Tag: nevin manimala

J Neuroimaging. 2021 Aug 13. doi: 10.1111/jon.12915. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Dueto motion artifacts, optic nerve (ON) findings of idiopathic intracranial hypertension (IIH) can easily be overlooked on T2-weighted (T2w) turbo spin-echo sequence. This study aimed to investigate the contribution of the apparent diffusion coefficient (ADC) map derived from the interleaved multi-shot (IMS) echoplanar imaging (EPI) to the ON findings of IIH in children.

METHODS: MRIs of 42 pediatric patients aged 3-17 years diagnosed with definite IIH according to modified Dandy criteria were retrospectively re-evaluated, between April 2018 and January 2021. Forty-two age- and sex-matched subjects with no IIH symptoms and reported as normal were included as a control group.

RESULTS: ON sheath distance (ONSD) on the ADC map (p = .005) and vertical tortuosity (p = .030) were significant single MRI parameters for predicting IIH. Other single parameters were not statistically significant. Flattening of the posterior sclera (FPS) and ON protrusion (ONP) were observed on ADC maps more frequently than T2w (42.8% vs. 19% and 19% vs. 4.7%, respectively). From combined MRI parameters, the presence of at least one of ONP, FPS, or ONSD on ADC maps (p = .001) showed greater significance than the presence of T2w (p = .048). The predictive values of other MRI findings evaluated together were not statistically significant (p > .05).

CONCLUSIONS: This study’s results show that due to the short readout time and less sensitivity to motion, the ADC map obtained from IMS-EPI can contribute to orbital findings of IIH, in addition to T2w.

PMID:34388272 | DOI:10.1111/jon.12915

Clin Oral Implants Res. 2021 Aug 13. doi: 10.1111/clr.13828. Online ahead of print.

ABSTRACT

AIM: To evaluate the Implant Disease Risk Assessment (IDRA) tool for the prediction of peri-implantitis in treated periodontitis patients with implant-supported fixed dental prostheses (FDPs) after at least 5 years of function.

MATERIAL AND METHODS: From the patient pool of implant patients enrolled in a regular supportive periodontal therapy program (SPT) for at least 5 years, 239 patients were screened. Eighty patients met the inclusion criteria and underwent evaluation through the criteria of the IDRA tool. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves including 95% confidence intervals were estimated.

RESULTS: Seventy-nine patients (43 males and 36 females, 8 smokers), aged on average 59.0 years (range: 40-79 years) at baseline (i.e. FDP delivery) were analyzed. The calculated IDRA-risk was in 34 patients (42.5%) a moderate risk, while 45 patients (56.3%) were considered at high IDRA-risk. One patient categorized at low IDRA-risk was excluded from the analysis. The AUC was 0.613 (95% CI: 0.464-0.762) if the IDRA-risk was associated with prevalence of peri-implantitis at the most recent follow-up. Peri-implantitis was diagnosed in 4 patients (12%) at moderate and in 12 patients (27%) at high IDRA-risk, respectively. The calculated odds ratio for developing peri-implantitis in patients with high IDRA-risk compared with patients with moderate IDRA-risk was 2.727 with no statistically significant difference between the two groups (95% CI: 0.793 – 9.376).

CONCLUSION: Within the limitations of the present retrospective study, the IDRA algorithm might represent a promising tool to assess patients at moderate or high risk of developing peri-implantitis.

PMID:34388276 | DOI:10.1111/clr.13828

PLoS One. 2021 Aug 13;16(8):e0256154. doi: 10.1371/journal.pone.0256154. eCollection 2021.

ABSTRACT

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

PMID:34388227 | DOI:10.1371/journal.pone.0256154

PLoS One. 2021 Aug 13;16(8):e0256236. doi: 10.1371/journal.pone.0256236. eCollection 2021.

ABSTRACT

OBJECTIVE: To assess the magnitude and factors associated with depression and anxiety among people with epilepsy and attending out-patient treatment at central Gondar zone primary public hospitals, northwest, Ethiopia.

