Traditional assembly process modeling methods often exhibit limited adaptability to changes in the production environment, leading to inflexibility when confronted with material shortages, equipment failures, and other production disturbances. In this paper, we introduce a novel adaptive assembly process modeling approach specifically tailored for aircraft manufacturing. Our method differentiates between product-specific assembly constraints and optimal assembly sequences derived from process expertise. This distinction enables the dynamic determination of subsequent assembly stages during actual production while accounting for production disruptions, such as material shortages and equipment malfunctions. By ensuring that product-specific assembly constraints are satisfied, the proposed method permits a controlled degradation of optimal assembly sequences to accommodate fluctuations arising from production disturbances. The efficacy and adaptability of our approach are demonstrated through its successful implementation in real-world aircraft manufacturing scenarios, yielding enhanced flexibility and resilience of the assembly process in the face of unpredictable production conditions.

The lack of a robust in vitro model of the human respiratory epithelium hinders the understanding of the biology and pathology of the respiratory system. We describe a defined protocol to derive human lung organoids from adult stem cells in the lung tissue and induce proximal differentiation to generate mature airway organoids. The lung organoids are then consecutively expanded for over 1 year with high stability, while the differentiated airway organoids are used to morphologically and functionally simulate human airway epithelium to a near-physiological level. Thus, we establish a robust organoid model of the human airway epithelium. The long-term expansion of lung organoids and differentiated airway organoids generates a stable and renewable source, enabling scientists to reconstruct and expand the human airway epithelial cells in culture dishes. The human lung organoid system provides a unique and physiologically active in vitro model for various applications, including studying virus-host interaction, drug testing, and disease modeling.

The airways and alveoli of the human respiratory tract are lined by two distinct types of epithelium, which are the primary targets of respiratory viruses. We previously established long-term expanding human lung epithelial organoids from lung tissues and developed a 'proximal' differentiation protocol to generate mucociliary airway organoids. However, a respiratory organoid system with bipotential of the airway and alveolar differentiation remains elusive. Here we defined a 'distal' differentiation approach to generate alveolar organoids from the same source for the derivation of airway organoids. The alveolar organoids consisting of type I and type II alveolar epithelial cells (AT1 and AT2, respectively) functionally simulate the alveolar epithelium. AT2 cells maintained in lung organoids serve as progenitor cells from which alveolar organoids derive. Moreover, alveolar organoids sustain a productive SARS-CoV-2 infection, albeit a lower replicative fitness was observed compared to that in airway organoids. We further optimized 2-dimensional (2D) airway organoids. Upon differentiation under a slightly acidic pH, the 2D airway organoids exhibit enhanced viral replication, representing an optimal in vitro correlate of respiratory epithelium for modeling the high infectivity of SARS-CoV-2. Notably, the higher infectivity and replicative fitness of the Omicron variant than an ancestral strain were accurately recapitulated in these optimized airway organoids. In conclusion, we have established a bipotential organoid culture system able to reproducibly expand the entire human respiratory epithelium in vitro for modeling respiratory diseases, including COVID-19.

An in vitro model of the nasal epithelium is imperative for understanding cell biology and virus-host interaction in the human upper respiratory tract. Here we report an organoid culture system of the nasal epithelium.

Background: The global mortality rate for coronavirus disease 2019 ( COVID-19) is 3.68%, but the mortality rate for critically ill patients is as high as 50%. Therefore, the exploration of prognostic predictors for patients with COVID-19 is vital for prompt clinical intervention. Our study aims to explore the predictive value of hematological parameters in the prognosis of patients with severe COVID-19. Methods: Ninety-eight patients who were diagnosed with COVID-19 at Jingzhou Central Hospital and Central Hospital of Wuhan, Hubei Province, were included in this study. Results: The median age of the patients was 59 [28-80] years; the median age of patients with a good prognosis was 56 [28-79] years, and the median age of patients with a poor outcome was 67 [35-80] years. The patients in the poor outcome group were older than the patients in the good outcome group (P<0.05). The comparison of hematological parameters showed that lymphocyte count (Lym#), red blood cells (RBCs), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) were significantly lower in the poor outcome group than in the good outcome group (P<0.05). Further, the red cell volume distribution width-CV (RDW-CV) and red cell volume distribution width-SD (RDW-SD) were significantly higher in the poor outcome group than in the good outcome group (P<0.0001). Receiver operating characteristic (ROC) curves showed RDW-SD, with an area under the ROC curve (AUC) of 0.870 [95% confidence interval (CI) 0.796-0.943], was the most significant single parameter for predicting the prognosis of severe patients. When the cut-off value was 42.15, the sensitivity and specificity of RDW-SD for predicting the prognosis of severe patients were 73.1% and 80.2%, respectively. Reticulocyte (RET) channel results showed the RET level was significantly higher in critical patients than in moderate patients and severe patients (P<0.05), which may be one cause of the elevated RDW in patients with a poor outcome. Conclusions: In this study, the hematological parameters of COVID-19 patients were statistically analyzed. RDW was found to be a prognostic predictor for patients with severe COVID-19, and the increase in RET may contribute to elevated RDW.

BACKGROUND:The third fatal coronavirus is the novel coronavirus (SARS-CoV-2) that causes novel coronavirus pneumonia (COVID-19) which first broke out in December 2019. Patients will develop rapidly if there is no any intervention, so the risk identification of severe patients is critical. The aim of this study was to investigate the characteristics and rules of hematology changes in patients with COVID-19, and to explore the possibility differentiating moderate and severe patients using conventional hematology parameters or combined parameters.METHODS:The clinical data of 45 moderate and severe type patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Jingzhou Central Hospital from January 23 to February 13, 2020 were collected. The epidemiological indexes, clinical symptoms, and laboratory test results of the patients were retrospectively analyzed. Those parameters with significant differences between moderate and severe cases were analyzed, and the combination parameters with the best diagnostic performance were selected using the linear discriminant analysis (LDA) method.RESULTS:Of the 45 patients with the novel 2019 corona virus (COVID-19) (35 moderate and 10 severe cases), 23 were male and 22 were female, with ages ranging from 16 to 62 years. The most common clinical symptoms were fever (89%) and dry cough (60%). As the disease progressed, white blood cell count (WBC), neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), red blood cell distribution width-coefficient of variation (RDW-CV), and red cell volume distribution width-standard deviation (RDW-SD) parameters in the severe group were significantly higher than those in the moderate group (P<0.05); meanwhile, lymphocyte count (Lym#), eosinophil count (Eos#), high fluorescent cell percentage (HFC%), red blood cell count (RBC), hemoglobin (HGB), and hematocrit (HCT) parameters in the severe group were significantly lower than those in the moderate group (P<0.05). For NLR parameter, it's area under the curve (AUC), cutoff, sensitivity and specificity were 0.890, 13.39, 83.3% and 82.4% respectively; meanwhile, for PLR parameter, it's AUC, cutoff, sensitivity and specificity were 0.842, 267.03, 83.3% and 74.0% respectively. The combined parameters of NLR and RDW-SD had the best diagnostic efficiency (AUC =0.938), and when the cutoff value was 1.046, the sensitivity and the specificity were 90.0% and 84.7% respectively, followed by the combined parameter NLR&RDW-CV (AUC =0.923). When the cut-off value was 0.62, the sensitivity and the specificity for distinguishing severe type from moderate cases of COVID-19 were 90.0% and 82.4% respectively.CONCLUSIONS:The combined NLR and RDW-SD parameter is the best hematology index. It may help clinicians to predict the severity of COVID-19 patients and can be used as a useful indicator to help prevent and control the epidemic.

Objectives Investigate the characteristics and rules of hematology changes in patients with COVID-19, and explore the possibility to identify moderate and severe patients using conventional hematology parameters or combined parameters. Methods The clinical data of 45 moderate and severe type patients with SARS-CoV-2 infections in Jingzhou Central Hospital from January 23 to February 13, 2020 were collected. The epidemiological indexes, clinical symptoms and laboratory test results of the patients were retrospectively analyzed. Those parameters with significant differences between the two groups were analyzed, and the combination parameters with best diagnostic performance were selected using the LDA method. Results Of the 45 patients with COVID-19 (35 moderate and 10 severe cases), 23 were male and 22 female, aged 16-62 years. The most common clinical symptoms were fever (89%) and dry cough (60%). As the disease progressed, WBC, Neu#, NLR, PLR, RDW-CV and RDW-SD parameters in the severe group were significantly higher than that in the moderate group ( P <0.05); meanwhile, Lym#, Eos#, HFC%, RBC, HGB and HCT parameters in the severe group were significantly lower than that in the moderate group ( P <0.05). For NLR, the AUC, the best cut-off value, the sensitivity and the specificity were 0.890, 13.39, 83.3% and 82.4% respectively, and for PLR, the AUC, the best cut-off, the sensitivity and the specificity were 0.842, 267.03, 83.3% and 74.0% respectively. The combined parameter NLR&RDW-SD had the best diagnostic efficiency (AUC was 0.938) and when the cut-off value was 1.046, the sensitivity and the specificity were 90.0% and 84.7% respectively, followed by the fitting parameter NLR&RDW-CV (AUC = 0.923). When the cut-off value was 0.62, the sensitivity and the specificity for distinguishing severe type from moderate cases of COVID-19 were 90.0% and 82.4% respectively. Conclusions The combined parameter NLR&RDW-SD is the best hematology index and can help clinicians to predict the severity of COVID-19 patients, and it can be used as a useful indicator to help prevent and control the epidemic.