Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety.

Flagging the presence of metal devices before a head MRI scan is essential to allow appropriate safety checks. There is an unmet need for an automated system which can flag aneurysm clips prior to MRI appointments. We assess the accuracy with which a machine learning model can classify the presence or absence of an aneurysm clip on CT images. A total of 280 CT head scans were collected, 140 with aneurysm clips visible and 140 without. The data were used to retrain a pre-trained image classification neural network to classify CT localizer images. Models were developed using fivefold cross-validation and then tested on a holdout test set. A mean sensitivity of 100% and a mean accuracy of 82% were achieved. Predictions were explained using SHapley Additive exPlanations (SHAP), which highlighted that appropriate regions of interest were informing the models. Models were also trained from scratch to classify three-dimensional CT head scans. These did not exceed the sensitivity of the localizer models. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.

Epidemiology of Klebsiella michiganensis Carrying Multidrug-Resistant IncHI5 Plasmids in the Southeast Coastal Area of China.

Purpose: This study aimed to explore the genomic characterization of multidrug-resistant IncHI5-carrying Klebsiella michiganensis strains and detailed genomic dissection of the IncHI5 plasmids. Materials and Methods: Through whole-genome sequencing, the IncHI5 plasmid pK92-qnrS was obtained from a single clinical K. michiganensis isolate K92. All complete genomes of K. michiganensis strains from the Genome database of NCBI were collected and used to construct a maximum likelihood (ML) phylogenetic tree. The epidemiology and geographic distribution of all the K. michiganensis strains were conducted. An extensive comparison of the seven IncHI5 plasmids of K. michiganensis (one from this study, six from GenBank) was applied. Results: This study revealed that all K. michiganensis strains carrying IncHI5 plasmids from different clonal groups were located in the southeast coastal area of China. The backbone regions of IncHI5 plasmids were composed of replicon (repHI5B and repFIB), partition (parABC), and conjugal transfer (tra1/tra2). The main accessory resistant regions of IncHI5 could be divided into two categories, Tn1696-related region and Tn6535-related region. These seven IncHI5 plasmids carried multiple drug-resistance genes which were all mediated by the mobile genetic elements (MGEs). Conclusion: Data presented here help to provide an overall in-depth understanding of epidemiology and geographic distribution of IncHI5-carrying K. michiganensis and the structure and evolutionary history of IncHI5 plasmids.

Robust EEG Based Biomarkers to Detect Alzheimer's Disease.

Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).

Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease.

It is widely accepted that early diagnosis of Alzheimer's disease (AD) makes it possible for patients to gain access to appropriate health care services and would facilitate the development of new therapies. AD starts many years before its clinical manifestations and a biomarker that provides a measure of changes in the brain in this period would be useful for early diagnosis of AD. Given the rapid increase in the number of older people suffering from AD, there is a need for an accurate, low-cost and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, the electroencephalogram (EEG) can play a vital role in this but at present, no reliable EEG biomarker exists for early diagnosis of AD. The gradual slowing of brain activity caused by AD starts from the back of the brain and spreads out towards other parts. Consequently, determining the brain regions that are first affected by AD may be useful in its early diagnosis. Higuchi fractal dimension (HFD) has characteristics which make it suited to capturing region-specific neural changes due to AD. The aim of this study is to investigate the potential of HFD of the EEG as a biomarker which is associated with the brain region first affected by AD. Mean HFD value was calculated for all channels of EEG signals recorded from 52 subjects (20-AD and 32-normal). Then, p-values were calculated between the two groups (AD and normal) to detect EEG channels that have a significant association with AD. k-nearest neighbor (KNN) algorithm was used to compute the distance between AD patients and normal subjects in the classification. Our results show that AD patients have significantly lower HFD values in the parietal areas. HFD values for channels in these areas were used to discriminate between AD and normal subjects with a sensitivity and specificity values of 100% and 80%, respectively.

Tsallis entropy as a biomarker for detection of Alzheimer's disease.

Alzheimer's disease (AD) and other forms of dementia are one of the major public health and social challenges of our time because of the large number of people affected. Early diagnosis is important for patients and their families to get maximum benefits from access to health and social care services and to plan for the future. EEG provides useful insight into brain functions and can play a useful role as a first line of decision-support tool for early detection and diagnosis of dementia. It is non-invasive, low-cost and has a high temporal resolution. The functions of brain cells are affected by damage caused by dementia and this in turn causes changes in the features of the EEG. Information theoretic methods have emerged as a potentially useful way to quantify changes in the EEG as biomarkers of dementia. Tsallis entropy has been shown to be one of the most promising information theoretic methods for quantifying changes in the EEG. In this paper, we develop the approach further. This has yielded an enhanced performance compared to existing approaches.

Chikungunya virus glycoproteins pseudotype with lentiviral vectors and reveal a broad spectrum of cellular tropism.

BACKGROUND: Outbreaks of the Chikungunya virus (CHIKV) infection has been documented in over 40 countries, resulting in clinical symptoms characterized by fever and joint pain. Diagnosing CHIKV in a clinical lab setting is often omitted because of the high lab safety requirement. An infection system that mimics CHIKV infection will permit clinical evaluation of the production of neutralizing antibody for both disease diagnostics and treatment. METHODOLOGY/PRINCIPAL FINDINGS: We generated a CHIKV construct expressing CHIKV structural proteins. This construct permits the production of CHIKV pseudo-viral particles with a luciferase reporter. The pseudo-virus was able to infect a wide range of cell lines. The pseudovirus could be neutralized by the addition of neutralizing antibodies from patients. CONCLUSIONS: Taken together, we have developed a powerful system that can be handled at biosafety level 2 laboratories for evaluation of existence of CHIKV neutralizing antibodies.