Nevin Manimala

Adv Ther. 2022 May 4. doi: 10.1007/s12325-022-02150-0. Online ahead of print.

ABSTRACT

INTRODUCTION: To investigate changes in refractive error in schoolchildren before and during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS: This study included 2792 students, who underwent a 3-year follow-up from 2018 to 2020. All participants underwent yearly noncycloplegic refraction and ocular examinations. Time-related changes in sphere, cylinder, and spherical equivalent (SE) measurements in both genders were analyzed.

RESULTS: The myopic sphere (- 0.78 ± 1.83 vs. – 1.03 ± 1.91 D; P = 0.025) and SE (- 1.04 ± 1.90 vs. – 1.32 ± 1.99 D; P = 0.015) progressed significantly from 2018 to 2019. Female participants had a significantly greater change in SE than male participants (P < 0.05), and the low hyperopia, emmetropia, and mild myopia groups significantly deteriorated (P < 0.001) from 2018 to 2019. Significant differences in sphere change (- 0.21 ± 0.97 vs. – 0.36 ± 0.96 D; P < 0.001) and SE change (- 0.23 ± 0.99 vs. – 0.38 ± 0.98 D; P < 0.001) were noted between 2019-2018 and 2020-2019, respectively. The respective changes in cylinder were statistically similar (- 0.03 ± 0.53 vs. – 0.05 ± 0.62 D; P = 0.400).

CONCLUSIONS: The refractive status of schoolchildren showed an increasing myopic shift trend before and during the COVID-19 pandemic. The low hyperopia, emmetropia, and mild myopia groups were more sensitive to environmental changes during COVID-19 than before. The myopic shift was greater in female participants than male participants.

PMID:35508845 | DOI:10.1007/s12325-022-02150-0

Neurosurg Rev. 2022 May 4. doi: 10.1007/s10143-022-01800-9. Online ahead of print.

ABSTRACT

Although every glioma patient varies in tumor size, location, histological grade and molecular biomarkers, non-tumoral morphological abnormalities are commonly detected by a statistical comparison among patient groups, missing the information of individual morphological alterations. In this study, we introduced an individual-level structural abnormality detection method for glioma patients and proposed several abnormality indexes to depict individual atrophy patterns. Forty-five patients with a glioma in the frontal lobe and fifty-one age-matched healthy controls participated in the study. Individual structural abnormality maps (SAM) were generated using patients’ preoperative T1 images, by calculating the degree of deviation of voxel volume in each patient with the normative model built from healthy controls. Based on SAM, a series of individual abnormality indexes were computed, and their relationship with glioma characteristics was explored. The results demonstrated that glioma patients showed unique non-tumoral atrophy patterns with overlapping atrophy regions mainly located at hippocampus, parahippocampus, amygdala, insula, middle temporal gyrus and inferior temporal gyrus, which are closely related to the human cognitive functions. The abnormality indexes were associated with several molecular biomarkers including isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion and telomerase reverse transcriptase (TERT) promoter mutation. Our study provides an effective way to access the individual-level non-tumoral structural abnormalities in glioma patients, which has the potential to significantly improve individualized precision medicine.

PMID:35508819 | DOI:10.1007/s10143-022-01800-9

Food Environ Virol. 2022 May 4. doi: 10.1007/s12560-022-09523-2. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has been monitored by applying different strategies, including SARS-CoV-2 detection with clinical testing or through wastewater-based epidemiology (WBE). We used the latter approach to follow SARS-CoV-2 dispersion in Tapachula city, located in Mexico’s tropical southern border region. Tapachula is a dynamic entry point for people seeking asylum in Mexico or traveling to the USA. Clinical testing facilities for SARS-CoV-2 monitoring are limited in the city. A total of eighty water samples were collected from urban and suburban rivers and sewage and a wastewater treatment plant over 4 months in Tapachula. We concentrated viral particles with a PEG-8000-based method, performed RNA extraction, and detected SARS-CoV-2 particles through RT-PCR. We considered the pepper mild mottle virus as a fecal water pollution biomarker and analytical control. SARS-CoV-2 viral loads (N1 and N2 markers) were quantified and correlated with official regional statistics of COVID-19 bed occupancy and confirmed cases (r > 91%). Our results concluded that WBE proved a valuable tool for tracing and tracking the COVID-19 pandemic in tropical countries with similar water temperatures (21-29 °C). Monitoring SARS-CoV-2 through urban and suburban river water sampling would be helpful in places lacking a wastewater treatment plant or water bodies with sewage discharges.

PMID:35508751 | DOI:10.1007/s12560-022-09523-2

Arch Sex Behav. 2022 May 4. doi: 10.1007/s10508-021-02193-4. Online ahead of print.

ABSTRACT

People who use crack cocaine (PWUCC) are a population severely impacted by a concentrated epidemic of HIV. Behavioral interventions to prevent and treat HIV among PWUCC have been implemented around the world including in low- and middle-income countries which have been disproportionately affected by HIV. However, few studies have validated and assessed psychometric properties of measures on PWUCC, especially in transnational populations. Our sample was comprised of 1324 PWUCC, Spanish mono-lingual speakers, residing in the metropolitan area of San Salvador, El Salvador. Exploratory factor analysis and subsequent confirmatory factor analysis using statistical softwares SPSS and Amos were conducted on three abbreviated and translated condom use attitude measures (i.e., Condom Use Attitudes Scale-Spanish Short Form, Condom Use Social Norm-Spanish Short Form [CUSN-SSF], Condom Use Self-Efficacy-Spanish Short Form). Convergent validity was examined by computing bivariate correlations between the scales and condom use and sexually transmitted disease diagnosis. Results indicated that a two-factor, 8-item correlated model for the CUAS-SSF scale had an excellent fit and adequate reliability (α = .76). The confirmatory factor analysis for the 5-item CUSN-SSF scale indicated a satisfactory fit with 3 of 6 fit indices indicating adequate fit. Analysis of the two-factor 5-item CUSE-SSF scale indicated satisfactory fit and adequate reliability (α = .84). There were significant correlations between all measures and with self-reported condom use. Results indicate that these brief measures are reliable and valid and can be utilized to assess the effectiveness of HIV risk reduction interventions among Spanish-speaking PWUCC.

PMID:35508750 | DOI:10.1007/s10508-021-02193-4

Pituitary. 2022 May 4. doi: 10.1007/s11102-022-01215-1. Online ahead of print.

ABSTRACT

PURPOSE: Transsphenoidal surgery (TSS) is the first-line treatment for patients with Cushing’s Disease (CD). Recurrence rates after a first TSS range between 3 and 22% within 3 years. Management of recurrent or persistent CD may include repeat TSS or stereotactic radiosurgery (SRS). We performed a meta-analysis to explore the overall efficacy of TSS and SRS for patients with CD after an initial surgical intervention.

METHODS: EMBASE, PubMed, SCOPUS, and Cochrane databases were searched from their dates-of-inception up to December 2021. Inclusion criteria were comprised of patients with an established diagnosis of CD who presented with persistent or biochemically recurrent disease after a first TSS for tumor resection and were treated with a second TSS or SRS.

RESULTS: Search criteria yielded 2,116 studies of which 37 articles from 15 countries were included for analysis. Mean age ranged between 29.9 and 47.9 years, and mean follow-up was 11-104 months. TSS was used in 669 (67.7%) patients, while SRS was used in 320 (32.4%) patients, and remission rates for CD were 59% (95%CI 0.49-0.68) and 74% (95%CI 0.54-0.88), respectively. There was no statistically significant difference in the remission rate between TSS and SRS (P = 0.15). The remission rate of patients with recurrent CD undergoing TSS was 53% (95%CI 0.32-0.73), and for persistent CD was 41% (95%CI 0.28-0.56) (P = 0.36).

CONCLUSION: Both TSS and SRS are possible approaches for the treatment of recurrent or persistent CD after a first TSS. Our data show that either TSS or SRS represent viable treatment options to achieve remission for this subset of patients.

PMID:35508745 | DOI:10.1007/s11102-022-01215-1

Risk Anal. 2022 May 4. doi: 10.1111/risa.13937. Online ahead of print.

