Implementation of esophageal endoscopic submucosal dissection in Spain: Results from the nationwide registry. / Implementación de la disección endoscópica submucosa esofágica en España: resultados del registro nacional.

INTRODUCTION AND AIMS: The outcomes of endoscopic submucosal dissection (ESD) in the esophagus have not been assessed in our country. Our primary aim was to analyze the effectiveness and safety of the technique. MATERIAL AND METHODS: Analysis of the prospectively maintained national registry of ESD. We included all superficial esophageal lesions removed by ESD in 17 hospitals (20 endoscopists) between January 2016 and December 2021. Subepithelial lesions were excluded. The primary outcome was curative resection. We conducted a survival analysis and used logistic regression analysis to assess predictors of non-curative resection. RESULTS: A total of 102 ESD were performed on 96 patients. The technical success rate was 100% and the percentage of en-bloc resection was 98%. The percentage of R0 and curative resection was 77.5% (n=79; 95%CI: 68%-84%) and 63.7% (n=65; 95%CI: 54%-72%), respectively. The most frequent histology was Barrett-related neoplasia (n=55 [53.9%]). The main reason for non-curative resection was deep submucosal invasion (n=25). The centers with a lower volume of ESD obtained worse results in terms of curative resection. The rate of perforation, delayed bleeding and post-procedural stenosis were 5%, 5% and 15.7%, respectively. No patient died or required surgery due to an adverse effect. After a median follow-up of 14months, 20patients (20.8%) underwent surgery and/or chemoradiotherapy, and 9 patients died (mortality 9.4%). CONCLUSIONS: In Spain, esophageal ESD is curative in approximately two out of three patients, with an acceptable risk of adverse events.

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia.

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues.

Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.

Use of fine capillaries for cryopreservation of Caenorhabditis elegans by vitrification.

Caenorhabditis elegans is an exceptional model organism. More than twenty thousand different strains have been developed, increasing knowledge on countless topics. However, the traditional method to cryopreserve this nematode, based on slow freezing, usually reaches recovery rates of around 35% for the L1 and L2 larval stages. Here, we propose two alternative methods to cryopreserve this nematode based on vitrification that are applicable in common laboratories and allow the selective individual cryopreservation of this organism. These new methods require ultra-high warming rates, which are achieved by employing very thin capillaries as the nematode container, and a very low final concentration of cryoprotectants, which, as compared to slow freezing, reduce toxicity damage. The recovery rate was 98.5% for larvae (L1 - L4) and 84.3% for adults. Given these results, our procedures offer an alternative to cryopreserve this nematode (larvae and adults) with higher recovery rates, avoiding expensive requirements. Indeed, it only needed a container with liquid nitrogen and a warming bath for water at 37 °C. The high performance of this approach has been revealed by preserving the long-term memory and, probably, the connectome of this nematode.

Long-term cryostorage does not negatively affect the recovery of Caenorhabditis elegans.

Cryostorage of Caenorhabditis elegans nematodes is important to maintain the many lines used for research. The standard method uses 15% of glycerol in M9-Buffer and a cooling rate of 1 °C/min; then worms can be stored in a -80 °C freezer or in liquid nitrogen. The recovery of C. elegans from stocks stored in liquid nitrogen is reported to be in the range of 35-45% and slightly decreases after years of storage. The storage at -80 °C is also considered safe, but the recovery is not as high as in liquid nitrogen. These observations have not been experimentally reported and therefore require verification. In this study, the standard methods were used in a set of experiments to compare the recovery of larvae and adult worms stored at -80 °C or in liquid nitrogen, after short- (a week) or long-term storage (3.5 years). No differences were observed in recovery, either for the time of storage or for the temperature of storage. Recovery of larvae was 32% at -80 °C and 36% in liquid nitrogen after 3.5 yr and that was not significantly different from the 7-d recovery rates. Adult worm recovery was below 5% for all treatments. These results suggest that both methods of storage can be used to successfully store C. elegans larvae for at least 3.5 years.

A quantitative biophysical principle to explain the 3D cellular connectivity in curved epithelia.

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended or folded epithelia is achieved by an energy minimization mechanism that leads to a complex cellular shape: the "scutoid". Here, we focus on the relationship between this shape and the connectivity between cells. We use a combination of computational, experimental, and biophysical approaches to examine how energy drivers affect the three-dimensional (3D) packing of tubular epithelia. We propose an energy-based stochastic model that explains the 3D cellular connectivity. Then, we challenge it by experimentally reducing the cell adhesion. As a result, we observed an increment in the appearance of scutoids that correlated with a decrease in the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle that links tissue geometry and energetics with the average cellular connectivity.

