Multi-task learning for predicting quality-of-life and independence in activities of daily living after stroke: a proof-of-concept study.

A health-related (HR) profile is a set of multiple health-related items recording the status of the patient at different follow-up times post-stroke. In order to support clinicians in designing rehabilitation treatment programs, we propose a novel multi-task learning (MTL) strategy for predicting post-stroke patient HR profiles. The HR profile in this study is measured by the Barthel index (BI) assessment or by the EQ-5D-3L questionnaire.

Predictors of social risk for post-ischemic stroke reintegration.

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients.

Comparison of sample preparation methodologies towards optimisation of Raman spectroscopy for peripheral blood mononuclear cells.

The exquisite sensitivity of Raman spectroscopy to the molecular composition of biological samples has been a particular strength in its development towards clinical applicates. Its strength in this regard also presents challenges in the development of its diagnostic capabilities owing to its sensitivity, not only to the sample biochemistry, but also the preparation methodology employed prior to analysis. Here we have examined and optimised several approaches to the preparation of peripheral blood mononuclear cells (PBMCs), or immune cell subtypes of whole blood, for Raman spectroscopic analysis.

Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model.

Activation and proliferation of immune cells such as lymphocytes and monocytes are appropriate inflammatory responses to invading pathogens and are key to overcoming an infection. In contrast, uncontrolled and prolonged activation of these cellular signalling pathways can be deleterious to the body and result in the development of autoimmune conditions. The understanding of cellular activatory status therefore plays a significant role in disease diagnosis and progression.

Vibrational spectroscopy of liquid biopsies for prostate cancer diagnosis.

Background: Screening for prostate cancer with prostate specific antigen and digital rectal examination allows early diagnosis of prostate malignancy but has been associated with poor sensitivity and specificity. There is also a considerable risk of over-diagnosis and over-treatment, which highlights the need for better tools for diagnosis of prostate cancer. This study investigates the potential of high throughput Raman and Fourier Transform Infrared (FTIR) spectroscopy of liquid biopsies for rapid and accurate diagnosis of prostate cancer.

Monitoring Radiotherapeutic Response in Prostate Cancer Patients Using High Throughput FTIR Spectroscopy of Liquid Biopsies.

Radiation therapy (RT) is used to treat approximately 50% of all cancer patients. However, RT causes a wide range of adverse late effects that can affect a patient's quality of life. There are currently no predictive assays in clinical use to identify patients at risk of normal tissue radiation toxicity.

Correction: Discrimination of breast cancer from benign tumours using Raman spectroscopy.

[This corrects the article DOI: 10.1371/journal.pone.

Discrimination of breast cancer from benign tumours using Raman spectroscopy.

Breast cancer is the most common cancer among women worldwide, with an estimated 1.7 million cases and 522,000 deaths in 2012. Breast cancer is diagnosed by histopathological examination of breast biopsy material but this is subjective and relies on morphological changes in the tissue.

Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon.

The coupling between Fourier-transform infrared (FTIR) imaging and unsupervised classification is effective in revealing the different structures of human tissues based on their specific biomolecular IR signatures; thus the spectral histology of the studied samples is achieved. However, the most widely applied clustering methods in spectral histology are local search algorithms, which converge to a local optimum, depending on initialization. Multiple runs of the techniques estimate multiple different solutions.

Fully unsupervised inter-individual IR spectral histology of paraffinized tissue sections of normal colon.

In label-free Fourier-transform infrared histology, spectral images are individually recorded from tissue sections, pre-processed and clustered. Each single resulting color-coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin-Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images.

Development of a hierarchical double application of crisp cluster validity indices: a proof-of-concept study for automated FTIR spectral histology.

Fourier-transform infrared (FTIR) spectral imaging is currently used as a non-destructive and label-free method for analyzing biological specimens. However, to highlight the different tissue regions, unsupervised clustering methods are commonly used leading to a subjective choice of the number of clusters. Here, we develop a hierarchical double application of 9 selected crisp cluster validity indices (CCVIs) using K-Means clustering.