Citations

BRCA2 is essential for homologous recombination DNA repair. BRCA2 mutations lead to genome instability and increased risk of breast and ovarian cancer. Similarly, mutations in BRCA2-interacting proteins are also known to modulate sensitivity to DNA damage agents and are established cancer risk factors. Here we identify the tumor suppressor CDK5RAP3 as a novel BRCA2 helical domain-interacting protein. CDK5RAP3 depletion induced DNA damage resistance, homologous recombination and single-strand annealing upregulation, and reduced spontaneous and DNA damage-induced genomic instability, suggesting that CDK5RAP3 negatively regulates double-strand break repair in the S-phase. Consistent with this cellular phenotype, analysis of transcriptomic data revealed an association between low CDK5RAP3 tumor expression and poor survival of breast cancer patients. Finally, we identified common genetic variations in the CDK5RAP3 locus as potentially associated with breast and ovarian cancer risk in BRCA1 and BRCA2 mutation carriers. Our results uncover CDK5RAP3 as a critical player in DNA repair and breast cancer outcomes.

Objective Alcohol-related liver disease (ALD) ranges from never-decompensated ALD (ndALD) to the life-threatening decompensated phenotype, known as alcohol-related hepatitis (AH). A multidimensional study of the clinical, histological and molecular features of these subtypes is lacking. Design Two large cohorts of patients were recruited in an international, observational multicentre study: a retrospective cohort of patients with ndALD (n=110) and a prospective cohort of patients with AH (n=225). Clinical, analytical, immunohistochemistry and hepatic RNA microarray analysis of both disease phenotypes were performed. Results Age and mean alcohol intake were similar in both groups. AH patients had greater aspartate amino transferase/alanine amino transferase ratio and lower gamma-glutamyl transferase levels than in ndALD patients. Patients with AH demonstrated profound liver failure and increased mortality. One-year mortality was 10% in ndALD and 50% in AH. Histologically, steatosis grade, ballooning and pericellular fibrosis were similar in both groups, while advanced fibrosis, Mallory-Denk bodies, bilirubinostasis, severe neutrophil infiltration and ductular reaction were more frequent among AH patients. Transcriptome analysis revealed a profound gene dysregulation within both phenotypes when compare to controls. While ndALD was characterised by deregulated expression of genes involved in matrisome and immune response, the development of AH resulted in a marked deregulation of genes involved in hepatocyte reprogramming and bile acid metabolism. Conclusions Despite comparable alcohol intake, AH patients presented with worse liver function compared with ndALD patients. Bilirubinostasis, severe fibrosis and ductular reaction were prominent features of AH. AH patients exhibited a more profound deregulation of gene expression compared with ndALD patients.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Abstract ARID1A (BAF250a) is a component of the SWI/SNF chromatin modifying complex, plays an important tumour suppressor role, and is considered prognostic in several malignancies. However, in ovarian carcinomas there are contradictory reports on its relationship to outcome, immune response, and correlation with clinicopathological features. We assembled a series of 1623 endometriosis‐associated ovarian carcinomas, including 1078 endometrioid (ENOC) and 545 clear cell (CCOC) ovarian carcinomas, through combining resources of the Ovarian Tumor Tissue Analysis (OTTA) Consortium, the Canadian Ovarian Unified Experimental Resource (COEUR), local, and collaborative networks. Validated immunohistochemical surrogate assays for ARID1A mutations were applied to all samples. We investigated associations between ARID1A loss/mutation, clinical features, outcome, CD8 + tumour‐infiltrating lymphocytes (CD8 + TILs), and DNA mismatch repair deficiency (MMRd). ARID1A loss was observed in 42% of CCOCs and 25% of ENOCs. We found no associations between ARID1A loss and outcomes, stage, age, or CD8 + TIL status in CCOC. Similarly, we found no association with outcome or stage in endometrioid cases. In ENOC, ARID1A loss was more prevalent in younger patients ( p = 0.012) and was associated with MMRd ( p < 0.001) and the presence of CD8 + TILs ( p = 0.008). Consistent with MMRd being causative of ARID1A mutations, in a subset of ENOCs we also observed an association with ARID1A loss‐of‐function mutation as a result of small indels ( p = 0.035, versus single nucleotide variants). In ENOC, the association with ARID1A loss, CD8 + TILs, and age appears confounded by MMRd status. Although this observation does not explicitly rule out a role for ARID1A influence on CD8 + TIL infiltration in ENOC, given current knowledge regarding MMRd, it seems more likely that effects are dominated by the hypermutation phenotype. This large dataset with consistently applied biomarker assessment now provides a benchmark for the prevalence of ARID1A loss‐of‐function mutations in endometriosis‐associated ovarian cancers and brings clarity to the prognostic significance. © 2021 The Pathological Society of Great Britain and Ireland.

