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Background In tissues and organisms, the coordination of neighboring cells is essential to maintain their properties and functions. Therefore, knowing which cells are adjacent is crucial to understand biological processes that involve physical interactions among them, e.g. cell migration and proliferation. In addition, some signaling pathways, such as Notch or extrinsic apoptosis, are highly dependent on cell–cell communication. While this is straightforward to obtain from membrane images, nuclei labelling is much more ubiquitous for technical reasons. However, there are no automatic and robust methods to find neighboring cells based only on nuclear markers. Results In this work, we describe Nfinder, a method to assess the cell’s local neighborhood from images with nuclei labeling. To achieve this goal, we approximate the cell–cell interaction graph by the Delaunay triangulation of nuclei centroids. Then, links are filtered by automatic thresholding in cell–cell distance (pairwise interaction) and the maximum angle that a pair of cells subtends with shared neighbors (non-pairwise interaction). We systematically characterized the detection performance by applying Nfinder to publicly available datasets from Drosophila melanogaster , Tribolium castaneum , Arabidopsis thaliana and C. elegans . In each case, the result of the algorithm was compared to a cell neighbor graph generated by manually annotating the original dataset. On average, our method detected 95% of true neighbors, with only 6% of false discoveries. Remarkably, our findings indicate that taking into account non-pairwise interactions might increase the Positive Predictive Value up to + 11.5%. Conclusion Nfinder is the first robust and automatic method for estimating neighboring cells in 2D and 3D based only on nuclear markers and without any free parameters. Using this tool, we found that taking non-pairwise interactions into account improves the detection performance significantly. We believe that using our method might improve the effectiveness of other workflows to study cell–cell interactions from microscopy images. Finally, we also provide a reference implementation in Python and an easy-to-use napari plugin.

Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to trade-offs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase-contrast with the functional specificity of fluorescence. We develop surveillance for events of interest in label-free microscopy using dynamics-informed neural networks that trigger smart fluorescence acquisitions upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging. Stepp and colleagues present hybrid-EDA, an event-driven acquisition (EDA) that enables gentle investigation of rare mitochondrial events. This approach combines continuous, low-phototoxicity phase-contrast surveillance with event-triggered fluorescence imaging, powered by dynamics-aware machine-learning event detection.

Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to tradeoffs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase contrast with the functional specificity of fluorescence. We developed surveillance for events of interest in label-free microscopy by novel dynamics-informed neural networks that trigger smart acquisitions in fluorescence upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable new insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging.

Estimating neighboring cells by using only nuclear markers is crucial in many biological applications. Although several strategies have been used for this purpose, most published methods lack a rigorous characterization of their efficiencies. Remarkably, previously described methods are not automatic and depend only on cell-cell distance, neglecting the importance of pair-neighborhood interaction. To develop a robust and automatic method for assessing cell local neighborhood, while analyzing the impact of the physical variables involved in this task. We inferred neighbors from images with nuclei labeling by approximating the cell-cell interaction graph by the Delaunay triangulation of nuclei centroids. Each edge of this graph was filtered by thresholding in cell-cell distance and the maximum angle that each pair subtends with shared neighbors (pair-neighborhood interaction). Thresholds were calculated by maximizing a new robust statistic that measures the communicability efficiency of the cell graph. Using a variety of images of diverse tissues with additional membrane labeling to find the ground truth, we characterized the assessment performance. On average, our method detected 95% of true neighbors, with only 6% of false discoveries. Even though our method’s performance and tissue regularity are correlated, it works with performance metrics over 86% in very different organisms, including Drosophila melanogaster, Tribolium castaneum, Arabidopsis thaliana and C. elegans. We automatically estimated neighboring relationships between cells in 2D and 3D using only nuclear markers. To achieve this goal, we filtered the Delaunay triangulation of nuclei centroids with a new measure of graph communicability efficiency. In addition, we found that taking pair-neighborhood interactions into account, in contrast to considering only cell-cell distances, leads to significant performance improvements. This becomes more notorious when the number of cells is low or the geometry of the cell graph is highly complex.

