Tag: pubmed

Forensic Sci Int. 2021 Nov 14;330:111105. doi: 10.1016/j.forsciint.2021.111105. Online ahead of print.

ABSTRACT

In the present study, the metric properties of dynamic footprints were analysed using non-colouring method in relation with body parameters and compared with static footprint measurements. The results of the study provide a better understanding of the relationship between static and dynamic footprints, which may be useful for biological profiling that allows more accurate identification. Stature, body weight, five length and two width parameters of dynamic (walking) footprints of young Slovak adults (65 females and 68 males) were analysed. Pearson correlation coefficients were calculated and equations for prediction of stature and body weight by linear regression analysis and multiple regression analysis were developed. Intersex differences were confirmed for all parameters and bilateral for some. Statistically significant differences were found in all measurements (p-value>0.05), except for the width of the standing and walking footprint in the mixed group. A positive correlation was found between the selected footprint diameters with stature (max – r = 0.82) and body weight (max – r = 0.70). Stature could be calculated with an accuracy of up to 4.40 cm and body weight with an accuracy of up to 9.56 kg. The results of the present study show that selected measurements of dynamic footprints correlate with stature and body weight. These results could be used in biological profiling in the medical and forensic fields.

PMID:34800909 | DOI:10.1016/j.forsciint.2021.111105

Biochem Biophys Res Commun. 2021 Nov 11;585:75-81. doi: 10.1016/j.bbrc.2021.11.005. Online ahead of print.

ABSTRACT

BACKGROUND: Intestinal mucositis is one of chemotherapeutics’ most common adverse effects, such as 5-fluorouracil (5-FU). Quercetin (QRC), a naturally occurring flavonoid, has approved antioxidant and anti-inflammatory properties. Thus, in this article, the preventive and curative effects of emulsion and nano-emulsion formulations of QRC were investigated in a model of 5-FU-induced intestinal mucositis using biochemical, histopathological, and molecular approaches.

METHOD: Thirty-six mice were divided into six different groups: Control (normal saline), 5-FU (a single dose of 5-FU 300 mg/kg), pre-treatment groups (pre-QRC, and pre-QRC-nano, receiving QRC 5 mg/kg emulsion and nano-emulsion before the induction of mucositis, respectively), and post-treatment groups (post-QRC, and post-QRC-nano, receiving QRC 5 mg/kg emulsion and nano-emulsion after the induction of mucositis, respectively).

FINDING: The administration of quercetin emulsion and nano-emulsion could significantly alleviate the oxidant-antioxidant balance of mice serum samples and reverse the destructive histopathologic changes induced by 5-FU in the intestine tissue. Nevertheless, although the expression of both pro-inflammatory genes, NF-κB and HIF-1α, was decreased when quercetin was administered to mice, this reduction was not statistically significant.

CONCLUSION: The administration of quercetin emulsion and nano-emulsion formulations could ameliorate the oxidative damage induced by chemotherapeutics, such as the 5-FU. Therefore, if confirmed in further studies, it could be used in clinical settings as a preventive and curative agent to decrease such catastrophic adverse events in chemotherapy patients.

PMID:34800883 | DOI:10.1016/j.bbrc.2021.11.005

Econ Hum Biol. 2021 Nov 8;44:101079. doi: 10.1016/j.ehb.2021.101079. Online ahead of print.

ABSTRACT

I examine the impacts of the Free Semester (FS) program, which is an exam-free semester for middle school students in South Korea, on students’ level of exam nervousness. I use a difference-in-differences approach that exploits the variation in FS-implementation across middle schools to estimate these impacts. I find that the FS program has statistically significant impacts on relieving exam nervousness during the semester in which it is run. In contrast, I find no statistically significant average medium-run impacts of the program. My subgroup analyses suggest that the reduction in exam nervousness during the FS is driven largely by high-achieving students.

PMID:34800899 | DOI:10.1016/j.ehb.2021.101079

Int J Med Inform. 2021 Nov 14;157:104635. doi: 10.1016/j.ijmedinf.2021.104635. Online ahead of print.

ABSTRACT

BACKGROUND: Applying machine learning to predicting oral cavity cancer prognosis is important in selecting candidates for aggressive treatment following diagnosis. However, models proposed so far have only considered cancer survival as discrete rather than dynamic outcomes.

OBJECTIVES: To compare the model performance of different machine learning-based algorithms that incorporate time-to-event data. These algorithms included DeepSurv, DeepHit, neural net-extended time-dependent cox model (Cox-Time), and random survival forest (RSF).

MATERIALS AND METHODS: Retrospective cohort of 313 oral cavity cancer patients were obtained from electronic health records. Models were trained on patient data following preprocessing. Predictors were based on demographic, clinicopathologic, and treatment information of the cases. Outcomes were the disease-specific and overall survival. Multivariable analyses were conducted to select significant prognostic features associated with tumor prognosis. Two models were generated per algorithm based on all-prognostic features and significant-prognostic features following statistical analysis. Concordance index (c-index) and integrated Brier scores were used as performance evaluators and model stability was assessed using intraclass correlation coefficients (ICC) calculated from these measures obtained from the cross-validation folds.

