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Background: Pulmonary fibrosis results from alveolar injury, leading to extracellular matrix remodelling and impaired lung function. This study aimed to classify patients with pulmonary fibrosis according to blood biomarkers to differentiate distinct disease patterns, known as endotypes., Methods: In this cluster analysis, we first classified patients from the PROFILE study, a multicentre, prospective, observational cohort of individuals with incident idiopathic pulmonary fibrosis or non-specific interstitial pneumonia in the UK (Nottingham University Hospitals, Nottingham; and Royal Brompton Hospital, London). 13 blood biomarkers representing extracellular matrix remodelling, epithelial stress, and thrombosis were measured by ELISA in the PROFILE study. We classified patients by unsupervised consensus clustering. To evaluate generalisability, a machine learning classifier trained on biomarker signatures derived from consensus clustering was applied to a replication dataset from the Australian Idiopathic Pulmonary Fibrosis Registry (AIPFR). Biomarker associations with mortality and change in percentage of predicted forced vital capacity (FVC%) were assessed, adjusting for age, gender, baseline FVC%, and antifibrotic treatment and steroid treatment before and after baseline. Mortality risk associated with the clusters in the PROFILE cohort was evaluated with Cox proportional hazards models, and mixed-effects models were used to analyse how clustering was associated with longitudinal FVC% in the PROFILE and AIPFR cohorts., Findings: 455 of 580 participants from the PROFILE study (348 [76%] men and 107 [24%] women; mean age 72·4 years [SD 8·3]) were included in the analysis. Within this group, three clusters were identified based on blood biomarkers. A basement membrane collagen (BM) cluster (n=248 [55%]) showed high concentrations of PRO-C4, PRO-C28, C3M, and C6M, whereas an epithelial injury (EI) cluster (n=109 [24%]) showed high concentrations of MMP-7, SP-D, CYFRA211, CA19-9, and CA-125. The third cluster (crosslinked fibrin [XF] cluster; n=98 [22%]) had high concentrations of X-FIB. In the replication dataset (117 of 833 patients from AIPFR; 87 [74%] men and 30 [26%] women; mean age 72·9 years [SD 7·9]), we identified the same three clusters (BM cluster, n=93 [79%]; EI cluster, n=8 [7%]; XF cluster, n=16 [14%]). These clusters showed similarities with clusters in the PROFILE dataset regarding blood biomarkers and phenotypic signatures. In the PROFILE dataset, the EI and XF clusters were associated with increased mortality risk compared with the BM cluster (EI vs BM: adjusted hazard ratio [HR] 1·88 [95% CI 1·42-2·49], p<0·0001; XF vs BM: adjusted HR 1·53 [1·13-2·06], p=0·0058). The EI cluster showed the greatest annual FVC% decline, followed by the BM and XF clusters. A similar FVC% decline pattern was observed in these clusters in the AIPFR replication dataset., Interpretation: Blood biomarker clustering in pulmonary fibrosis identified three distinct blood biomarker signatures associated with lung function and prognosis, suggesting unique pulmonary fibrosis biomarker patterns. These findings support the presence of pulmonary fibrosis endotypes with the potential to guide targeted therapy development., Funding: None., Competing Interests: Declaration of interests YM reports personal fees for advisory board work from Boehringer Ingelheim and Roche, outside the submitted work. TJC reports fees for work on scientific advisory boards for Boehringer Ingelheim, Roche, Bristol Myers Squibb, Vicore, Pliant, Endeavour Bioscience, and DevPro, and grants from Boehringer Ingelheim, Roche, Bristol Myers Squibb, Galapagos, and Biogen, outside the submitted work. DJL and JMBS are employees and shareholders of Nordic Bioscience. MAK is an employee and shareholder of Nordic Bioscience and holds patents relating to neoepitopes. AUW reports receiving consulting, lecture, or educational fees from Boehringer Ingelheim and Veracyte, and holds the honorary position of President of the World Association of Sarcoidosis and Other Granulomatous Diseases. LVW reports funding from, consultancy for, or collaboration with GSK, Genentech, Orion Pharma, and Galapagos. PLM has received, via his institution, industry-academic funding from AstraZeneca and has received speaker and consultancy fees from Boehringer Ingelheim and Roche, outside the submitted work. TMM has received industry-academic funding from GSK R&D and UCB, and consultancy or speaker's fees from Apellis, AstraZeneca, Bayer, Biogen Idec, Boehringer Ingelheim, Galapagos, GSK R&D, Indalo, Pliant, ProMetic, Roche, Samumed, UCB, and Celgene. RGJ has grants or contracts from AstraZeneca, Biogen, Galecto, GSK, Nordic Bioscience, Redx, and Pliant, with all payments going to his institutions. RGJ has also served as a consultant to Bristol Myers Squibb, Chiesi, Daewoong, Veracyte, Resolution Therapeutics, and Pliant, has received payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing, or educational events from Boehringer Ingelheim, Chiesi, Roche, PatientMPower, and AstraZeneca, has participated on a data safety monitoring board or advisory board for Boehringer Ingelheim, Galapagos, and Vicore, and has an unpaid role in an advisory board at NuMedii. RGJ is also a trustee of Action for Pulmonary Fibrosis. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Local Interpretability Model-agnostic Explanations (LIME) is a well-known post-hoc technique for explaining black-box models. While very useful, recent research highlights challenges around the explanations generated. In particular, there is a potential lack of stability, where the explanations provided vary over repeated runs of the algorithm, casting doubt on their reliability. This paper investigates the stability of LIME when applied to multivariate time series classification. We demonstrate that the traditional methods for generating neighbours used in LIME carry a high risk of creating 'fake' neighbours, which are out-of-distribution in respect to the trained model and far away from the input to be explained. This risk is particularly pronounced for time series data because of their substantial temporal dependencies. We discuss how these out-of-distribution neighbours contribute to unstable explanations. Furthermore, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune. We show how unsuitable hyperparameters can impact the stability of explanations. We propose a two-fold approach to address these issues. First, a generative model is employed to approximate the distribution of the training data set, from which within-distribution samples and thus meaningful neighbours can be created for LIME. Second, an adaptive weighting method is designed in which the hyperparameters are easier to tune than those of the traditional method. Experiments on real-world data sets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework. In addition, in-depth discussions are provided on the reasons behind these results., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Han Meng reports financial support was provided by China Scholarship Council. Han Meng reports financial support was provided by University of Nottingham. Isaac Triguero reports equipment, drugs, or supplies was provided by NVIDIA Corp., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The settlement of cold and arid environments by Pleistocene hunter-gatherers has been a heated topic in Paleolithic Archaeology and the Quaternary Sciences for years. In the Iberian Peninsula, a key area for studying human adaptations to such environments is composed by the large interior and upland regions of the northern and southern plateaus (Mesetas) and bordering areas. As, traditionally, these regions have been relatively under-investigated compared to the ecologically more favored coastal areas of the peninsula, our knowledge of the human settlement of the whole Iberian hinterland remains scarce for the Last Glacial. In this paper we present the discovery and first geoarcheological, paleoenvironmental and chronometric evidence obtained at Charco Verde II, a new site close to the southwestern foothills of the Iberian system range (Guadalajara province, Spain), bearing a sequence of Magdalenian human occupations starting at least at 20.8-21.4 ka cal BP during the Last Glacial Maximum, and covering Greenland Stadial 2 until ∼15.1-16.6 ka cal BP, including Heinrich stadial 1. As this site is located in an upland region which today faces one of the harshest climates in Iberia, such occupation sequence, occurred during some of the coldest and most arid phases of the Last Glacial, has relevant implications for our understanding of human-environment-climate interactions and population dynamics in Iberia and Western Europe. These findings support the hypothesis that the Iberian hinterland was not avoided by Upper Paleolithic hunter-gatherers due to ecological constraints, but it hosted a complex and relatively dense settlement at least in some areas, even during cold periods. This suggest, one more time, that the historical scarcity of Upper Paleolithic sites in inland Iberia is, to a significant extent, an artifact of research bias., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Aragoncillo-del Río et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The gut is a selective barrier that not only allows the translocation of nutrients from food, but also microbe-derived metabolites to the systemic circulation that flows through the liver. Microbiota dysbiosis occurs when energy imbalances appear due to an unhealthy diet and a sedentary lifestyle. Dysbiosis has a critical impact on increasing intestinal permeability and epithelial barrier deterioration, contributing to bacterial and antigen translocation to the liver, triggering non-alcoholic fatty liver disease (NAFLD) progression. In this study, the potential therapeutic/beneficial effects of a combination of metabolic cofactors (a multi-ingredient; MI) (betaine, N-acetylcysteine, L-carnitine, and nicotinamide riboside) against NAFLD were evaluated. In addition, we investigated the effects of this metabolic cofactors' combination as a modulator of other players of the gut-liver axis during the disease, including gut barrier dysfunction and microbiota dysbiosis. Diet-induced NAFLD mice were distributed into two groups, treated with the vehicle (NAFLD group) or with a combination of metabolic cofactors (NAFLD-MI group), and small intestines were harvested from all animals for histological, molecular, and omics analysis. The MI treatment ameliorated gut morphological changes, decreased gut barrier permeability, and reduced gene expression of some proinflammatory cytokines. Moreover, epithelial cell proliferation and the number of goblet cells were increased after MI supplementation. In addition, supplementation with the MI combination promoted changes in the intestinal microbiota composition and diversity, as well as modulating short-chain fatty acids (SCFAs) concentrations in feces. Taken together, this specific combination of metabolic cofactors can reverse gut barrier disruption and microbiota dysbiosis contributing to the amelioration of NAFLD progression by modulating key players of the gut-liver axis.

