Chronic lung allograft dysfunction (CLAD) is a serious complication after lung transplantation (LuTx) and is associated with elevated proportions of neutrophils in bronchoalveolar lavage (BAL). Induced sputum is a less-invasive sampling method than BAL and assesses markers of inflammation on the surfaces of large central airways. We wanted to examine whether % neutrophil levels in induced sputum were elevated prior to CLAD diagnosis among LuTx recipients, and whether sputum markers of inflammation can be used as a tool for predicting the development of CLAD. Induced sputum samples were collected at 1, 3, 6, 12, and 24 months post-LuTx in 36 patients with a history of COPD or pulmonary fibrosis, and of these, 16 developed CLAD either during or after the sputum surveillance period. At 2 years, median (IQR) % neutrophils in induced sputum were significantly higher among patients with CLAD compared with those without CLAD [73 (52-80) % vs 59 (41-76) %, p = .01]. Interestingly, we found a significant increase in the rate of change in % neutrophils beginning at 90 days preceding the diagnosis of CLAD. This suggests using sputum neutrophil percentage as a surveillance modality for monitoring lung allograft function after LuTx.Serum amyloid A (SAA) is an acute-phase protein (APP) to which multiple immunological functions have been attributed. Regardless, the true biological role of SAA remains poorly understood. SAA is remarkably conserved in mammalian evolution, thereby suggesting an important biological function. Since its discovery in the 1970s, the majority of researchers have investigated SAA using recombinant forms made available through bacterial expression. Nevertheless, recent studies indicate that these recombinant forms of SAA are unreliable. Indeed, commercial SAA variants have been shown to be contaminated with bacterial products including lipopolysaccharides and lipoproteins. As such, biological activities and receptor usage (TLR2, TLR4) revealed through the use of commercial SAA variants may not reflect the inherent nature of this APP. Within this review, we discuss the biological effects of SAA that have been demonstrated through more solid experimental approaches. SAA takes part in the innate immune response via the recruitment of leucocytes and executes, through pathogen recognition, antimicrobial activity. Knockout animal models implicate SAA in a range of functions, such as regulation of T-cell-mediated responses and monopoiesis. Moreover, through its structural motifs, not only does SAA function as an extracellular matrix protein, but it also binds extracellular matrix proteins. Finally, we here also provide an overview of definite SAA receptor-mediated functions and highlight those that are yet to be validated. The role of FPR2 in SAA-mediated leucocyte recruitment has been confirmed; nevertheless, SAA has been linked to a range of other receptors including CD36, SR-BI/II, RAGE and P2RX7.Artificial intelligence holds great promise for improved health-care outcomes. But it also poses substantial new hazards, including algorithmic discrimination. For example, an algorithm used to identify candidates for beneficial "high risk care management" programs routinely failed to select racial minorities. Furthermore, some algorithms deliberately adjust for race in ways that divert resources away from minority patients. find more To illustrate, algorithms have underestimated African Americans' risks of kidney stones and death from heart failure. Algorithmic discrimination can violate Title VI of the Civil Rights Act and Section 1557 of the Affordable Care Act when it unjustifiably disadvantages underserved populations. This article urges that both legal and technical tools be deployed to promote AI fairness. Plaintiffs should be able to assert disparate impact claims in health-care litigation, and Congress should enact an Algorithmic Accountability Act. In addition, fairness should be a key element in designing, implementing, validating, and employing AI.As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package. Unsupervised clustering is a method used to identify heterogeneity among groups and homogeneity within a group of patients. Without a prespecified outcome entry, the resulting model deciphers patterns that may not be disclosed using traditional methods. This is the first time such clustering analysis is applied in identifying unique subgroups at high risk for periodontitis in National Health and Nutrition Examination Surveys (NHANES 2009 to 2014 data sets using >500 variables. Questionnaire, examination, and laboratory data (33 tables) for >1,000 variables were merged from 14,072 respondents who underwent clinical periodontal examination. Participants with≥6 teeth and available data for all selected categories were included (N=1,222). Data wrangling produced 519 variables. k-means/modes clustering (k=214) was deployed. The optimal k-value was determined through the elbow method, formula=∑ (x ) - ((∑ x )2 /n). The 5-cluster model showing the highest variability (63.08%) was selected. The 2012 C effectively identifies characteristics that are statistically significantly related to periodontitis status and hence detects subpopulations at high risk for periodontitis without costly clinical examinations.A global socio-bioethics is called upon to address the ethical challenges arising from the revolutionary gene editing technologies such as CRISPR-Cas9, which offers the capability to rewrite the human genome. The ethical inquiry Françoise Baylis has undertaken in the book Altered Inheritance CRISPR and the Ethics of Human Genome Editing (Harvard University Press, 2019) operates at individual, societal and global levels. Baylis has not only presented insights on how to practice "slow science" and achieve broad societal consensus through empowering the public, but she also shown what a global socio-bioethics approach can offer for the further development of bioethics.