Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions

Deep learning of chest X‑rays can predict mechanical ventilation outcome in ICU‑admitted COVID‑19 patients

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model

Neuroinflammation, Mast Cells, and Glia: Dangerous Liaisons

The perspective of neuroinflammation as an epiphenomenon following neuron damage is being replaced by the awareness of glia and their importance in neural functions and disorders. Systemic inflammation generates signals that communicate with the brain and leads to changes in metabolism and behavior, with microglia assuming a pro-inflammatory phenotype. Identification of potential peripheral-to-central cellular links is thus a critical step in designing effective therapeutics. Mast cells may fulfill such a role. These resident immune cells are found close to and within peripheral nerves and in brain parenchyma/meninges, where they exercise a key role in orchestrating the inflammatory process from initiation through chronic activation. Mast cells and glia engage in crosstalk that contributes to accelerate disease progression; such interactions become exaggerated with aging and increased cell sensitivity to stress. Emerging evidence for oligodendrocytes, independent of myelin and support of axonal integrity, points to their having strong immune functions, innate immune receptor expression, and production/response to chemokines and cytokines that modulate immune responses in the central nervous system while engaging in crosstalk with microglia and astrocytes. In this review, we summarize the findings related to our understanding of the biology and cellular signaling mechanisms of neuroinflammation, with emphasis on mast cell-glia interactions

Tetrodotoxin (TTX) as a Therapeutic Agent for Pain

Tetrodotoxin (TTX) is a potent neurotoxin that blocks voltage-gated sodium channels (VGSCs). VGSCs play a critical role in neuronal function under both physiological and pathological conditions. TTX has been extensively used to functionally characterize VGSCs, which can be classified as TTX-sensitive or TTX-resistant channels according to their sensitivity to this toxin. Alterations in the expression and/or function of some specific TTX-sensitive VGSCs have been implicated in a number of chronic pain conditions. The administration of TTX at doses below those that interfere with the generation and conduction of action potentials in normal (non-injured) nerves has been used in humans and experimental animals under different pain conditions. These data indicate a role for TTX as a potential therapeutic agent for pain. This review focuses on the preclinical and clinical evidence supporting a potential analgesic role for TTX. In addition, the contribution of specific TTX-sensitive VGSCs to pain is reviewed

Discovery of Prostamide F2α and Its Role in Inflammatory Pain and Dorsal Horn Nociceptive Neuron Hyperexcitability

It was suggested that endocannabinoids are metabolized by cyclooxygenase (COX)-2 in the spinal cord of rats with kaolin/λ-carrageenan-induced knee inflammation, and that this mechanism contributes to the analgesic effects of COX-2 inhibitors in this experimental model. We report the development of a specific method for the identification of endocannabinoid COX-2 metabolites, its application to measure the levels of these compounds in tissues, and the finding of prostamide F2α (PMF2α) in mice with knee inflammation. Whereas the levels of spinal endocannabinoids were not significantly altered by kaolin/λ-carrageenan-induced knee inflammation, those of the COX-2 metabolite of AEA, PMF2α, were strongly elevated. The formation of PMF2α was reduced by indomethacin (a non-selective COX inhibitor), NS-398 (a selective COX-2 inhibitor) and SC-560 (a selective COX-1 inhibitor). In healthy mice, spinal application of PMF2α increased the firing of nociceptive (NS) neurons, and correspondingly reduced the threshold of paw withdrawal latency (PWL). These effects were attenuated by the PMF2α receptor antagonist AGN211336, but not by the FP receptor antagonist AL8810. Also prostaglandin F2α increased NS neuron firing and reduced the threshold of PWL in healthy mice, and these effects were antagonized by AL8810, and not by AGN211336. In mice with kaolin/λ-carrageenan-induced knee inflammation, AGN211336, but not AL8810, reduced the inflammation-induced NS neuron firing and reduction of PWL. These findings suggest that inflammation-induced, and prostanoid-mediated, enhancement of dorsal horn NS neuron firing stimulates the production of spinal PMF2α, which in turn contributes to further NS neuron firing and pain transmission by activating specific receptors