Background: COVID-19 vaccines are safe and effective at preventing severe disease. Among the rare complications that may compromise vaccine acceptance are allergic reactions. This study aimed at comparing the incidence and potential triggers of the most commonly reported allergic reactions related to licensed COVID-19 vaccines in Europe and the United States (US) based on data of the two of the world’s largest vaccine adverse event reporting systems, EudraVigilance and VAERS. Methods: Data pertaining to allergic reactions post COVID-19 vaccination reported from week 52/2020 to week 39/2021 were collected from EudraVigilance and VAERS databases and analyzed. Incidence rates were calculated using the corresponding administered vaccine doses as denominators for all licensed vaccines and both platform types (mRNA or vectored). The composition of the novel mRNA and vectored vaccines was examined to identify potential allergic triggers. Results: Anaphylactic reactions and anaphylactic shock were the most common allergic reactions, predominantly reported by females, at estimated incidence rates of 9.91/million and 1.36/million vaccine doses, respectively. A 2- to 5-fold higher incidence of both allergic reactions was found in Europe compared to the US for both vaccine platforms. Most cases were benign. Fatalities were extremely rare and associated with vectored rather than mRNA vaccines. Conclusions: The precise mechanism(s) for allergic reactions after vaccination with COVID-19 vaccines are not fully known. Plausible explanations include exposure to components of the final pharmaceutical product and cross-reactivity to ingredients or unintentional impurities in the final formula. Additional research is warranted to further improve vaccine safety.

Abstract Cerebral small vessel disease is a leading cause of stroke and a major contributor to cognitive decline and dementia, but our understanding of specific genes underlying the cause of sporadic cerebral small vessel disease is limited. We report a genome-wide association study and a whole-exome association study on a composite extreme phenotype of cerebral small vessel disease derived from its most common MRI features: white matter hyperintensities and lacunes. Seventeen population-based cohorts of older persons with MRI measurements and genome-wide genotyping (n = 41 326), whole-exome sequencing (n = 15 965), or exome chip (n = 5249) data contributed 13 776 and 7079 extreme small vessel disease samples for the genome-wide association study and whole-exome association study, respectively. The genome-wide association study identified significant association of common variants in 11 loci with extreme small vessel disease, of which the chr12q24.11 locus was not previously reported to be associated with any MRI marker of cerebral small vessel disease. The whole-exome association study identified significant associations of extreme small vessel disease with common variants in the 5′ UTR region of EFEMP1 (chr2p16.1) and one probably damaging common missense variant in TRIM47 (chr17q25.1). Mendelian randomization supports the causal association of extensive small vessel disease severity with increased risk of stroke and Alzheimer’s disease. Combined evidence from summary-based Mendelian randomization studies and profiling of human loss-of-function allele carriers showed an inverse relation between TRIM47 expression in the brain and blood vessels and extensive small vessel disease severity. We observed significant enrichment of Trim47 in isolated brain vessel preparations compared to total brain fraction in mice, in line with the literature showing Trim47 enrichment in brain endothelial cells at single cell level. Functional evaluation of TRIM47 by small interfering RNAs-mediated knockdown in human brain endothelial cells showed increased endothelial permeability, an important hallmark of cerebral small vessel disease pathology. Overall, our comprehensive gene-mapping study and preliminary functional evaluation suggests a putative role of TRIM47 in the pathophysiology of cerebral small vessel disease, making it an important candidate for extensive in vivo explorations and future translational work.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Abstract Background Niemann-Pick Disease Type C (NPC) is an autosomal recessive rare disease characterised by progressive neurovisceral manifestations. The collection of on-going large-scale NPC clinical data may generate better understandings of the natural history of the disease. Here we report NPC patient data from the International Niemann-Pick Disease Registry (INPDR). Method The INPDR is a web-based, patient-led independent registry for the collection of prospective and retrospective clinical data from Niemann-Pick Disease patients. Baseline data from NPC patients enrolled into the INPDR from September 2014 to December 2019 was extracted to analyse the demographic, genetic and clinical features. Results A total of 203 NPC patients from six European countries were included in this study. The mean age (SD) at diagnosis was 11.2 years (14.2). Among enrolled patients, 168 had known neurological manifestations; 43 (24.2%) had early-infantile onset, 47 (26.4%) had late-infantile onset, 41 (23.0%) had juvenile onset, and 37 (20.8%) had adult onset. 10 (5.6%) patients had the neonatal rapidly fatal systemic form. Among the 97 patients with identified NPC1 variants, the most common variant was the c. 3182T > C variant responsible for the p.lle1061Thr protein change, reported in 35.1% (N = 34) of patients. The frequencies of hepatomegaly and neonatal jaundice were greatest in patients with early-infantile and late-infantile onset. Splenomegaly was the most commonly reported observation, including 80% of adult-onset patients. The most commonly reported neurological manifestations were cognitive impairment (78.5%), dysarthria (75.9%), ataxia (75.9%), vertical supranuclear gaze palsy (VSGP) (70.9%), dysphagia (69.6%). A 6-domain composite disability scale was used to calculate the overall disability score according to neurological-onset. Across all with neurological onset, the majority of patients showed moderate to severe impairments in all domains, excluding ‘swallowing’ and ‘seizure’. The age at diagnosis and death increased with increased age of neurological symptom onset. Miglustat use was recorded in 62.43% of patients and the most common symptomatic therapies used by patients were antiepileptics (32.94%), antidepressants (11.76%) and antacids (9.41%). Conclusion The proportion of participants at each age of neurological onset was relatively consistent across the cohort. Neurological manifestations, such as ataxia, dysphagia, and dysarthria, were frequently observed across all age categories.