Citations

AbstractObjectivesThe assessment of physical function is fundamental in the management of patients with idiopathic inflammatory myopathies (IIMs). We aimed to investigate the physical function of patients with IIMs compared with those with non-IIM autoimmune rheumatic diseases (AIRDs) utilizing Patient-Reported Outcome Measurement Information System (PROMIS) Physical Function (PF) data obtained in the COVAD study, an international self-reported e-survey assessing the safety of COVID-19 vaccines in AIRDs.MethodsDemographics, AIRD diagnosis, disease activity, and PROMIS PF short form-10a data were extracted from the COVAD database. PROMIS PF-10a scores were compared between disease categories and stratified by disease activity. Factors affecting PROMIS PF-10a scores other than disease activity were identified by multivariable regression analysis in patients with inactive disease.ResultsA total of 1057 IIM patients, 3635 non-IIM AIRD patients and 3981 healthy controls (HCs) responded to the COVAD e-survey from April to August 2021. Using a binomial regression model, the predicted mean of PROMIS PF-10a scores was significantly lower in IIM patients compared with non-IIM AIRD patients or HCs [36.3 (95% CI 35.5, 37.1) vs 41.3 (95% CI 40.2, 42.5) vs 46.2 (95% CI 45.8, 46.6), P < 0.001], irrespective of disease activity. The independent factors for lower PROMIS PF-10a scores in patients with inactive disease were older age, female, longer disease duration, and a diagnosis of inclusion body myositis or polymyositis.ConclusionPhysical function is significantly impaired in IIMs compared with non-IIM AIRDs or HCs, even in patients with inactive disease. Our study highlights a critical need for better strategies to minimize functional disability in patients with IIMs.

Abstract Extracellular vesicles (EVs) are emerging as powerful players in cell‐to‐cell communication both in healthy and diseased brain. In Parkinson's disease (PD)—characterized by selective dopaminergic neuron death in ventral midbrain (VMB) and degeneration of their terminals in striatum (STR)—astrocytes exert dual harmful/protective functions, with mechanisms not fully elucidated. Here, this study shows that astrocytes from the VMB‐, STR‐, and VMB/STR‐depleted brains release a population of small EVs in a region‐specific manner. Interestingly, VMB‐astrocytes secreted the highest rate of EVs, which is further exclusively increased in response to CCL3, a chemokine that promotes robust dopaminergic neuroprotection in different PD models. The neuroprotective potential of nigrostriatal astrocyte‐EVs is investigated in differentiated versus undifferentiated SH‐SY5Y cells exposed to oxidative stress and mitochondrial toxicity. EVs from both VMB‐ and STR‐astrocytes counteract H 2 O 2 ‐induced caspase‐3 activation specifically in differentiated cells, with EVs from CCL3‐treated astrocytes showing a higher protective effect. High resolution respirometry further reveals that nigrostriatal astrocyte‐EVs rescue neuronal mitochondrial complex I function impaired by the neurotoxin MPP + . Notably, only EVs from VMB‐astrocyte fully restore ATP production, again specifically in differentiated SH‐SY5Y. These results highlight a regional diversity in the nigrostriatal system for the secretion and activities of astrocyte‐EVs, with neuroprotective implications for PD.

AbstractBackgroundStudies suggest that immune checkpoint inhibitors may represent a promising strategy for boosting immune responses and improving the antitumor activity of standard therapies in patients with relapsed/refractory hematologic malignancies.AimsPhase 1/2 FUSION NHL 001 was designed to determine the safety and efficacy of durvalumab, an anti‐programmed death ligand 1 (PD‐L1) antibody, combined with standard‐of‐care therapies for lymphoma or chronic lymphocytic leukemia (CLL).Methods and ResultsThe primary endpoints were to determine the recommended phase 2 dose of the drugs used in combination with durvalumab (durvalumab was administered at the previously recommended dose of 1500 mg every 4 weeks) and to assess safety and tolerability. Patients were enrolled into one of four arms: durvalumab monotherapy (Arm D) or durvalumab in combination with lenalidomide ± rituximab (Arm A), ibrutinib (Arm B), or rituximab ± bendamustine (Arm C). A total of 106 patients with relapsed/refractory lymphoma were enrolled. All but two patients experienced at least one treatment‐emergent adverse event (TEAE); those not experiencing a TEAE were in Arm C (diffuse large B‐cell lymphoma [DLBCL]) and Arm D (DLBCL during the durvalumab monotherapy treatment period). No new safety signals were identified, and TEAEs were consistent with the respective safety profiles for each study treatment. Across the study, patients with follicular lymphoma (FL; n = 23) had an overall response rate (ORR) of 59%; ORR among DLBCL patients (n = 37) was 18%. Exploratory biomarker analysis showed that response to durvalumab monotherapy or combination therapy was associated with higher interferon‐γ signature scores in patients with FL (p = .02).ConclusionDurvalumab as monotherapy or in combination is tolerable but requires close monitoring. The high rate of TEAEs during this study may reflect on the difficulty in combining durvalumab with full doses of other agents. Durvalumab alone or in combination appeared to add limited benefit to therapy.

Abstract The classification of myeloid neoplasms and acute leukemias was last updated in 2016 within a collaboration between the World Health Organization (WHO), the Society for Hematopathology, and the European Association for Haematopathology. This collaboration was primarily based on input from a clinical advisory committees (CACs) composed of pathologists, hematologists, oncologists, geneticists, and bioinformaticians from around the world. The recent advances in our understanding of the biology of hematologic malignancies, the experience with the use of the 2016 WHO classification in clinical practice, and the results of clinical trials have indicated the need for further revising and updating the classification. As a continuation of this CAC-based process, the authors, a group with expertise in the clinical, pathologic, and genetic aspects of these disorders, developed the International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias. Using a multiparameter approach, the main objective of the consensus process was the definition of real disease entities, including the introduction of new entities and refined criteria for existing diagnostic categories, based on accumulated data. The ICC is aimed at facilitating diagnosis and prognostication of these neoplasms, improving treatment of affected patients, and allowing the design of innovative clinical trials.

