Nevin Manimala

JMIR Med Inform. 2021 May 6;9(5):e28413. doi: 10.2196/28413.

ABSTRACT

BACKGROUND: Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation.

OBJECTIVE: We aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities.

METHODS: Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models.

RESULTS: We found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials.

CONCLUSIONS: Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.

PMID:33955834 | DOI:10.2196/28413

Subst Abus. 2021 May 6:1-2. doi: 10.1080/08897077.2021.1915919. Online ahead of print.

ABSTRACT

Lebanon, a small middle-income nation in western Asia, has been crippled by decades of political turmoil and armed conflict. A “quadruple crisis” hit the country over the past years, starting with the protracted humanitarian Syrian refugee crisis, followed by a severe socioeconomic collapse, the global COVID-19 pandemic, and lastly the Beirut port catastrophic blast. With the exposure to repetitive traumatic events and associated organic brain injury, the Lebanese population has become at a higher risk of addiction, among other psychiatric comorbidities. With the scarce statistics about the topic and limited addiction services in the country, collaborative local efforts and international help are urgently needed to fight the upcoming substance use epidemic. Raising awareness, providing adequate training, and securing resources for the management of both addiction and trauma are of utmost importance.

PMID:33955819 | DOI:10.1080/08897077.2021.1915919

J Environ Sci Health C Toxicol Carcinog. 2021 May 6:1-24. doi: 10.1080/26896583.2021.1883964. Online ahead of print.

ABSTRACT

Cancer is a main concern for human health and there is a need of alternative methodologies to rapidly screen large quantitative of compounds that may represent a toxicological risk. Here a statistical analyses is performed on a benchmark database of experimental Ames data to identify chemical descriptors discriminating mutagens and non-mutagens. A total of 53 activating and deactivating modulators are identified, that flagged respectively a percentage of mutagen and non-mutagen up to 87%. Modulators are further combined to form synergistic cross-terms, accounting for the effect that combined properties may have on the final toxicity. Exclusion rules are defined as exception to the modulators. Synergistic cross-terms and exclusion rules improve the enrichment of mutagens/non-mutagens with respect of the original abundance in the dataset to values higher than 95%. The external predictivity of modulators and cross-terms reach balanced accuracy up to 0.775 that is analogous to other mutagenicity models from the literature, confirming the suitability of the rules to real-life screening of chemicals. Modulators are discussed for their mechanistic link to mutagenicity. This analysis confirms the key role of some properties (polarizability, shape, mass, presence of reactive functional groups or unsaturated planar systems) as driving elements for the initiation of the mutagenicity.

PMID:33955817 | DOI:10.1080/26896583.2021.1883964

Psychother Res. 2021 May 6:1-15. doi: 10.1080/10503307.2021.1921303. Online ahead of print.

ABSTRACT

Objective: The purpose of this paper is to describe an approach to dynamical systems (DS) using a set of differential equations, and how an application of these equations can be used to address a critical element of the therapeutic relationship. Using APA’s Three Approaches to Psychotherapy with a Female Client: The Next Generation and Three Approaches to Psychotherapy with a Male Client: The Next Generation videos, DS models were created for each of the six sessions with expert clinicians (Judith Beck, Leslie Greenberg, and Nancy McWilliams) from the three theoretical approaches. Method: A second-by-second observational coding system of the emotional exchanges of the therapists and clients was used as the data for the equations. Results: DS modeling allowed for a side-by-side comparison between the three approaches as well as between the two clients. Examining the graphs created by plotting the results of the DS equations (in particular, phase-space portraits) revealed that there were similarities among the three theoretical approaches, and there were notable differences between the two clients. Conclusions: DS modelling can provide researchers and clinicians with a powerful tool to investigate the complex phenomenon that is psychotherapy.

PMID:33955816 | DOI:10.1080/10503307.2021.1921303

West J Nurs Res. 2021 May 6:1939459211013580. doi: 10.1177/01939459211013580. Online ahead of print.

