Nevin Manimala

PLoS One. 2021 Sep 20;16(9):e0257609. doi: 10.1371/journal.pone.0257609. eCollection 2021.

ABSTRACT

INTRODUCTION: The widespread use of face masks by the general public may help to prevent the spread of viruses. Face masks are thought to be a good strategy to protect against respiratory diseases such as the Coronavirus. Identifying student knowledge, attitude, and practice about the use of face masks is crucial to detect vulnerabilities and respond rapidly to avoid the spread of the infection. This study aimed to determine the knowledge, attitude, and practices of face mask utilization and associated factors in the COVID-19 pandemic among college students.

METHODS: A cross-sectional study was performed from February to March 2021 among 764 students from Wachemo University, Southern Ethiopia. A multistage sampling technique was used in the study. The sample size for each department was allocated in proportion to the number of students in that department, and each respondent was chosen using a simple random sampling procedure. Data were collected using a pre-tested self-administered questionnaire and analyzed using SPSS version 26. To predict the relationship between the predictor and outcome variables, a logistic regression model was used. At a p-value of 0.05, statistical significance was declared.

RESULTS: The study showed that the overall knowledge of the students was 223 (29.2%), their attitude was 673 (88.1%), and their practice was 684 (89.5%). The students from the college natural and computational sciences (AOR: 0.23; 95%CI: 0.13, 0.40) and students having good knowledge (AOR = 4.40; 95%CI; 2.13, 9.14) were found to be independently associated with face mask utilization.

CONCLUSION: When compared to other researches, the knowledge about the usage of face masks in this study was low, but the attitudes and practices were high. Authorities in areas that are in danger of a COVID-19 pandemic should plan and implement public awareness and education initiatives.

PMID:34543358 | DOI:10.1371/journal.pone.0257609

PLoS One. 2021 Sep 20;16(9):e0257520. doi: 10.1371/journal.pone.0257520. eCollection 2021.

ABSTRACT

INTRODUCTION: Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort.

METHODS: A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict IBD flare within 6 months of each visit. A 70% training and 30% validation approach was used.

RESULTS: A total of 95,878 patients across 780,559 visits were identified. Of these, 22,245 (23.2%) patients had at least one IBD flare. Patients were predominantly White (87.7%) and female (57.1%), with a mean age of 48.0 years. The logistic regression model had an area under the receiver operating curve (AuROC) of 0.66 (95% CI: 0.65-0.66), sensitivity of 0.69 (95% CI: 0.68-0.70), and specificity of 0.74 (95% CI: 0.73-0.74) in the validation cohort. The RF model had an AuROC of 0.80 (95% CI: 0.80-0.81), sensitivity of 0.74 (95% CI: 0.73-0.74), and specificity of 0.72 (95% CI: 0.72-0.72) in the validation cohort. Important predictors of IBD flare in the RF model were the number of previous flares, age, potassium, and white blood cell count.

CONCLUSION: The machine learning modeling framework was replicated and results showed a similar predictive accuracy in a nationally representative cohort of IBD patients. This modeling framework could be embedded in routine practice as a tool to distinguish high-risk patients for disease activity.

PMID:34543353 | DOI:10.1371/journal.pone.0257520

PLoS One. 2021 Sep 20;16(9):e0257627. doi: 10.1371/journal.pone.0257627. eCollection 2021.

ABSTRACT

OBJECTIVES: Most research into the management of pelvic inflammatory disease (PID) is in younger women and focuses on sexually transmitted pathogens such as N. gonorrhoeae or C. trachomatis. Non-sexually transmitted bacterial pathogens and PID in older women are rarely examined. The objective of this study is to explore cervical culture pathogens in women of different age groups in a medical center in eastern Taiwan.

METHODS: We enrolled patients whose medical records were diagnosed with PID (ICD-9-CM 614.0 [N70.01-03], 614.1[N70.11-13], 614.9 [N73.5, N73.9]) at our hospital from October 2014 to March 2020. Patients were divided into three groups according to age: the age <25 years, age 25-44 years, and the ≥ 45 years group. Chi-square test, ANOVA and logistic regression were used for statistical analysis. In subgroup analysis, endocervical pathogens were further stratified into vaginal, respiratory, enteric, skin, oral, and other.