RESULTS: While all models were satisfactory, better discriminatory performance and calibration was observed for disease-specific than overall survival (mean c-index: 0.85 vs 0.74; mean integrated Brier score: 0.12 vs 0.17). DeepSurv performed best in terms of discrimination for both outcomes (c-indices: 0.76 -0.89) while RSF produced better calibrated survival estimates (integrated Brier score: 0.06 -0.09). Model stability of the algorithms varied with the outcomes as Cox-Time had the best intraclass correlation coefficient (mean ICC: 1.00) for disease-specific survival while DeepSurv was most stable for overall survival prediction (mean ICC: 0.99).

CONCLUSIONS: Machine learning algorithms based on time-to-event outcomes are successful in predicting oral cavity cancer prognosis with DeepSurv and RSF producing the best discriminative performance and calibration.

PMID:34800847 | DOI:10.1016/j.ijmedinf.2021.104635

Eur J Radiol. 2021 Nov 15;145:110032. doi: 10.1016/j.ejrad.2021.110032. Online ahead of print.

ABSTRACT

BACKGROUND: Filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) are ubiquitously applied in the reconstruction of coronary CT angiography (CCTA) datasets. However, currently no data is available on the impact of a model-based adaptive filter (MBAF2), recently developed for a dedicated cardiac scanner.

PURPOSE: Our aim was to determine the effect of MBAF2 on subjective and objective image quality parameters of coronary arteries on CCTA.

METHODS: Images of 102 consecutive patients referred for CCTA were evaluated. Four reconstructions of coronary images (FBP, ASIR, MBAF2, ASIR + MBAF2) were co-registered and cross-section were assessed for qualitative (graininess, sharpness, overall image quality) and quantitative [image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)] image quality parameters. Image noise and signal were measured in the aortic root and the left main coronary artery, respectively. Graininess, sharpness, and overall image quality was assessed on a 4-point Likert scale.

RESULTS: As compared to FBP, ASIR, and MBAF2, ASIR + MBAF2 resulted in reduced image noise [53.1 ± 12.3, 30.6 ± 8.5, 36.3 ± 4.2, 26.3 ± 4.0 Hounsfield units (HU), respectively; p < 0.001], improved SNR (8.4 ± 2.6, 14.1 ± 3.6, 11.8 ± 2.3, 16.3 ± 3.3 HU, respectively; p < 0.001) and CNR (9.4 ± 2.7, 15.9 ± 4.0, 13.3 ± 2.5, 18.3 ± 3.5 HU, respectively; p < 0.001). No difference in sharpness was observed amongst the reconstructions (p = 0.08). Although ASIR + MBAF2 was non-superior to ASIR regarding overall image quality (p = 0.99), it performed better than FBP (p < 0.001) and MBAF2 (p < 0.001) alone.

CONCLUSION: The combination of ASIR and MBAF2 resulted in reduced image noise and improved SNR and CNR. The implementation of MBAF2 in clinical practice may result in improved noise reduction performance and could potentiate radiation dose reduction.

PMID:34800835 | DOI:10.1016/j.ejrad.2021.110032

Comput Methods Programs Biomed. 2021 Nov 6;213:106509. doi: 10.1016/j.cmpb.2021.106509. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist’s reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject’s performance in cognitive tasks and may find significant differences between the patient’s data and controls. The input data of classifiers may imply alterations in diagnosis; therefore, it is essential to ensure an adequate representation to describe the entire dataset classified.

METHODS: We propose a supervoxels-based representation calculated by two main steps: the short-range connectivity, supervoxels’ generation using a Fuzzy Iterative Clustering algorithm, and the long-range connectivity, employing Detrended Cross-Correlation Analysis among supervoxels. The unrelated supervoxels, through a statistical test based on critical points calculated empirically, are removed. The remainder supervoxels are the input for feature selectors to extract the discriminative supervoxels. We implement support vector machine classifiers using the correlation coefficient of the significant supervoxels. The dataset of 1.5 Tesla was downloaded from the SchizConnect site, where the fMRI data, during an auditory oddball task, was acquired. We calculate the performance of the classifiers using a leave-one-out cross-validation and compute the area under the Receiver Operating Characteristic curve and a permutation test to ensure no bias in the classifiers.

RESULTS: According to the permutation test, with p-values less than the significance level of 0.05, the classifiers extract discriminative class structure from data where no bias is shown. Our supervoxels-based representation gets the maximum values of sensitivity, specificity, and accuracy of 92.9%, 100%, and 96.4%, respectively. The discriminative brain regions, to discern among patients and controls, are extracted; these regions also are mentioned by the related works.

CONCLUSIONS: The proposed representation, based on supervoxels, is a data-driven model that does not use predefined models of the signal nor pre-relocated brain regions of interest. The results are competitive against the related works, and the relevant supervoxels are related to the schizophrenia diagnosis.