METHOD: An institutional based cross-sectional study was conducted from May-June, 2020 at central Gondar zone primary public hospitals. A total of 589 participants were chosen by systematic sampling technique. Data was collected by utilizing Amharic version interviewer-administered structured and semi-structured questioners. Depression and anxiety were assessed by using hospital anxiety and depression scale. Bivariate and multivariate logistic regression analysis was done to recognize variables related to both depression and anxiety. Association was described by using “adjusted odds ratio” (AOR) along with 95% full Confidence interval (CI). Finally, P-values < 0.05 in adjusted analysis were taken as a cut off for significant association.

RESULT: Out of 556 participants included in the study, 30.9%, 33.1% had depression and anxiety respectively. Being divorced/widowed (AOR = 2.43, 95% CI, 1.18-4.99), using two and above number of antiepileptic medications (AOR = 1.77,95% CI,1.02-3.09), very frequent seizure frequency (AOR = 2.68, 95% CI,1.30-5.51), current substance use (AOR = 1.82, 95% CI, 1.03-3.22), perceived stigma (AOR = 5.67,95% CI,3.14-8.18), and hazardous alcohol use (AOR = 2.84, 95% CI,1.32-6.09) were statistically associated with depression. While, being a single (AOR = 1.65, 95% CI, 1.04-2.63), using two and above number of antiepileptic medications (AOR = 2.27, 95% CI, 1.42-3.62), duration of illness ≥16 years (AOR = 2.82, 95% CI, 1.26-6.31), and perceived stigma (AOR = 2.49, 95% CI, 1.63-3.82) were statistically associated with anxiety at a p-value < 0.05.

CONCLUSION: This study showed that the magnitude of depression and anxiety were relatively high among people with epilepsy. Using two and above number of antiepileptic medications and perceived stigma were statistically associated with both depression and anxiety. Screening, early identification and providing appropriate intervention of depression and anxiety among people with epilepsy should be great concern for the health care providers.

PMID:34388228 | DOI:10.1371/journal.pone.0256236

PLoS One. 2021 Aug 13;16(8):e0255689. doi: 10.1371/journal.pone.0255689. eCollection 2021.

ABSTRACT

The aim of this study was to examine psychometric properties of the revised Posttraumatic Stress Checklist (PCL-5) for Diagnostic and Statistical Manual- 5th Edition (DSM-5) in Greek postpartum women after Cesarean Section(CS) (emergency-elective).So far, there was no study in Greece assessing psychometric properties of the PCL-5 in women after CS. The participating women (N = 469), who gave birth with emergency and elective CS at the Greek University Hospital of Larisa, have consented to participate in two phases of the survey and completed self-report questionnaires, the 2nd day after CS and at the 6th week after CS. Measures used in this study were the PCL-5 for DSM-5, the Life Events Checklist (LEC-5), Criteria B, C, D, E, and Criterion A, specifically designed for detection of posttraumatic stress disorder (PTSD) symptoms in postpartum period. To evaluate the internal reliability of the PCL-5 two different indices of internal consistency were calculated, i.e., Cronbach’s alpha (.97) and Guttman’ssplit-half (.95), demonstrating high reliability level. The data were positively skewed, suggesting that the reported levels of PTSD among our participants were low. Factor analyses demonstrated acceptable construct validity; a comparison of thePCL-5 with the other measures of the same concept showed a good convergent validity of the scale. Overall, all the results suggest that the four-factor PCL-5 seemed to work adequately for the Greek sample of women after CS.

PMID:34388199 | DOI:10.1371/journal.pone.0255689

PLoS One. 2021 Aug 13;16(8):e0256113. doi: 10.1371/journal.pone.0256113. eCollection 2021.

ABSTRACT

BACKGROUND: Separating ill or possibly infectious people from their healthy community is one of the core principles of non-pharmaceutical interventions. However, there is scarce evidence on how to successfully implement quarantine orders. We investigated a community quarantine for an entire village in Germany (Neustadt am Rennsteig, March 2020) with the aim of better understanding the successful implementation of quarantine measures.

METHODS: This cross-sectional survey was conducted in Neustadt am Rennsteig six weeks after the end of a 14-day mandatory community quarantine. The sample size consisted of 562 adults (64% of the community), and the response rate was 295 adults, or 52% (33% of the community).