ABSTRACT

The purpose of this study was to examine individuals’ knowledge of cardiovascular risk-related biometric numbers and to compare self-reported and investigator-measured numbers in a convenience sample of adults in the Midwest region. Sociodemographic data and personal knowledge of cardiovascular risk-related biometric numbers were assessed using self-reported questionnaires. Investigators conducted health assessments to obtain biometric numbers. Among the 224 participants, participants’ reported knowledge about their cardiovascular risk-related biometric numbers was low, especially for high-density lipoprotein and fasting blood glucose levels. Participants’ knowledge was associated with education level and the recency of their last healthcare visit for health assessment. We found statistically significant mean differences between self-reported and investigator-measured blood pressure, and weight. This study found that there were discrepancies between self-reported and investigator-measured cardiovascular risk-related numbers. Future research is needed to develop educational interventions to improve personal knowledge of cardiovascular risks.

PMID:33955791 | DOI:10.1177/01939459211013580

J Urol. 2021 May 6:101097JU0000000000001869. doi: 10.1097/JU.0000000000001869. Online ahead of print.

ABSTRACT

PURPOSE: We assessed the long-term surgical, functional urinary and sexual outcome of adolescent and young adult men who underwent childhood hypospadias repair.

MATERIALS AND METHODS: Men born with non-syndromic hypospadias and healthy male controls, aged 16-21 years old, were recruited and their surgical, urinary, sexual functional and aesthetic outcomes assessed. Good outcome was defined as a patent and orthotopic meatus without fistulae and straight erections (<30° curvature) without erectile or ejaculatory problems. Statistics included regression analyses, Chi²/Fisher Exact tests and Student’s t/Mann-Whitney U and Kruskal Wallis tests.

RESULTS: 193 cases and 50 controls participated, 16.4 [8.2-21.2] years after initial repair. At least one re-intervention was performed in 39.2%. The highest re-intervention rate was found in those younger than 12 months at initial repair, even when excluding proximal hypospadias cases. A disturbed urinary and/or suboptimal sexual functional outcome was seen in 52.9% of cases. Suboptimal voiding was found in 22.1%, although few had relevant residual urine. More re-interventions and proximal hypospadias were associated with suboptimal urinary outcome and the latter also with impaired sexual function. Poor inter-observer agreements were found between physician’s and patient’s genital appraisal.

CONCLUSIONS: In 52.9% of cases, at least one concern was identified that required long-term follow-up. Hypospadias repair below twelve months was associated with more re-interventions. Adopting a restrictive attitude towards aesthetic refinement unless on the patient’s own request, could improve urinary outcomes.

PMID:33955779 | DOI:10.1097/JU.0000000000001869

J Agric Food Chem. 2021 May 6. doi: 10.1021/acs.jafc.0c07942. Online ahead of print.

ABSTRACT

An intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the lipidomic profiling of fresh and frozen-thawed beef muscle. The data were obtained by REIMS and then processed using multivariate statistical analysis methods including principal component analysis-linear discriminant analysis (PCA-LDA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The discrimination of fresh and frozen-thawed meat has been achieved, and the real-time identification accuracy was 92-100%. Changes in the composition and content of fatty acids and phospholipids were statistically analyzed by OPLS-DA, and the ions of m/z 279.2317, m/z 681.4830, and m/z 697.4882 were selected as differential compounds/metabolites. The developed method was also successfully applied in the discrimination of fresh and frozen-thawed meat samples. These results showed that REIMS as a high-throughput, rapid, and real-time mass spectrometry detection technology can be used for the identification of fresh and frozen-thawed meat samples.

PMID:33955749 | DOI:10.1021/acs.jafc.0c07942

J Occup Health. 2021 Jan;63(1):e12224. doi: 10.1002/1348-9585.12224.

ABSTRACT

OBJECTIVES: Little is known about workplace measures against coronavirus disease 2019 (COVID-19) in Japan during the winter of 2020, especially in micro-, small-, and medium-sized enterprises (MSMEs). This study aimed to provide an overview of the current situation of anti-COVID-19 measures in Japanese enterprises during the winter, considering company size.