Regulator of G-Protein Signaling 14 (RGS14) Is a Selective H-Ras Effector

Background: Regulator of G-protein signaling (RGS) proteins have been well-described as accelerators of Ga-mediated GTP hydrolysis (‘‘GTPase-accelerating proteins’’ or GAPs). However, RGS proteins with complex domain architectures are now known to regulate much more than Ga GTPase activity. RGS14 contains tandem Ras-binding domains that have been reported to bind to Rap- but not Ras GTPases in vitro, leading to the suggestion that RGS14 is a Rap-specific effector. However, more recent data from mammals and Drosophila imply that, in vivo, RGS14 may instead be an effector of Ras.Methodology/Principal Findings: Full-length and truncated forms of purified RGS14 protein were found to bind indiscriminately in vitro to both Rap- and Ras-family GTPases, consistent with prior literature reports. In stark contrast, however, we found that in a cellular context RGS14 selectively binds to activated H-Ras and not to Rap isoforms. Co- transfection / co-immunoprecipitation experiments demonstrated the ability of full-length RGS14 to assemble a multiprotein complex with components of the ERK MAPK pathway in a manner dependent on activated H-Ras. Small interfering RNA-mediated knockdown of RGS14 inhibited both nerve growth factor- and basic fibrobast growth factor- mediated neuronal differentiation of PC12 cells, a process which is known to be dependent on Ras-ERK signaling.Conclusions/Significance: In cells, RGS14 facilitates the formation of a selective Ras?GTP-Raf-MEK-ERK multiprotein complex to promote sustained ERK activation and regulate H-Ras-dependent neuritogenesis. This cellular function for RGS14 is similar but distinct from that recently described for its closely-related paralogue, RGS12, which shares the tandem Ras- binding domain architecture with RGS14

Interactive Responses of a Thalamic Neuron to Formalin Induced Lasting Pain in Behaving Mice

Thalamocortical (TC) neurons are known to relay incoming sensory information to the cortex via firing in tonic or burst mode. However, it is still unclear how respective firing modes of a single thalamic relay neuron contribute to pain perception under consciousness. Some studies report that bursting could increase pain in hyperalgesic conditions while others suggest the contrary. However, since previous studies were done under either neuropathic pain conditions or often under anesthesia, the mechanism of thalamic pain modulation under awake conditions is not well understood. We therefore characterized the thalamic firing patterns of behaving mice in response to nociceptive pain induced by inflammation. Our results demonstrated that nociceptive pain responses were positively correlated with tonic firing and negatively correlated with burst firing of individual TC neurons. Furthermore, burst properties such as intra-burst-interval (IntraBI) also turned out to be reliably correlated with the changes of nociceptive pain responses. In addition, brain stimulation experiments revealed that only bursts with specific bursting patterns could significantly abolish behavioral nociceptive responses. The results indicate that specific patterns of bursting activity in thalamocortical relay neurons play a critical role in controlling long-lasting inflammatory pain in awake and behaving mice

A narrative review on the similarities and dissimilarities between myalgic encephalomyelitis/chronic fatigue syndrome (me/cfs) and sickness behavior

It is of importance whether myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a variant of sickness behavior. The latter is induced by acute infections/injury being principally mediated through proinflammatory cytokines. Sickness is a beneficial behavioral response that serves to enhance recovery, conserves energy and plays a role in the resolution of inflammation. There are behavioral/symptomatic similarities (for example, fatigue, malaise, hyperalgesia) and dissimilarities (gastrointestinal symptoms, anorexia and weight loss) between sickness and ME/CFS. While sickness is an adaptive response induced by proinflammatory cytokines, ME/CFS is a chronic, disabling disorder, where the pathophysiology is related to activation of immunoinflammatory and oxidative pathways and autoimmune responses. While sickness behavior is a state of energy conservation, which plays a role in combating pathogens, ME/CFS is a chronic disease underpinned by a state of energy depletion. While sickness is an acute response to infection/injury, the trigger factors in ME/CFS are less well defined and encompass acute and chronic infections, as well as inflammatory or autoimmune diseases. It is concluded that sickness behavior and ME/CFS are two different conditions