Intratumoral pro-oxidants promote cancer immunotherapy by recruiting and reprogramming neutrophils to eliminate tumors.

Neutrophils have recently gained recognition for their potential in the fight against cancer. Neutrophil plasticity between the N1 anti-tumor and N2 pro-tumor subtypes is now apparent, as is the ability to polarize these individual subtypes by interventions such as intratumoral injection of various agents including bacterial products or pro-oxidants. Metabolic responses and the production of reactive oxygen species (ROS) such as hydrogen peroxide act as potent chemoattractants and activators of N1 neutrophils that facilitates their recruitment and ensuing activation of a toxic respiratory burst in tumors. Greater understanding of the precise mechanism of N1 neutrophil activation, recruitment and regulation is now needed to fully exploit their anti-tumor potential against cancers both locally and at distant sites. This systematic review critically analyzes these new developments in cancer immunotherapy.

DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images.

Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature extraction solution for many downstream tasks, as it only uses unlabeled data. Recently, SSL methods based on instance discrimination have gained popularity in the medical imaging domain. However, SSL pre-trained encoders may use many clues in the image to discriminate an instance that are not necessarily disease-related. Moreover, pathological patterns are often subtle and heterogeneous, requiring the ability of the desired method to represent anatomy-specific features that are sensitive to abnormal changes in different body parts. In this work, we present a novel SSL framework, named DrasCLR, for 3D lung CT images to overcome these challenges. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions. We formulate the encoder using conditional hyper-parameterized network, in which the parameters are dependant on the anatomical location, to extract anatomically sensitive features. Extensive experiments on large-scale datasets of lung CT scans show that our method improves the performance of many downstream prediction and segmentation tasks. The patient-level representation improves the performance of the patient survival prediction task. We show how our method can detect emphysema subtypes via dense prediction. We demonstrate that fine-tuning the pre-trained model can significantly reduce annotation efforts without sacrificing emphysema detection accuracy. Our ablation study highlights the importance of incorporating anatomical context into the SSL framework. Our codes are available at https://github.com/batmanlab/DrasCLR.

Melaleuca alternifolia concentrate inhibits in vitro entry of influenza virus into host cells.

Influenza virus causes high morbidity among the infected population annually and occasionally the spread of pandemics. Melaleuca alternifolia Concentrate (MAC) is an essential oil derived from a native Australian tea tree. Our aim was to investigate whether MAC has any in vitro inhibitory effect on influenza virus infection and what mechanism does the MAC use to fight the virus infection. In this study, the antiviral activity of MAC was examined by its inhibition of cytopathic effects. In silico prediction was performed to evaluate the interaction between MAC and the viral haemagglutinin. We found that when the influenza virus was incubated with 0.010% MAC for one hour, no cytopathic effect on MDCK cells was found after the virus infection and no immunofluorescence signal was detected in the host cells. Electron microscopy showed that the virus treated with MAC retained its structural integrity. By computational simulations, we found that terpinen-4-ol, which is the major bioactive component of MAC, could combine with the membrane fusion site of haemagglutinin. Thus, we proved that MAC could prevent influenza virus from entering the host cells by disturbing the normal viral membrane fusion procedure.

ComBat Harmonization: Empirical Bayes versus fully Bayes approaches.

Studying small effects or subtle neuroanatomical variation requires large-scale sample size data. As a result, combining neuroimaging data from multiple datasets is necessary. Variation in acquisition protocols, magnetic field strength, scanner build, and many other non-biologically related factors can introduce undesirable bias into studies. Hence, harmonization is required to remove the bias-inducing factors from the data. ComBat is one of the most common methods applied to features from structural images. ComBat models the data using a hierarchical Bayesian model and uses the empirical Bayes approach to infer the distribution of the unknown factors. The empirical Bayes harmonization method is computationally efficient and provides valid point estimates. However, it tends to underestimate uncertainty. This paper investigates a new approach, fully Bayesian ComBat, where Monte Carlo sampling is used for statistical inference. When comparing fully Bayesian and empirical Bayesian ComBat, we found Empirical Bayesian ComBat more effectively removed scanner strength information and was much more computationally efficient. Conversely, fully Bayesian ComBat better preserved biological disease and age-related information while performing more accurate harmonization on traveling subjects. The fully Bayesian approach generates a rich posterior distribution, which is useful for generating simulated imaging features for improving classifier performance in a limited data setting. We show the generative capacity of our model for augmenting and improving the detection of patients with Alzheimer's disease. Posterior distributions for harmonized imaging measures can also be used for brain-wide uncertainty comparison and more principled downstream statistical analysis.Code for our new fully Bayesian ComBat extension is available at https://github.com/batmanlab/BayesComBat.

