Rapid diagnosis is one key pillar to end tuberculosis (TB). Point-of-care tests (POCTs) facilitate early detection, immediate treatment, and reduced transmission of TB disease. This Review evaluates current diagnostic assays endorsed by the World Health Organization and identifies the gaps between existing conventional tests and the ideal POCT. We discuss the commercial development of new rapid tests and research studies on nonsputum-based diagnostic biomarkers from both pathogen and host. Last, we highlight advances in integrated microfluidics technology that may aid the development of new POCTs.

BACKGROUND. Matrix metalloproteinases (MMPs) are key regulators of tissue destruction in tuberculosis (TB) and may be targets for host-directed therapy. We conducted a phase II double-blind, randomized, controlled trial investigating doxycycline, a licensed broad-spectrum MMP inhibitor, in patients with pulmonary TB. METHODS. Thirty patients with pulmonary TB were enrolled within 7 days of initiating anti-TB treatment and randomly assigned to receive either 100 mg doxycycline or placebo twice a day for 14 days, in addition to standard care. RESULTS. Whole blood RNA-sequencing demonstrated that doxycycline accelerated restoration of dysregulated gene expression in TB towards normality, rapidly down-regulating type I and II interferon and innate immune response genes, and up-regulating B-cell modules relative to placebo. The effects persisted for 6 weeks after doxycycline discontinuation, concurrent with suppressed plasma MMP-1. Doxycycline significantly reduced sputum MMP-1, -8, -9, -12 and -13, suppressed type I collagen and elastin destruction, reduced pulmonary cavity volume without altering sputum mycobacterial loads, and was safe. CONCLUSION. Adjunctive doxycycline with standard anti-TB treatment suppressed pathological MMPs in PTB patients. Larger studies on adjunctive doxycycline to limit TB immunopathology are merited. TRIAL REGISTRATION. ClinicalTrials.gov NCT02774993. FUNDING. Singapore National Medical Research Council (NMRC/CNIG/1120/2014, NMRC/Seedfunding/0010/2014, NMRC/CISSP/2015/009a); the Singapore Infectious Diseases Initiative (SIDI/2013/013); National University Health System (PFFR-28 January 14, NUHSRO/2014/039/BSL3-SeedFunding/Jul/01); the Singapore Immunology Network Immunomonitoring platform (BMRC/IAF/311006, H16/99/b0/011, NRF2017_SISFP09); an ExxonMobil Research Fellowship, NUHS Clinician Scientist Program (NMRC/TA/0042/2015, CSAINV17nov014); the UK Medical Research Council (MR/P023754/1, MR/N006631/1); a NUS Postdoctoral Fellowship (NUHSRO/2017/073/PDF/03); The Royal Society Challenge Grant (CHG\R1\170084); the Sir Henry Dale Fellowship, Wellcome Trust (109377/Z/15/Z); and A*STAR.

Relaxin‐3 has been proposed to modulate emotional–behavioural functions such as arousal and behavioural activation, appetite regulation, stress responses, anxiety, memory, sleep and circadian rhythm. The nucleus incertus (NI), in the midline tegmentum close to the fourth ventricle, projects widely throughout the brain and is the primary site of relaxin‐3 neurons. Over recent years, a number of preclinical studies have explored the function of the NI and relaxin‐3 signalling, including reports of mRNA or peptide expression changes in the NI in response to behavioural or pharmacological manipulations, effects of lesions or electrical or pharmacological manipulations of the NI, effects of central microinfusions of relaxin‐3 or related agonist or antagonist ligands on physiology and behaviour, and the impact of relaxin‐3 gene deletion or knockdown. Although these individual studies reveal facets of the likely functional relevance of the NI and relaxin‐3 systems for human physiology and behaviour, the differences observed in responses between species (e.g. rat vs. mouse), the clearly identified heterogeneity of NI neurons and procedural differences between laboratories are some of the factors that have prevented a precise understanding of their function. This review aims to draw attention to the current preclinical evidence available that suggests the relevance of the NI/relaxin‐3 system to the pathology and/or symptoms of certain neuropsychiatric disorders and to provide cognizant directions for future research to effectively and efficiently uncover its therapeutic potential.Linked Articles: This article is part of a themed section on Recent Progress in the Understanding of Relaxin Family Peptides and their Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.10/issuetoc

The performance of automatic tumor tissue classification using traditional machine learning methods greatly depends on the choice of meaningful descriptive features derived from the tissue images.

In histopathological classification and diagnosis of cancer cases, pathologists perform visual assessments of immunohistochemistry (IHC)-stained biomarkers in cells to determine tumor versus non-tumor tissues. One of the prerequisites for such assessments is the correct identification of regions-of-interest (ROIs) with relevant histological features. Advances in image processing and machine learning give rise to the possibility of full automation in ROI identification by identifying image features such as colors and textures. Such computer-aided diagnostic systems could enhance research output and efficiency in identifying the pathology (normal, non-tumor or tumor) of a tissue pattern from ROI images. In this paper, a computational method using color-texture based extreme learning machines (ELM) is proposed for automatic tissue tumor classification. Our approach consists of three steps: (1) ROIs are manually identified and annotated from individual cores of tissue microarrays (TMAs); (2) color and texture features are extracted from the ROIs images; (3) ELM is applied to the extracted features to classify the ROIs into non-tumor or tumor categories. The proposed approach is tested on 100 sets of images from a kidney cancer TMA and the results show that ELM is able to achieve classification accuracies of 91.19% and 88.72% with a Gaussian radial basis function (RBF) and linear kernel, respectively, which is superior to using SVM with the same kernels.