The Arabidopsis Protein CONSERVED ONLY IN THE GREEN LINEAGE160 Promotes the Assembly of the Membranous Part of the Chloroplast ATP Synthase

The chloroplast F(1)F(o)-ATP synthase/ATPase (cpATPase) couples ATP synthesis to the light-driven electrochemical proton gradient. The cpATPase is a multiprotein complex and consists of a membrane-spanning protein channel (comprising subunit types a, b, b′, and c) and a peripheral domain (subunits α, β, γ, δ, and ε). We report the characterization of the Arabidopsis (Arabidopsis thaliana) CONSERVED ONLY IN THE GREEN LINEAGE160 (AtCGL160) protein (AtCGL160), conserved in green algae and plants. AtCGL160 is an integral thylakoid protein, and its carboxyl-terminal portion is distantly related to prokaryotic ATP SYNTHASE PROTEIN1 (Atp1/UncI) proteins that are thought to function in ATP synthase assembly. Plants without AtCGL160 display an increase in xanthophyll cycle activity and energy-dependent nonphotochemical quenching. These photosynthetic perturbations can be attributed to a severe reduction in cpATPase levels that result in increased acidification of the thylakoid lumen. AtCGL160 is not an integral cpATPase component but is specifically required for the efficient incorporation of the c-subunit into the cpATPase. AtCGL160, as well as a chimeric protein containing the amino-terminal part of AtCGL160 and Synechocystis sp. PCC6803 Atp1, physically interact with the c-subunit. We conclude that AtCGL160 and Atp1 facilitate the assembly of the membranous part of the cpATPase in their hosts, but loss of their functions provokes a unique compensatory response in each organism

Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction

Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural networks to reduce reliance on computational capacity. This reduction of computational capacity is of critical importance in time-limited or resource-constrained circumstances, such as the early stages of a pandemic. On a dataset of 49 patients, comprising over 20,000 images, we demonstrate that the use of existing methods as feature extractors results in the effective classification of COVID-19-related pneumonia severity while requiring only minutes of training time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity score scale and provides comparable per-patient region and global scores compared to expert annotated ground truths. These results demonstrate the capability for rapid deployment and use of such minimally-adapted methods for progress monitoring, patient stratification and management in clinical practice for COVID-19 patients, and potentially in other respiratory diseases.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Long-term salvage therapy with cyclosporin A in refractory idiopathic thrombocytopenic purpura

Treatment of severe, chronic idiopathic thrombocytopenic purpura (ITP) refractory to most usual therapies is a difficult challenge. Little information exists on the clinical use of cyclosporin A (CyA) in the treatment of ITP. This report describes long-term treatment with CyA (median, 40 months) and follow-up (median, 36.8 months) in 12 adult patients with resistant ITP. CyA used in relatively low doses (2.5-3 mg/kg of body weight per day) led to a clinical improvement in 10 patients (83.3%). Five had a complete response (41.1%), 4 a complete response to maintenance therapy (33.3%), and one a partial response (8.3%). Two patients had no response. Most patients with a response (60%) had a long-term remission (mean, 28.6 months) after discontinuation of CyA. One patient had a relapse of ITP 4 years after CyA therapy was stopped. Side effects were moderate and transient, even in patients dependent on continued CyA treatment. CyA seems to represent reasonable salvage treatment in severe, potentially life-threatening, refractory ITP

Degenerative Myelopathy in Hovawart Dogs: Molecular Characterization, Pathological Features and Accumulation of Mutant Superoxide Dismutase 1 Protein

Degenerative myelopathy (DM) is an adult-onset, progressive neurological disease affecting several breeds of dog. Homozygosity or compound heterozygosity for the canine superoxide dismutase 1 (SOD1) gene mutations, possibly modulated by the modifier SP110 locus, are associated with a high risk for DM. Although the pathophysiological mechanisms are largely unknown, a role for mutant SOD1 in causing neuronal degeneration has been postulated. Three Hovawart dogs, 9e12 years of age, developed slowly progressive incoordination and weakness of the pelvic limbs leading to non-ambulatory flaccid paraparesis and muscle atrophy. Neuropathological lesions comprised axonal degeneration and loss of ascending and descending spinal pathways, which were most severe in the mid- to caudal thoracic segments. Accumulation of mutant SOD1 protein in neurons and reactive astrocytes was demonstrated by immunolabelling with the 16G9 antibody against the mutant SOD1 protein (p.E40K amino acid substitution). All three dogs were homozygous for the c.118A allele, but none had the SP110 ‘risk’ haplotype, suggesting a weak association of SP110 with the onset of DM in this breed. Our data suggest that the Hovawart breed is predisposed to the SOD1:c.118G>A mutation, which is associated with the development of DM. Prevention of DM could be achieved with the help of strategies based on epidemiological and genetic testing