Background.Nucleoside reverse-transcriptase inhibitor (NRTI)-related mitochondrial toxicity has been suggested as a key factor in the induction of antiretroviral-related lipoatrophy. This study aimed to evaluate in vivo the effects of NRTI withdrawal on mitochondrial parameters and body fat distribution. Methods.A multicenter, prospective, randomized trial assessed the efficacy and tolerability of switching to lopinavir-ritonavir plus nevirapine (nevirapine group; n=34), compared with lopinavir-ritonavir plus 2 NRTIs (control group; n=33) in a group of human immunodeficiency virus-infected adults with virological suppression. A subset of 35 individuals (20 from the nevirapine group and 15 from the control group) were evaluated for changes in the mitochondrial DNA (mtDNA) to nuclear DNA ratio and cytochrome c oxidase (COX) activity after NRTI withdrawal. Dual-energy X-ray absorptiometry (DEXA) scans were used to objectively quantify fat redistribution over time. Results.The nevirapine group experienced a progressive increase in mtDNA content (a 40% increase at week 48; P=.039 for comparison between groups) and in the COX activity (26% and 32% at weeks 24 and 48, respectively; P=.01 and P=.09 for comparison between groups, respectively). There were no statistically significant between-group differences in DEXA scans at week 48, although a higher fat increase in extremities was observed in the nevirapine group. No virologic failures occurred in either treatment arm. Conclusions.Switching to a nucleoside-sparing regimen of nevirapine and lopinavir-ritonavir maintained full antiviral efficacy and led to an improvement in mitochondrial parameters, which suggests a reversion of nucleoside-associated mitochondrial toxicity. Although DEXA scans performed during the study only revealed slight changes in fat redistribution, a longer follow-up period may show a positive correlation between reduced mitochondrial toxicity and a clinical improvement of lipodystrophy.

Hydroxyethyl starch (HES) solutions increase the risk of acute kidney injury (AKI) in critically ill patients admitted to intensive care unit (ICU) for medical indications. We conducted a cohort study to evaluate the renal safety of modern 6% HES solutions in high-risk patients having cardiac surgery. In this multicentre prospective cohort study, we recruited 261 consecutive patients at high-risk for developing cardiac surgery-associated AKI, based on a Cleveland score ≥ 4 points, from July to December 2017th in 14 hospitals in Spain and the United Kingdom. Multivariable logistic regression modeling and propensity-score matched-pairs analysis were used to determine the adjusted association between administration of HES and AKI. Of the cohort, 95 patients (36.4%) received 6% HES 130/0.4 either intraoperatively or postoperatively. Postoperative AKI occurred in 145 patients (55.5%). The unadjusted odds of AKI was significantly higher in the HES group, when compared to those not receiving HES (OR 2.22, 95% CI 1.30–3.80, p = 0.003). In multivariable logistic regression models, modern HES was not associated with significantly increased risk of AKI (adjusted OR 0.84, 95% CI 0.41–1.71, p = 0.63). In propensity score match-pairs analysis of 188 patients, the HES group experienced similar adjusted odds of AKI (OR 1.05, CI 95% 0.87–1.27, p = 0.57) and RRT (OR 1.06, CI 95% 0.92–1.22, p = 0.36). The use of modern hydroxyethyl starch 6% HES 130/0.4 was not associated with an increased risk of AKI nor dialysis in this cohort of patients at elevated risk for developing AKI after cardiac surgery. •This is a multicenter prospective cohort study, with high-risk patients for developing CSA-AKI.•Modern Hydroxyethyl starch was not associated with an increased risk of AKI in high-risk patients for developing CSA-AKI.•Multivariable logistic regression modeling and propensity-score matched-pairs analysis were used.

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy –performed in a large and diverse chemolibrary– complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective ( in silico ) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC 50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC 50 = 0.12–20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC 50 7–45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known “garbage in, garbage out” machine learning principle.

