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Rationale High circulating galectin-3 is associated with poor outcomes in patients with coronavirus disease (COVID-19). We hypothesized that GB0139, a potent inhaled thiodigalactoside galectin-3 inhibitor with antiinflammatory and antifibrotic actions, would be safely and effectively delivered in COVID-19 pneumonitis. Objectives Primary outcomes were safety and tolerability of inhaled GB0139 as an add-on therapy for patients hospitalized with COVID-19 pneumonitis. Methods We present the findings of two arms of a phase Ib/IIa randomized controlled platform trial in hospitalized patients with confirmed COVID-19 pneumonitis. Patients received standard of care (SoC) or SoC plus 10 mg inhaled GB0139 twice daily for 48 hours, then once daily for up to 14 days or discharge. Measurements and Main Results Data are reported from 41 patients, 20 of which were assigned randomly to receive GB0139. Primary outcomes: the GB0139 group experienced no treatment-related serious adverse events. Incidences of adverse events were similar between treatment arms (40 with GB0139 + SoC vs. 35 with SoC). Secondary outcomes: plasma GB0139 was measurable in all patients after inhaled exposure and demonstrated target engagement with decreased circulating galectin (overall treatment effect post-hoc analysis of covariance [ANCOVA] over days 2–7; P = 0.0099 vs. SoC). Plasma biomarkers associated with inflammation, fibrosis, coagulopathy, and major organ function were evaluated. Conclusions In COVID-19 pneumonitis, inhaled GB0139 was well-tolerated and achieved clinically relevant plasma concentrations with target engagement. The data support larger clinical trials to determine clinical efficacy. Clinical trial registered with ClinicalTrials.gov (NCT04473053) and EudraCT (2020–002230–32).

The presence of functionally efficient cytotoxic T lymphocytes (CTL) in the Tumour nest is crucial in mediating a successful immune response to cancer. The detection and elimination of cancer cells by CTL can be impaired by cancer-mediated immune evasion. In recent years, it has become increasingly clear that not only neoplastic cells themselves, but also cells of the tumour microenvironment (TME) exert immunosuppressive functions and thereby play an integral part in the immune escape of cancer. The most abundant stromal cells of the TME, cancer associated fibroblasts (CAFs), promote tumour progression via multiple pathways and play a role in dampening the immune response to cancer. Recent research indicates that T cells react to CAF signalling and establish bidirectional crosstalk that plays a significant role in the tumour immune response. This review discusses the various mechanisms by which the CAF/T cell crosstalk may impede anti-cancer immunity.

Background Targeted lung cancer screening is effective in reducing mortality by upwards of twenty percent. However, screening is not universally available and uptake is variable and socially patterned. Understanding screening behaviour is integral to designing a service that serves its population and promotes equitable uptake. We sought to review the literature to identify barriers and facilitators to screening to inform the development of a pilot lung screening study in Scotland. Methods We used Arksey and O’Malley’s scoping review methodology and PRISMA-ScR framework to identify relevant literature to meet the study aims. Qualitative, quantitative and mixed methods primary studies published between January 2000 and May 2021 were identified and reviewed by two reviewers for inclusion, using a list of search terms developed by the study team and adapted for chosen databases. Results Twenty-one articles met the final inclusion criteria. Articles were published between 2003 and 2021 and came from high income countries. Following data extraction and synthesis, findings were organised into four categories: Awareness of lung screening, Enthusiasm for lung screening, Barriers to lung screening, and Facilitators or ways of promoting uptake of lung screening. Awareness of lung screening was low while enthusiasm was high. Barriers to screening included fear of a cancer diagnosis, low perceived risk of lung cancer as well as practical barriers of cost, travel and time off work. Being health conscious, provider endorsement and seeking reassurance were all identified as facilitators of screening participation. Conclusions Understanding patient reported barriers and facilitators to lung screening can help inform the implementation of future lung screening pilots and national lung screening programmes.

Abstract:We address the task of estimating bacterial and cellular load in the human distal lung with fibered confocal fluorescence microscopy (FCFM). In pulmonary FCFM some cells can display autofluorescence, and they appear as disc like objects in the FCFM images, whereas bacteria, although not autofluorescent, appear as bright blinking dots when exposed to a targeted smartprobe. Estimating bacterial and cellular load becomes a challenging task due to the presence of background from autofluorescent human lung tissues, i.e., elastin, and imaging artifacts from motion etc. We create a database of annotated images for both these tasks where bacteria and cells were annotated, and use these databases for supervised learning. We extract image patches around each pixel as features, and train a classifier to predict if a bacterium or cell is present at that pixel. We apply our approach on two datasets for detecting bacteria and cells respectively. For the bacteria dataset, we show that the estimated bacterial load increases after introducing the targeted smartprobe in the presence of bacteria. For the cell dataset, we show that the estimated cellular load agrees with a clinician's assessment.

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In this paper, we propose an unsupervised approach for bacterial detection in optical endomicroscopy images. This approach splits each image into a set of overlapping patches and assumes that observed intensities are linear combinations of the actual intensity values associated with background image structures, corrupted by additive Gaussian noise and potentially by a sparse outlier term modelling anomalies (which are considered to be candidate bacteria). The actual intensity term representing background structures is modelled as a linear combination of a few atoms drawn from a dictionary which is learned from bacteria-free data and then fixed while analyzing new images. The bacteria detection task is formulated as a minimization problem and an alternating direction method of multipliers (ADMM) is then used to estimate the unknown parameters. Simulations conducted using two ex vivo lung datasets show good detection and correlation performance between bacteria counts identified by a trained clinician and those of the proposed method.

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