ABSTRACT

While children are one of the groups at risk in disasters, they can also take an active part in disaster management, provided that the opportunity is given. This research examined the effect of disaster experience, disaster education, country, and city socioeconomic status on children’s perceived risk and preparedness with a survey of 1335 children between 11 and 14 years old, in Nepal and Turkey. The survey used questionnaires and the pictorial representation of illness and self measure (PRISM) tool. Results showed that (1) children’s risk perceptions were in line with their country-specific objective risks; (2) there were differences between the countries in relation to perception of risk for all the hazards except wildfire; (3) socioeconomic status had a statistically significant effect on children’s perceptions of risk and preparedness for earthquakes, wildfires, that is, children who live in wealthier places had higher perceived risk and preparedness; (4) children in both countries showed similar trends in their knowledge of the correct protective actions to take in the event of a hazard occurrence. However, there is still room to enhance children’s knowledge, in terms of safety behaviors, as the children selected many incorrect protective actions. There are important implications in terms of child-centered disaster management which hopefully will make life safer and help to create more resilience to disaster in society as a whole.

PMID:35508707 | DOI:10.1111/risa.13937

Sci Rep. 2022 May 4;12(1):7243. doi: 10.1038/s41598-022-11296-4.

ABSTRACT

Knee osteoarthritis (OA) is the most prevalent type of OA, and Toll-like receptor 7 (TLR7) may lead to the pathogenesis of OA. Recently, X-linked TLR7 polymorphism has been confirmed to be associated with arthritis. However, there is a lack of studies on TLR7 gene polymorphism associated with knee OA susceptibility. The current study aimed to determine whether TLR7 gene polymorphism is associated with the risk of knee OA. Genotyping of two polymorphic sites (rs3853839 and rs179010) in the TLR7 gene was performed in 252 OA patients, and 265 healthy controls using the SNaPshot sequencing technique. Data were analyzed statistically by Chi-square tests and logistic regression. Rs3853839-C allele showed frequencies of 28% and 27% in the healthy control and female knee OA groups, respectively. The differences were not statistically significant (P > 0.05). The rs3853839-CG genotype frequency was significantly lower in the female knee OA group as compared to the healthy control group (OR 0.60; 95%CI 0.36-0.99; P = 0.044). In the male hemizygote population, the rs3853839-CC showed significantly lower frequencies in the male knee OA group as compared to the healthy control group (OR 0.35; 95%CI 0.17-0.71; P = 0.0025). Regarding rs179010, there were no differences in the genotype distribution and allele frequencies between OA patients and healthy subjects under any models (P > 0.05). Stratified analysis showed that the frequency of the rs3853839-CG genotypes was lower in high Kellgren-Lawrence grades (KLG) (OR 0.48; 95%CI 0.21-1.08; P = 0.066), and significantly lower in OA patients with effusion synovitis (OR 0.38; 95%CI 0.17-0.88; P = 0.013). TLR7 rs3853839 polymorphism may play a role in the susceptibility of knee OA in the Chinese Han Population and may be associated with OA severity and the risk of effusion synovitis in Knee OA.

PMID:35508687 | DOI:10.1038/s41598-022-11296-4

J Gastrointest Surg. 2022 May 4. doi: 10.1007/s11605-022-05332-x. Online ahead of print.

ABSTRACT

BACKGROUND: Procedure-specific complications can have devastating consequences. Machine learning-based tools have the potential to outperform traditional statistical modeling in predicting their risk and guiding decision-making. We sought to develop and compare deep neural network (NN) models, a type of machine learning, to logistic regression (LR) for predicting anastomotic leak after colectomy, bile leak after hepatectomy, and pancreatic fistula after pancreaticoduodenectomy (PD).

METHODS: The colectomy, hepatectomy, and PD National Surgical Quality Improvement Program (NSQIP) databases were analyzed. Each dataset was split into training, validation, and testing sets in a 60/20/20 ratio, with fivefold cross-validation. Models were created using NN and LR for each outcome. Models were evaluated primarily with area under the receiver operating characteristic curve (AUROC).

