Chronic pulmonary aspergillosis - a guide for the general physician.

This collaborative article presents a review of chronic pulmonary aspergillosis (CPA) from the perspective of a multidisciplinary team comprising of respiratory physicians, radiologists, mycologists, dietitians, pharmacists, physiotherapists and palliative care specialists. The review synthesises current knowledge on CPA, emphasising the intricate interplay between clinical, radiological, and microbiological aspects. We highlight the importance of assessing each patient as multidisciplinary team to ensure personalised treatment strategies and a holistic approach to patient care.

An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields.

Background: Despite knowledge of qualitative changes that occur on ultrasound in tendinopathy, there is currently no objective and reliable means to quantify the severity or prognosis of tendinopathy on ultrasound.

Objective: The primary objective of this study is to produce a quantitative and automated means of inferring potential structural changes in tendinopathy by developing and implementing an algorithm which performs a texture based segmentation of tendon ultrasound (US) images.

Method: A model-based segmentation approach is used which combines Gaussian mixture models, Markov random field theory and grey-level co-occurrence (GLCM) features.

Introduction of allergenic food to infants and allergic and autoimmune conditions: a systematic review and meta-analysis.

Objectives: To evaluate the effects of early introduction to allergenic foods compared with late introduction and its impact on food allergy, food sensitisation and autoimmune disease risk.

Design And Setting: The systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Four electronic databases (MEDLINE, CENTRAL, EMBASE and CINAHL) were searched from inception till 24 October 2022 using keywords and MeSH without limitations on publication's language or date.

Robust, scalable, and informative clustering for diverse biological networks.

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne.

Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study.

The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected.