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Abstract To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spectralis). DL-based algorithms quantified ellipsoid zone (EZ)-thickness, hyperreflective foci (HRF) and drusen volume. Outer nuclear layer (ONL)-thickness and subretinal drusenoid deposits (SDD) were quantified by human experts. All patients completed four MP examinations using an identical custom 45 stimuli grid on MP-3 (NIDEK) and MAIA (CenterVue). MP stimuli were co-registered with corresponding OCT using image registration algorithms. Multivariable mixed-effect models were calculated. 3.600 PWS from 20 eyes of 20 patients were analyzed. Decreased EZ thickness, decreased ONL thickness, increased HRF and increased drusen volume had a significant negative effect on PWS (all p

Abstract To evaluate the intra- and interdevice repeatability of microperimetry (MP) assessments in patients with diabetic macular edema (DME) two consecutive MP testings (45 fovea-centered stimuli, 4–2 staircase strategy) were performed using MP3 (NIDEK, Aichi, Japan) and MAIA (CenterVue, Padova, Italy), respectively. Intraretinal fluid (IRF) and ellipsoid zone (EZ) thickness were automatically segmented by published deep learning algorithms. Hard exudates (HEs) were annotated semi-automatically and disorganization of retinal inner layers (DRIL) was segmented manually. Point-to-point registration of MP stimuli to corresponding spectral-domain OCT (Spectralis, Heidelberg Engineering, Germany) locations was performed for both devices. Repeatability was assessed overall and in areas of disease-specific OCT biomarkers using Bland-Altmann coefficients of repeatability (CoR). A total of 3600 microperimetry stimuli were tested in 20 eyes with DME. Global CoR was high using both devices (MP3: ± 6.55 dB, MAIA: ± 7.69 dB). Higher retest variances were observed in stimuli with IRF (MP3: CoR ± 7.4 dB vs. ± 6.0 dB, p = 0.001, MAIA: CoR ± 9.2dB vs. ± 6.8 dB, p = 0.002) and DRIL on MP3 (CoR ± 6.9 dB vs. ± 3.2 dB, p

Abstract With the approval of the first two substances for the treatment of geographic atrophy (GA) secondary to age-related macular degeneration (AMD), a standardized monitoring of patients treated with complement inhibitors in clinical practice is needed. Optical coherence tomography (OCT) provides high-resolution access to the retinal pigment epithelium (RPE) and neurosensory layers, such as the ellipsoid zone (EZ), which further enhances the understanding of disease progression and therapeutic effects in GA compared to conventional fundus autofluorescence (FAF). In addition, artificial intelligence-based methodology allows the identification and quantification of GA-related pathology on OCT in an objective and standardized manner. The purpose of this study was to comprehensively evaluate automated OCT monitoring for GA compared to reading center-based manual FAF measurements in the largest successful phase 3 clinical trial data of complement inhibitor therapy to date. Automated OCT analysis of RPE loss showed a high and consistent correlation to manual GA measurements on conventional FAF. EZ loss on OCT was generally larger than areas of RPE loss, supporting the hypothesis that EZ loss exceeds underlying RPE loss as a fundamental pathophysiology in GA progression. Automated OCT analysis is well suited to monitor disease progression in GA patients treated in clinical practice and clinical trials.

Abstract Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

Abstract Microperimetry (MP) is a psychometric examination combining retinal imaging and functional sensitivity testing with an increasing importance due to its potential use as clinical study outcome. We investigated the repeatability of pointwise retinal sensitivity (PWS) on the most advanced commercially available MP devices under their standard setting in a healthy aging population. Two successive MP examinations on both MP-3 (NIDEK CO., Ltd., Gamagori, Japan) and MAIA (CenterVue S.p.A. (iCare), Padova, Italy) were performed on healthy aging subjects in a randomized order. PWS repeatability was analysed for different macular regions and age groups using Bland-Altmann coefficients of repeatability (CoR). A total of 3600 stimuli from 20 healthy individuals with a mean age of 70 (11) years were included. Mean CoR in dB were ±4.61 for MAIA and ±4.55 for MP-3 examinations. A lower repeatability (p=0.005) was detected in the central millimetre on MAIA examinations. Higher subject age was associated with a lower repeatability of PWS on both devices (both p=0.003). Intra-device correlation was good (MAIA: 0.79 [0.76–0.81]; MP-3: 0.72 [0.68–0.76]) whereas a moderate mean inter-device correlation (0.6 [0.55–0.65]) could be detected. In conclusion, older subjects and the foveal region are associated with a worse pointwise repeatability.

Objective: Treatment decisions in neovascular age-related macular degeneration (nAMD) are mainly based on subjective evaluation of OCT. The purpose of this cross-sectional study was to provide a comparison of qualitative and quantitative differences between OCT devices in a systematic manner. Design: Prospective, cross-sectional study. Subjects: One hundred sixty OCT volumes, 40 eyes of 40 patients with nAMD. Methods: Patients from clinical practice were imaged with 4 different OCT devices during one visit: (1) Spectralis Heidelberg; (2) Cirrus; (3) Topcon Maestro2; and (4) Topcon Triton. Intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) were manually annotated in all cubes by trained human experts to establish fluid measurements based on expert-reader annotations. Intraretinal fluid, SRF, and PED volume were quantified in nanoliters (nL). Bland–Altman plots were created to analyze the agreement of measurements in the central 1 and 6 mm. The Friedman test was performed to test for significant differences in the central 1, 3, and 6 mm. Main Outcome Measures: Intraretinal fluid, SRF, and PED volume. Results: In the central 6 mm, there was a trend toward higher IRF and PED volumes in Spectralis images compared with the other devices and no differences in SRF volume. In the central 1 mm, the standard deviation of the differences ranged from ± 3 nL to ± 6 nL for IRF, from ± 3 nL to ± 4 nL for SRF, and from ± 7 nL to ± 10 nL for PED in all pairwise comparisons. Manually annotated IRF and SRF volumes showed no significant differences in the central 1 mm. Conclusions: Fluid volume quantification achieved excellent reliability in all 3 retinal compartments on images obtained from 4 OCT devices, particularly for clinically relevant IRF and SRF values. Although fluid volume quantification is reliable in all 4 OCT devices, switching OCT devices might lead to deviating fluid volume measurements with higher agreement in the central 1 mm compared with the central 6 mm, with highest agreement for SRF volume in the central 1 mm. Understanding device-dependent differences is essential for expanding the interpretation and implementation of pixel-wise fluid volume measurements in clinical practice and in clinical trials. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Abstract Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.

Abstract Childhood acute lymphoblastic leukemia (ALL) genomes show that relapses often arise from subclonal outgrowths. However, the impact of clonal evolution on the actionable proteome and response to targeted therapy is not known. Here, we present a comprehensive retrospective analysis of paired ALL diagnosis and relapsed specimen. Targeted next generation sequencing and proteome analysis indicate persistence of actionable genome variants and stable proteomes through disease progression. Paired viably-frozen biopsies show high correlation of drug response to variant-targeted therapies but in vitro selectivity is low. Proteome analysis prioritizes PARP1 as a pan-ALL target candidate needed for survival following cellular stress; diagnostic and relapsed ALL samples demonstrate robust sensitivity to treatment with two PARP1/2 inhibitors. Together, these findings support initiating prospective precision oncology approaches at ALL diagnosis and emphasize the need to incorporate proteome analysis to prospectively determine tumor sensitivities, which are likely to be retained at disease relapse.

Abstract Geographic atrophy (GA) represents a late stage of age-related macular degeneration, which leads to irreversible vision loss. With the first successful therapeutic approach, namely complement inhibition, huge numbers of patients will have to be monitored regularly. Given these perspectives, a strong need for automated GA segmentation has evolved. The main purpose of this study was the clinical validation of an artificial intelligence (AI)-based algorithm to segment a topographic 2D GA area on a 3D optical coherence tomography (OCT) volume, and to evaluate its potential for AI-based monitoring of GA progression under complement-targeted treatment. 100 GA patients from routine clinical care at the Medical University of Vienna for internal validation and 113 patients from the FILLY phase 2 clinical trial for external validation were included. Mean Dice Similarity Coefficient (DSC) was 0.86 ± 0.12 and 0.91 ± 0.05 for total GA area on the internal and external validation, respectively. Mean DSC for the GA growth area at month 12 on the external test set was 0.46 ± 0.16. Importantly, the automated segmentation by the algorithm corresponded to the outcome of the original FILLY trial measured manually on fundus autofluorescence. The proposed AI approach can reliably segment GA area on OCT with high accuracy. The availability of such tools represents an important step towards AI-based monitoring of GA progression under treatment on OCT for clinical management as well as regulatory trials.