FINDINGS: National television was reported as the most important channel of information. Contact with local authorities was very limited, and partners or spouses played a more important role in sharing information. Generally, the self-reported information level was judged to be good (211/289 [73.0%]). The majority of participants (212/289 [73.4%]) approved of the quarantine, and the reported compliance was 217/289 (75.1%). A self-reported higher level of concern as well as a higher level of information correlated positively with both a greater acceptance of quarantine and self-reported compliant behaviour.

INTERPRETATION: The community quarantine presented a rare opportunity to investigate a public health intervention for an entire community. In order to improve the implementation of public health interventions, public health risk communication activities should be intensified to increase both the information level (potentially leading to better compliance with community quarantine) and the communication level (to facilitate rapport and trust between public health authorities and their communities).

PMID:34388211 | DOI:10.1371/journal.pone.0256113

PLoS One. 2021 Aug 13;16(8):e0255922. doi: 10.1371/journal.pone.0255922. eCollection 2021.

ABSTRACT

Tillering and secondary branching are two plastic traits with high agronomic importance, especially in terms of the ability of plants to adapt to changing environments. We describe a quantitative trait analysis of tillering and secondary branching in two novel BC1F2 populations totaling 246 genotypes derived from backcrossing two Sorghum bicolor x S. halepense F1 plants to a tetraploidized S. bicolor. A two-year, two-environment phenotypic evaluation in Bogart, GA and Salina, KS permitted us to identify major effect and environment specific QTLs. Significant correlation between tillering and secondary branching followed by discovery of overlapping sets of QTLs continue to support the developmental relationship between these two organs and suggest the possibility of pleiotropy. Comparisons with two other populations sharing S. bicolor BTx623 as a common parent but sampling the breadth of the Sorghum genus, increase confidence in QTL detected for these two plastic traits and provide insight into the evolution of morphological diversity in the Eusorghum clade. Correspondence between flowering time and vegetative branching supports other evidence in suggesting a pleiotropic effect of flowering genes. We propose a model to predict biomass weight from plant architecture related traits, quantifying contribution of each trait to biomass and providing guidance for future breeding experiments.

PMID:34388196 | DOI:10.1371/journal.pone.0255922

PLoS One. 2021 Aug 13;16(8):e0255935. doi: 10.1371/journal.pone.0255935. eCollection 2021.

ABSTRACT

INTRODUCTION: Breast cancer is an important public health problem worldwide, with important disparities in incidence, mortality, and survival rates between developed and developing countries due to inequalities regarding access to measures for the prevention and treatment of the disease. In Brazil, there are higher rates of incidence and a downward trend in mortality in regions of greater socioeconomic development.

OBJECTIVE: To evaluate the effect of age, period, and birth cohort on breast cancer mortality in women aged 20 years and older in the states of the Northeast Region of Brazil, an area of high socioeconomic vulnerability, from 1980 to 2019.

METHODS: The death records were extracted from the DATASUS Mortality Information System website (Department of National Health Informatics) from the Ministry of Health of Brazil. Estimable functions were used to estimate the age-period and cohort models (APC) using the Epi library from the R statistical software version 6.4.1.

RESULTS: The average breast cancer mortality rate for the period was 20.45 deaths per 100,000 women. The highest coefficients per 100,000 women were observed in the states of Pernambuco (21.09 deaths) and Ceará (20.85 deaths), and the lowest in Maranhão (13.58 deaths) and Piauí (15.43 deaths). In all of the locations, there was a progressive increase in mortality rates in individuals over 40 years of age, with higher rates in the last five-year period (2015-2019). There was an increase in the risk of death for the five-year period of the 2000s in relation to the reference period (1995-1999) in the Northeast region and in the states of Alagoas, Bahia, Maranhão, Paraíba, and Piauí. In addition, there was an increased risk of death for women born after the 1950s in all locations.

CONCLUSION: The highest mortality rates in all five-year periods analyzed were observed in states with greater socioeconomic development, with an increase in mortality rates in the 2000s, and a higher risk of death in the younger cohorts.

PMID:34388198 | DOI:10.1371/journal.pone.0255935

PLoS One. 2021 Aug 13;16(8):e0256086. doi: 10.1371/journal.pone.0256086. eCollection 2021.

ABSTRACT

BACKGROUND: Quality water, sanitation, and hygiene facilities act as barricades to the transmission of COVID-19 in health care facilities. These facilities ought to also be available, accessible, and functional in temporary treatment centers. Despite numerous studies on health care facilities, however, there is limited information on the status of WASH facilities in such centers.

