Nevin Manimala

Rev Med Inst Mex Seguro Soc. 2021 Sep 1;59(5):404-411.

ABSTRACT

BACKGROUND: The pandemic caused by COVID-19 is one of the main problems of public health around the world. Of the individuals with infection, a large amount corresponds to first-level health workers.

OBJECTIVE: To determine the relationship between length of stay in respiratory offices and SARS-CoV-2 infection in health workers at a first-level health center.

MATERIAL AND METHODS: Cross-sectional analytic study in health workers of a first-level unit of San Luis Potosí, Mexico, who developed suspected symptomatology of SARS-CoV-2 infection from March 2020 to January 2021. Two groups were formed according to the result of the RT-PCR. Demographic variables, occupation, work area, work in respiratory area, hours of work accumulated within the respiratory area to develop symptomatology, and date of development of symptomatology were registered.

RESULTS: Of 350 health workers active, 144 developed respiratory symptomatology; of these, 66 had positive RT-PCR for SARS-CoV-2 infection. Working in an area with no respiratory patients but in contact with other patients confers an OR 2.49 (1.04-6.26), when compared with working in a respiratory area, p 0.0446. The length in a filter for respiratory patients gives a protective OR of 0.3062 (0.08-0.99) for developing SARS-CoV-2 infection, p 0.0608. Each hour accumulated in a respiratory area confers an OR 1.001 (0.99-1.00) without statistical significance, p 0.3046.

CONCLUSIONS: Working in a respiratory area and the accumulated hours of work in this place are not risk factors for developing COVID-19 in health workers.

PMID:34918890

Adv Clin Exp Med. 2021 Dec 17. doi: 10.17219/acem/143586. Online ahead of print.

ABSTRACT

BACKGROUND: Behçet’s disease (BD) is a chronic inflammatory vasculitis affecting multiple organs. Uveitis is frequently seen in patients with BD, especially in Turkish population.

OBJECTIVES: To investigate vascular endothelial growth factor (VEGF) gene polymorphisms along with the levels of VEGF and VEGF receptors in patients with Behçet’s uveitis (BU).

MATERIAL AND METHODS: Fifty-five BD-associated uveitis patients and 30 ageand sex-matched controls were included in this case-control study. The genotypes of the single nucleotide poymorphisms (SNPs): rs2010963 (+405G), rs3025039 (+936T) and rs699947 (-2598A) of the VEGF-A gene were determined using real-time polymerase chain reaction (RT-PCR) and serum levels of VEGF and VEGF receptors were measured using enzyme-linked immunosorbent assay (ELISA).

RESULTS: No associations of the VEGF gene polymorphisms were observed in BD uveitis patients, but arthritis was present in 53.3% of patients not possessing CT genotype in C3025039→T polymorphism (p = 0.024). Although there were no statistically significant differences in serum VEGF-A, VEGF-C and soluble vascular endothelial growth factor receptor-3 (sVEGFR-3) levels (p < 0.05), serum vascular endothelial growth factor receptor-1 (VEGFR-1) and sVEGFR-3 levels were significantly higher in the BD group (p < 0.001 and p = 0.001, respectively). In addition, VEGF-C/soluble vascular endothelial growth factor receptor-2 (sVEGFR-2) ratio was significantly higher (p < 0.001), while VEGF-A/VEGFR-1 and VEGF-C/sVEGFR-3 ratios were significantly lower (p < 0.001 and p = 0.033, respectively) in BD patients compared to controls. Also, VEGF-C/sVEGFR-3 (p = 0.024, r = 0.37) and VEGF-C/sVEGFR-2 (p = 0.020, r = 0.38) ratios were positively correlated with disease duration.

CONCLUSIONS: The significant changes in sVEGFR-3 levels and VEGF-C/sVEGFR-3 ratio has shown that lymphangiogenesis processes might take place in the pathogenesis of BD uveitis, and these parameters can be important indicators of evaluation of BD patients with uveitis together with disease duration.

PMID:34918882 | DOI:10.17219/acem/143586

J Appl Clin Med Phys. 2021 Dec 17. doi: 10.1002/acm2.13508. Online ahead of print.

ABSTRACT

PURPOSE: Methodologies for optimization of SPECT image acquisition can be challenging due to imaging throughput, physiological bias, and patient comfort constraints. We evaluated a vendor-independent method for simulating lower count image acquisitions.

