Laparoscopic pyeloplasty proficiency during a residency program after adoption of a standardized simulation training program is maintained during the COVID pandemic despite reduced surgery volume

ABSTRACT Purpose: To evaluate the effect of the standardized laparoscopic simulation training program in pyeloplasty, following its implementation and during the COVID-19 pandemic. Material and Methods: A retrospective chart review was performed at Hospital de Clínicas de Porto Alegre, a tertiary referral center in south Brazil, in which 151 patients underwent laparoscopic pyeloplasty performed by residents between 2006-2021. They were divided into three groups: before and after adoption of a standardized laparoscopic simulation training program and during the COVID-19 pandemic. The main outcome was a combined negative outcome of conversion to open surgery, major postoperative complications (Clavien-Dindo III or higher) or unsuccessful procedure, defined as need for redo pyeloplasty. Results: There was a significant reduction in the combined negative outcome (21.1% vs 6.3%), surgical time (mean 200.0 min vs 177.4 min) and length of stay (median 5 days vs 3 days) after the adoption of simulation training program. These results were maintained during the COVID-19 pandemic (combined negative outcome of 6.3%, mean surgical time of 160.1 min and median length of stay of 3 days) despite a reduction in 55.4% of the surgical volume. Conclusion: A structured laparoscopic simulation program can improve outcomes of laparoscopic pyeloplasty during the learning curve

Bronchoalveolar Lavage Proteomics in Patients with Suspected Lung Cancer

All experiments including MS analysis were supported by Fundacao para a Ciencia e Tecnologia project EXPL/DTP-PIC/0616/2013. RM is supported by FCT investigator program 2012 (IF/01002/2012). ASC is supported by grant SFRH/BPD/85569/2012 funded by Fundacao para a Ciencia e Tecnologia.Lung cancer configures as one of the deadliest types of cancer. The future implementation of early screening methods such as exhaled breath condensate analysis and low dose computed tomography (CT) as an alternative to current chest imaging based screening will lead to an increased burden on bronchoscopy units. New approaches for improvement of diagnosis in bronchoscopy units, regarding patient management, are likely to have clinical impact in the future. Diagnostic approaches to address mortality of lung cancer include improved early detection and stratification of the cancers according to its prognosis and further response to drug treatment. In this study, we performed a detailed mass spectrometry based proteome analysis of acellular bronchoalveolar lavage (BAL) fluid samples on an observational prospective cohort consisting of 90 suspected lung cancer cases which were followed during two years. The thirteen new lung cancer cases diagnosed during the follow up time period clustered, based on liquid chromatography-mass spectrometry (LC-MS) data, with lung cancer cases at the time of BAL collection. Hundred and thirty-tree potential biomarkers were identified showing significantly differential expression when comparing lung cancer versus non-lung cancer. The regulated biomarkers showed a large overlap with biomarkers detected in tissue samples.publishersversionpublishe

Is the proteome of bronchoalveolar lavage extracellular vesicles a marker of advanced lung cancer?

Funding: R.M. is supported by Fundação para a Ciência e a Tecnologia (CEEC position, 2019–2025 investigator). This article is a result of the projects (iNOVA4Health—UID/Multi/04462/2013), supported by Lisboa Portugal Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work is also funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT—Portuguese Foundation for Science and Technology under the projects number PTDC/BTM-TEC/30087/2017 and PTDC/BTM-TEC/30088/2017. This work was supported by the Wellcome Trust/DBT India Alliance Margdarshi Fellowship (grant number IA/M/15/1/502023) awarded to A.P. B.C.-S., M.C.S.C. and C.B. are supported by the Champalimaud Foundation and the EMBO Installation Grant 3921.Acellular bronchoalveolar lavage (BAL) proteomics can partially separate lung cancer from non-lung cancer patients based on principal component analysis and multivariate analysis. Furthermore, the variance in the proteomics data sets is correlated mainly with lung cancer status and, to a lesser extent, smoking status and gender. Despite these advances BAL small and large extracellular vehicles (EVs) proteomes reveal aberrant protein expression in paracrine signaling mechanisms in cancer initiation and progression. We consequently present a case-control study of 24 bronchoalveolar lavage extracellular vesicle samples which were analyzed by state-of-the-art liquid chromatography-mass spectrometry (LC-MS). We obtained evidence that BAL EVs proteome complexity correlated with lung cancer stage 4 and mortality within two years´ follow-up (p value = 0.006). The potential therapeutic target DNMT3B complex is significantly up-regulated in tumor tissue and BAL EVs. The computational analysis of the immune and fibroblast cell markers in EVs suggests that patients who deceased within the follow-up period display higher marker expression indicative of innate immune and fibroblast cells (four out of five cases). This study provides insights into the proteome content of BAL EVs and their correlation to clinical outcomes.publishersversionpublishe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Comparative analysis of the bronchoalveolar microbiome in Portuguese patients with different chronic lung disorders.

The lung is inhabited by a diverse microbiome that originates from the oropharynx by a mechanism of micro-aspiration. Its bacterial biomass is usually low; however, this condition shifts in lung cancer (LC), chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD). These chronic lung disorders (CLD) may coexist in the same patient as comorbidities and share common risk factors, among which the microbiome is included. We characterized the microbiome of 106 bronchoalveolar lavages. Samples were initially subdivided into cancer and non-cancer and high-throughput sequenced for the 16S rRNA gene. Additionally, we used a cohort of 25 CLD patients where crossed comorbidities were excluded. Firmicutes, Proteobacteria and Bacteroidetes were the most prevalent phyla independently of the analyzed group. Streptococcus and Prevotella were associated with LC and Haemophilus was enhanced in COPD versus ILD. Although no significant discrepancies in microbial diversity were observed between cancer and non-cancer samples, statistical tests suggested a gradient across CLD where COPD and ILD displayed the highest and lowest alpha diversities, respectively. Moreover, COPD and ILD were separated in two clusters by the unweighted UniFrac distance (P value = 0.0068). Our results support the association of Streptoccocus and Prevotella with LC and of Haemophilus with COPD, and advocate for specific CLD signatures