RESULTS: A total of 96 patients were included in the study. There were 31 patients in the age ≥ 45 years group, 52 patients in the age 25-44 years group, and 13 patients in the age <25 years group. Vagina and enteric pathogens were the most common pathogens among all groups. The isolated respiratory and other pathogens were more in the age ≥ 45 years group than in the other two groups. Prevotella bivia was more common in the age <25 years and 25-44 years groups.

CONCLUSIONS: This may be due to different pathogeneses of PID in the age ≥ 45 years patients. Our study can be used as a reference for antibiotic choice of non-sexually transmitted PID and to prevent long-term sequelae of PID.

PMID:34543349 | DOI:10.1371/journal.pone.0257627

PLoS One. 2021 Sep 20;16(9):e0257613. doi: 10.1371/journal.pone.0257613. eCollection 2021.

ABSTRACT

This paper analyses COVID-19 patients’ dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.

PMID:34543345 | DOI:10.1371/journal.pone.0257613

JBRA Assist Reprod. 2021 Sep 20. doi: 10.5935/1518-0557.20210058. Online ahead of print.

ABSTRACT

This article reports the case of a 28-year-old female 31.6 weeks pregnant with twins diagnosed with SARS-CoV-2 infection, who delivered a boy and a girl. The newborns underwent RT-PCR testing for SARS-CoV-2; the male tested negative and the female newborn tested positive, in that the female placenta was SARS-CoV-2 positive and the male placenta negative. Clinical and laboratory findings evincing vertical transmission of SARS-CoV-2 were identified. Strict, multidisciplinary prenatal care is recommended for this group of patients. This case report alone does not provide statistical evidence of vertical transmission, but it is an account of a relevant matter.

PMID:34542252 | DOI:10.5935/1518-0557.20210058

J Esthet Restor Dent. 2021 Sep 20. doi: 10.1111/jerd.12818. Online ahead of print.

ABSTRACT

OBJECTIVE: This in vitro study investigated the influence of resin coating on the bonding performance of self-adhesive resin cements in single-visit computer-aided design (CAD)/computer-aided manufacturing (CAM) resin restorations.

MATERIALS AND METHOD: CAD/CAM resin (1.5-mm thick) was mounted on 20 noncoated and 20 resin-coated human dentin surfaces using dual-cured self-adhesive resin cements (Panavia SA Cement Plus or Panavia SA Cement Universal, Kuraray Noritake Dental) in either self-curing or dual-curing mode. These specimens were sectioned into beam-shaped sticks and subjected to microtensile bond strength tests after 24 h of water storage. The obtained data were statistically analyzed with three-way analysis of variance (ANOVA) and t tests (α = 0.05).

RESULTS: The three-way ANOVA results revealed the significant influence of resin coating, resin cement, and curing mode. Resin coating and light curing led to higher bond strengths in almost all groups. Resin-coated dentin with Panavia SA Cement Plus exhibited a mean bond strength greater than 35 MPa in both self-curing and dual-curing modes.

CONCLUSIONS: In single-visit CAD/CAM resin restorations, resin coating, resin cement selection, and curing mode influenced the bonding performance of self-adhesive resin cements. In addition, resin coating and light curing increased the bond strength of self-adhesive resin cements. Resin coating and light curing are encouraged for predictable bonding performance of dual-cured self-adhesive resin cements in single-visit CAD/CAM resin restorations.

PMID:34542233 | DOI:10.1111/jerd.12818

J Intellect Disabil Res. 2021 Sep 20. doi: 10.1111/jir.12885. Online ahead of print.

ABSTRACT

BACKGROUND: There is certain empirical evidence of, on the one hand, a positive correlation between executive functions (EFs) and intelligence in people with intellectual disability (ID) and, on the other hand, a slower rate of development of EFs in these people relative to people without ID. This evidence is not, however, unequivocal, and further studies are required.

METHODS: We analysed the relationship between development of EFs and both age and intellectual capacity, in a sample of 106 students with either ID or borderline intellectual functioning (BIF) at a special education centre [63 boys and 43 girls, 11-18 years old, mean total intelligence quotient (TIQ) of 59.6]. We applied nine instruments to evaluate both neuropsychological development (working memory, inhibitory control, cognitive flexibility, planning, processing speed and verbal fluency) and behavioural development [teachers’ perceptions of the EFs of their students by Behavior Rating Inventory of Executive Function – Second Edition (BRIEF-2) School]. ID and BIF groups were statistically compared in terms of mean performance measures in EF tests. We looked at the correlation between EFs and age, and correlations between EFs and intelligence: TIQ, fluid intelligence [measured by the perceptual reasoning (PR) sub-index of Wechsler Intelligence Scale for Children-IV (WISC-IV)] and crystallised intelligence (measured by the verbal comprehension (VC) sub-index of WISC-IV). Regression models were built for variables with strong correlation.