Left Atrial Mechanics and Diastolic Function Among People Living With Human Immunodeficiency Virus (from the Veterans Aging Cohort Study).

Human immunodeficiency virus (HIV) infection is associated with subclinical cardiomyopathy, diastolic dysfunction, and increased risk of cardiovascular death. However, the relationship between left atrial (LA) mechanics and left ventricular (LV) diastolic function has not been evaluated in people living with HIV (PLWH) relative to HIV-uninfected (HIV-) controls. This is a multicenter, cross-sectional cohort analysis using the HIV Cardiovascular Disease substudy of the Veterans Aging Cohort Study database, which aimed to examine a cohort of PLWH and HIV- veterans without known cardiovascular disease. A total of 277 subjects (180 PLWH, 97 HIV-) with echocardiograms were identified. LV and LA phasic strain were derived and diastolic function was evaluated. Relationship between LA strain, LV strain, and the degree of diastolic dysfunction were assessed using analysis of variance and ordinal logistic regression with propensity weighting. In the PLWH cohort, 91.7% were on antiretroviral therapy and 86.1% had HIV viral loads <500 copies/ml. The mean (± SD) duration of infection was 9.7 ± 4.9 years. Relative to HIV- veterans, PLWH did not differ in LA mechanics and proportion of diastolic dysfunction (p = 0.31). Using logistic regression with propensity weighting, we found no association between HIV status and degree of diastolic dysfunction. In both cohorts, LA reservoir strain and LA conduit strain were inversely and independently associated with the degree of diastolic dysfunction. Compared with HIV- veterans, PLWH who are primarily virally suppressed and antiretroviral-treated did not differ in LA strain or LV diastolic dysfunction. If confirmed in other cohorts, HIV viral suppression may curtail adverse alterations in cardiac structure and function.

Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation.

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a single domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.

The in vitro and in vivo antiviral properties of combined monoterpene alcohols against West Nile virus infection.

West Nile Virus (WNV) is a mosquito-borne flavivirus that can cause neuroinvasive disease in humans and animals for which no therapies are currently available. We studied an established combination of monoterpene alcohols (CMA) derived from Melaleuca alternifolia, against WNV infection. The in vitro results show that CMA exhibits virucidal activity, as well as reduces the viral titres and percentage of infected cells. The antiviral mechanism of action of CMA was studied. We found that CMA did not alter the intracellular pH, neither induced apoptosis, but did induce cell cycle arrest in the G0/G1-phase although that was not the antiviral mechanism. Furthermore, we tested CMA in vivo using IRF 3(-)(/)(-)/7(-/-)mice and it was found that CMA treatment significantly delayed morbidity due to WNV infection, reduced the loss of body weight and reduced the viral titres in brain. These findings suggest that CMA could be a therapeutic agent against WNV infection.

Immunomodulatory activity of Melaleuca alternifolia concentrate (MAC): inhibition of LPS-induced NF-κB activation and cytokine production in myeloid cell lines.

Melaleuca alternifolia concentrate (MAC) is a mixture predominantly composed of monoterpenoids and sesquiterpenes, refined from the essential oil of the tea tree by removing up to 99% of the more toxic, hydrophobic monoterpenes. MAC was examined here for its immunomodulatory effects on the human THP1 and murine RAW264.7 myeloid leukemic cell lines as models for macrophage-like cells. Firstly, MAC levels were determined that did not affect either the survival or proliferation of these cell lines in vitro. Next, the levels of lipopolysaccharide (LPS)-induced production of cytokines (IL-6, TNFα, IL-10, GM-CSF, IFNγ and IL-3) were examined from the myeloid cell lines using multiplex assays. Many of the LPS-inducible cytokines produced by either cell lines could be significantly inhibited by MAC. Closer examination of the mechanism of action of MAC showed that it inhibited the LPS-induced activation of IκB phosphorylation and nuclear factor (NF)-κB signalling and translocation, inhibiting iNOS protein expression and NO production. These results demonstrate that MAC exerts its immunomodulatory effects by inhibiting NF-κB signalling activation and levels of cytokine production by macrophage-like cell lines.