Locally Advanced Esophageal Cancer Patients With Complete Clinical Response Post Neoadjuvant Chemoradiotherapy: A Markov Decision Analysis of Esophagectomy versus Active Surveillance

OBJECTIVE

Chemoradiation followed by esophagectomy is a standard treatment option for locally advanced esophageal cancer (LAEC) patients. Esophagectomy is a high-risk procedure, and recent evidence suggests select patients may benefit from omitting or delaying surgery. This study aims to compare surgery versus active surveillance for LAEC patients with complete clinical response (cCR) after neoadjuvant chemoradiotherapy (nCRT).

METHODS

Decision analysis with Markov modelling was utilized. The base case was a 60-year-old male with T3N0M0 esophageal cancer with cCR after nCRT. The decision was modelled for a 5-year time horizon. Primary outcomes were life-years (LY) and quality-adjusted life-years (QALYs). Probabilities and utilities were derived through literature. Deterministic sensitivity analyses were performed using ranges from literature with consideration for clinical plausibility.

RESULTS

Surgery was favoured for survival with an expected LY of 2.89 versus 2.64. After incorporating quality of life, active surveillance was favoured with an expected QALY of 1.70 versus 1.56. The model was sensitive to probability of recurrence on active surveillance (threshold value 0.598), probability of recurrence being resectable (0.318) and disutility of prior esophagectomy (-0.091). The model was not sensitive to perioperative morbidity and mortality.

CONCLUSIONS

Our study finds that surgery increases life expectancy but decreases quality-adjusted life years. Although the incremental change in QALY for either modality is insufficient to make broad clinical recommendations, our study demonstrates that either approach is acceptable. As probabilities of key factors are further defined in the literature, treatment decisions for patients with LAEC and a cCR after nCRT should consider histology, patient values, and quality of life.

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions

Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model's performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusions This is the first proof-of-concept artificial intelligence deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings.

Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors

INTRODUCTION

High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation).

METHODS

A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated.

RESULTS

The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00.

DISCUSSION

The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.

Performance of Intracystic Glucose Measurement for the Characterization of Pancreatic Cystic Lesions

BACKGROUND AND AIMS

Endoscopic ultrasound (EUS)-guided fine-needle aspiration (FNA) is essential for the classification of pancreatic cystic lesions (PCLs). Recently, intracystic glucose has been suggested as an alternative to carcinoembryonic antigen (CEA) level as a predictor of mucinous cystic lesions (M-PCLs). This study aims to evaluate the diagnostic performance of intra-cystic glucose in distinguishing between M-PCLs and non M-PCLs (NM-PCLs) and to analyze the possibility of on-site glucose measurement with a standard glucometer.

METHODS

Patients with PCLs submitted to EUS-FNA with simultaneous intracystic glucose measurement between 2017 and 2022 were included. The diagnostic performance of glucose versus CEA for the differentiation between M-PCLs and NM-PCLs was compared to a final diagnosis based on the analysis of surgical specimen, intracystic biopsy or, if this data was unavailable, multidisciplinary evaluation. A cut-off of <50 mg/dL was used for the diagnosis of MCLs. Additionally, the agreement between on-site glucose determination with a standard glucometer and laboratory glucose measurement was assessed.

RESULTS

Mucinous lesions accounted for 56% of all PCLs. The median values of glucose and CEA for M-PCLs were 18 mg/dL and 286 ng/mL, respectively. Intracystic glucose had a sensitivity and specificity of 93.2% and 76.5%, respectively, for the diagnosis of MCLs (versus 55.6% and 87.5%, respectively, for CEA). The area under the curve was 0.870 for on-site glucose (versus 0.806 for CEA). An excellent correlation was observed between on-site and laboratory glucose measurement (ρ=0.919).

CONCLUSIONS

The measurement of intracystic glucose showed superior performance compared with CEA in distinguishing between M-PCLs and NM-PCLs, with excellent correlation between on-site and conventional lab glucose measurement. Thus, on-site intracystic glucose appears to be an excellent biomarker for the characterization of PCLs due to its low cost, high availability, and the need for a minimal cyst fluid volume for its determination.

Differential amplification of the subtelomeric satellite DNA JcSAT1 in the genus Jatropha L. (Euphorbiaceae)

Satellite DNAs (satDNAs) are highly repetitive sequences that occur in virtually all eukaryotic genomes and can undergo rapid copy number and nucleotide sequence variation among relatives. After chromosomal mapping of the satDNA JcSAT1, it was found a large accumulation at subtelomeres of Jatropha curcas (subgenus Curcas), but an absence of these monomers in J. integerrima (subgenus Jatropha). This fact suggests a dynamic scenario for this satellite repeat in Jatropha genomes. Here, we used a multitasking approach (sequence analysis, DNA blotting and chromosomal mapping) to investigate the molecular organization and chromosomal abundance and distribution of JcSAT1 in a broader group of species from the subgenus Jatropha (J. gossypiifolia, J. mollissima, J. podagrica, and J. multifida) in addition to J. curcas, with the aiming of understanding the evolution of this satDNA. Based on the analysis of BAC clone sequences of J. curcas, a large array (~ 30 kb) of 80 homogeneous monomers of JcSAT1 was identified in BAC 23J11. The monomer size was conserved (~ 358 bp) and contained a telomeric motif at the 5' end. PCR amplification coupled with a Southern blot revealed the presence of JcSAT1-like sequences in all species examined. However, a large set of genome copies was identified only in J. curcas, where a ladder-like pattern with multimers of different sizes was observed. In situ hybridization of BAC 23J11 confirmed the subtelomeric pattern for J. curcas, but showed no signals on chromosomes of species from the subgenus Jatropha. Our data indicate that JcSAT1 is a highly homogeneous satDNA that originated from a region near the telomeres and spread throughout the chromosomal subtermini, possibly due to frequent ectopic recombination between these regions. The abundance of JcSAT1 in the genome of J. curcas suggests that an amplification event occurred either at the base of the subgenus Curcas or at least in this species, although the repeat is shared by all species of the genus studied so far.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

Estimating the environmental impact of endoscopic activity at a tertiary center: a pilot study

INTRODUCTION

The growing number of endoscopic procedures, frequently requiring single-use disposable instruments, is responsible for the production of a large amount of waste. To this date, the reality of waste production at large European Gastroenterology centers is unknown. This study aimed to estimate the amount of waste due to endoscopic practice at a tertiary center in Portugal.

METHODS

We performed a prospective study to calculate the mass (in kg) of residues generated during a period of 5 working days of endoscopic practice. We included residues produced at endoscopy suites, pre and postprocedure areas and during endoscope reprocessing. Residues were categorized as non-dangerous (groups I/II), of biologic risk (group III) and specific hazardous hospital residues (group IV). The production of residues separated for recycling/valorization (paper/card and plastic) was also quantified. The volume of water used for reprocessing an endoscope was also assessed.

RESULTS

During the analyzed period, 241 endoscopic procedures were performed. A total of 443.2 kg of waste (22.6 kg from groups I/II, 266.9 kg from group III and 3.9 kg from group IV) were produced, most from group III (75%). For each endoscopic procedure, 1.8 kg of waste was generated. Of the total waste mass, 17.8% was separated for recycling/valorization. A volume of 55L of water was required for reprocessing one endoscope.

CONCLUSION

Each endoscopic procedure generated a significant amount of waste and water consumption during reprocessing. These real-life analyses are a pivotal step before implementing effective measures to improve resource utilization and more sustainable practices.

Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy

Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights

In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

Shear Testing of Topologically Optimised Web Cover Plates in Splice Connections-Experiment Design and Results

Testing shear-resisting plates in steel connections is one of the most challenging laboratory undertakings in steel construction, as the most common experimental layout design includes simulating the connection with its adjoining members. This significant hindrance gained particular magnitude as the need to test prototypes of topologically optimised shear cover plates became more pressing. Indeed, new code-compliant topology optimisation approaches for steel construction have recently been offered, and physically non-linear analyses have been demonstrated to be vital for assessing these elements. Hence, a rapid and reliable experimental process has become a fundamental necessity. To answer this need, a novel layout is herein proposed, in which topologically optimised and previously numerically examined bolted shear plates of a well-known steel joint were tested. The results allowed for the definition of the material trilinear model for use in subsequent numerical analysis, as well as the validation of the numerical simulation results. The discrepancy between the previously mathematically anticipated and empirically determined ultimate resistance did not exceed 1.7%.

Predicting Factors of Clinical Outcomes in Patients Hospitalized after Esophageal Foreign Body or Caustic Injuries: The Experience of a Tertiary Center

Ingestion of foreign bodies (IFB) and ingestion of caustic agents are frequent non-hemorrhagic causes of endoscopic urgencies, with the potential for severe complications. This study aimed to evaluate the predicting factors of the clinical outcomes of patients hospitalized as a result of IFB or ingestion of caustics (IC). This was a retrospective single-center study of patients admitted for IFB or IC between 2000 and 2019 at a tertiary center. Demographic and clinical data, as well as preliminary exams, were evaluated. Also, variables of the clinical outcomes, including the length of stay (LS) and other inpatient complications, were assessed. Sixty-six patients were included (44 IFB and 22 IC). The median LS was 7 days, with no differences between the groups (p = 0.07). The values of C-reactive protein (CRP) upon admission correlated with the LS in the IFB group (p < 0.01) but not with that of those admitted after IC. In the IFB patients, a diagnosis of perforation on both an endoscopy (p = 0.02) and CT scan (p < 0.01) was correlated with the LS. The Zargar classification was not correlated with the LS in the IC patients (p = 0.36). However, it was correlated with antibiotics, nosocomial pneumonia and an increased need for intensive care treatment. CT assessment of the severity of the caustic lesions did not correlate with the LS. In patients admitted for IFB, CRP values may help stratify the probability of complications. In patients admitted due to IC, the Zargar classification may help to predict inpatient complications, but it does not correlate with the LS.

Comparative analysis of repetitive DNA in dysploid and non-dysploid Phaseolus beans

Structural karyotype changes result from ectopic recombination events frequently associated with repetitive DNA. Although most Phaseolus species present relatively stable karyotypes with 2n = 22 chromosomes, the karyotypes of species of the Leptostachyus group show high rates of structural rearrangements, including a nested chromosome fusion that led to the dysploid chromosome number of the group (2n = 20). We examined the roles of repetitive landscapes in the rearrangements of species of the Leptostachyus group using genome-skimming data to characterize the repeatome in a range of Phaseolus species and compared them to species of that group (P. leptostachyus and P. macvaughii). LTR retrotransposons, especially the Ty3/gypsy lineage Chromovirus, were the most abundant elements in the genomes. Differences in the abundance of Tekay, Retand, and SIRE elements between P. macvaughii and P. leptostachyus were reflected in their total amounts of Ty3/gypsy and Ty1/copia. The satellite DNA fraction was the most divergent among the species, varying both in abundance and distribution, even between P. leptostachyus and P. macvaughii. The rapid turnover of repeats in the Leptostachyus group may be associated with the several rearrangements observed.

Outcomes of Elective and Non-elective Fenestrated-branched Endovascular Aortic Repair for Treatment of Thoracoabdominal Aortic Aneurysms

OBJECTIVE

To describe outcomes after elective and non-elective fenestrated-branched endovascular aortic repair (FB-EVAR) for thoracoabdominal aortic aneurysms (TAAAs).

BACKGROUND

FB-EVAR has been increasingly utilized to treat TAAAs; however, outcomes after non-elective versus elective repair are not well described.

METHODS

Clinical data of consecutive patients undergoing FB-EVAR for TAAAs at 24 centers (2006-2021) were reviewed. Endpoints including early mortality and major adverse events (MAEs), all-cause mortality, and aortic-related mortality (ARM), were analyzed and compared in patients who had non-elective versus elective repair.

RESULTS

A total of 2603 patients (69% males; mean age 72±10 year old) underwent FB-EVAR for TAAAs. Elective repair was performed in 2187 patients (84%) and non-elective repair in 416 patients [16%; 268 (64%) symptomatic, 148 (36%) ruptured]. Non-elective FB-EVAR was associated with higher early mortality (17% vs 5%, P <0.001) and rates of MAEs (34% vs 20%, P <0.001). Median follow-up was 15 months (interquartile range, 7-37 months). Survival and cumulative incidence of ARM at 3 years were both lower for non-elective versus elective patients (50±4% vs 70±1% and 21±3% vs 7±1%, P <0.001). On multivariable analysis, non-elective repair was associated with increased risk of all-cause mortality (hazard ratio, 1.92; 95% CI] 1.50-2.44; P <0.001) and ARM (hazard ratio, 2.43; 95% CI, 1.63-3.62; P <0.001).

CONCLUSIONS

Non-elective FB-EVAR of symptomatic or ruptured TAAAs is feasible, but carries higher incidence of early MAEs and increased all-cause mortality and ARM than elective repair. Long-term follow-up is warranted to justify the treatment.

Deep Learning and Minimally Invasive Endoscopy: Automatic Classification of Pleomorphic Gastric Lesions in Capsule Endoscopy

INTRODUCTION

Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield for detecting gastric lesions is suboptimal. Convolutional neural networks (CNNs) are artificial intelligence models with great performance for image analysis. Nonetheless, their role in gastric evaluation by wireless CE (WCE) has not been explored.

METHODS

Our group developed a CNN-based algorithm for the automatic classification of pleomorphic gastric lesions, including vascular lesions (angiectasia, varices, and red spots), protruding lesions, ulcers, and erosions. A total of 12,918 gastric images from 3 different CE devices (PillCam Crohn's; PillCam SB3; OMOM HD CE system) were used from the construction of the CNN: 1,407 from protruding lesions; 994 from ulcers and erosions; 822 from vascular lesions; and 2,851 from hematic residues and the remaining images from normal mucosa. The images were divided into a training (split for three-fold cross-validation) and validation data set. The model's output was compared with a consensus classification by 2 WCE-experienced gastroenterologists. The network's performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value and negative predictive value, and area under the precision-recall curve.

RESULTS

The trained CNN had a 97.4% sensitivity; 95.9% specificity; and positive predictive value and negative predictive value of 95.0% and 97.8%, respectively, for gastric lesions, with 96.6% overall accuracy. The CNN had an image processing time of 115 images per second.

DISCUSSION

Our group developed, for the first time, a CNN capable of automatically detecting pleomorphic gastric lesions in both small bowel and colon CE devices.

Artificial Intelligence and Anorectal Manometry: Automatic Detection and Differentiation of Anorectal Motility Patterns-A Proof-of-Concept Study

INTRODUCTION

Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this examination is limited, and the complexity of data analysis and report is a significant drawback. This pilot study aimed to develop and validate an artificial intelligence model to automatically differentiate motility patterns of fecal incontinence (FI) from obstructed defecation (OD) using ARM data.

METHODS

We developed and tested multiple machine learning algorithms for the automatic interpretation of ARM data. Four models were tested: k-nearest neighbors, support vector machines, random forests, and gradient boosting (xGB). These models were trained using a stratified 5-fold strategy. Their performance was assessed after fine-tuning of each model's hyperparameters, using 90% of data for training and 10% of data for testing.

RESULTS

A total of 827 ARM examinations were used in this study. After fine-tuning, the xGB model presented an overall accuracy (84.6% ± 2.9%), similar to that of random forests (82.7% ± 4.8%) and support vector machines (81.0% ± 8.0%) and higher that of k-nearest neighbors (74.4% ± 3.8%). The xGB models showed the highest discriminating performance between OD and FI, with an area under the curve of 0.939.

DISCUSSION

The tested machine learning algorithms, particularly the xGB model, accurately differentiated between FI and OD manometric patterns. Subsequent development of these tools may optimize the access to ARM studies, which may have a significant impact on the management of patients with anorectal functional diseases.

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

Integration of the social determinants of health into quality indicators for colorectal cancer surgery: a scoping review protocol

INTRODUCTION

Quality monitoring is a critical component of high-performing cancer care systems. Quality indicators (QIs) are standardised, evidence-based measures of healthcare quality that allow healthcare systems to track performance, identify gaps in healthcare delivery and inform areas of priority for strategic planning. Social structures and economic systems that allow for unequal access to power and resources that shape health and health inequities can be described through the social determinants of health (SDoH) framework. Therefore, granular analysis of healthcare quality through SDoH frameworks is required to identify patient subgroups who may experience health inequity. Given the high burden of disease of colorectal cancer (CRC) and well-defined cancer care pathways, CRC is often the first disease site targeted by health systems for quality improvement. The objective of this review is to examine how SDoH have been integrated into QIs for CRC surgery. This review aims to address three primary questions: (1) Have SDoH been integrated into the development, reporting and assessment of CRC surgery QIs? (2) When integrated, what measures and statistical methods have been applied? (3) In which direction do individual SDoH influence QIs outputs?

METHODS

This review will follow Arksey and O'Malley frameworks for scoping reviews. We will search MEDLINE, EMBASE, HealthSTAR databases for papers that examine QIs for CRC surgery applicable to healthcare systems from database inception until January 2023. Interventional trials, prospective and retrospective observational studies, reviews, case series and qualitative study designs will be included. Two authors will independently review all titles, abstracts and full texts to determine which studies meet the inclusion criteria.

ETHICS & DISSEMINATION

No ethics approval is required for this review. Results will be disseminated through scientific presentation and relevant conferences targeted for researchers examining healthcare quality and equity in cancer care.

REGISTRATION DETAILS

osf.io/vfzd3-Open Science Framework.

Metabolite Profile Characterization of Cyanobacterial Strains with Bioactivity on Lipid Metabolism Using In Vivo and In Vitro Approaches

Cyanobacteria have demonstrated their therapeutic potential for many human diseases. In this work, cyanobacterial extracts were screened for lipid reducing activity in zebrafish larvae and in fatty-acid-overloaded human hepatocytes, as well as for glucose uptake in human hepatocytes and ucp1 mRNA induction in murine brown adipocytes. A total of 39 cyanobacteria strains were grown and their biomass fractionated, resulting in 117 chemical fractions. Reduction of neutral lipids in zebrafish larvae was observed for 12 fractions and in the human hepatocyte steatosis cell model for five fractions. The induction of ucp1 expression in murine brown adipocytes was observed in six fractions, resulting in a total of 23 bioactive non-toxic fractions. All extracts were analyzed by untargeted UPLC-Q-TOF-MS mass spectrometry followed by multivariate statistical analysis to prioritize bioactive strains. The metabolite profiling led to the identification of two markers with lipid reducing activity in zebrafish larvae. Putative compound identification using mass spectrometry databases identified them as phosphatidic acid and aromatic polyketides derivatives-two compound classes, which were previously associated with effects on metabolic disorders. In summary, we have identified cyanobacterial strains with promising lipid reducing activity, whose bioactive compounds needs to be identified in the future.

Seasonality and Objective Physical Activity and Sedentary Behaviour among Older Adults from Four European Countries

OBJECTIVE

The present study aimed to explore the relationship between objective physical activity and sedentary behaviour with seasonality among a sample of older adults living in four European countries.

METHODS

A sample of 169 older adults living in Croatia, Greece, Portugal, and Poland (mean age = 72.2 ± 6.0, 68% female) had valid objective physical activity and sedentary behaviour data collected in different seasons of the year: spring and autumn/winter. Physical activity and sedentary behaviour were collected with accelerometers (ActiGraph, GT3X), over 7 consecutive days, in both periods. A valid record was defined as at least two weekdays and one weekend day with 10 hours of wearing time. Analyses were performed with IBM SPSS 28.0, using t-test, ANOVA, and binary logistic regressions.

RESULTS

Most older adults from the four countries met the physical activity guidelines in spring and autumn/winter. No significant variations were found across seasons for sedentary behaviour and physical activity both for light and vigorous intensity, regardless of sex, country, education, and body mass index (BMI). A decline in moderate physical activity intensity from spring to autumn/winter was found for those with lower education and higher BMI.

CONCLUSION

The promotion of physical activity must be considered in programs to promote healthy aging throughout the year, especially considering the moderate intensity and those populations with higher BMI and lower educational levels.