RESULTS: In most of the tests used to evaluate EFs, the ID subgroup performed significantly worse than the subgroup with BIF. In general, teachers’ thought that participants had ‘medium-low’ levels of EFs. TIQ, by WISC-IV scale, correlated significantly with scores in all tests for all EFs. The PR sub-index correlated significantly with 14 of the tests for EFs; 35% of the variation in PR can be explained by variation in performance in Picture Span (working memory) and Mazes (planning). The VC sub-index correlated weakly with seven of the EF tests. We found significant correlations in the ID group between age and scores in all tests of working memory and inhibitory control. Age – considering all participants – did not correlate with any of the variables of teachers’ perception except for working memory, and this correlation was not strong.

CONCLUSIONS: The results of our study are consistent with descriptions of the typical population: (1) fluid intelligence is more related to EFs than crystallised intelligence is; and (2) working memory capacity is the EF most strongly related with general, fluid and crystallised forms of intelligence. The results suggest that as children and adolescents with ID/BIF get older, their capacities for working memory and inhibitory control increase; development of the other EFs studied was less evident. Teachers’ perceptions of the EFs of children with ID or BIF were independent of intellectual capacity and age. More research is needed to delve further into the development of EFs in people with ID/BIF.

PMID:34542219 | DOI:10.1111/jir.12885

Stat Med. 2021 Sep 20. doi: 10.1002/sim.9196. Online ahead of print.

ABSTRACT

With the uptake of targeted therapies, instead of the “one-fits-all” approach, modern randomized controlled trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with time-to-event outcomes. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which appropriately control both within- and across-marker multiplicity. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups identified from AREDS. This multiple-testing-based simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

PMID:34542190 | DOI:10.1002/sim.9196

Stat Med. 2021 Sep 20. doi: 10.1002/sim.9204. Online ahead of print.

ABSTRACT

A novel model-free interaction screening approach called the hybrid metrics is introduced for high-dimensional heterogeneous data analysis. The metrics established based on the variation of conditional joint distribution function are measurements of interaction that include both size and direction. They are robust and can work with many types of response variables, including continuous, discrete, and categorical variables. We can apply the hybrid metrics to effective interaction selection for classification, response index models, and Poisson regression, among others. When dealing with classification, the hybrid metrics are capable of capturing both nonlinear category-general and category-specific interaction effects, providing us with a comprehensive overview and precise discovery of category information. When faced with a continuous response, the hybrid metrics perform fairly well even if the signal strength is weak, behaving as if the true interactions were known. To facilitate implementation, a fast two-stage procedure which naturally and efficiently enforces both strong and weak heredity is advocated. We further demonstrate their superior performances over popular competitors by exhaustive simulations and a SRBCT real data example. Supplementary materials for this article are available online.

PMID:34542189 | DOI:10.1002/sim.9204

Stat Med. 2021 Sep 20. doi: 10.1002/sim.9202. Online ahead of print.

ABSTRACT

Increasing evidence has shown that gene-gene interactions have important effects in biological processes of human diseases. Due to the high dimensionality of genetic measurements, interaction analysis usually suffers from a lack of sufficient information and has unsatisfactory results. Biological network information has been massively accumulated, allowing researchers to identify biomarkers while taking a system perspective, conducting network selection (of functionally related biomarkers), and accommodating network structures. In main-effect-only analysis, network information has been incorporated. However, effort has been limited in interaction analysis. Recently, link networks that describe the relationships between genetic interactions have been demonstrated as effective for revealing multiscale hierarchical organizations in networks and providing interesting findings beyond node networks. In this study, we develop a novel structured Bayesian interaction analysis approach to effectively incorporate network information. This study is among the first to identify gene-gene interactions with the assistance of network selection, while simultaneously accommodating the underlying network structures of both main effects and interactions. It innovatively respects multiple hierarchies among main effects, interactions, and networks. The Bayesian technique is adopted, which may be more informative for estimation and prediction over some other techniques. An efficient variational Bayesian expectation-maximization algorithm is developed to explore the posterior distribution. Extensive simulation studies demonstrate the practical superiority of the proposed approach. The analysis of TCGA data on melanoma and lung cancer leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

PMID:34542187 | DOI:10.1002/sim.9202