Early determinants of acute kidney injury during experimental intra-abdominal sepsis

Indexación: Web of Science; Scielo.Background: Sepsis-induced acute kidney injury (AKI) is an early and frequent organ dysfunction, associated with increased mortality. Aim: To evaluate the impact of macrohemodynamic and microcirculatory changes on renal function and histology during an experimental model of intra-abdominal sepsis. Material and Methods: In 18 anaesthetized pigs, catheters were installed to measure hemodynamic parameters in the carotid, right renal and pulmonary arteries. After baseline assessment and stabilization, animals were randomly divided to receive and intra-abdominal infusion of autologous feces or saline. Animals were observed for 18 hours thereafter. Results: In all septic animals, serum lactate levels increased, but only eight developed AKI (66%). These animals had higher creatinine and interleukin-6 levels, lower inulin and paraaminohippurate clearance (decreased glomerular filtration and renal plasma flow), and a negative lactate uptake. Septic animals with AKI had lower values of mean end arterial pressure, renal blood flow and kidney perfusion pressure, with an associated increase in kidney oxygen extraction. No tubular necrosis was observed in kidney histology. Conclusions: The reduction in renal blood flow and renal perfusion pressure were the main mechanisms associated with AKI, but were not associated with necrosis. Probably other mechanisms, such as microcirculatory vasoconstriction and inflammation also contributes to AKI development. (Rev Med Chile 2014; 142: 551-558) Key words: Acute kidney injury; Renal circulation; Sepsis

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss 4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa), Fusobacterium nucleatum (Fn), Parvimonas micra (Pm), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Tannerella forsythia (Tf), and Treponema denticola (Td). The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ≥0.760 and a sensitivity and specificity ≥75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky’s complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa, while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitisThis work was supported by the EM2014/025 project from the Regional Ministry of Culture, Education and University (regional government of Galicia, Spain), which is integrated in the Regional Plan of Research, Innovation and Development 2011–2015; and grants PI13/02390-PI16/01163 awarded to MT within the State Plan for R+D+I 2013–2016 (National Plan for Scientific Research, Technological Development and Innovation 2008–2011) and co-financed by the ISCIII-Deputy General Directorate of evaluation and Promotion of Research-European Regional Development Fund “A way of Making Europe” and Instituto de Salud Carlos III FEDERS

Accuracy of periodontitis diagnosis obtained using multiple molecular biomarkers in oral fluids: A systematic review and meta‐analysis

Aim To determine the accuracy of biomarker combinations in gingival crevicular fluid (GCF) and saliva through meta-analysis to diagnose periodontitis in systemically healthy subjects. Methods Studies on combining two or more biomarkers providing a binary classification table, sensitivity/specificity values or group sizes in subjects diagnosed with periodontitis were included. The search was performed in August 2022 through PUBMED, EMBASE, Cochrane, LILACS, SCOPUS and Web of Science. The methodological quality of the articles selected was evaluated using the QUADAS-2 checklist. Hierarchical summary receiver operating characteristic modelling was employed to perform the meta-analyses (CRD42020175021). Results Twenty-one combinations in GCF and 47 in saliva were evaluated. Meta-analyses were possible for six salivary combinations (median sensitivity/specificity values): IL-6 with MMP-8 (86.2%/80.5%); IL-1β with IL-6 (83.0%/83.7%); IL-1β with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1β, IL-6 and MMP-8 (81.8%/84.3%); and IL-1β, IL-6, MIP-1α and MMP-8 (76.6%/79.7%). Conclusions Two-biomarker combinations in oral fluids show high diagnostic accuracy for periodontitis, which is not substantially improved by incorporating more biomarkers. In saliva, the dual combinations of IL-1β, IL-6 and MMP-8 have an excellent ability to detect periodontitis and a good capacity to detect non-periodontitis. Because of the limited number of biomarker combinations evaluated, further research is required to corroborate these observationsThis study was funded by the Instituto de Salud Carlos III (ISCIII) through the project PI21/00588 and co-funded by the European UnionS

Relationship between dental and periodontal health status and the salivary microbiome: bacterial diversity, co-occurrence networks and predictive models