METHODS: The assessment of health care facilities for the COVID-19 response checklist and key informant interviews, were used for data collection. 35 treatment centers in Southern Ethiopia were surveyed. Eightkey informants were interviewed to gain an understanding of the WASH conditions in the treatment centers. The Quantitative data was entered using EPI-INFO 7 and exported to SPSS 20 for analysis. Results are presented using descriptive statistics. Open Code 4.02 was used for the thematic analysis of the qualitative data.

RESULTS: Daily water supply interruptions occurred at 27 (77.1%) of the surveyed sites. Only 30 (85.72%) had bathrooms that were segregated for personnel and patients, and only 3 (3.57%) had toilets that were handicapped accessible. 20(57.2%) of the treatment centers did not have a hand hygiene protocol that satisfied WHO guidelines. In terms of infection prevention and control, 16 (45.71%) of the facilities lacked adequate personal protective equipment stocks. Between urban and rural areas, there was also a significant difference in latrine maintenance, hand hygiene protocol design and implementation, and incineration capacity.

CONCLUSION: The results reveal crucial deficiencies in the provision of WASH in the temporary COVID-19 treatment centers. Efforts to improve WASH should offer priority to hygiene service interventions to minimize the risk of healthcare-acquired infections. The sustainable provision of hygiene services, such as hand washing soap, should also be given priority.

PMID:34388184 | DOI:10.1371/journal.pone.0256086

PLoS One. 2021 Aug 13;16(8):e0255886. doi: 10.1371/journal.pone.0255886. eCollection 2021.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, especially in underdeveloped countries. There is a clear need to develop novel computer-assisted diagnosis tools to provide rapid and cost-effective screening in places where massive traditional testing is not feasible. Lung ultrasound is a portable, easy to disinfect, low cost and non-invasive tool that can be used to identify lung diseases. Computer-assisted analysis of lung ultrasound imagery is a relatively recent approach that has shown great potential for diagnosing pulmonary conditions, being a viable alternative for screening and diagnosing COVID-19.

OBJECTIVE: To evaluate and compare the performance of deep-learning techniques for detecting COVID-19 infections from lung ultrasound imagery.

METHODS: We adapted different pre-trained deep learning architectures, including VGG19, InceptionV3, Xception, and ResNet50. We used the publicly available POCUS dataset comprising 3326 lung ultrasound frames of healthy, COVID-19, and pneumonia patients for training and fine-tuning. We conducted two experiments considering three classes (COVID-19, pneumonia, and healthy) and two classes (COVID-19 versus pneumonia and COVID-19 versus non-COVID-19) of predictive models. The obtained results were also compared with the POCOVID-net model. For performance evaluation, we calculated per-class classification metrics (Precision, Recall, and F1-score) and overall metrics (Accuracy, Balanced Accuracy, and Area Under the Receiver Operating Characteristic Curve). Lastly, we performed a statistical analysis of performance results using ANOVA and Friedman tests followed by post-hoc analysis using the Wilcoxon signed-rank test with the Holm’s step-down correction.

RESULTS: InceptionV3 network achieved the best average accuracy (89.1%), balanced accuracy (89.3%), and area under the receiver operating curve (97.1%) for COVID-19 detection from bacterial pneumonia and healthy lung ultrasound data. The ANOVA and Friedman tests found statistically significant performance differences between models for accuracy, balanced accuracy and area under the receiver operating curve. Post-hoc analysis showed statistically significant differences between the performance obtained with the InceptionV3-based model and POCOVID-net, VGG19-, and ResNet50-based models. No statistically significant differences were found in the performance obtained with InceptionV3- and Xception-based models.

CONCLUSIONS: Deep learning techniques for computer-assisted analysis of lung ultrasound imagery provide a promising avenue for COVID-19 screening and diagnosis. Particularly, we found that the InceptionV3 network provides the most promising predictive results from all AI-based techniques evaluated in this work. InceptionV3- and Xception-based models can be used to further develop a viable computer-assisted screening tool for COVID-19 based on ultrasound imagery.

PMID:34388187 | DOI:10.1371/journal.pone.0255886