Background: Asthma is a chronic inflammatory disorder of the airways and one of the most important non-communicable diseases worldwide. Analyzing crowdsourced data can help understand public interest and unmet needs as well as potential factors influencing search behavior. Objective: The study aimed to investigate asthma-related web search data in Europe to identify possible regional and seasonal variations and to assess public interest. Methods: Google Ads Keyword Planner was used to measure search volume for search terms related to asthma, allergic asthma, and bronchial asthma in 21 European countries between January 2018 and December 2021. The top 10 keywords of each country were categorized qualitatively. Search volume per 100 000 inhabitants was descriptively assessed in terms of regional and seasonal trends. Spearman correlations between search volume and pollen concentration as well as coronavirus disease (COVID-19) cases were investigated. Results: The median search volume per 100 000 inhabitants for asthma and allergic asthma was highest in Northern and Western Europe, while the highest search volume for bronchial asthma was observed in Western and Eastern regions. A seasonal trend was identified for all search terms and in all regions. Correlations were found between search frequency and pollen load and search behavior and COVID-19 cases. Overall, Europeans were most interested in the diseases in general, their treatment options, and symptoms. Conclusion: These results highlighted the need for reliable and region-specific information about the disease and for public campaigns to improve asthma control. The study also emphasizes the importance of using crowdsourced data for a more encompassing overview beyond conventional healthcare data.

Abstract Background Spirometry and conventional chest x-ray have limitations in investigating early emphysema, while computed tomography, the reference imaging method in this context, is not part of routine patient care due to its higher radiation dose. In this work, we investigated a novel low-dose imaging modality, dark-field chest x-ray, for the evaluation of emphysema in patients with alpha1-antitrypsin deficiency. Methods By exploiting wave properties of x-rays for contrast formation, dark-field chest x-ray visualises the structural integrity of the alveoli, represented by a high signal over the lungs in the dark-field image. We investigated four patients with alpha1-antitrypsin deficiency with a novel dark-field x-ray prototype and simultaneous conventional chest x-ray. The extent of pulmonary function impairment was assessed by pulmonary function measurement and regional emphysema distribution was compared with CT in one patient. Results We show that dark-field chest x-ray visualises the extent of pulmonary emphysema displaying severity and regional differences. Areas with low dark-field signal correlate with emphysematous changes detected by computed tomography using a threshold of -950 Hounsfield units. The airway parameters obtained by whole-body plethysmography and single breath diffusing capacity of the lungs for carbon monoxide demonstrated typical changes of advanced emphysema. Conclusions Dark-field chest x-ray directly visualised the severity and regional distribution of pulmonary emphysema compared to conventional chest x-ray in patients with alpha1-antitrypsin deficiency. Due to the ultra-low radiation dose in comparison to computed tomography, dark-field chest x-ray could be beneficial for long-term follow-up in these patients.

Abstract The aim of this study is to investigate the impact of age-related lens opacities and advanced cataract, quantified by LOCS III grading, on quantitative autofluorescence (qAF) measurements in patients before and after cataract surgery. Images from a randomized controlled trial evaluating the impact of femtosecond-laser assisted cataract surgery (FLACS) on retinal thickness were analyzed post-hoc. One-hundred and twenty eyes from 60 consecutive patients with age-related cataract were included and assessed with qAF and optical coherence tomography (OCT) before, 1, 3 and 6 weeks after cataract surgery (randomized 1:1 to FLACS or phacoemulsification). LOCS III grading was performed before surgery. Pre- to post-surgical qAF values, as well as the impact of LOCS III gradings, surgery technique, gender, axial length and age on post-surgery qAF values was investigated using generalized linear mixed models. For this analysis, 106 eyes from 53 patients were usable. No difference in qAF was found between FLACS and phacoemulsification (p > 0.05) and results were pooled for the total cohort. Mean pre-surgical qAF was 89.45 ± 44.9 qAF units, with a significant mean increase of 178.4–191.6% after surgery (p 0.05). Higher LOCS III cortical opacity quantifications were associated with a significantly greater increase in qAF after surgery (estimate: 98.56, p = 0.006) and nuclear opacities showed a trend toward an increased change (estimate: 48.8, p = 0.095). Considerable interactions were identified between baseline qAF and cortical opacities, nuclear opacities and posterior subcapsular opacities, as well as nuclear opacities and cortical opacities (p = 0.012, p = 0.064 and p = 0.069, respectively). Quantitative autofluorescence signals are significantly reconstituted after cataract surgery and LOCS III gradings are well associated with post-surgical qAF values. Careful consideration of age-related lens opacities is vital for the correct interpretation of qAF, especially in retinal diseases affecting the elderly. ClinicalTrials.gov Identifier: NCT03465124.

PurposeTo predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.Materials and methodsStudy eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.ResultsData of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.ConclusionThe proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD.

Fibroblasts, or their homolog stromal cells, are present in most tissues and play an essential role in tissue homeostasis and regeneration. As a result, fibroblast-based strategies have been widely employed in tissue engineering. However, while considered to have immunosuppressive properties, the survival and functionality of allogeneic fibroblasts after transplantation remain controversial. Here, we evaluated innate and adaptive immune responses against allogeneic fibroblasts following intradermal injection into different immune-deficient mouse strains. While allogeneic fibroblasts were rejected 1 week after transplantation in immunocompetent mice, rejection did not occur in immunodeficient γ chain–deficient NOD-SCID (NSG) mice. T-cell- and B-cell-deficient RAG1 knockout mice showed greater loss of fibroblasts by day 5 after transplantation compared with NSG mice ( P ≤ 0.05) but prolonged persistence compared with wild-type recipient ( P ≤ 0.005). Loss of fibroblasts correlated with the expression of proinflammatory chemokine genes and infiltration of myeloid cells in the transplantation site. Depletion of macrophages and neutrophils delayed rejection, revealing the role of innate immune cells in an early elimination of fibroblasts that is followed by T-cell-mediated rejection in the second week. These findings indicate that the application of allogeneic fibroblasts in tissue engineering products requires further improvements to overcome cell rejection by innate and adaptive immune cells.

Abstract Background Murine xenografts of pediatric leukemia accurately recapitulate genomic aberrations. How this translates to the functional capacity of cells remains unclear. Here, we studied global protein abundance, phosphorylation, and protein maturation by proteolytic processing in 11 pediatric B- and T- cell ALL patients and 19 corresponding xenografts. Methods Xenograft models were generated for each pediatric patient leukemia. Mass spectrometry-based methods were used to investigate global protein abundance, protein phosphorylation, and limited proteolysis in paired patient and xenografted pediatric acute B- and T- cell lymphocytic leukemia, as well as in pediatric leukemia cell lines. Targeted next-generation sequencing was utilized to examine genetic abnormalities in patients and in corresponding xenografts. Bioinformatic and statistical analysis were performed to identify functional mechanisms associated with proteins and protein post-translational modifications. Results Overall, we found xenograft proteomes to be most equivalent with their patient of origin. Protein level differences that stratified disease subtypes at diagnostic and relapse stages were largely recapitulated in xenografts. As expected, PDXs lacked multiple human leukocyte antigens and complement proteins. We found increased expression of cell cycle proteins indicating a high proliferative capacity of xenografted cells. Structural genomic changes and mutations were reflected at the protein level in patients. In contrast, the post-translational modification landscape was shaped by leukemia type and host and only to a limited degree by the patient of origin. Of 201 known pediatric oncogenic drivers and drug-targetable proteins, the KMT2 protein family showed consistently high variability between patient and corresponding xenografts. Comprehensive N terminomics revealed deregulated proteolytic processing in leukemic cells, in particular from caspase-driven cleavages found in patient cells. Conclusion Genomic and host factors shape protein and post-translational modification landscapes differently. This study highlights select areas of diverging biology while confirming murine patient-derived xenografts as a generally accurate model system.

