Editorial: Next generation in vitro models to study chronic pulmonary diseases
Notice bibliographique
Résumé
Chronic pulmonary diseases such as Chronic Obstructive Pulmonary Disease (COPD), Tuberculosis (TB) and Idiopathic Pulmonary Fibrosis (IPF) impact millions of people and are leading causes of death worldwide [1][2][3] . Hence, a vast amount of research effort has been made to find early detection and curative therapies for these diseases. Researchers utilize several novel in vitro and ex vivo models to investigate the underlying mechanisms behind respiratory diseases. This is intended to help create targeted treatments that can aid in assessing and understanding novel disease mechanisms to subsequently improve the prognosis and quality of life of patients living with chronic pulmonary illnesses.The use of bioinformatics and surgical models to improve diagnostics in respiratory diseases have the potential to impact clinical outcomes. In this Research-topic, studies from Oloko-oba et al. and Gupta et al., assessed computer aided diagnostic (CAD) systems related to TB diagnosis from the common and sensitive chest X-Ray (CXR), that use deep learning techniques as well as the use of a cellulose matrix absorptive probe for bronchial epithelial lining fluid (bELF) in the airways respectively. From the systematic review of Oloko-oba et al. it was found that although most studies were developmental instead of being used in the clinic, the use of public and training datasets presented great potential for the use of CAD systems to improve TB diagnosis 7 . In line with lung disease clinical diagnostics, Gupta et al., also discovered that the newly established bELF probes maintained their integrity with no residual fibers in vivo and obtained samples rich in proteins and with higher levels of inflammatory cytokines compared to the samples obtained from bronchial wash fluid 8 . Ultimately, this high-precision probe is a novel technique for analyzing biomarkers in a consistent and accurate manner that will aid in early detection of lung disease 8 .In addition to diagnostics, the use of air liquid interface (ALI) cultured cells, has been used to assess different factors in disease severity that are necessary to advance therapeutic treatments. In this Research-topic, Ito et al., used ALIs to show that age was a significant factor affecting respiratory syncytial virus (RSV) infection, with increased viral load and viral genome copies, lower viral clearance, higher inflammation, increased cell damage, mucin production and cellular senescence in ALIs, of older people (>65 years) compared to younger individuals ≤60 years 9 .Additionally, Kasper and colleagues compared an ALI model to a submerged culture model 10 , to show that SARS-CoV-2 entry genes such as angiotensin converting enzyme 2 (ACE2), transmembrane serine protease 2 (TMPRSS2), cathepsin L (CTSL) and tyrosine protein kinase receptor UFO (AXL) in human primary small airway epithelial cells (SAEC) or bronchial epithelial cells (HBEC) are affected more by culture conditions than individual donor conditions.Lung organoids is another in vitro model that is used to assess cell mechanisms and their implications in pulmonary diseases [11][12][13] . Here, Wisman et al., developed organoids of MRC-5 and unfractionated lung cell suspensions or isolated EpCAM+ distal lung tissue pulmonary epithelial cells from individuals with/ without IPF 11 . Organoids from IPF-derived cells were larger compared to isolated EpCAM + cell-organoids suggesting intrinsic progenitor dysfunction. Unfractionated cell suspensions from IPF-derived lungs also resulted in a higher number of organoids, suggesting a dysregulated communication between epithelial and stroma cells in IPF which may lead to distal lung alveolar impairment 11 .Another important model is the precision cut lung slice (PCLS) model where thin slices of lung tissue are cultured in vitro for studies into disease mechanisms 14,15 . In this Research-Topic, Cervantes et al., established PCLS from donors without a history of disease and exposed them to particulate matter from Afghanistan (PMa) or particulate matter from California as the control (PMc) to investigate the mechanisms related to unique military deployment airway symptoms 16 .Interestingly, PCLS was used to show that PMa increased airway hyperresponsiveness (AHR), but PMc had no effect 16 . Additionally, PMa co-stimulated with IL-13 resulted in significantly amplified AHR compared to PMc co-stimulations 16 .Another important mechanism underlying respiratory disease pathogenesis is the disruption of the epithelial barrier integrity 18 . Hsieh & Yang et al., used the Electric Cell-Substrate Impedance Sensing (ECIS) system to assess the effect of different ECM substrates on the barrier integrity and attachment of basal airway epithelial cells 19 . It was shown that airway epithelial cells attached faster on Fibronectin, collagen I and collagen III compared to collagen IV and laminin. Further, fibronectin and collagen-I enabled the fastest epithelial barrier formation, compared to the other ECM proteins. This study demonstrated a potential protective role of these ECM proteins in pathological lung conditions 19 .Respiratory cancers are a prominent source of cancer incidence and mortality, therefore investigations into their mechanisms of drug resistance are essential to identify novel treatment targets 20,21 . Tuffour et al. used CRISPER-Cas-9 gene editing to knockout the CASD1 and SIAE genes 22 which are an important part of the breast cancer resistance protein (BCRP) a main ATPbinding cassette (ABC) transporter protein involved in multidrug resistant (MDR) pathways 22 .Here, using CRISPR-gene editing, and drug sensitivity analysis, it was shown that deacetylated Sias are utilized by cancer cells to overexpress BCRP as a pathway of MDR, which can be used to further advance the effectiveness of chemotherapies.Ex vivo models have also been utilized to study the biological methods of disease and potentially identify mechanisms for therapeutics due to their ability to mimic the in vivo physiology. Ievlev et al., created a ferret tracheal model for injury and cell engraftment using tracheal explants and found a semblance to surface airway epithelium (SAE) and submucosal glands (SMGs) 23 . Consistent results in line with published data on in vivo injury systems were found after injury-experiments.A 3D-printed culture chamber that allows imaging of ferret tissue explants was set-up that aided ferret cell ALI establishment.In this Research-topic, various studies utilised a breadth of tools including CAD modeling, pulmonary sampling devices, ALIs, PCLS, ECIS, CRISPR and ex-vivo-systems to study different mechanisms of pulmonary diseases as well as diagnostics and to perform drug studies. These prove the utility and adaptability of these systems for future studies and the advancement of the pulmonary field.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,004 | 0,001 |
| Intégrité de la recherche | 0,014 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».