Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis

Background. The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. Methods. Semi-structured interviews and an online survey were used. Results. Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation.This work is part of the PICCOLO project, which has received funding from the European Union’s Horizon 2020 research and innovation Programme under grant agreement No. 732111. GR18199, funded by “Consejería de Economía, Ciencia y Agenda Digital, Junta de Extremadura” and co-funded by European Union (ERDF “A way to make Europe”). The funding bodies did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for e_ective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four di_erent models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.This work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 732111. Furthermore, this publication has also been partially supported by GR18199 from Consejería de Economía, Ciencia y Agenda Digital of Junta de Extremadura (co-funded by European Regional Development Fund–ERDF. “A way to make Europe”/ “Investing in your future”. This work has been performed by the ICTS “NANBIOSIS” at the Jesús Usón Minimally Invasive Surgery Centre

The Venus' Cloud Discontinuity in 2022

First identified in 2016 by JAXA's Akatsuki mission, the discontinuity/disruption is a recurrent wave observed to propagate during decades at the deeper clouds of Venus (47--56 km above the surface), while its absence at the clouds' top ($\sim$70 km) suggests that it dissipates at the upper clouds and contributes in the maintenance of the puzzling atmospheric superrotation of Venus through wave-mean flow interaction. Taking advantage of the campaign of ground-based observations undertaken in coordination with the Akatsuki mission since December 2021 until July 2022, we aimed to undertake the longest uninterrupted monitoring of the cloud discontinuity up to date to obtain a pioneering long-term characterization of its main properties and better constrain its recurrence and lifetime. The dayside upper, middle and nightside lower clouds were studied with images with suitable filters acquired by Akatsuki/UVI, amateur observers and NASA's IRTF/SpeX, respectively. Hundreds of images were inspected in search of manifestations of the discontinuity events and to measure key properties like its dimensions, orientation or rotation period. We succeeded in tracking the discontinuity at the middle clouds during 109 days without interruption. The discontinuity exhibited properties nearly identical to measurements in 2016 and 2020, with an orientation of $91^{\circ}\pm 8^{\circ}$, length/width of $4100\pm 800$ / $500\pm 100$ km and a rotation period of $5.11\pm 0.09$ days. Ultraviolet images during 13-14 June 2022 suggest that the discontinuity may have manifested at the top of the clouds during $\sim$21 hours as a result of an altitude change in the critical level for this wave due to slower zonal winds.Comment: 8 pages, 4 figures, 2 animated figures, 1 tabl

Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis

Colorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 µm/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.This work was supported by the PICCOLO project. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein. This research has also been supported by the project ONKOTOOLS (KK2020/00069) funded by the Basque Government Industry Department under the ELKARTEK program

Learning curves of basic laparoscopic psychomotor skills in SINERGIA VR simulator

Purpose: Surgical simulators are currently essential within any laparoscopic training program because they provide a low-stakes, reproducible and reliable environment to acquire basic skills. The purpose of this study is to determine the training learning curve based on different metrics corresponding to five tasks included in SINERGIA laparoscopic virtual reality simulator. Methods: Thirty medical students without surgical experience participated in the study. Five tasks of SINERGIA were included: Coordination, Navigation, Navigation and touch, Accurate grasping and Coordinated pulling. Each participant was trained in SINERGIA. This training consisted of eight sessions (R1–R8) of the five mentioned tasks and was carried out in two consecutive days with four sessions per day. A statistical analysis was made, and the results of R1, R4 and R8 were pair-wise compared with Wilcoxon signed-rank test. Significance is considered at P value <0.005. Results: In total, 84.38% of the metrics provided by SINERGIA and included in this study show significant differences when comparing R1 and R8. Metrics are mostly improved in the first session of training (75.00% when R1 and R4 are compared vs. 37.50% when R4 and R8 are compared). In tasks Coordination and Navigation and touch, all metrics are improved. On the other hand, Navigation just improves 60% of the analyzed metrics. Most learning curves show an improvement with better results in the fulfillment of the different tasks. Conclusions: Learning curves of metrics that assess the basic psychomotor laparoscopic skills acquired in SINERGIA virtual reality simulator show a faster learning rate during the first part of the training. Nevertheless, eight repetitions of the tasks are not enough to acquire all psychomotor skills that can be trained in SINERGIA. Therefore, and based on these results together with previous works, SINERGIA could be used as training tool with a properly designed training program

A few StePS forward in unveiling the complexity of galaxy evolution: Light-weighted stellar ages of intermediate-redshift galaxies with WEAVE

The upcoming new generation of optical spectrographs on four-meter-class telescopes will provide invaluable information for reconstructing the history of star formation in individual galaxies up to redshifts of about 0.7. We aim at defining simple but robust and meaningful physical parameters that can be used to trace the coexistence of widely diverse stellar components: younger stellar populations superimposed on the bulk of older ones. We produce spectra of galaxies closely mimicking data from the forthcoming Stellar Populations at intermediate redshifts Survey (StePS), a survey that uses the WEAVE spectrograph on the William Herschel Telescope. First, we assess our ability to reliably measure both ultraviolet and optical spectral indices in galaxies of different spectral types for typically expected signal-to-noise levels. Then, we analyze such mock spectra with a Bayesian approach, deriving the probability density function of r- and u-band light-weighted ages as well as of their difference. We find that the ultraviolet indices significantly narrow the uncertainties in estimating the r- and u-band light-weighted ages and their difference in individual galaxies. These diagnostics, robustly retrievable for large galaxy samples even when observed at moderate signal-to-noise ratios, allow us to identify secondary episodes of star formation up to an age of ~0.1 Gyr for stellar populations older than ~1.5 Gyr, pushing up to an age of ~1 Gyr for stellar populations older than ~5 Gyr. The difference between r-band and u-band light-weighted ages is shown to be a powerful diagnostic to characterize and constrain extended star-formation histories and the presence of young stellar populations on top of older ones. This parameter can be used to explore the interplay between different galaxy star-formation histories and physical parameters such as galaxy mass, size, morphology, and environment

Systems and technologies for objective evaluation of technical skills in laparoscopic surgery

Minimally invasive surgery is a highly demanding surgical approach regarding technical requirements for the surgeon, who must be trained in order to perform a safe surgical intervention. Traditional surgical education in minimally invasive surgery is commonly based on subjective criteria to quantify and evaluate surgical abilities, which could be potentially unsafe for the patient. Authors, surgeons and associations are increasingly demanding the development of more objective assessment tools that can accredit surgeons as technically competent. This paper describes the state of the art in objective assessment methods of surgical skills. It gives an overview on assessment systems based on structured checklists and rating scales, surgical simulators, and instrument motion analysis. As a future work, an objective and automatic assessment method of surgical skills should be standardized as a means towards proficiency-based curricula for training in laparoscopic surgery and its certification

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data