The retinal world has been revolutionized by optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy. The numbers of intravitreal injections are on a constant rise and management in neovascular age-related macular degeneration (nAMD) is mainly driven by the qualitative assessment of macular fluid as detected on OCT scans. The presence of macular fluid, particularly subretinal fluid (SRF) and intraretinal fluid (IRF), has been used to trigger re-treatments in clinical trials and the real world. However, large discrepancies can be found between the evaluations of different readers or experts and especially small amounts of macular fluid might be missed during this process. Pixel-wise detection of macular fluid uses an entire OCT volume to calculate exact volumes of retinal fluid. While manual annotations of such pixel-wise fluid detection are unfeasible in a clinical setting, artificial intelligence (AI) is able to overcome this hurdle by providing real-time results of macular fluid in different retinal compartments. Quantitative fluid assessments have been used for various post hoc analyses of randomized controlled trials, providing novel insights into anti-VEGF treatment regimens. Nonetheless, the application of AI-algorithms in a prospective patient care setting is still limited. In this review, we discuss the use of quantitative fluid assessment in nAMD during anti-VEGF therapy and provide an outlook to novel forms of patient care with the support of AI quantifications.

Summary: Background: Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). Methods: In this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)—which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison. Findings: Between October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=–0·27, p=0·089 and visual emphysema ρ=–0·45, p=0·0028), the dark-field signal (ρ=0·62, p

PurposeTo assess the influence of the SARS-CoV-2 lockdown in spring on frequency, severity and quality of care of rhegmatogeneous retinal detachments (RRD) in a tertiary referral center in Vienna, Austria.MethodsSingle center, consecutive case series with historical controls. Patients presenting with primary RRD during the first Austrian SARS-CoV-2 lockdown (March 16th-May 3rd 2020) and a corresponding control group consisting of the same time period of the preceding 3 years.ResultsThe mean number of patients with RD in the reference group (RG) was 22 (± 1) and in the lockdown group (LG) 15. Median total delay, defined as onset of symptoms until surgery, in the RG was 5 (lower quartile: 3.0; upper quartile: 8.0) compared to 7 (3.0; 12.0) days in the LG, (p = 0.740). During the lockdown 67% of patients were referred from an external ophthalmologist compared to 52% in the RG, (p = 0.395). 34% of patients in the RG presented with an attached macula compared to 33% in the LG (p = 0.597). PVR was present in 49% of cases in the RG compared to 73% in the LG. Single surgery success (SSS) rates were lower in the LG (73.3%) compared to the RG (85.3%), (p = 0.275).ConclusionPatients with RRD during the SARS-CoV-2 lockdown presented and were treated within acceptable time limits, showed the same macula-on ratios but a higher PVR rate and a tendency towards worse SSS rates compared to the time period of the preceding 3 years.

Ulcerative colitis (UC) is a chronic inflammatory condition linked to intestinal microbial dysbiosis, including the expansion of E. coli strains related to extra-intestinal pathogenic E. coli. These “pathobionts” exhibit pathogenic properties, but their potential to promote UC is unclear due to the lack of relevant animal models. Here, we established a mouse model using a representative UC pathobiont strain (p19A), and mice lacking single immunoglobulin and toll-interleukin 1 receptor domain (SIGIRR), a deficiency increasing susceptibility to gut infections. Strain p19A was found to adhere to the cecal mucosa of Sigirr -/- mice, causing modest inflammation. Moreover, it dramatically worsened dextran sodium sulfate-induced colitis. This potentiation was attenuated using a p19A strain lacking α-hemolysin genes, or when we targeted pathobiont adherence using a p19A strain lacking the adhesin FimH, or following treatment with FimH antagonists. Thus, UC pathobionts adhere to the intestinal mucosa, and worsen the course of colitis in susceptible hosts.

BackgroundSince the outbreak of the COVID-19 pandemic, a number of risk factors for a poor outcome have been identified. Thereby, cardiovascular comorbidity has a major impact on mortality. We investigated whether coronary calcification as a marker for coronary artery disease (CAD) is appropriate for risk prediction in COVID-19.MethodsHospitalized patients with COVID-19 (n = 109) were analyzed regarding clinical outcome after native computed tomography (CT) imaging for COVID-19 screening. CAC (coronary calcium score) and clinical outcome (need for intensive care treatment or death) data were calculated following a standardized protocol. We defined three endpoints: critical COVID-19 and transfer to ICU, fatal COVID-19 and death, composite endpoint critical and fatal COVID-19, a composite of ICU treatment and death. We evaluated the association of clinical outcome with the CAC. Patients were dichotomized by the median of CAC. Hazard ratios and odds ratios were calculated for the events death or ICU or a composite of death and ICU.ResultsWe observed significantly more events for patients with CAC above the group's median of 31 for critical outcome (HR: 1.97[1.09,3.57], p = 0.026), for fatal outcome (HR: 4.95[1.07,22.9], p = 0.041) and the composite endpoint (HR: 2.31[1.28,4.17], p = 0.0056. Also, odds ratio was significantly increased for critical outcome (OR: 3.01 [1.37, 6.61], p = 0.01) and for fatal outcome (OR: 5.3 [1.09, 25.8], p = 0.02).ConclusionThe results indicate a significant association between CAC and clinical outcome in COVID-19. Our data therefore suggest that CAC might be useful in risk prediction in patients with COVID-19.

Abstract Background Stromal gene expression patterns predict patient outcomes in colorectal cancer. TRIM28 is a transcriptional co-repressor that regulates an abundance of genes through the KRAB domain family of transcription factors. We have previously shown that stromal expression of TRIM28 is a marker of disease relapse and poor survival in colorectal cancer. Here, we perform differential epithelium-stroma proteomic network analyses to characterize signaling pathways associated with TRIM28 within the tumor microenvironment. Methods Reverse phase protein arrays were generated from laser capture micro-dissected carcinoma and stromal cells from fresh frozen colorectal cancer tissues. Phosphorylation and total protein levels were measured for 30 cancer-related signaling pathway endpoints. Strength and direction of associations between signaling endpoints were identified using Spearman’s rank-order correlation analysis and compared to TRIM28 levels. Expression status of TRIM28 in tumor epithelium and stromal fibroblasts was assessed using IHC in formalin fixed tissue and the epithelium to stroma protein expression ratio method. Results We found distinct proteomic networks in the epithelial and stromal compartments which were linked to expression levels of TRIM28. Low levels of TRIM28 in tumor stroma (high epithelium: stroma ratio) were found in 10 out of 19 cases. Upon proteomic network analyses, these stromal high ratio cases revealed moderate signaling pathway similarity exemplified by 76 significant Spearman correlations (ρ ≥ 0.75, p ≤ 0.01). Furthermore, low levels of stromal TRIM28 correlated with elevated MDM2 levels in tumor epithelium (p = 0.01) and COX-2 levels in tumor stroma (p = 0.002). Low TRIM28 epithelium to stroma ratios were associated with elevated levels of caspases 3 and 7 in stroma (p = 0.041 and p = 0.036) and an increased signaling pathway similarity in stromal cells with 81 significant Spearman correlations (ρ ≥ 0.75, p ≤ 0.01). Conclusions By dissecting TRIM28-associated pathways in stromal fibroblasts and epithelial tumor cells, we performed comprehensive proteomic analyses of molecular networks within the tumor microenvironment. We found modulation of several signaling pathways associated with TRIM28, which may be attributed to the pleiotropic properties of TRIM28 through its translational suppression of the family of KRAB domain transcription factors in tumor stromal compartments.

Therapy-induced presentation of cell surface calreticulin (CRT) is a pro-phagocytic immunogen beneficial for invoking anti-tumor immunity. Here, we characterized the roles of ERp57 and α-integrins as CRT-interacting proteins that coordinately regulate CRT translocation from the ER to the surface during immunogenic cell death. Using T-lymphoblasts as a genetic cell model, we found that drug-induced surface CRT is dependent on ERp57, while drug-induced surface ERp57 is independent of CRT. Differential subcellular immunostaining assays revealed that ERp57−/− cells have minimal cytosolic CRT, indicating that ERp57 is indispensable for extra-ER accumulation of CRT. Stimulation of integrin activity, with either cell adhesion or molecular agonists, resulted in decreased drug-induced surface CRT and ERp57 levels. Similarly, surface CRT and ERp57 was reduced in cells expressing GFFKR, a conserved α-integrin cytosolic motif that binds CRT. Drug-induced surface ERp57 levels were consistently higher in CRT−/− cells, suggesting integrin inhibition of surface ERp57 is an indirect consequence of α-integrin binding to CRT within the CRT-ERp57 complex. Furthermore, β1−/− cells with reduced expression of multiple α-integrins, exhibit enhanced levels of drug-induced surface CRT and ERp57. Our findings highlight the coordinate involvement of plasma membrane integrins as inhibitors, and ERp57 originating from the ER as promoters, of CRT translocation from the ER to the cell surface.