Compartmental tongue surgery for intermediate-advanced squamous cell carcinoma: A multicentric study

Background: A multicentric study was conducted on technical reproducibility of compartmental tongue surgery (CTS) in advanced tongue cancers (OTSCC) and comparison to standard wide margin surgery (SWMS). Methods: We studied 551 patients with OTSCC treated by CTS and 50 by SWMS. Oncological outcomes were analyzed. A propensity score was performed to compare survival endpoints for the two cohorts. Results: In the CTS group, survival and prognosis were significantly associated with positive lymph-nodes, extranodal extension, depth of invasion and involvement of the soft tissue connecting the tongue primary tumor to neck lymph nodes (T-N tract), independently from the center performing the surgery. SWMS versus CTS showed a HR Cause-Specific Survival (CSS) of 3.24 (95% CI: 1.71-6.11; p < 0.001); HR Loco-Regional Recurrence Free Survival (LRRFS) of 2.54 (95% CI: 1.47-4.40; p < 0.001); HR Overall Survival (OS) of 0.11 (95% CI: 0.01-0.77; p = 0.03). Conclusion: Performing the CTS could provide better CSS and LRRFS than SWMS regardless of the center performing the surgery, in advanced OTSSC

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

BACKGROUND No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. METHODS Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. RESULTS Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). CONCLUSIONS Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models

Flow-diverter treatment for renal artery aneurysms: One-year follow-up of a multicentric preliminary experience

PURPOSERenal artery aneurysms (RAAs) are rare in the general population, although the true incidence and natural history remain elusive. Conventional endovascular therapies such as coil embolization or covered stent graft may cause sidebranches occlusion, leading to organ infarction. Flow-diverters (FD) have been firstly designed to treat cerebrovascular aneurysms, but their use may be useful to treat complex RAAs presenting sidebraches arising from aneurysmal sac. To evaluate mid-term follow-up (FUP) safety and efficacy of FD during treatment of complex RAAs.METHODSBetween November 2019 and April 2020, 7 RAAs were identified in 7 patients (4 men, 3 women; age range 55-82 years; median 67 years) and treated by FD. Procedural details, complications, morbidity and mortality, aneurysm occlusion and segmental artery patency were retrospectively reviewed. Twelve months computed tomography angiography (CTA) FUP was evaluated for all cases.RESULTDeployment of FD was successful in all cases. One intraprocedural technical complication was encountered with one FD felt down into aneurism sac which requiring additional telescopic stenting. One case at 3 months CTA FUP presented same complication, requiring same rescue technique. At 12 months CTA FUP 5 cases of size shrinkage and 2 cases of stable size were documented. No rescue surgery or major intraprocedural or mid-term FUP complication was seen.CONCLUSIONComplex RAAs with two or more sidebranches can be safely treated by FD. FD efficacy for RAA needs a further validation at long term FUP by additional large prospective studies

Mitochondrial DNA Backgrounds Might Modulate Diabetes Complications Rather than T2DM as a Whole

Mitochondrial dysfunction has been implicated in rare and common forms of type 2 diabetes (T2DM). Additionally, rare mitochondrial DNA (mtDNA) mutations have been shown to be causal for T2DM pathogenesis. So far, many studies have investigated the possibility that mtDNA variation might affect the risk of T2DM, however, when found, haplogroup association has been rarely replicated, even in related populations, possibly due to an inadequate level of haplogroup resolution. Effects of mtDNA variation on diabetes complications have also been proposed. However, additional studies evaluating the mitochondrial role on both T2DM and related complications are badly needed. To test the hypothesis of a mitochondrial genome effect on diabetes and its complications, we genotyped the mtDNAs of 466 T2DM patients and 438 controls from a regional population of central Italy (Marche). Based on the most updated mtDNA phylogeny, all 904 samples were classified into 57 different mitochondrial sub-haplogroups, thus reaching an unprecedented level of resolution. We then evaluated whether the susceptibility of developing T2DM or its complications differed among the identified haplogroups, considering also the potential effects of phenotypical and clinical variables. MtDNA backgrounds, even when based on a refined haplogroup classification, do not appear to play a role in developing T2DM despite a possible protective effect for the common European haplogroup H1, which harbors the G3010A transition in the MTRNR2 gene. In contrast, our data indicate that different mitochondrial haplogroups are significantly associated with an increased risk of specific diabetes complications: H (the most frequent European haplogroup) with retinopathy, H3 with neuropathy, U3 with nephropathy, and V with renal failure

Quality assurance for automatically generated contours with additional deep learning

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours. Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions. Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time. Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior