Radiation necrosis in the brain commonly occurs in three distinct clinical scenarios, namely, radiation therapy for head and neck malignancy or intracranial extraaxial tumor, stereotactic radiation therapy (including radiosurgery) for brain metastasis, and radiation therapy for primary brain tumors. Knowledge of the radiation treatment plan, amount of brain tissue included in the radiation port, type of radiation, location of the primary malignancy, and amount of time elapsed since radiation therapy is extremely important in determining whether the imaging abnormality represents radiation necrosis or recurrent tumor. Conventional magnetic resonance (MR) imaging findings of these two entities overlap considerably, and even at histopathologic analysis, tumor mixed with radiation necrosis is a common finding. Advanced imaging modalities such as diffusion tensor imaging and perfusion MR imaging (with calculation of certain specific parameters such as apparent diffusion coefficient ratios, relative peak height, and percentage of signal recovery), MR spectroscopy, and positron emission tomography can be useful in differentiating between recurrent tumor and radiation necrosis. In everyday practice, the visual assessment of diffusion-weighted and perfusion images may also be helpful by favoring one diagnosis over the other, with restricted diffusion and an elevated relative cerebral blood volume being seen much more frequently in recurrent tumor than in radiation necrosis.

Radiation necrosis in the brain: imaging features and differentiation from tumor recurrence

The development of protective agents against harmful radiations has been a subject of investigation for decades. However, effective (ideal) radioprotectors and radiomitigators remain an unsolved problem. Because ionizing radiation-induced cellular damage is primarily attributed to free radicals, radical scavengers are promising as potential radioprotectors. Early development of such agents focused on thiol synthetic compounds, e.g., amifostine (2-(3-aminopropylamino) ethylsulfanylphosphonic acid), approved as a radioprotector by the Food and Drug Administration (FDA, USA) but for limited clinical indications and not for nonclinical uses. To date, no new chemical entity has been approved by the FDA as a radiation countermeasure for acute radiation syndrome (ARS). All FDA-approved radiation countermeasures (filgrastim, a recombinant DNA form of the naturally occurring granulocyte colony-stimulating factor, G-CSF; pegfilgrastim, a PEGylated form of the recombinant human G-CSF; sargramostim, a recombinant granulocyte macrophage colony-stimulating factor, GM-CSF) are classified as radiomitigators. No radioprotector that can be administered prior to exposure has been approved for ARS. This differentiates radioprotectors (reduce direct damage caused by radiation) and radiomitigators (minimize toxicity even after radiation has been delivered). Molecules under development with the aim of reaching clinical practice and other nonclinical applications are discussed. Assays to evaluate the biological effects of ionizing radiations are also analyzed.

https://www.mdpi.com/2227-9059/8/11/461/pdf

Radioprotection and Radiomitigation: From the Bench to Clinical Practice.

Pyroptosis is a programmed and inflammatory cell death initiated by inflammasome. During pyroptosis, cytosolic pattern recognition receptors, apoptosis-associated speck-like protein and pro-Caspase-1 form activated inflammasome together. Caspase-1 activated by inflammasome results in generating an N-terminal cleavage product of gasdermin D (GSDMD), which is a major executor of pyroptosis. As a consequence of pyroptosis, a large number of pro-inflammatory cytokines are released including IL-1β and IL-18. Nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) and absent in melanoma 2 (AIM2)-like receptors (ALRs) belong to cytosolic pattern recognition receptors and assemble inflammasomes by detecting host cell damage signals. Pyroptosis pathways are divided into canonical and non-canonical pathways according to the identification of damage signals by cytoplasmic protein sensors. Pyroptosis not only plays an important role in infection, but also plays a vital role in inflammatory immune diseases. This article reviews the advances research of pyroptosis initiated by inflammasome in inflammatory and immune diseases.

The advances in pyroptosis initiated by inflammasome in inflammatory and immune diseases.

The biological effects of indium-tin-oxide (ITO) are of considerable importance because workers exposed to indium compounds have been diagnosed with interstitial lung disease or pulmonary alveolar proteinosis; however, the pathophysiology of these diseases is undefined. Here, mice intraperitoneally inoculated with ITO-nanoparticles (ITO-NPs) resulted in peritonitis dependent in NLRP3 inflammasome, with neutrophils recruitment and interleukin-1β (IL-1β) production. Withal peritoneal macrophages exposed ex vivo to ITO-NPs caused IL-1β secretion and cytolysis. Further, alveolar macrophages exposed to ITO-NPs in vitro showed ITO-NP endocytosis and production of tumor necrosis factor-α (TNF-α) and IL-1β, ensued cell death by cytolysis. This cell death was RIPK1-independent but caspase1-dependent, and thus identified as pyroptosis. Endocytosis of ITO-NPs by activated THP-1 cells induced pyroptosis with IL-1β/TNF-α production and cytolysis, but not in activated THP-1 cells with knockdown of NLRP3, ASC, or caspase1. However, exposing activated THP-1 cells with NLRP3 or ASC knockdown to ITO-NPs resulted in cell death but without cytolysis, with deficiency in IL-1β/TNF-α, and revealing features of apoptosis. While, mesenchymal stem cells (MSCs) co-cultured with macrophages impaired both inflammation and cell death induced by ITO-NPs. Together, our findings provide crucial insights to the pathophysiology of respiratory diseases caused by ITO particles, and identify MSCs as a potent therapeutic.

