Category: Statistics

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

PLoS One. 2021 Aug 13;16(8):e0256031. doi: 10.1371/journal.pone.0256031. eCollection 2021.

ABSTRACT

PURPOSE: The credibility and the reliability of Internet webpages to seek medication-related information is questionable. The main objective of the current study was to evaluate perception and experience of pharmacists with the use of Internet-based medication information by their patients.

METHODS: This is a cross-sectional descriptive study that was conducted to evaluate perception and experience of pharmacists with the use of Internet-based medication information by their patients. During the study period, 200 pharmacists were approached to participate in the study using a paper-based survey to assess their perceptions and current experience with the use of Internet-based medication information by their patients. Data were analyzed using descriptive statistics (mean/standard deviation for continuous variables, and frequency/percentages for qualitative variables). Also, simple linear regression was utilized to screen factors affecting pharmacists’ perception scores of the use of Internet-based medication information.

RESULTS: Among 161 recruited pharmacists, the majority (n = 129, 80.1%) reported receiving inquiries from patients about Internet-based medication information within the last year. Among them, only 22.6% (n = 29) of pharmacists believed that Internet-based medication information is somewhat or very accurate. Unfortunately, only 24.2% (n = 31) of them stated that they always had enough time for their patient to discuss their Internet-based medication information. Regarding pharmacists’ perception of the use of Internet-based medication information by their patients, more than half of the pharmacists (>50%) believe that Internet-based medication information could increase the patient’s role in taking responsibility. On the other hand, 54.7% (n = 88) of the pharmacists believed that Internet-based medication information would contribute to rising the healthcare cost by obtaining unnecessary medications by patients. Finally, pharmacists’ educational level was found to significantly affect their perception scores toward patient use of Internet-based medication information where those with higher educational level showed lower perception score (r = -0.200, P-value = 0.011).

CONCLUSION: Although pharmacists felt that usage of Internet-based data by patients is beneficial, they also have believed that it has a negative impact in terms of rising the healthcare cost, and it promotes unnecessary fear or concern about medications. We suggest that pharmacists be trained on principles of critical appraisal to become professional in retrieval information on the Internet that might improve their delivery of healthcare information and their recommendations to patients.

PMID:34388191 | DOI:10.1371/journal.pone.0256031

PLoS One. 2021 Aug 13;16(8):e0255637. doi: 10.1371/journal.pone.0255637. eCollection 2021.

ABSTRACT

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

PMID:34388173 | DOI:10.1371/journal.pone.0255637

PLoS One. 2021 Aug 13;16(8):e0256105. doi: 10.1371/journal.pone.0256105. eCollection 2021.

ABSTRACT

Keel bone fractures (KBF) in commercial poultry production systems are a major welfare problem with possible economic consequences for the poultry industry. Recent investigations suggest that the overall situation may be worsening. Depending on the housing system, fracture prevalences exceeding 80% have been reported from different countries. No specific causes have yet been identified and this has consequently hampered risk factor identification. The objective of the current study was to investigate the prevalence of KBF in Danish layer hens and to identify risk factors in relation to KBF in all major productions systems, including parent stock production. For risk factor identification, production data from the included flocks was used. In total, 4794 birds from 40 flocks were investigated at end-of-lay. All birds were euthanized on farm and underwent inspection and palpation followed by necropsy. All observations were recorded and subsequently analysed using the SAS statistical software package. In flocks from non-caged systems, fracture prevalence in the range 53%-100%, was observed whereas the prevalence in flocks from enriched cages ranged between 50-98%. Furthermore, often multiple fractures (≥4) were observed in individual birds (range 5-81% of the birds with fractures) depending on the flock. The localization of the fractures at the distal end of the keel bone is highly consistent in all flocks (>96%). Macroscopically the fractures varied morphologically from an appearance with an almost total absence of callus, most frequently observed in caged birds, to large callus formations in and around the fracture lines, which was a typical finding in non-caged birds. Despite being housed under cage-free conditions, parent birds had significantly fewer fractures (all flocks were 60 weeks old) per bird, than other birds from cage-free systems. The body weight at end-of-lay had an effect on the risk of having fractures, heavy hens have significantly fewer fractures at end-of-lay. The older the hens were at onset of lay, the lower was the flock prevalence at end-of-lay. Additionally, the daily egg size at onset of lay was of importance for the risk of developing fractures, the production of heavier eggs initially, resulted in higher fracture prevalence at depopulation. The odds ratio of body weight, (+100 g) was 0.97, age at onset of lay (+1 week) was 0.87 and daily egg weight at onset (+1 gram) was 1.03. In conclusion, the study demonstrated a very high prevalence of KBF in hens from all production systems and identified hen size, age at onset of lay and daily egg weight at onset of lay to be major risk factors for development of KBF in the modern laying hen. Further research regarding this is warranted to strengthen the longevity and enhance the welfare of laying hens.

PMID:34388183 | DOI:10.1371/journal.pone.0256105

PLoS One. 2021 Aug 13;16(8):e0255398. doi: 10.1371/journal.pone.0255398. eCollection 2021.

ABSTRACT

Stable isotope analyses of bone collagen are often used in palaeoecological studies to reveal environmental conditions in the habitats of different herbivore species. However, such studies require valuable reference data, obtained from analyses of modern individuals, in habitats of well-known conditions. In this article, we present the stable carbon and nitrogen isotope composition of bone collagen from modern red deer (N = 242 individuals) dwelling in various habitats (N = 15 study sites) in Europe. We investigated which of the selected climatic and environmental factors affected the δ13C and δ15N values in bone collagen of the studied specimens. Among all analyzed factors, the percent of forest cover influenced the carbon isotopic composition most significantly, and decreasing forest cover caused an increase in δ13C values. The δ15N was positively related to the proportion of open area and (only in the coastal areas) negatively related to the distance to the seashore. Using rigorous statistical methods and a large number of samples, we confirmed that δ13C and δ15N values can be used as a proxy of past habitats of red deer.