PMID:34800805 | DOI:10.1016/j.cmpb.2021.106509

Value Health Reg Issues. 2021 Nov 17;29:21-27. doi: 10.1016/j.vhri.2021.09.001. Online ahead of print.

ABSTRACT

OBJECTIVES: Myanmar faces a growing epidemic of type 2 diabetes mellitus, which has significant impact on the individual health and health service system; nevertheless, reliable cost estimate for treating diabetes is still unknown. Therefore, this study aimed to explore the treatment cost of hospitalization by type 2 diabetes mellitus and the association of complications and comorbidities with the treatment cost.

METHODS: The retrospective incidence-based cost of illness analysis was performed at the diabetes ward of 800-bed teaching hospital in Yangon, Myanmar. The data were retrieved from hospital financial reports and patient’s medical records for the fiscal year 2017 to 2018. Data was analyzed by using descriptive statistics and multivariate statistics. One-way sensitivity analysis was used to assess the uncertainty of input parameters.

RESULTS: This study involved 87 inpatients with type 2 diabetes mellitus with an average length of stay of 16.1 ± 12.6 days. Of the study sample, 67% had complications whereas 74% had comorbidities. The average treatment cost per admission was $718.7 (equal to 58% of gross domestic product – GDP per capita) at 2018 prices. Based on the multiple regression analysis, cost savings per admission were $276.5, $307.3, and $319.5 from preventing foot ulcer, nephropathy, and retinopathy, respectively.

CONCLUSIONS: This study found that the treatment of diabetes is costly because of its preventable health consequences. Better disease management to prevent complications results in considerable cost savings. This quantitative evidence would increase awareness in health service system.

PMID:34800825 | DOI:10.1016/j.vhri.2021.09.001

J Surg Res. 2021 Nov 17;270:471-476. doi: 10.1016/j.jss.2021.10.007. Online ahead of print.

ABSTRACT

BACKGROUND: Pandemic related changes have radically altered the delivery of medical teaching. The practical skills of medicine which students should be proficient in at time of graduation have tended to require in-person tutelage, with reduced access resulting in the risk of skill deficits in newly qualified doctors. Small group teaching sessions are amenable to a virtual mode of delivery, with the ability of the virtual platform to confer practical skills unproven. The objective of the study was to evaluate the use of teleproctoring in acquisition of suturing skills in medical students.

METHODS: This was a single blinded two- armed randomized control trial. Medical students undergoing clinical rotations in their penultimate and final years who were able to complete the suturing tutorial were invited to participate in this study. Control groups underwent conventional suturing training under direct supervision, with the interventional group undergoing the tutorial in a remote learning setting via live streaming. Pre- and post-test assessment was carried out using validated suturing Global Rating Scale tool.

RESULTS: A total of 24 participants were recruited, with 23 participants completing the task. Adequacy of sampling was demonstrated in both groups using Box’s M test (P = 0.9). Participants’ individual and composite scores were comparable at baseline (P = 0.28) and following the tutorial (P = 0.52). Participants improved to a statistically significant degree regardless of method of teaching delivery, in all skill parameters (P < 0.001).

CONCLUSIONS: Teleproctoring is an effective tool in the provision of teaching basic suturing skills in medical students. Research on its use in more complex practical skills is warranted.

PMID:34800793 | DOI:10.1016/j.jss.2021.10.007

J Equine Vet Sci. 2021 Oct 14;108:103794. doi: 10.1016/j.jevs.2021.103794. Online ahead of print.

ABSTRACT

Analysis of the cerebrospinal fluid (CSF) is important as a complementary test in horses with neurologic diseases, and sequential analysis may provide information about the treatment response or evolution and quantitative measures of the CSF drug concentration during treatment. The aim of this study was to compare erythrocyte and nucleated cell counts and protein concentration in multiple CSF samples obtained sequentially from two different puncture sites in clinically healthy horses. Eight and 12 horses, with no evidence of neurologic disease, were subjected to CSF collection from the atlanto-occipital (AO) and C1-C2 spaces, respectively. Cytologic and chemical analyses were performed on the CSF obtained at five sampling times (T1, T2, T3, T4, and T5). Repeated measures models were used to compare the mean erythrocyte count, nucleated cell count, and total protein concentration between the AO and C1-C2 groups at each sampling time. C1-C2 CSF had a significantly higher total protein concentration at T1 and T4 than that of AO CSF. All total protein concentration values remained within the reference interval (<90 mg/dL) for all sampling times and groups. No statistical difference was present between results at T2, T3, T4, and T5 and at T1 in both groups for all analyses. In conclusion, five consecutive AO or C1-C2 CSF collections with at least a 7-d interval did not result in alterations in the CSF erythrocyte and nucleated cell counts and total protein concentrations and did not interfere with the CSF analysis results.

PMID:34800797 | DOI:10.1016/j.jevs.2021.103794

J Environ Manage. 2021 Nov 11;302(Pt B):114085. doi: 10.1016/j.jenvman.2021.114085. Online ahead of print.

ABSTRACT

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

PMID:34800764 | DOI:10.1016/j.jenvman.2021.114085