RESULTS: A total of 197,488 patients were included for colectomy, 25,403 for hepatectomy, and 23,333 for PD. For anastomotic leak, AUROC for NN was 0.676 (95% 0.666-0.687), compared with 0.633 (95% CI 0.620-0.647) for LR. For bile leak, AUROC for NN was 0.750 (95% CI 0.739-0.761), compared with 0.722 (95% CI 0.698-0.746) for LR. For pancreatic fistula, AUROC for NN was 0.746 (95% CI 0.733-0.760), compared with 0.713 (95% CI 0.703-0.723) for LR. Variables related to intra-operative information, such as surgical approach, biliary reconstruction, and pancreatic gland texture were highly important for model predictions.

DISCUSSION: Machine learning showed a marginal advantage over traditional statistical techniques in predicting procedure-specific outcomes. However, models that included intra-operative information performed better than those that did not, suggesting that NSQIP procedure-targeted datasets may be strengthened by including relevant intra-operative information.

PMID:35508684 | DOI:10.1007/s11605-022-05332-x

Sci Rep. 2022 May 4;12(1):7216. doi: 10.1038/s41598-022-10902-9.

ABSTRACT

Infertility is a significant health problem and assisted reproductive technologies to treat infertility. Despite all efforts, the success rate of these methods is still low. Also, each of these methods has side effects and costs. Therefore, accurate prediction of treatment success rate is a clinical challenge. This retrospective study aimed to internally validate and compare various machine learning models for predicting the clinical pregnancy rate (CPR) of infertility treatment. For this purpose, data from 1931 patients consisting of in vitro fertilization (IVF) or intra cytoplasmic sperm injection (ICSI) (733) and intra uterine insemination (IUI) (1196) treatments were included. Also, no egg or sperm donation data were used. The performance of machine learning algorithms to predict clinical pregnancy were expressed in terms of accuracy, recall, F-score, positive predictive value (PPV), brier score (BS), Matthew correlation coefficient (MCC), and receiver operating characteristic. The significance of the features with CPR and AUCs was evaluated by Student’s t test and DeLong’s algorithm. Random forest (RF) model had the highest accuracy in the IVF/ICSI treatment. The sensitivity, F1 score, PPV, and MCC of the RF model were 0.76, 0.73, 0.80, and 0.5, respectively. These values for IUI treatment were 0.84, 0.80, 0.82, and 0.34, respectively. The BS was 0.13 and 0.15 for IVF/ICS and IUI, respectively. In addition, the estimated AUCs of the RF model for IVF/ICS and IUI were 0.73 and 0.7, respectively. Some essential features were obtained based on RF ranking for the two datasets, including age, follicle stimulation hormone, endometrial thickness, and infertility duration. The results showed a strong relationship between clinical pregnancy and a woman’s age. Also, endometrial thickness and the number of follicles decreased with increasing female age in both treatments.

PMID:35508641 | DOI:10.1038/s41598-022-10902-9

Sci Rep. 2022 May 4;12(1):7295. doi: 10.1038/s41598-022-10459-7.

ABSTRACT

Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary computations downstream and inflates the time required to compute functional connectivity networks by a factor that is at least inversely proportional to the true network density. As these noisy solutions have distorted coefficients, thresholding them as per some criterion, statistical or otherwise, does not alleviate the problem. Thus obtaining a sparse representation of such data is important since it provides an efficient representation of the data and facilitates its further analysis. We propose a fast Sparse Vector Autoregressive Greedy Search (SVARGS) method that works well for high dimensional data, even when the number of time points is relatively low, by incorporating only statistically significant coefficients. In numerical experiments, our methods show high accuracy in recovering the true sparse model. The relative absence of spurious coefficients permits accurate, stable and fast evaluation of derived quantities such as power spectrum, coherence and Granger causality. Consequently, sparse functional connectivity networks can be computed, in a reasonable time, from data comprising tens of thousands of channels/voxels. This enables a much higher resolution analysis of functional connectivity patterns and community structures in such large networks than is possible using existing time series methods. We apply our method to EEG data where computed network measures and community structures are used to distinguish emotional states as well as to ADHD fMRI data where it is used to distinguish children with ADHD from typically developing children.

PMID:35508638 | DOI:10.1038/s41598-022-10459-7