PMID:34388162 | DOI:10.1371/journal.pone.0255398

Zh Nevrol Psikhiatr Im S S Korsakova. 2021;121(7. Vyp. 2):94-98. doi: 10.17116/jnevro202112107294.

ABSTRACT

One of the leading symptoms in patients with multiple sclerosis (MS) is cognitive impairment. It often affects aspects of cognition such as learning ability, memory, processing speed, and attention. It has been proven that patients often complain of difficulties in multitasking and choosing the right words. These problems are often underestimated. Various studies show that regular physical activity, mainly aerobic exercise, can potentially improve cognitive function. Positive effects on concentration, memory, and multitasking were described. In March 2019, the Tyumen regional center of MS, together with the clinical Institute of the brain (Yekaterinburg), launched a clinical study of methods for rehabilitation of cognitive disorders in patients with MS. There was a statistically significant improvement in MOCA-test scores, according to SDMT and PASSAT data in the main group of MS patients. Despite a significant improvement in cognitive function, the self-assessment of mental function according to the MSQOL54-MN test in this group of patients did not change. Our preliminary results suggest that a comprehensive and well-controlled training program can improve cognitive abilities in MS patients even after a short course of treatment.

PMID:34387454 | DOI:10.17116/jnevro202112107294

J Comput Chem. 2021 Aug 13. doi: 10.1002/jcc.26734. Online ahead of print.

ABSTRACT

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin.

PMID:34387374 | DOI:10.1002/jcc.26734

J Ultrasound Med. 2021 Aug 13. doi: 10.1002/jum.15804. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore the clinical value of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of benign and malignant subpleural pulmonary lesions (SPLs).

METHODS: Among 959 patients with SPLs who were scheduled to undergo ultrasound-guided puncture in our department between January 2019 and June 2019, 506 patients were included and their B-mode ultrasound and CEUS features, including the lesion’s location, size, margin, echo, perfusion pattern of ultrasound contrast agent, degree of enhancement, homogeneity, vascular signs, and necrosis, were retrospectively investigated. All malignant cases were diagnosed by pathology, while benign cases were diagnosed by two respiratory physicians after comprehensive analysis of pathology, etiology, imaging, and clinical symptoms. Statistical differences in these features between the benign and malignant groups were then analyzed.

RESULTS: There were 506 cases in this study, including 219 benign cases and 287 malignant cases. Among them, 351 were males and 155 were females, with an average age of 59 ± 16 years. There were statistically significant differences between benign and malignant groups in the perfusion pattern, the degree of enhancement, and vascular signs. The features of the malignant group included local-to-whole perfusion pattern, hypo-enhancement, and curly hair sign, while those of the benign group included a centrifugal perfusion pattern, iso-enhancement and hyper-enhancement, and dendritic sign. There was no statistically significant difference between the two groups in homogeneity and necrosis.

CONCLUSIONS: CEUS enhancement mode is different between benign and malignant SPLs, which can provide supplementary information for the differential diagnosis of SPLs in the existing imaging diagnosis.

PMID:34387377 | DOI:10.1002/jum.15804

Med Phys. 2021 Aug 13. doi: 10.1002/mp.15161. Online ahead of print.

ABSTRACT

PURPOSE: Convolutional neural network (CNN)-based denoising is an effective method for reducing complex computed tomography (CT) noise. However, the image blur induced by denoising processes is a major concern. The main source of image blur is the pixel-level loss (e.g., mean-squared-error (MSE) and mean-absolute-error (MAE)) used to train a CNN denoiser. To reduce the image blur, feature-level loss is utilized to train a CNN denoiser. A CNN denoiser trained using VGG loss can preserve the small structures, edges, and texture of the image. However, VGG loss, derived from an ImageNet-pretrained image classifier, is not optimal for training a CNN denoiser for CT images. ImageNet contains natural RGB images, so the features extracted by the ImageNet-pretrained model cannot represent the characteristics of CT images that are highly correlated with diagnosis. Furthermore, a CNN denoiser trained with VGG loss causes bias in CT number. Therefore, we propose to use a binary classification network trained using CT images as a feature extractor and newly define the feature-level loss as observer loss.

METHODS: As obtaining labeled CT images for training classification network is difficult, we create labels by inserting simulated lesions. We conduct two separate classification tasks, signal-known-exactly (SKE) and signal-known-statistically (SKS), and define the corresponding feature-level losses as SKE loss and SKS loss, respectively. We use SKE loss and SKS loss to train CNN denoiser.

RESULTS: Compared to pixel-level losses, a CNN denoiser trained using observer loss (i.e., SKE loss and SKS loss) is effective in preserving structure, edge, and texture. Observer loss also resolves the bias in CT number, which is a problem of VGG loss. Comparing observer losses using SKE and SKS tasks, SKS yields images having a more similar noise structure to reference images.

CONCLUSIONS: Using observer loss for training CNN denoiser is effective to preserve structure, edge, and texture in denoised images and prevent the CT number bias. In particular, when using SKS loss, denoised images having a similar noise structure to reference images are generated.

PMID:34387360 | DOI:10.1002/mp.15161