The present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. Our scale of overall oral health was used to produce a convenience sample of 81 patients from 270 who were initially recruited. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 × 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the phyloseq, DESeq2, Microbiome, SpiecEasi, igraph, MixOmics packages. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or oral disease, especially in high grades. Supragingival dental parameters influence the microbiota’s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that oral health has on the salivary microbiome. The possible keystone OTUs are different in the oral health and disease, and even these vary between dental and periodontal disease: half of them belongs to the core microbiome and are independent of the abundance parameters. The salivary microbiome, involving a considerable number of OTUs, shows an excellent discriminatory potential for distinguishing different grades of dental, periodontal or oral disease; considering the number of predictive OTUs, the best model is that which predicts the combined dental and periodontal statusThis investigation was supported by the Instituto de Salud Carlos III (General Division of Evaluation and Research Promotion, Madrid, Spain) and co-financed by FEDER (“A way of making Europe”) under grant ISCIII/PI17/01722, and the CESPU under grants MVOS2016 and MVOS-PT-IINFACTS-2019S

Cytokine thresholds in gingival crevicular fluid with potential diagnosis of chronic periodontitis differentiating by smoking status

The objective of the present study was to determine cytokine thresholds derived from predictive models for the diagnosis of chronic periodontitis, differentiating by smoking status. Seventy-five periodontally healthy controls and 75 subjects affected by chronic periodontitis were recruited. Sixteen mediators were measured in gingival crevicular fluid (GCF) using multiplexed bead immunoassays. The models were obtained using binary logistic regression, distinguishing between non-smokers and smokers. The area under the curve (AUC) and numerous classification measures were obtained. Model curves were constructed graphically and the cytokine thresholds calculated for the values of maximum accuracy (ACC). There were three cytokine-based models and three cytokine ratio-based models, which presented with a bias-corrected AUC > 0.91 and > 0.83, respectively. These models were (cytokine thresholds in pg/ml for the median ACC using bootstrapping for smokers and non-smokers): IL1alpha (46099 and 65644); IL1beta (4732 and 5827); IL17A (11.03 and 17.13); IL1alpha/IL2 (4210 and 7118); IL1beta/IL2 (260 and 628); and IL17A/IL2 (0.810 and 1.919). IL1alpha, IL1beta and IL17A, and their ratios with IL2, are excellent diagnostic biomarkers in GCF for distinguishing periodontitis patients from periodontally healthy individuals. Cytokine thresholds in GCF with diagnostic potential are defined, showing that smokers have lower threshold values than non-smokers.This work was supported by the Instituto de Salud Carlos III (General Division of Evaluation and Research Promotion, Madrid, Spain) and co-financed by FEDER (“A way of making Europe”) under Grant ISCIII/PI17/01722, and the Consellería de Cultura, Educación e Ordenación Universitaria da Xunta de Galicia (Spain) under Grant ED431B 2017/029 and A. Regueira-Iglesias support ED481A-2017S

Corpus y construcciones: perspectivas hispánicas

Los trabajos que integran este volumen constituyen una muestra significativa de las aportaciones actuales de la lingüística de corpus en el ámbito hispánico. La primera parte está dedicada al análisis de fenómenos gramaticales con datos de corpus. La segunda se centra en el diseño y elaboración de corpus textuales de diverso tipo, con especial atención a las posibilidades de recuperación y explotación de los datos que contienen. En ambos bloques se pone de manifiesto la variedad de recursos y métodos de análisis propiciados por el desarrollo de corpus tanto del español como de otras lenguas (gallego, portugués, alemán). Varios capítulos muestran la necesidad de un enfoque plurilingüe, bien para dar cuenta de fenómenos de variación y cambio en situaciones de contacto, bien para desarrollar recursos lingüísticos para la enseñanza de lenguas extranjeras o para la traducción. La autoría plural de la obra configura un panorama diverso y estimulante de las posibilidades que ofrece la lingüística de corpus para profundizar en el conocimiento de las lenguas.Axencia Galega de Innovación, ref. nº ED431B 2017/3