Search

Humoral immunity is a major waypoint towards chronic allograft dysfunction in lung transplantation (LT) recipients. Though allo-immunization and antibody-mediated rejection (AMR) are well-known entities, some diagnostic gaps need to be addressed. Morphological analysis could be enhanced by digital pathology and artificial intelligence-based companion tools. Graft transcriptomics can help to identify graft failure phenotypes or endotypes. Donor-derived cell free DNA is being evaluated for graft-loss risk stratification and tailored surveillance. Preventative therapies should be tailored according to risk. The donor pool can be enlarged for candidates with HLA sensitization, with strategies combining plasma exchange, intravenous immunoglobulin and immune cell depletion, or with emerging or innovative therapies such as imlifidase or immunoadsorption. In cases of insufficient pre-transplant desensitization, the effects of antibodies on the allograft can be prevented by targeting the complement cascade, although evidence for this strategy in LT is limited. In LT recipients with a humoral response, strategies are combined, including depletion of immune cells (plasmapheresis or immunoadsorption), inhibition of immune pathways, or modulation of the inflammatory cascade, which can be achieved with photopheresis. Altogether, these innovative techniques offer promising perspectives for LT recipients and shape the 21st century’s armamentarium against AMR.

IntroductionPrior studies assessing outcomes of lung transplants from cigarette-smoking donors found mixed results. Oscillometry, a non-invasive test of respiratory impedance, detects changes in lung function of smokers prior to diagnosis of COPD, and identifies spirometrically silent episodes of rejection post-transplant. We hypothesise that oscillometry could identify abnormalities in recipients of smoking donor lungs and discriminate from non-smoking donors.MethodsThis prospective single-center cohort study analysed 233 double-lung recipients. Oscillometry was performed alongside routine conventional pulmonary function tests (PFT) post-transplant. Multivariable regression models were constructed to compare oscillometry and conventional PFT parameters between recipients of lungs from smoking vs non-smoking donors.ResultsThe analysis included 109 patients who received lungs from non-smokers and 124 from smokers. Multivariable analysis identified significant differences between recipients of smoking and non-smoking lungs in the oscillometric measurements R5-19, X5, AX, R5z and X5z, but no differences in %predicted FEV1, FEV1/FVC, %predicted TLC or %predicted DLCO. An analysis of the smoking group also demonstrated associations between increasing smoke exposure, quantified in pack years, and all the oscillometry parameters, but not the conventional PFT parameters.ConclusionAn interaction was identified between donor-recipient sex match and the effect of smoking. The association between donor smoking and oscillometry outcomes was significant predominantly in the female donor/female recipient group.

BackgroundChronic lung allograft dysfunction (CLAD) is the major cause of death post-lung transplantation, with acute cellular rejection (ACR) being the biggest contributing risk factor. Although patients are routinely monitored with spirometry, FEV1 is stable or improving in most ACR episodes. In contrast, oscillometry is highly sensitive to respiratory mechanics and shown to track graft injury associated with ACR and its improvement following treatment. We hypothesize that intra-subject variability in oscillometry measurements correlates with ACR and risk of CLAD.MethodsOf 289 bilateral lung recipients enrolled for oscillometry prior to laboratory-based spirometry between December 2017 and March 2020, 230 had ≥ 3 months and 175 had ≥ 6 months of follow-up. While 37 patients developed CLAD, only 29 had oscillometry at time of CLAD onset and were included for analysis. These 29 CLAD patients were time-matched with 129 CLAD-free recipients. We performed multivariable regression to investigate the associations between variance in spirometry/oscillometry and the A-score, a cumulative index of ACR, as our predictor of primary interest. Conditional logistic regression models were built to investigate associations with CLAD.ResultsMultivariable regression showed that the A-score was positively associated with the variance in oscillometry measurements. Conditional logistic regression models revealed that higher variance in the oscillometry metrics of ventilatory inhomogeneity, X5, AX, and R5-19, was independently associated with increased risk of CLAD (p < 0.05); no association was found for variance in %predicted FEV1.ConclusionOscillometry tracks graft injury and recovery post-transplant. Monitoring with oscillometry could facilitate earlier identification of graft injury, prompting investigation to identify treatable causes and decrease the risk of CLAD.

