Esophageal and Gastric Malignancies After Bariatric Surgery: a Retrospective Global Study

Background: Bariatric surgery can influence the presentation, diagnosis, and management of gastrointestinal cancers. Esophagogastric (EG) malignancies in patients who have had a prior bariatric procedure have not been fully characterized. Objective: To characterize EG malignancies after bariatric procedures. Setting: University Hospital, United Kingdom. Methods: We performed a retrospective, multicenter observational study of patients with EG malignancies after bariatric surgery to characterize this condition. Results: This study includes 170 patients from 75 centers in 25 countries who underwent bariatric procedures between 1985 and 2020. At the time of the bariatric procedure, the mean age was 50.2 ± 10 years, and the mean weight 128.8 ± 28.9 kg. Women composed 57.3% (n = 98) of the population. Most (n = 64) patients underwent a Roux-en-Y gastric bypass (RYGB) followed by adjustable gastric band (AGB; n = 46) and sleeve gastrectomy (SG; n = 43). Time to cancer diagnosis after bariatric surgery was 9.5 ± 7.4 years, and mean weight at diagnosis was 87.4 ± 21.9 kg. The time lag was 5.9 ± 4.1 years after SG compared to 9.4 ± 7.1 years after RYGB and 10.5 ± 5.7 years after AGB. One third of patients presented with metastatic disease. The majority of tumors were adenocarcinoma (82.9%). Approximately 1 in 5 patients underwent palliative treatment from the outset. Time from diagnosis to mortality was under 1 year for most patients who died over the intervening period. Conclusion: The Oesophago-Gastric Malignancies After Obesity/Bariatric Surgery study presents the largest series to date of patients developing EG malignancies after bariatric surgery and attempts to characterize this condition.info:eu-repo/semantics/publishedVersio

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page

Energy Resolution Performance of the CMS Electromagnetic Calorimeter

The energy resolution performance of the CMS lead tungstate crystal electromagnetic calorimeter is presented. Measurements were made with an electron beam using a fully equipped supermodule of the calorimeter barrel. Results are given both for electrons incident on the centre of crystals and for electrons distributed uniformly over the calorimeter surface. The electron energy is reconstructed in matrices of 3 times 3 or 5 times 5 crystals centred on the crystal containing the maximum energy. Corrections for variations in the shower containment are applied in the case of uniform incidence. The resolution measured is consistent with the design goals

Differentiation of oral bacteria in in vitro cultures and human saliva by secondary electrospray ionization – mass spectrometry

ISSN:2045-232

Differentiation of oral bacteria in in vitro cultures and human saliva by secondary electrospray ionization – mass spectrometry

The detection of bacterial-specific volatile metabolites may be a valuable tool to predict infection. Here we applied a real-time mass spectrometric technique to investigate differences in volatile metabolic profiles of oral bacteria that cause periodontitis. We coupled a secondary electrospray ionization (SESI) source to a commercial high-resolution mass spectrometer to interrogate the headspace from bacterial cultures and human saliva. We identified 120 potential markers characteristic for periodontal pathogens Aggregatibacter actinomycetemcomitans (n = 13), Porphyromonas gingivalis (n = 70), Tanerella forsythia (n = 30) and Treponema denticola (n = 7) in in vitro cultures. In a second proof-of-principle phase, we found 18 (P. gingivalis, T. forsythia and T. denticola) of the 120 in vitro compounds in the saliva from a periodontitis patient with confirmed infection with P. gingivalis, T. forsythia and T. denticola with enhanced ion intensity compared to two healthy controls. In conclusion, this method has the ability to identify individual metabolites of microbial pathogens in a complex medium such as saliva

Esophageal and gastric malignancies after bariatric surgery: a retrospective global study

Background: Bariatric surgery can influence the presentation, diagnosis, and management of gastrointestinal cancers. Esophagogastric (EG) malignancies in patients who have had a prior bariatric procedure have not been fully characterized. Objective: To characterize EG malignancies after bariatric procedures. Setting: University Hospital, United Kingdom. Methods: We performed a retrospective, multicenter observational study of patients with EG malignancies after bariatric surgery to characterize this condition. Results: This study includes 170 patients from 75 centers in 25 countries who underwent bariatric procedures between 1985 and 2020. At the time of the bariatric procedure, the mean age was 50.2 ± 10 years, and the mean weight 128.8 ± 28.9 kg. Women composed 57.3% (n = 98) of the population. Most (n = 64) patients underwent a Roux-en-Y gastric bypass (RYGB) followed by adjustable gastric band (AGB; n = 46) and sleeve gastrectomy (SG; n = 43). Time to cancer diagnosis after bariatric surgery was 9.5 ± 7.4 years, and mean weight at diagnosis was 87.4 ± 21.9 kg. The time lag was 5.9 ± 4.1 years after SG compared to 9.4 ± 7.1 years after RYGB and 10.5 ± 5.7 years after AGB. One third of patients presented with metastatic disease. The majority of tumors were adenocarcinoma (82.9%). Approximately 1 in 5 patients underwent palliative treatment from the outset. Time from diagnosis to mortality was under 1 year for most patients who died over the intervening period. Conclusion: The Oesophago-Gastric Malignancies After Obesity/Bariatric Surgery study presents the largest series to date of patients developing EG malignancies after bariatric surgery and attempts to characterize this condition

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

Energy Resolution of the Barrel of the CMS Electromagnetic Calorimeter

The energy resolution of the barrel part of the CMS Electromagnetic Calorimeter has been studied using electrons of 20 to 250 GeV in a test beam. The incident electron's energy was reconstructed by summing the energy measured in arrays of 3x3 or 5x5 channels. There was no significant amount of correlated noise observed within these arrays. For electrons incident at the centre of the studied 3x3 arrays of crystals, the mean stochastic term was measured to be 2.8% and the mean constant term to be 0.3%. The amount of the incident electron's energy which is contained within the array depends on its position of incidence. The variation of the containment with position is corrected for using the distribution of the measured energy within the array. For uniform illumination of a crystal with 120 GeV electrons a resolution of 0.5% was achieved. The energy resolution meets the design goal for the detector