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Zhongguo Xue Xi Chong Bing Fang Zhi Za... Jun 2024To investigate the feasibility of constructing the risk index of infection based on the classification of echinococcosis lesions, so as to provide insights into the...
OBJECTIVE
To investigate the feasibility of constructing the risk index of infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis.
METHODS
The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of infection were examined using a univariate linear regression model.
RESULTS
A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ = 4.12 to 708.65, all values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ = 6.74 to 122.60, all values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of infection and the detection of incident cystic echinococcosis cases during the study period ( = -0.518 to 2.265, all values > 0.05), and strong correlations were found between the risk indices of infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all values > 0.7, = 2.521 to 3.692, all values < 0.05). Linear regression models were established between the risk index of infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant ( = 0.214 to 2.168, = 2.458 to 3.692, = 6.044 to 13.629, all values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models ( = 16.479 to 197.304, = 271.570 to 38 928.860, all values < 0.05).
CONCLUSIONS
The risk index of infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.
Topics: Echinococcosis; Humans; China; Echinococcus; Risk Factors; Animals
PubMed: 38952312
DOI: 10.16250/j.32.1374.2024068 -
Statistics in Medicine Jul 2024Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this...
Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model's performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals' experience with COVID-19, some unaccounted-for latent variables were associated with the disease.
PubMed: 38951953
DOI: 10.1002/sim.10156 -
BMC Pregnancy and Childbirth Jun 2024An individualized education using visual aids, allowing the woman to demonstrate what she has learned, and providing the opportunity for the woman to ask questions are... (Randomized Controlled Trial)
Randomized Controlled Trial
The effect of breastfeeding education given through the teach-back method on mothers' breastfeeding self-efficacy and breastfeeding success: a randomized controlled study.
BACKGROUND
An individualized education using visual aids, allowing the woman to demonstrate what she has learned, and providing the opportunity for the woman to ask questions are important in terms of breastfeeding self-efficacy, breastfeeding success, and the sustainability of the education. This study is original in evaluating the effectiveness and sustainability of breastfeeding education provided through the teach-back method in terms of breastfeeding self-efficacy and success in a short period of time. Therefore, the aim of this study is to examine the impact of teach-back method on mothers' breastfeeding self-efficacy and breastfeeding success.
MATERIALS AND METHODS
This is a randomized controlled study. The population of this study consisted of women who gave birth in the obstetrics and gynecology department of a state hospital located in Çorlu, in the northwest region of Turkey, between March 2022 and August 2022. The sample of this study consisted of a total of 100 postpartum women, with 50 participants in the experimental group and 50 participants in the control group, who gave birth in the obstetrics and gynecology department of Çorlu State Hospital. Computer-assisted simple randomization was employed to ensure the homogeneous distribution of the women into the experimental and control groups. The women in the experimental group received education and counseling services using the Teach-Back Method, based on the content of the prepared Breastfeeding Education Guide. The control group mothers, on the other hand, received standard breastfeeding education and counseling services. The data were collected through face-to-face interviews during the first 24 h postpartum and at the 1-month follow-up visits. In the study, the data collection tools used were a Personal Information Form, LATCH Breastfeeding Assessment and Evaluation Scale, Postpartum Breastfeeding Self-Efficacy Scale (short form), and the Teach-Back Observation Tool. In the evaluation of the research findings, the SPSS (Statistical Package for the Social Sciences) version 25.0 (IBM Corp., Armonk, NY, USA) program was used for statistical analyses. Descriptive, graphical, and statistical methods were employed to examine whether the scores obtained from each continuous variable followed a normal distribution. The Kolmogorov-Smirnov test was used to assess the normality of the scores derived from a continuous variable using statistical methods.
RESULTS
In the study, no significant difference was found in the distribution of the socio-demographic characteristics of the participants according to the study groups. In the experimental group, which received training with the tell-what-you-learned method, the mothers' average EÖYÖ scores before the training, at the 24th hour after the training and at the 1st month after the training were 46.41 ± 11.26, respectively; It was determined to be 66.23 ± 6.94 and 67.84 ± 6.27. In the measurements made during the follow-up, it was determined that there was a significant difference in the study group's EÖYÖ score averages (p < 0,001). For mothers in the experimental group, the average LATCH score of the mothers before training, 24 h after training and 1 month after training was 7.73 ± 1.81, respectively; It was determined that these values were 8.66 ± 1.61 and 9.95 ± 0.30, and there was a significant difference in the mean LATCH scores of the study group in the measurements made during the follow-up (p < 0.001).