METHODS: We developed an algorithm that recombines the ECG-gated raw data into reduced counting acquisitions. We then tested the algorithm to simulate reduction of counting statistics from phantom SPECT image acquisition, which was synchronized with an ECG simulator. The datasets were reconstructed with a resolution recovery algorithm and the summed stress score (SSS) was assessed by three readers (two experts and one automatic).

RESULTS: The algorithm generated varying counting levels, simulating multiple examinations at the same time. The error between the expected and the simulated countings ranged from approximately 5% to 10% for the ungated simulations and 0% for the gated simulations.

CONCLUSIONS: The vendor-independent algorithm successfully generated lower counting statistics datasets from single-gated SPECT raw data. This method can be readily implemented for optimal SPECT research aiming to lower the injected activity and/ or to shorten the acquisition time.

PMID:34918865 | DOI:10.1002/acm2.13508

Mol Ecol. 2021 Dec 17. doi: 10.1111/mec.16329. Online ahead of print.

ABSTRACT

Selection leaves signatures in the DNA sequence of genes, with many test statistics devised to detect its action. While these statistics are frequently used to support hypotheses about the adaptive significance of particular genes, the effect these genes have on reproductive fitness is rarely quantified experimentally. Consequently, it is unclear how gene-level signatures of selection are associated with empirical estimates of gene effect on fitness. Eukaryotic datasets that permit this comparison are very limited. Using the model plant Arabidopsis thaliana, for which these resources are available, we calculated seven gene-level substitution and polymorphism-based statistics commonly used to infer selection (dN/dS, NI, DOS, Tajima’s D, Fu and Li’s D*, Fay and Wu’s H, and Zeng’s E) and, using knockout lines, compared these to gene-level estimates of effect on fitness. We found that consistent with expectations, essential genes were more likely to be classified as negatively selected. By contrast, using 379 Arabidopsis genes for which data was available, we found no evidence that genes predicted to be positively selected had a significantly different effect on fitness than genes evolving more neutrally. We discuss these results in the context of the analytic challenges posed by Arabidopsis, one of the only systems in which this study could be conducted, and advocate for examination in additional systems. These results are relevant to the evaluation of genome-wide studies across species where experimental fitness data is unavailable, as well as highlighting an increasing need for the latter.

PMID:34918851 | DOI:10.1111/mec.16329

Br J Math Stat Psychol. 2021 Dec 17. doi: 10.1111/bmsp.12261. Online ahead of print.

ABSTRACT

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.

PMID:34918834 | DOI:10.1111/bmsp.12261

Mov Disord. 2021 Dec 17. doi: 10.1002/mds.28888. Online ahead of print.

ABSTRACT

BACKGROUND: The “dual syndrome hypothesis” distinguished two subtypes in mild cognitive impairment (MCI) in Parkinson’s disease: frontostriatal, characterized by attentional and executive deficits; and posterior cortical, characterized by visuospatial, memory, and language deficits.

OBJECTIVE: The aim was to identify resting-state functional modifications associated with these subtypes.

METHODS: Ninety-five nondemented patients categorized as having normal cognition (n = 31), frontostriatal (n = 14), posterior cortical (n = 20), or mixed (n = 30) cognitive subtype had a 3 T resting-state functional magnetic resonance imaging scan. Twenty-four age-matched healthy controls (HCs) were also included. A group-level independent component analysis was performed to identify resting-state networks, and the selected components were subdivided into 564 cortical regions in addition to 26 basal ganglia regions. Global intra- and inter-network connectivity along with global and local efficiencies was compared between groups. The network-based statistics approach was used to identify connections significantly different between groups.

RESULTS: Patients with posterior cortical deficits had increased intra-network functional connectivity (FC) within the basal ganglia network compared with patients with frontostriatal deficits. Patients with frontostriatal deficits had reduced inter-network FC between several networks, including the visual, default-mode, sensorimotor, salience, dorsal attentional, basal ganglia, and frontoparietal networks, compared with HCs, patients with normal cognition, and patients with a posterior cortical subtype. Similar results were also found between patients with a mixed subtype and HCs.

CONCLUSION: MCI subtypes are associated with specific changes in resting-state FC. Longitudinal studies are needed to determine the predictive potential of these markers regarding the risk of developing dementia. © 2021 International Parkinson and Movement Disorder Society.

PMID:34918782 | DOI:10.1002/mds.28888

Acta Chim Slov. 2021 Dec 15;68(4):997-1007. doi: 10.17344/acsi.2021.6998.