Background: Tobacco addiction is the leading cause of preventable morbidity and mortality worldwide, but only 1 in 20 cessation attempts is supervised by a health professional. The potential advantages of mobile health (mHealth) can circumvent this problem and facilitate tobacco cessation interventions for public health systems. Given its easy scalability to large populations and great potential, chatbots are a potentially useful complement to usual treatment. Objective: This study aims to assess the effectiveness of an evidence-based intervention to quit smoking via a chatbot in smartphones compared with usual clinical practice in primary care. Methods: This is a pragmatic, multicenter, controlled, and randomized clinical trial involving 34 primary health care centers within the Madrid Health Service (Spain). Smokers over the age of 18 years who attended on-site consultation and accepted help to quit tobacco were recruited by their doctor or nurse and randomly allocated to receive usual care (control group [CG]) or an evidence-based chatbot intervention (intervention group [IG]). The interventions in both arms were based on the 5A’s (ie, Ask, Advise, Assess, Assist, and Arrange) in the US Clinical Practice Guideline, which combines behavioral and pharmacological treatments and is structured in several follow-up appointments. The primary outcome was continuous abstinence from smoking that was biochemically validated after 6 months by the collaborators. The outcome analysis was blinded to allocation of patients, although participants were unblinded to group assignment. An intention-to-treat analysis, using the baseline-observation-carried-forward approach for missing data, and logistic regression models with robust estimators were employed for assessing the primary outcomes. Results: The trial was conducted between October 1, 2018, and March 31, 2019. The sample included 513 patients (242 in the IG and 271 in the CG), with an average age of 49.8 (SD 10.82) years and gender ratio of 59.3% (304/513) women and 40.7% (209/513) men. Of them, 232 patients (45.2%) completed the follow-up, 104/242 (42.9%) in the IG and 128/271 (47.2%) in the CG. In the intention-to-treat analysis, the biochemically validated abstinence rate at 6 months was higher in the IG (63/242, 26%) compared with that in the CG (51/271, 18.8%; odds ratio 1.52, 95% CI 1.00-2.31; P=.05). After adjusting for basal CO-oximetry and bupropion intake, no substantial changes were observed (odds ratio 1.52, 95% CI 0.99-2.33; P=.05; pseudo-R2=0.045). In the IG, 61.2% (148/242) of users accessed the chatbot, average chatbot-patient interaction time was 121 (95% CI 121.1-140.0) minutes, and average number of contacts was 45.56 (SD 36.32). Conclusions: A treatment including a chatbot for helping with tobacco cessation was more effective than usual clinical practice in primary care. However, this outcome was at the limit of statistical significance, and therefore these promising results must be interpreted with caution. Trial Registration: Clinicaltrials.gov NCT 03445507; https://tinyurl.com/mrnfcmtd International Registered Report Identifier (IRRID): RR2-10.1186/s12911-019-0972-z

Background Atypical carcinoids (AC) of the lung are rare intermediate-grade neuroendocrine neoplasms. Prognostic factors for these tumours are undefined. Methods Our cooperative group retrieved data on 127 patients operated between 1980 and 2009 because of an AC. Several clinical and pathological features were studied. Results In a univariable analysis, T-status (p=0.005), N-status (p=0.021), preoperative M-status (previously treated) (p=0.04), and distant recurrence developed during the outcome (p<0.001) presented statistically significant differences related to survival of these patients. In a multivariable analysis, only distant recurrence was demonstrated to be an independent risk factor for survival (p<0.001; HR: 13.1). During the monitoring, 25.2% of the patients presented some kind of recurrence. When we studied recurrence factors in a univariable manner, sublobar resections presented significant relationship with locoregional recurrence (p<0.001). In the case of distant recurrence, T and N status presented significant differences. Patients with preoperative M1 status presented higher frequencies of locoregional and distant recurrence (p=0.004 and p<0.001, respectively). In a multivariable analysis, sublobar resection was an independent prognostic factor to predict locoregional recurrence (p=0.002; HR: 18.1). Conclusions Complete standard surgical resection with radical lymphadenectomy is essential for AC. Sublobar resections are related to locoregional recurrence, so they should be avoided except for carefully selected patients. Nodal status is an important prognostic factor to predict survival and recurrence. Distant recurrence is related to poor outcome.