Deep learning based domain adaptation for mitochondria segmentation on EM volumes.

BACKGROUND AND OBJECTIVE: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species. METHODS: We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation. RESULTS: We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets. CONCLUSIONS: The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.

Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes.

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications make neither the code nor the full training details public, leading to reproducibility issues and dubious model comparisons. Thus, following a recent code of best practices in the field, we present an extensive study of the state-of-the-art architectures and compare them to different variations of U-Net-like models for this task. To unveil the impact of architectural novelties, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters has been performed, running each configuration multiple times to measure their stability. Using this methodology, we found very stable architectures and training configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset and outperform all previous works on two other available datasets: Lucchi++ and Kasthuri++. The code and its documentation are publicly available at https://github.com/danifranco/EM_Image_Segmentation .

MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. However, public mitochondria segmentation datasets only contain hundreds of instances with simple shapes. It is unclear if existing methods achieving human-level accuracy on these small datasets are robust in practice. To this end, we introduce the MitoEM dataset, a 3D mitochondria instance segmentation dataset with two (30µm)3 volumes from human and rat cortices respectively, 3, 600× larger than previous benchmarks. With around 40K instances, we find a great diversity of mitochondria in terms of shape and density. For evaluation, we tailor the implementation of the average precision (AP) metric for 3D data with a 45× speedup. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances. Thus, our MitoEM dataset poses new challenges to the field. We release our code and data: https://donglaiw.github.io/page/mitoEM/index.html.

Avoiding sexual interference: herkogamy and dichogamy in style dimorphic flowers of Narcissus broussonetii (Amaryllidaceae).

Spatial (herkogamy) or temporal (dichogamy) separation of sex organs are mechanisms considered to restrict self-pollination and promote outcrossing. Additionally, avoidance of self-interference is proposed to be the driving force for the evolution of these mechanisms, particularly in self-incompatible species. However, species with anthers and stigmas at different levels may increase the rate of imprecise pollen transfer, resulting in pollen discounting. Non-reciprocal stylar dimorphism has been considered a transitional, unstable stage towards the evolution of reciprocal style dimorphism (distyly), to simultaneously avoid interference and lack of precision. In this study we investigate the spatial and temporal separation of sex organs in a population of the style dimorphic and self-incompatible Narcissus broussonetii and their consequences in the reciprocity between the sex organs of morphs and their fecundity. First, we evaluated the relative growth of sex organs after anthesis. Then, we studied the stigma receptivity along the flower lifespan including its effect on seed production in both morphs. Finally, given the weak reciprocity between the sex organs of morphs of this species, we estimated population genetic diversity parameters in Long- and Short-styled plants to explore differences between them as a result of rates of inbreeding due to different mating strategies. We observed that Long-styled plants and Short-styled plants present different strategies to avoid sexual interference and both of them had negative consequences in the reciprocity between the sex organs of morphs. Long-styled plants exhibited a delay in stigma receptivity and a higher growth rate of the style after anthesis, while Short-styled plants presented higher herkogamy and no delay in stigma receptivity. These findings suggest that the avoidance of self-interference, in stylar dimorphic Narcissus species, seems to be more critical than improving of reciprocity between the sex organs of morphs. This might explain why reciprocal herkogamy (distyly) is rare in the genus.

Persistence of Long-Term Memory in Vitrified and Revived Caenorhabditis elegans.

Can memory be retained after cryopreservation? Our research has attempted to answer this long-standing question by using the nematode worm Caenorhabditis elegans, a well-known model organism for biological research that has generated revolutionary findings but has not been tested for memory retention after cryopreservation. Our study's goal was to test C. elegans' memory recall after vitrification and reviving. Using a method of sensory imprinting in the young C. elegans, we establish that learning acquired through olfactory cues shapes the animal's behavior and the learning is retained at the adult stage after vitrification. Our research method included olfactory imprinting with the chemical benzaldehyde (C6H5CHO) for phase-sense olfactory imprinting at the L1 stage, the fast-cooling SafeSpeed method for vitrification at the L2 stage, reviving, and a chemotaxis assay for testing memory retention of learning at the adult stage. Our results in testing memory retention after cryopreservation show that the mechanisms that regulate the odorant imprinting (a form of long-term memory) in C. elegans have not been modified by the process of vitrification or by slow freezing.