CONCLUSIONS
Breastfeeding education provided through the teach-back method is more effective in increasing both breastfeeding success and breastfeeding self-efficacy when compared to standard breastfeeding education.
TRIAL REGISTRATION
Iran Randomized Clinical Trial Center IRCT20220509054795N2 Date of first registration: 10/11/2022.
Topics: Humans; Breast Feeding; Female; Self Efficacy; Adult; Mothers; Turkey; Patient Education as Topic; Young Adult
PubMed: 38951771
DOI: 10.1186/s12884-024-06601-0 -
Scientific Reports Jul 2024A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation...
Compounded wind gusts and maximum temperature via semiparametric copula in the risk assessments of power blackouts and air conditioning demands for major cities in Canada.
A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas. Their joint probability estimates allow for a better understanding of the risk of power blackouts and the demand for air conditioning in the community. The bivariate framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. A thorough model performance comparison indicated that GKDE outperformed the parametric models in defining the marginal distribution of selected univariate series. Significant positive correlations were observed among extreme pairs, except for Calgary and Halifax stations, with inconsistent correlation variations based on selected cities and lag time. Various parametric 2-D copulas were selected to model the dependence structure of bivariate pairs at different time lags for selected stations. AMWGS or AHMT events, when considered independently, would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles, indicating a higher risk of power blackouts and heightened air-conditioning demands, which varied inconsistently with time lags across the station. The bivariate AMWGS and corresponding MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AHMT and corresponding WGS events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT and corresponding WGS events. These hazard statistics can help in better planning for community well-being during extreme weather.
PubMed: 38951564
DOI: 10.1038/s41598-024-65413-6 -
Journal of Patient-reported Outcomes Jun 2024The Insomnia Severity Index (ISI) is a widely used measure of insomnia severity. Various ISI research findings suggest different factor solutions and meaningful... (Randomized Controlled Trial)
Randomized Controlled Trial
Re-examining the factor structure of the Insomnia Severity Index (ISI) and defining the meaningful within-individual change (MWIC) for subjects with insomnia disorder in two phase III clinical trials of the efficacy of lemborexant.
BACKGROUND
The Insomnia Severity Index (ISI) is a widely used measure of insomnia severity. Various ISI research findings suggest different factor solutions and meaningful within-individual change (MWIC) to detect treatment response in patients with insomnia. This study examined an ISI factor solution and psychometric indices to define MWIC in a robust patient sample from clinical trial settings.
METHODS
We endeavored to improve upon previous validation of ISI by examining structural components of confirmatory factor analysis (CFA) models using two large, placebo-controlled clinical trials of lemborexant for insomnia. Using the best-fitting two-factor solution, we evaluated anchor-based, distribution-based and receiver operating characteristic (ROC) curve methods to derive an estimate of the MWIC.
RESULTS
The model structure for the 7-item scale proposed in other research did not fit the observed data from our two lemborexant clinical trials (N = 1956) as well as a two-factor solution based on 6 items did. Using triangulation of anchor-based, distribution-based, and ROC methods, we determined that a 5-point reduction using 6 items best represented a clinically meaningful improvement in individuals with insomnia in our patient sample.
CONCLUSIONS
A 6-item two-factor scale had better psychometric properties than the 7-item scale in this patient sample. On the 6-item scale, a reduction of 5 points in the ISI total score represented the MWIC. Generalizability of the proposed MWIC may be limited to patient populations with similar demographic and clinical characteristics.
Topics: Humans; Sleep Initiation and Maintenance Disorders; Male; Female; Severity of Illness Index; Middle Aged; Psychometrics; Adult; Factor Analysis, Statistical; Treatment Outcome; ROC Curve; Pyridines; Pyrimidines
PubMed: 38951287
DOI: 10.1186/s41687-024-00744-6 -
Zhonghua Fu Chan Ke Za Zhi Jun 2024To investigate the relationship between the polymorphism of endoplasmic reticulum aminopeptidase 1 (ERAP-1) gene and the occurrence of pre-eclampsia (PE). A...
To investigate the relationship between the polymorphism of endoplasmic reticulum aminopeptidase 1 (ERAP-1) gene and the occurrence of pre-eclampsia (PE). A case-control study was conducted in Beijing Obstetrics and Gynecology Hospital from October 2018 to October 2021. A total of 51 PE pregnant women with onset gestational age<34 weeks were selected as the PE group, and 48 normal pregnant women during the same period were selected as the control group. Venous blood samples were collected from the pregnant women before delivery and umbilical cord within 5 minutes after delivery. Single nucleotide polymorphisms (SNP) of ERAP-1 gene in the pregnant women and their fetus were detected by next-generation sequencing. Univariate analysis and multivariate logistic regression analysis were used to analyze all the SNP loci and alleles detected in the two groups, and the significant SNP were screened. (1) A total of 13 target SNP loci of maternal ERAP-1 gene were selected by univariate analysis. Among them, the frequency distribution of genotypes at 96096828, 96121524, 96121715, 96122260 and 96122281 showed statistically significant differences between PE group and control group (all <0.05). Multivariate logistic regression analysis showed that the risk of PE in pregnant women with TC genotype at locus 96121524 was 2.002 times higher than those with TT genotype (95%: 0.687-5.831, =0.020). (2) A total of 4 target SNP loci of ERAP-1 gene in fetal were selected by univariate analysis, and there was no statistical significance in gene polymorphism of the 4 loci between PE group and control group (all 0.05). Multivariate logistic regression analysis showed that the risk of PE in fetus with genotype AA at locus 96121406 was 0.236 times that of fetus with genotype GG (95%: 0.055-1.025, =0.016). ERAP-1 gene with TC genotype at 96121524 in the mother and GG genotype at 96121406 in the fetus might be related to the incidence of PE.
Topics: Humans; Female; Pregnancy; Pre-Eclampsia; Aminopeptidases; Minor Histocompatibility Antigens; Case-Control Studies; Polymorphism, Single Nucleotide; Genotype; Genetic Predisposition to Disease; Alleles; Adult; Gene Frequency; Fetus
PubMed: 38951078
DOI: 10.3760/cma.j.cn112141-20240201-00070 -
Bioinformatics (Oxford, England) Jul 2024Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding...
MOTIVATION
Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges.
RESULTS
In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained.
AVAILABILITY
https://github.com/Teddy-XiongGZ/DeepGSEA.
PubMed: 38950178
DOI: 10.1093/bioinformatics/btae434 -
PloS One 2024Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the...
Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p<0.03). Although the sample size in this analysis was limited, these results support a dual and complementary analysis approach from independently derived retinal arterial and venous non-invasive intracranial pressure predictions. Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters.
Topics: Humans; Intracranial Pressure; Machine Learning; Probability; Female; Male; Algorithms; Adult; Middle Aged
PubMed: 38950055
DOI: 10.1371/journal.pone.0306028 -
Biometrics Jul 2024The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear...
The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.
Topics: Bayes Theorem; Multivariate Analysis; Computer Simulation; Models, Statistical; Humans; Linear Models; Biometry; Normal Distribution
PubMed: 38949889
DOI: 10.1093/biomtc/ujae059 -
Physical Chemistry Chemical Physics :... Jul 2024The radiative cooling of naphthalene dimer cations, (CH) was studied experimentally through action spectroscopy using two different electrostatic ion-beam storage rings,...
The radiative cooling of naphthalene dimer cations, (CH) was studied experimentally through action spectroscopy using two different electrostatic ion-beam storage rings, DESIREE in Stockholm and Mini-Ring in Lyon. The spectral characteristics of the charge resonance (CR) band were observed to vary significantly with a storage time of up to 30 seconds in DESIREE. In particular, the position of the CR band shifts to the blue, with specific times (inverse of rates) of 0.64 s and 8.0 s in the 0-5 s and 5-30 s storage time ranges, respectively. These long-time scales indicate that the internal energy distribution of the stored ions evolves by vibrational radiative cooling, which is consistent with the absence of fast radiative cooling recurrent fluorescence for (CH). Density functional based tight binding calculations with local excitations and configuration interactions (DFTB-EXCI) were used to simulate the absorption spectrum for ion temperatures between 10 and 500 K. The evolution of the bandwidth and position with temperature is in qualitative agreement with the experimental findings. Furthermore, these calculations yielded linear temperature dependencies for both the shift and the broadening. Combining the relationship between the CR band position and the ion temperature with the results of the statistical model, we demonstrate that the observed blue shift can be used to determine the radiative cooling rate of (CH).
PubMed: 38949429
DOI: 10.1039/d4cp01200c