Protein arrays are frequently used to profile antibody repertoires in humans and animals. High-throughput protein array characterisation of complex antibody repertoires necessitates the use of extensively validated secondary detection antibodies. This article details the validation of an affinity-isolated anti-chicken IgY antibody produced in rabbit and a goat anti-rabbit IgG antibody conjugated with alkaline phosphatase using protein arrays consisting of 7,390 distinct human proteins. Probing protein arrays with secondary antibodies in absence of chicken serum revealed non-specific binding to 61 distinct human proteins. Despite the identified non-specific binding, the tested antibodies are well suited for use in protein array experiments as the cross-reactive binding partners can be readily excluded from further analysis. The evident cross-reactivity of the tested secondary detection antibodies points towards the necessity of platform-specific antibody characterisation studies for all secondary immunoreagents. Furthermore, secondary antibody characterisation using protein arrays enables the generation of reference lists of cross-reactive proteins, which can be then marked as potential false positives in follow-up experiments. Providing such cross-reactivity reference lists accessible to the wider research community may help to interpret data generated with the same antibodies in applications not only related to protein arrays such as immunoprecipitation, Western blots or other immunoassays.

Protein arrays are frequently used to profile antibody repertoires in humans and animals. High-throughput protein array characterisation of complex antibody repertoires requires a platform-dependent, lot-to-lot validation of secondary detection antibodies. This article details the validation of an affinity-isolated anti-chicken IgY antibody produced in rabbit and a goat anti-rabbit IgG antibody conjugated with alkaline phosphatase using protein arrays consisting of 7,390 distinct human proteins. Probing protein arrays with secondary antibodies in absence of chicken serum revealed non-specific binding to 61 distinct human proteins. The cross-reactivity of the tested secondary detection antibodies points towards the necessity of platform-specific antibody characterisation studies for all secondary immunoreagents. Secondary antibody characterisation using protein arrays enables generation of reference lists of cross-reactive proteins, which can be then excluded from analysis in follow-up experiments. Furthermore, making such cross-reactivity lists accessible to the wider research community may help to interpret data generated by the same antibodies in applications not related to protein arrays such as immunoprecipitation, Western blots or other immunoassays.

Colorectal cancer is one of the most common cancers worldwide with almost 700,000 deaths every year. Detection of colorectal cancer at an early stage significantly improves patient survival. Cancer-specific autoantibodies found in sera of cancer patients can be used for pre-symptomatic detection of the disease. In this study we assess the zinc finger proteins ZNF346, ZNF638, ZNF700 and ZNF768 as capture antigens for the detection of autoantibodies in colorectal cancer. Sera from 96 patients with colorectal cancer and 35 control patients with no evidence of cancer on colonoscopy were analysed for the presence of ZNF-specific autoantibodies using an indirect ELISA. Autoantibodies to individual ZNF proteins were detected in 10-20% of colorectal cancer patients and in 0-5.7% of controls. A panel of all four ZNF proteins resulted in an assay specificity of 91.4% and sensitivity of 41.7% for the detection of cancer patients in a cohort of non-cancer controls and colorectal cancer patients. Clinicopathological and survival analysis revealed that ZNF autoantibodies were independent of disease stage and did not correlate with disease outcome. Since ZNF autoantibodies were shared between patients and corresponding ZNF proteins showed similarities in their zinc finger motifs, we performed an in silico epitope sequence analysis. Zinc finger proteins ZNF700 and ZNF768 showed the highest sequence similarity with a bl2seq score of 262 (E-value 1E-81) and their classical C2H2 ZNF motifs were identified as potential epitopes contributing to their elevated immunogenic potential. Our findings show an enhanced and specific immunogenicity to zinc finger proteins, thereby providing a multiplexed autoantibody assay for minimally invasive detection of colorectal cancer.