METHODS: This study was an Internet-based nationwide cross-sectional study. Individuals who were registered as full-time workers were invited to participate in the survey. Data were collected using an online self-administered questionnaire in December 2020. The chi-squared test for trend was performed to calculate the P-value for trend for each workplace measure across company sizes.

RESULTS: For the 27 036 participants, across company sizes, the most prevalent workplace measure was encouraging mask wearing at work, followed by requesting that employees refrain from going to work when ill and restricting work-related social gatherings and entertainment. These measures were implemented by approximately 90% of large-scale enterprises and by more than 40% of micro- and small-scale enterprises. In contrast, encouraging remote working was implemented by less than half of large-scale enterprises and by around 20% of micro- and small-scale enterprises. There were statistically significant differences in all workplace measures by company size (all P < .001).

CONCLUSIONS: We found that various responses to COVID-19 had been taken in workplaces. However, some measures, including remote working, were still not well-implemented, especially in smaller enterprises. The findings suggest that occupational health support for MSMEs is urgently needed to mitigate the current wave of COVID-19.

PMID:33955633 | DOI:10.1002/1348-9585.12224

Respirology. 2021 May 6. doi: 10.1111/resp.14049. Online ahead of print.

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by infection with severe acute respiratory syndrome coronavirus 2 was first detected in Wuhan, China, in late 2019 and continues to spread worldwide. Persistent questions remain about the relationship between the severity of COVID-19 and comorbid diseases, as well as other chronic pulmonary conditions. In this systematic review and meta-analysis, we aimed to examine in detail whether the underlying chronic obstructive pulmonary diseases (COPD), asthma and chronic respiratory diseases (CRDs) were associated with an increased risk of more severe COVID-19. A comprehensive literature search was performed using five international search engines. In the initial search, 722 articles were identified. After eliminating duplicate records and further consideration of eligibility criteria, 53 studies with 658,073 patients were included in the final analysis. COPD was present in 5.2% (2191/42,373) of patients with severe COVID-19 and in 1.4% (4203/306,151) of patients with non-severe COVID-19 (random-effects model; OR = 2.58, 95% CI = 1.99-3.34, Z = 7.15, p < 0.001). CRD was present in 8.6% (3780/44,041) of patients with severe COVID-19 and in 5.7% (16,057/280,447) of patients with non-severe COVID-19 (random-effects model; OR = 2.14, 95% CI = 1.74-2.64, Z = 7.1, p < 0.001). Asthma was present in 2.3% (1873/81,319) of patients with severe COVID-19 and in 2.2% (11,796/538,737) of patients with non-severe COVID-19 (random-effects model; OR = 1.13, 95% CI = 0.79-1.60, Z = 0.66, p = 0.50). In conclusion, comorbid COPD and CRD were clearly associated with a higher severity of COVID-19; however, no association between asthma and severe COVID-19 was identified.

PMID:33955623 | DOI:10.1111/resp.14049

J Magn Reson Imaging. 2021 May 6. doi: 10.1002/jmri.27651. Online ahead of print.

ABSTRACT

BACKGROUND: Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated.

PURPOSE: To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast-enhanced MRI (DCE-MRI) for prediction of HER-2 and Ki-67 status.

STUDY TYPE: Retrospective.

POPULATION: A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts.

FIELD STRENGTH/SEQUENCE: 3.0T, T₁ gradient echo.

ASSESSMENT: Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time-intensity curves of DCE-MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively.

STATISTICAL TESTS: Wilcoxon rank-sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test.

RESULTS: The intratumoral and peritumoral RSs for prediction of HER-2 and Ki-67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574-0.793) and 0.690 (95% CI, 0.577-0.804), and 0.714 (95% CI, 0.616-0.812) and 0.692 (95% CI, 0.590-0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604-0.823) and 0.749 (95% CI, 0.656-0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8).

DATA CONCLUSION: Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE-MRI had the potential to identify HER-2 and Ki-67 status.

LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2.

PMID:33955619 | DOI:10.1002/jmri.27651