Replicability takes on special meaning when researching phenomena that are embedded in space and time, including phenomena distributed on the surface and near surface of the Earth. Two principles, spatial dependence and spatial heterogeneity, are generally characteristic of such phenomena. Various practices have evolved in dealing with spatial heterogeneity, including the use of place-based models. We review the rapidly emerging applications of artificial intelligence to phenomena distributed in space and time and speculate on how the principle of spatial heterogeneity might be addressed. We introduce a concept of weak replicability and discuss possible approaches to its measurement.

The ability of melatonin treatment of aged animals to partially restore the pattern of gene expression characterizing the younger animal has been frequently reported. The current study examines the effect of melatonin upon age-related changes of some key proteins relevant to the aging process. Male B6C3F1 mice, aged 5.5 months and 23.4 months were used as a model for aging and half of each group received a diet supplemented with 40-ppm (w/w) melatonin for 9.3 weeks. Protein components of the globus pallidus were studied including glial fibrillary acidic protein (GFAP), NF-κB, protein disulfide isomerase (PDI), and Nissl staining. Some age-related changes were in an upward direction (GFAP and NF-κB), while others were depressed with age (PDI and intensity of Nissl staining). However, in either case, melatonin treatment of aged mice generally altered these parameters so that they came to more closely resemble the levels found in younger animals. The extent of this reversal to a more youthful profile, ranged from complete (for NF-κB) to very minor (for Nissl staining and PDI). Overall, these findings are in accord with prior data on the effect of melatonin on cortical gene expression and confirm the value of melatonin as a means of retarding events associated with senescence.

A pandemic of metabolic diseases, consisting of type 2 diabetes, nonalcoholic fatty liver disease, and obesity, has imposed critical challenges for societies worldwide, prompting investigation of underlying mechanisms and exploration of low-cost and effective treatment. In this report, we demonstrate that metabolic disorders in mice generated by feeding with a high-fat diet without dietary vitamin D can be prevented by oral administration of polycationic amine resin. Oral administration of cholestyramine, but not the control uncharged polystyrene, was able to sequester negatively charged bacterial endotoxin in the gut, leading to 1) reduced plasma endotoxin levels, 2) resolved systemic inflammation and hepatic steatohepatitis, and 3) improved insulin sensitivity. Gut dysbiosis, characterized as an increase of the phylum Firmicutes and a decrease of Bacteroidetes and Akkermansia muciniphila, was fully corrected by cholestyramine, indicating that the negatively charged components in the gut are critical for the dysbiosis. Furthermore, fecal bacteria transplant, derived from cholestyramine-treated animals, was sufficient to antagonize the metabolic disorders of the recipient mice. These results indicate that the negatively charged components produced by dysbiosis are critical for biogenesis of metabolic disorders and also show a potential application of cationic polystyrene to treat metabolic disorders through promoting gut eubiosis.

Background

Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS.

Methods

In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable.

Findings

The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group).

Interpretation

Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated.

Funding

US National Institutes of Health and European Society of Intensive Care Medicine.