ABSTRACT

In this study, Box-Behnken design (BBD) in response surface methodology (RSM) was employed to optimize As(V) removal from an aqueous solution onto synthesized crosslinked carboxymethylchitosan-epichlorohydrin/Fe3O4 nanaocomposite. The factors like solution pH, adsorbent dose, contact time and temperature were optimized by the method which shows high correlation coefficient (R2=0.9406), and a predictive quadratic polynomial model equation. The adequacy of the model and parameters were evaluated by analysis of variance (ANOVA) with their significant factors of Fischer’s F – test (p<0.05). Seven significant parameters with interaction effects in the experiment with p-value < 0.0001 was observed, having a maximum removal efficiency of As(V) is 95.1%. Optimal conditions of dosage, pH, temperature, initial ion concentration and contact time in the process were found to be 0.7 g, pH 6.5, 308K, 10 mg/L and 60 min respectively. Langmuir isotherm model fitted better than the Freundlich model having a maximum adsorption capacity of 28.99 mg/g, a high regression value of 0.9988, least chi-square value of 0.1781. The process was found to follow monolayer adsorption and pseudo-second-order kinetics. Thermodynamic parameters indicate the process is spontaneous, endothermic and physisorption in nature. Successful regeneration of the adsorbent implies its applicability to the removal of arsenic from real life wastewater.

PMID:34918765 | DOI:10.17344/acsi.2021.6998

Acta Chim Slov. 2021 Dec 15;68(4):882-895. doi: 10.17344/acsi.2021.6875.

ABSTRACT

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) statistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

PMID:34918764 | DOI:10.17344/acsi.2021.6875

Biosci Rep. 2021 Dec 22;41(12):BSR20211769. doi: 10.1042/BSR20211769.

ABSTRACT

Among chronic liver diseases, fatty liver has the highest incidence worldwide. Coexistence of fatty liver and other chronic diseases, such as diabetes, hepatitis B virus (HBV) and Helicobacter pylori (Hp) infection, is common in clinical practice. The present study was conducted to analyze the prevalence and association of coexisting diseases in patients with fatty liver and to investigate how coexisting diseases contribute to abnormal transaminase and lipid profiles. We enrolled participants who were diagnosed with fatty liver via ultrasound in the physical examination center of West China Hospital. Multivariable logistic regression was used to determine the adjusted odds ratios (ORs). We found that 23.6% of patients who underwent physical examinations were diagnosed with fatty liver. These patients had higher risks of metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), and hypertension and a lower risk of HBV infection. The risks of Hp infection and hyperthyroidism did not statistically differ. When fatty liver coexisted with T2DM, MetS and thyroid dysfunction, it conferred a higher risk of elevated transaminase. Fatty liver was positively correlated with triglycerides, cholesterol and low-density lipoprotein cholesterol (LDL-C) and negatively correlated with HBV; thus, HBV had a neutralizing effect on lipid metabolism when coexisting with fatty liver. In conclusion, patients with fatty liver that coexists with T2DM, MetS and thyroid dysfunction are more prone to elevated transaminase levels. Patients with both fatty liver and HBV may experience a neutralizing effect on their lipid metabolism. Thus, lipid alterations should be monitored in these patients during antiviral treatment for HBV.

PMID:34918746 | DOI:10.1042/BSR20211769

Demography. 2021 Dec 17:9648346. doi: 10.1215/00703370-9648346. Online ahead of print.

ABSTRACT

This study contributes to the literature on union dissolution by adopting a machine learning (ML) approach, specifically Random Survival Forests (RSF). We used RSF to analyze data on 2,038 married or cohabiting couples who participated in the German Socio-Economic Panel Survey, and found that RSF had considerably better predictive accuracy than conventional regression models. The man’s and the woman’s life satisfaction and the woman’s percentage of housework were the most important predictors of union dissolution; several other variables (e.g., woman’s working hours, being married) also showed substantial predictive power. RSF was able to detect complex patterns of association, and some predictors examined in previous studies showed marginal or null predictive power. Finally, while we found that some personality traits were strongly predictive of union dissolution, no interactions between those traits were evident, possibly reflecting assortative mating by personality traits. From a methodological point of view, the study demonstrates the potential benefits of ML techniques for the analysis of union dissolution and for demographic research in general. Key features of ML include the ability to handle a large number of predictors, the automatic detection of nonlinearities and nonadditivities between predictors and the outcome, generally superior predictive accuracy, and robustness against multicollinearity.

PMID:34918743 | DOI:10.1215/00703370-9648346