Lymph node density in silicosis: its relationship with lung function and clinical parameters

published_or_final_versio

Cryptic silver resistance is prevalent and readily activated in certain Gram-negative pathogens

Objectives: To assess the prevalence of cryptic silver (Ag+) resistance amongst clinical isolates of Gram-negative bacteria, and to examine how overt Ag+ resistance becomes activated in such strains. Methods: Established methods were used to determine the susceptibility of 444 recent clinical isolates to Ag+, and to evaluate the potential for overt Ag+ resistance to emerge from these isolates by spontaneous mutation. The genetic basis for Ag+ resistance was investigated using PCR amplification and DNA sequencing. Results: None of the isolates tested displayed overt Ag+ resistance. However, upon silver challenge, high-level Ag+ resistance (silver nitrate MIC >128 mg/L) was selected at high frequency (10¯⁷ to 10¯⁸) in ˜76% isolates of Enterobacter spp., ˜58% isolates of Klebsiella spp., and ˜0.7% isolates of E. coli. All strains in which Ag+ resistance could be selected harboured the sil operon, with resistance in each case apparently resulting from activation of this system as a consequence of a single missense mutation in silS. By contrast, Ag+ resistance could not be selected in isolates lacking sil, which included all tested representatives of Acinetobacter baumannii, Pseudomonas aeruginosa, Proteus spp and Citrobacter spp. Conclusions: Whilst overt Ag+ resistance in Gram-negative pathogens is uncommon, cryptic Ag+ resistance pertaining to the sil operon is prevalent and readily activated in particular genera (Enterobacter and Klebsiella)

GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.</jats:p

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.</jats:p

Epidemiological risk factors for adult dengue in Singapore: an 8-year nested test negative case control study

10.1186/s12879-016-1662-4BMC Infectious Diseases16132

Where does the transport current flow in Bi2Sr2CaCu2O8 crystals?

A new measurement technique for investigation of vortex dynamics is introduced. The distribution of the transport current across a crystal is derived by a sensitive measurement of the self-induced magnetic field of the transport current. We are able to clearly mark where the flow of the transport current is characterized by bulk pinning, surface barrier, or a uniform current distribution. One of the novel results is that in BSCCO crystals most of the vortex liquid phase is affected by surface barriers resulting in a thermally activated apparent resistivity. As a result the standard transport measurements in BSCCO do not probe the dynamics of vortices in the bulk, but rather measure surface barrier properties.Comment: 11 pages, 4 figures, accepted for publication in Natur

Sponsor ownership in Asian REITs

This study examines the relationship between sponsor ownership and firm performance proxied by firm value, operating cash flow, and dividend policy with Asian real estate investment trusts (REITs) in Japan, Hong Kong, Malaysia, and Singapore for the period from 2002 to 2012, focusing on both the incentive alignment effect and the entrenchment effect. Our study sheds new light on effective corporate governance for Asian REITs that are prone to agency problems. Such agency problems arise from the inequitable distribution of power to sponsors that results from the external management structure. The findings suggest that larger sponsor ownership aligns the interests of sponsors and minority shareholders and enhances the performance of Asian REITs, while such an effect diminishes as sponsors become more entrenched. We find that the incentive alignment effect and entrenchment effect are primarily driven by developer-sponsored REITs. Also evident is that the presence of institutional investors mitigates agency problems and increases firm performance

Media Reporting of Health Interventions: Signs of Improvement, but Major Problems Persist

Background: Studies have persistently shown deficiencies in medical reporting by the mainstream media. We have been monitoring the accuracy and comprehensiveness of medical news reporting in Australia since mid 2004. This analysis of more than 1200 stories in the Australian media compares different types of media outlets and examines reporting trends over time. Methods and Findings: Between March 2004 and June 2008 1230 news stories were rated on a national medical news monitoring web site, Media Doctor Australia. These covered a variety of health interventions ranging from drugs, diagnostic tests and surgery to dietary and complementary therapies. Each story was independently assessed by two reviewers using ten criteria. Scores were expressed as percentages of total assessable items deemed satisfactory according to a coding guide. Analysis of variance was used to compare mean scores and Fishers exact test to compare proportions. Trends over time were analysed using un-weighted linear regression analysis. Broadsheet newspapers had the highest average satisfactory scores: 58% (95% CI 56–60%), compared with tabloid newspapers and online news outlets, 48% (95% CI 44–52) and 48% (95% CI 46–50) respectively. The lowest scores were assigned to stories broadcast by human interest/current affairs television programmes (average score 33% (95% CI 28–38)). While there was a non- significant increase in average scores for all outlets, a significant improvement was seen in the online news media: a rise of 5.1% (95%CI 1.32, 8.97; P 0.009). Statistically significant improvements were seen in coverage of the potential harms of interventions, the availability of treatment or diagnostic options, and accurate quantification of benefits. Conclusion: Although the overall quality of medical reporting in the general media remains poor, this study showed modest improvements in some areas. However, the most striking finding was the continuing very poor coverage of health news by commercial current affairs television programs