Self-labeled techniques are semi-supervised classification methods that address the shortage of labeled examples via a self-learning process based on supervised models. They progressively classify unlabeled data and use them to modify the hypothesis learned from labeled samples. Most relevant proposals are currently inspired by boosting schemes to iteratively enlarge the labeled set. Despite their effectiveness, these methods are constrained by the number of labeled examples and their distribution, which in many cases is sparse and scattered. The aim of this paper is to design a framework, named synthetic examples generation for self-labeled semi-supervised classification, to improve the classification performance of any given self-labeled method by using synthetic labeled data. These are generated via an oversampling technique and a positioning adjustment model that use both labeled and unlabeled examples as reference. Next, these examples are incorporated in the main stages of the self-labeling process. The principal aspects of the proposed framework are: 1) introducing diversity to the multiple classifiers used by using more (new) labeled data; 2) fulfilling labeled data distribution with the aid of unlabeled data; and 3) being applicable to any kind of self-labeled method. In our empirical studies, we have applied this scheme to four recent self-labeled methods, testing their capabilities with a large number of data sets. We show that this framework significantly improves the classification capabilities of self-labeled techniques.

Oxidative stress and inflammation underpin most diseases; their mechanisms are inextricably linked. Chronic inflammation is associated with oxidation, anti-inflammatory cascades are linked to decreased oxidation, increased oxidative stress triggers inflammation, and redox balance inhibits the inflammatory cellular response. Whether or not oxidative stress and inflammation represent the cause or consequence of cellular pathology, they contribute significantly to the pathogenesis of noncommunicable diseases (NCD). The incidence of obesity and other related metabolic disturbances are increasing, as are age-related diseases due to a progressively aging population. Relationships between oxidative stress, inflammatory signaling, and metabolism are, in the broad sense of energy transformation, being increasingly recognized as part of the problem in NCD. In this chapter, we summarize the pathologic consequences of an imbalance between circulating and cellular paraoxonases, the system for scavenging excessive reactive oxygen species and circulating chemokines. They act as inducers of migration and infiltration of immune cells in target tissues as well as in the pathogenesis of disease that perturbs normal metabolic function. This disruption involves pathways controlling lipid and glucose homeostasis as well as metabolically driven chronic inflammatory states that encompass several response pathways. Dysfunction in the endoplasmic reticulum and/or mitochondria represents an important feature of chronic disease linked to oxidation and inflammation seen as self-reinforcing in NCD. Therefore, correct management requires a thorough understanding of these relationships and precise interpretation of laboratory test results.

We studied the influence of PON1 on metabolic alterations induced by oxidized LDL when incubated with endothelial cells. HUVEC cells were incubated with native LDL, oxidized LDL, oxidized LDL plus HDL from wild type mice, and oxidized LDL plus HDL from PON1-deficient mice. Results showed alterations in carbohydrate and phospholipid metabolism and increased apoptosis in cells incubated with oxidized LDL. These changes were partially prevented by wild type mouse HDL, but the effects were less effective with HDL from PON1-deficient mice. Our results suggest that PON1 may play a significant role in endothelial cell survival by protecting cells from alterations in the respiratory chain induced by oxidized LDL. These results extend current knowledge on the protective role of HDL and PON1 against oxidation and apoptosis in endothelial cells.