The article is devoted to the study of the peculiarities of the transformation of the media industry due to the development and introduction of information and communication technologies. The main directions of the development of printing activity as one of the components of the media industry using modern digital technologies are considered. A qualitative analysis of the impact of digital technologies on transformational processes in the media industry has been carried out, the possibilities and potential consequences of changes for the printing industry have been assessed. In the course of the study, the main stages of digital transformation were identified, trends in changes in printing works/ services during digitalization were revealed. It is noted that the transformation of printing activity provides modern printing houses with new channels of communication with counterparties and ensures the choice of the most effective business models. Digitalization of printing activity leads to changes in the business processes of the organization, the structure of demand for printing works (services) and production technology. The authors made an attempt to assess the degree of digitalization of the media sphere (using the example of printing organizations), to identify its main directions and changes. The study was based on a review of scientific literature and the results of practical activities of printing organizations. The stages of planning and implementation of the processes of digital transformation of printing and packaging industries are determined. The digital maturity level of printing organizations was assessed using modern techniques. An attempt is made to predict further options for the use of digital technologies in printing activities. The main factors constraining the digitalization of the printing industry are formulated. The further prospects of digital transformation in the printing industry are determined.

Rationale Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone.Methods We built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow25–75), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree.Results Accuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p

Background: Chronic lung allograft dysfunction (CLAD) is the major cause of death beyond 2 years after lung transplantation and develops in 50% of all patients by 5 years post-transplant. CLAD is diagnosed on the basis of a sustained drop of 20% for at least 3 months in the forced expiratory volume (FEV1), compared to the best baseline value achieved post-transplant. CLAD presents as two main phenotypes: bronchiolitis obliterans syndrome (BOS) is more common and has better prognosis than restrictive allograft syndrome (RAS). Respiratory oscillometry is a different modality of lung function testing that is highly sensitive to lung mechanics. The current study investigated whether spectral and intrabreath oscillometry can differentiate between CLAD-free, BOS- and RAS-CLAD at CLAD onset, i.e., at the time of the initial 20% drop in the FEV1.Methods: A retrospective, cross-sectional analysis of 263 double lung transplant recipients who underwent paired testing with oscillometry and spirometry at the Toronto General Pulmonary Function Laboratory from 2017 to 2022 was conducted. All pulmonary function testing and CLAD diagnostics were performed following international guidelines. Statistical analysis was conducted using multiple comparisons.Findings: The RAS (n = 6) spectral oscillometry pattern differs from CLAD-free (n = 225) by right-ward shift of reactance curve similar to idiopathic pulmonary fibrosis whereas BOS (n = 32) has a pattern similar to obstructive lung disease. Significant differences were found in most spectral and intrabreath parameters between BOS, RAS, and time-matched CLAD-free patients. Post-hoc analysis revealed these differences were primarily driven by BOS instead of RAS. While no differences were found between CLAD-free and RAS patients with regards to spectral oscillometry, the intrabreath metric of reactance at end-inspiration (XeI) was significantly different (p < 0.05). BOS and RAS were differentiated by spectral oscillometry measure R5, and intrabreath resistance at end expiration, ReE (p < 0.05 for both).Conclusion: Both spectral and intrabreath oscillometry can differentiate BOS-CLAD from CLAD-free states while intrabreath oscillometry, specifically XeI, can uniquely distinguish RAS-CLAD from CLAD-free. Spectral and intrabreath oscillometry offer complementary information regarding lung mechanics in CLAD patients to help distinguish the two phenotypes and could prove useful in prognostication.