/pdf/endocytosis-of-indium-tin-oxide-nanoparticles-by-macrophages-5hqfpaj8dl.pdf

Endocytosis of indium-tin-oxide nanoparticles by macrophages provokes pyroptosis requiring NLRP3-ASC-Caspase1 axis that can be prevented by mesenchymal stem cells

Association between vision impairment and mortality: a systematic review and meta-analysis

Radiation-induced brain injury (RI) commonly occurs in patients who received head and neck radiotherapy. However, the mechanism of RI remains unclear. We aimed to evaluate whether pyroptosis was involved in RI and the impact of mesenchymal stem cells (MSCs) on it. BALB/c male mice (6–8 weeks) were cranially irradiated (15 Gy), and MSCs were transplanted into the bilateral cortex 2 days later; then mice were sacrificed 1 month later. Meanwhile, irradiated BV-2 microglia cells (10 Gy) were cocultured with MSCs for 24 hours. We observed that irradiated mice brains presented NLRP3 and caspase-1 activation. RT-PCR then indicated that it mainly occurred in microglia cells but not in neurons. Further, irradiated BV-2 cells showed pyroptosis and increased production of IL-18 and IL-1β. RT-PCR also demonstrated an increased expression of several inflammasome genes in irradiated BV-2 cells, including NLRP3 and AIM2. Particularly, NLRP3 was activated. Knockdown of NLRP3 resulted in decreased LDH release. Noteworthily, in vivo, MSCs transplantation alleviated radiation-induced NLRP3 and caspase-1 activation. Moreover, in vitro, MSCs could decrease caspase-1 dependent pyroptosis, NLRP3 inflammasome activation, and ROS production induced by radiation. Thus, our findings proved that microglia pyroptosis occurred in RI. MSCs may act as a potent therapeutic tool in attenuating pyroptosis.

/pdf/mesenchymal-stem-cells-attenuate-radiation-induced-brain-4tftblhcd0.pdf

Mesenchymal Stem Cells Attenuate Radiation-Induced Brain Injury by Inhibiting Microglia Pyroptosis

Aim To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

Retinal age gap as a predictive biomarker for mortality risk

Abstract Introduction retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age–chronological age) and incident Parkinson’s disease (PD). Methods a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. Results a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01–1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13–6.22, P = 0.024; HR = 4.86, 95% CI: 1.59–14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). Conclusion retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.

/pdf/retinal-age-gap-as-a-predictive-biomarker-of-future-risk-of-uj4gr6ii.pdf

Retinal age gap as a predictive biomarker of future risk of Parkinson’s disease

Objective: To assess the joint impact of cognitive performance and visual acuity on mortality over 13-year follow-up in a representative US sample. Methods: Data from National Health and Nutrition Examination Survey (NHANES) participants (≥18 years old) were linked with the death record data of the National Death Index (NDI) with mortality follow-up through December 31, 2011. Cognitive performance was evaluated by the Digit Symbol Substitution Test (DSST) and cognitive performance impairment was defined as the DSST score equal to or less than the median value in the study population. Visual impairment (VI) was defined as presenting visual acuity worse than 20/40 in the better-seeing eye. Risks of all-cause and specific-cause mortality were estimated with Cox proportional hazards models after adjusting for confounders. Results: A total of 2,550 participants 60 years and older from two waves of (NHANES, 1999-2000, 2001-2002) were included in the current analysis. Over a median follow-up period of 9.92 years, 952 (35.2%) died of all causes, of whom 239 (23.1%), 224 (24.0%), and 489 (52.9%) died from cardiovascular disease (CVD), cancer, and non-CVD/non-cancer mortality, respectively. Cognitive performance impairment and VI increased the odds for mortality. Co-presence of VI among cognitive impaired elderly persons predicted nearly a threefold increased risk of all-cause mortality [hazard ratios (HRs), 2.74; 95% confidence interval (CI), 2.02-3.70; P < 0.001) and almost a fourfold higher risk of non-CVD/non-cancer mortality (HR, 3.72; 95% CI, 2.30-6.00; P < 0.001) compared to having neither impairment. Conclusion: People aged 60 years and over with poorer cognitive performance were at higher risk of long-term mortality, and were especially vulnerable to further mortality when concomitant with VI. It is informative for clinical implication in terms of early preventive interventions.

/pdf/cognitive-performance-concomitant-with-vision-acuity-7foxalzt9l.pdf

Cognitive Performance Concomitant With Vision Acuity Predicts 13-Year Risk for Mortality.

Background: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). Methods: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. Results: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (β=0.002 [95% CI, 0.001–0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73–5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01–1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00–1.10]; P-overall <0.0001; P-nonlinear=0.0681). Conclusions: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

https://doi.org/10.1161/strokeaha.122.038809

Huan Liao

Papers

Mesenchymal Stem Cells Attenuate Radiation-Induced Brain Injury by Inhibiting Microglia Pyroptosis

Retinal age gap as a predictive biomarker for mortality risk

Retinal age gap as a predictive biomarker of future risk of Parkinson’s disease

Cognitive Performance Concomitant With Vision Acuity Predicts 13-Year Risk for Mortality.

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease