An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms

Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.

Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging

The most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today's state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance.

The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets

Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly. It emerges when the number of healthy class instances being much larger than the disease class instances. To solve this problem, we proposed undersampling the healthy class instances to improve disease class classification. This model is named Hellinger Distance Undersampling (HDUS). It employs the Hellinger Distance to measure the resemblance between majority class instance and its neighbouring minority class instances to separate classes effectively and boost the discrimination power for each class. An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models. The outcomes display that HDUS can perform better than other models in terms of sensitivity, F1 measure, and balanced accuracy.

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

ABSTRACTPrecise identification of causative variants from whole-genome sequencing data, including both coding and non-coding variants, is challenging. The CAGI5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline. The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multi-phenotype-matching score. Mutations in the top scoring genes for each phenotype profile were ranked on a six-point scale of pathogenicity probability, resulting in an approximately equal number of top ranked coding and non-coding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the post-submission phase, after careful screening of the genes in the correct genome we identified additional potential diagnostic variants, a high proportion of which are non-coding.

Vernakalant in Atrial Fibrillation: A Relatively New Weapon in the Armamentarium Against an Old Enemy

Atrial fibrillation is the most common sustained cardiac arrhythmia, and its prevalence is increasing with age; also it is associated with significant morbidity and mortality. Rhythm control is advised in recent-onset atrial fibrillation, and in highly symptomatic patients, also in young and active individuals. Moreover, rhythm control is associated with lower incidence of progression to permanent atrial fibrillation. Vernakalant is a relatively new anti-arrhythmic drug that showed efficacy and safety in recent-onset atrial fibrillation. Vernakalant is indicated in atrial fibrillation (⩽7 days) in patients with no heart disease (class I, level A) or in patients with mild or moderate structural heart disease (class IIb, level B). Moreover, Vernakalant may be considered for recent-onset atrial fibrillation (⩽3 days) post cardiac surgery (class IIb, level B). Although it is mainly indicated in patients with recent-onset atrial fibrillation and with no structural heart disease, it can be given in moderate stable cardiac disease as alternative to Amiodarone. Similarly to electrical cardioversion, pharmacological cardioversion requires a minimal evaluation and cardioversion should be included in a comprehensive management strategy for better outcome.

More great saphenous vein valves - less varicose veins?

Background: In patients with chronic venous disease (CVD) the number of venous valves and the degree of valve deterioration have not been extensively investigated and are poorly understood. The aim of this prospective study was to quantitatively and qualitatively investigate the venous valves in CVD patients in view of their clinical classification. Patients and methods: Within two years a consecutive series of 152 patients (223 limbs) undergoing primary surgery for great saphenous vein varicose veins was investigated. In all patients the ‘C’ class according to the basic CEAP-classification was registered preoperatively (C2 to C6) for each limb. Both the quantity and quality of venous valves were assessed in the GSV’s after removal. Qualitative evaluation of the valves was based on macroscopic appearance using a classification from 0 to 5 and described as ‘valve disease class’. Results: A negative correlation between age and the number of valves was detected (p = 0.0035). There was an increase of C-class with increasing age. No significant correlation between the average number of valves per meter and the C-class was detected. For all C-classes an average of between four and five valves per meter was counted. Valve disease class was positively correlated with the C-class although the valve disease class was never higher than the C-class (p < 0.05). Conclusions: The valve disease class of the great saphenous vein correlates with the C-class of the CEAP-classification. The number of valves did not correlate with the ‘C’-class. With each increase in the CEAP class the age increased as well.