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Intracardiac masses are unusual findings in infants, and most of them are benign. Nevertheless, they may be associated with a significant degree of hemodynamic instability and/or arrhythmias. Malignant tumors of the heart rarely occur in children. Rhabdoid tumors are aggressive tumors with a dismal prognosis even when diagnosed early. Although rhabdomyomas are common cardiac tumors in infants, they are mostly benign. The most common sites of involvement are the kidneys and central nervous system, but soft tissues, lungs, and ovaries may also be affected. The diagnosis can be challenging, particularly in sites where they do not usually occur. In the present paper, we report the case of a 2-year-old boy diagnosed with cardiac rhabdoid tumor highlighting the importance of molecular studies and recent genetic discoveries with the purpose of improving the management of such cases. The aim of this educational case report and literature review is to raise awareness of cardiac masses in children and to point out diagnostic hints toward a cardiac tumor on various imaging modalities. Given the rarity of all tumors involving the heart and the lack of symptom specificity, a high degree of suspicion is needed to arrive at the correct diagnosis.

The advent and refinement of complex endovascular techniques in the last two decades has revolutionized the field of vascular surgery. This has allowed an effective minimally invasive treatment of extensive disease involving the pararenal and the thoracoabdominal aorta. Fenestrated-branched EVAR (F/BEVAR) now represents a feasible technical solution to address these complex diseases, moving the proximal sealing zone above the renal-visceral vessels take-off and preserving their patency. The aim of this paper was to provide a narrative review on the peri-operative management of patients undergoing F/BEVAR procedures for juxtarenal abdominal aortic aneurysm (JAAA), pararenal abdominal aortic aneurysm (PRAA) or thoracoabdominal aortic aneurism (TAAA). It will focus on how to prevent, diagnose, and manage the complications ensuing from these complex interventions, in order to improve clinical outcomes. Indeed, F/BEVAR remains a technically, physiologically, and mentally demanding procedure. Intraoperative adverse events often require prolonged or additional procedures and complications may significantly impact a patient’s quality of life, health status, and overall cost of care. The presence of standardized preoperative, perioperative, and postoperative pathways of care, together with surgeons and teams with significant experience in aortic surgery, should be considered as crucial points to improve clinical outcomes. Aggressive prevention, prompt diagnosis and timely rescue of any major adverse events following the procedure remain paramount clinical needs.

This study (ReCORD-FL) sought to construct a historical control cohort to augment single-arm trials in relapsed/refractory follicular lymphoma (r/r FL). A retrospective study in 10 centers across North America and Europe was conducted. Adults with grade 1–3A FL were required to be r/r after ≥2 therapy lines including an anti-CD20 and an alkylator. After first becoming r/r, patients were required to initiate ≥1 additional therapy line, which defined the study index date. Endpoints were observed from start of each therapy line (including index line) until death, last follow-up, or December 31, 2020. Endpoints were complete response (CR) rate, overall response rate (ORR), time to next treatment or death (TNT-D), event-free survival (EFS), and overall survival (OS). One hundred eighty-seven patients were identified. Most patients’ (80.2%) index therapy occurred in third line (3L) (range, 3L–6L). Median follow-up from FL diagnosis was 9 years (range, 1–21 years). CR and ORR to the index therapy were 39.0% and 70.6%, respectively. Median (95% confidence interval) EFS from index was 14.6 (11.0-18.0) months; median OS from index was 10.6 years. Outcomes worsened across successive treatment lines and for patients who were double refractory (r/r to both an anti-CD20 monoclonal antibody and an alkylator) or POD24 (progressed ≤24 months after front-line anti-CD20) at index. Findings demonstrate the unmet need of FL patients with multiply relapsed, double refractory, or POD24 disease. Based on robustness of the historical data collected and comparability with a previous study (SCHOLAR-5), ReCORD-FL presents a valuable source of control data for comparative studies in r/r FL.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Background: Histological changes induced by gluten in the duodenal mucosa of patients with non-coeliac gluten sensitivity (NCGS) are poorly defined. Objectives: To evaluate the structural and inflammatory features of NCGS compared to controls and coeliac disease (CeD) with milder enteropathy (Marsh I-II). Methods: Well-oriented biopsies of 262 control cases with normal gastroscopy and histologic findings, 261 CeD, and 175 NCGS biopsies from 9 contributing countries were examined. Villus height (VH, in μm), crypt depth (CrD, in μm), villus-to-crypt ratios (VCR), IELs (intraepithelial lymphocytes/100 enterocytes), and other relevant histological, serologic, and demographic parameters were quantified. Results: The median VH in NCGS was significantly shorter (600, IQR: 400–705) than controls (900, IQR: 667–1112) (p < 0.001). NCGS patients with Marsh I-II had similar VH and VCR to CeD [465 µm (IQR: 390–620) vs. 427 µm (IQR: 348–569, p = 0·176)]. The VCR in NCGS with Marsh 0 was lower than controls (p < 0.001). The median IEL in NCGS with Marsh 0 was higher than controls (23.0 vs. 13.7, p < 0.001). To distinguish Marsh 0 NCGS from controls, an IEL cut-off of 14 showed 79% sensitivity and 55% specificity. IEL densities in Marsh I-II NCGS and CeD groups were similar. Conclusion: NCGS duodenal mucosa exhibits distinctive changes consistent with an intestinal response to luminal antigens, even at the Marsh 0 stage of villus architecture.