457) for the low (n = 90), medium (n = 98), and high (n = 86) in-hospital RSC tertiles (p = 0.21), respectively. In-hospital and 30-day mortality rates did not differ significantly across the in-hospital RSC tertiles; however, 30-day readmission rates were higher at hospitals with higher in-hospital RSCs: median = 17.5, 17.8, and 18.0% at low, medium, and high in-hospital RSC tertiles, respectively (p = 0.005 for test of trend across tertiles).Conclusions: In our study of patients hospitalized with AMI, greater resource utilization during the hospitalization was not associated with meaningful differences in costs or mortality during the post-acute period. These findings suggest that it may be possible for higher cost hospitals to improve efficiency in care without increasing post-acute care utilization or worsening outcomes. [ABSTRACT FROM AUTHOR]

This document analyzes the variation in excess mortality rates across different states in the United States during the COVID-19 pandemic. It estimates the number of excess deaths and the potential number of deaths that could have been averted if all states had matched the lowest excess death rates. The findings suggest that a significant number of excess deaths could have potentially been prevented if all states had matched the rates of the ten states with the lowest excess mortality. The study also examines the correlation between excess mortality and vaccination rates, finding that the inverse correlation decreased over time. The document emphasizes the importance of reducing health disparities, increasing vaccination rates, and implementing effective measures during future outbreaks. [Extracted from the article]

Background The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection. Methods This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms. Results Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months. Conclusions We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration.  NCT04610515. [ABSTRACT FROM AUTHOR]

Background While prior work examining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern focused on hospitalization and death, less is known about differences in clinical presentation. We compared the prevalence of acute symptoms across pre-Delta, Delta, and Omicron. Methods We conducted an analysis of the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE), a cohort study enrolling symptomatic SARS-CoV-2-positive participants. We determined the association between the pre-Delta, Delta, and Omicron time periods and the prevalence of 21 coronavirus disease 2019 (COVID-19) acute symptoms. Results We enrolled 4113 participants from December 2020 to June 2022. Pre-Delta vs Delta vs Omicron participants had increasing sore throat (40.9%, 54.6%, 70.6%; P <.001), cough (50.9%, 63.3%, 66.7%; P <.001), and runny noses (48.9%, 71.3%, 72.9%; P <.001). We observed reductions during Omicron in chest pain (31.1%, 24.2%, 20.9%; P <.001), shortness of breath (42.7%, 29.5%, 27.5%; P <.001), loss of taste (47.1%, 61.8%, 19.2%; P <.001), and loss of smell (47.5%, 55.6%, 20.0%; P <.001). After adjustment, those infected during Omicron had significantly higher odds of sore throat vs pre-Delta (odds ratio [OR], 2.76; 95% CI, 2.26–3.35) and Delta (OR, 1.96; 95% CI, 1.69–2.28). Conclusions Participants infected during Omicron were more likely to report symptoms of common respiratory viruses, such as sore throat, and less likely to report loss of smell and taste. Trial registration NCT04610515. [ABSTRACT FROM AUTHOR]

Background Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. Methods We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID−), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. Results Among the first 1000 participants, 722 were COVID+ and 278 were COVID−. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID− group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID− groups at 3 months. Conclusions Approximately half of COVID+ participants, as compared with one-quarter of COVID− participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. Clinical Trials Registration NCT04610515. [ABSTRACT FROM AUTHOR]

After the solution treatment and cold rolling deformation of 00Cr22Ni5Mo3N duplex stainless steel, its super plastic deformation test is made at 850 ~ 1 000 °C for 5 minutes. The effect of deformation temperature on the superplasticity of 00Cr22Ni5Mo3N is studied. Its microstructure before deformation is observed, and the influence of sigma phase and microstructure changes on the superplasticity is researched. The reason why the deformation temperature affects the superplasticity of 00Cr22Ni5Mo3N is revealed. The results show that, when the deformation temperature changes, the microstructure and quantity of σ phase changes, and so does the superplasticity. After solution treatment at 1 300 °C, 85% of cold rolling deformation, and holding at 850 - 1 000 °C for 5 rain, its microstructure ~s recrystallized, the uniform equiaxed grains become more and more with the increase in deformation temperature, and the number of sigma phase gets smaller. Meanwhile, the elongation of00Cr22Ni5Mo3N duplex stainless steel increases from 480% to 1290%, and the peak stress decreases from 125 MPa to 32 MPa. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged <25 years (-2.9 to 14.1). Among persons aged <65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of >1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates. [ABSTRACT FROM AUTHOR]

Background: Infection fatality rate and infection hospitalization rate, defined as the proportion of deaths and hospitalizations, respectively, of the total infected individuals, can estimate the actual toll of coronavirus disease 2019 (COVID-19) on a community, as the denominator is ideally based on a representative sample of a population, which captures the full spectrum of illness, including asymptomatic and untested individuals.Objective: To determine the COVID-19 infection hospitalization rate and infection fatality rate among the non-congregate population in Connecticut between March 1 and June 1, 2020.Methods: The infection hospitalization rate and infection fatality rate were calculated for adults residing in non-congregate settings in Connecticut prior to June 2020. Individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies were estimated using the seroprevalence estimates from the recently conducted Post-Infection Prevalence study. Information on total hospitalizations and deaths was obtained from the Connecticut Hospital Association and the Connecticut Department of Public Health, respectively.Results: Prior to June 1, 2020, nearly 113,515 (90% confidence interval [CI] 56,758-170,273) individuals were estimated to have SARS-CoV-2 antibodies, and there were 7792 hospitalizations and 1079 deaths among the non-congregate population. The overall COVID-19 infection hospitalization rate and infection fatality rate were estimated to be 6.86% (90% CI, 4.58%-13.72%) and 0.95% (90% CI, 0.63%-1.90%), respectively, and there was variation in these rate estimates across subgroups; older people, men, non-Hispanic Black people, and those belonging to 2 of the counties had a higher burden of adverse outcomes, although the differences between most subgroups were not statistically significant.Conclusions: Using representative seroprevalence estimates, the overall COVID-19 infection hospitalization rate and infection fatality rate were estimated to be 6.86% and 0.95%, respectively, among community residents in Connecticut. [ABSTRACT FROM AUTHOR]

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US. [ABSTRACT FROM AUTHOR]

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors.Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies. [ABSTRACT FROM AUTHOR]

In this paper, the author presented a strategy for classifying local multi-channels MS waveform, triggering by a single event. There were three steps to achieve the goal. Firstly, based on STA/LTA method, the first arrival and terminated time had been picked up, using the MS signal preprocessed; secondly, the author extracted the waveform features, time-frequency (L,f), amplitude (A), statistics of amplitude distribution (AD) and threshold algorithm (TS), and correlation coefficient (R); thirdly, before establishing an effective judgment mechanism, this method employed a hierarchical recognition framework with 3 layers, which integrated preliminary judgment, combined recognition and optimization judgment. This method was validated through analyzing a coal mine visual event in Shandong Province, and the result shows that it is successfully used to classify electromagnetic interference wave, background noise and MS events. The result can basically meet the requirements of classification accuracy. [ABSTRACT FROM AUTHOR]