Integrating equity frameworks into surgical quality improvement and health administrative databases: A narrative review

Ensuring safe, timely, and effective surgery is critical for high-quality healthcare and is the goal of surgical quality monitoring systems. At the heart of these systems are health administrative databases which house patient clinico-demographic information, healthcare processes and outcomes. Through analysis of monitoring systems outputs, we can identify gaps within healthcare delivery, patient experience, and surgical outcomes. However, gaps in our healthcare can only be measured by the variables we collect. Equity stratifiers are sociodemographic descriptors that can identify patient populations who experience differences in health and healthcare that may be considered unjust or unfair. They include age, education, gender, geographic location, income, Indigenous identity, racialized group, and sex at birth. These equity stratifiers represent measurable components of the social determinants of health housed within health administrative databases and allow for standardized analysis and reporting of health inequity. However, not all databases collect these stratifiers - making granular analysis of patient subgroups who may experience health inequity impossible to measure. Moreover, in databases that do collect this information, a wide range in the classification systems used makes for comparisons across jurisdictions challenging. The focus of this narrative review will be to apply the principles of the equity stratifier framework to examine what measures are collected in surgical quality improvement databases, cancer monitoring systems and provincial/state health administrative databases in the United States of America and Canada. The goal of this narrative review is to 1) inform researchers, surgeons, and policymakers of the current landscape of social variables collected within common health administrative databases. 2) Outline the pros and cons of the current collection system. 3) Issue a call to action for policymakers to incorporate health equity frameworks into the collection and reporting of data.

Triple-sized intragastric balloon due to spontaneous hyperinflation

A 38-year-old female with medical history of breast cancer, hypertension, diabetes mellitus and obesity (body max index 55kg/m2), was admitted to the emergency room with complains of nausea and vomits. Three weeks prior to the presentation, she placed an intragastric balloon (IGB) (Orbera365™, Apollo endosurgery Inc., Austin, TX), for weight loss, filled with 600ml solution of saline and methylene blue dye. Upon physical examination, she was dehydrated and presented with a bulging of the upper abdominal wall associated with mild abdominal pain. Laboratory tests showed severe metabolic alkalosis, hypocalcemia and hypokalemia. Abdominal x-ray revealed gastric distension with an increased size IGB, measuring 164.3*145.6*144.1 mm (estimated volume of 1800mL), with an air-fluid level. Upper endoscopy revealed the balloon stuck in the antrum. A catheter needle was used to puncture and deflate the balloon. Once deflated it was removed with endoscopic forceps. The fluid was not sent for microbiologic culture. After IGB removal, hydroelectrolytic disturbances were resolved and oral feeding was promptly resumed without further complications.

Infectious proctitis: what every gastroenterologist needs to know

The incidence of sexually transmitted infections (STI) is rising, especially in high-risk groups, namely people living with human immunodeficiency virus (HIV), men who have sex with men, and people with multiple sexual partners. Additionally, the growing availability and use of pre-exposure prophylaxis to prevent HIV infection appears to be associated with an increased risk of infection by venereal agents. The correct recognition of these infections is crucial, not only for individual patients, but also in terms of public health. Furthermore, a diligent diagnostic assessment is key for an efficient therapeutic approach. Infectious proctitis (IP) predominantly occurs in individuals with a history of receptive anal exposure, being a frequent cause for referral to a gastroenterology specialist. The most frequently identified agents are Neisseria gonorrhoeae, Chlamydia trachomatis, Herpes simplex virus, and Treponema pallidum. This paper aims to provide a practice-oriented and up-to-date review regarding the diagnostic and therapeutic approaches to patients with suspected IP. The authors reviewed the most important issues in terms of clinical history, physical examination, and specific diagnostic and therapeutic methods. It is also highlighted the most important topics regarding vaccination, screening for other STIs and differential diagnosis with inflammatory bowel disease. Identification of high-risk groups, screening of potential STIs, and notification of diagnosed anorectal diseases are extremely important and essential to prevent transmission and other complications.

Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents

With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.

Advances, Challenges, and Future Perspectives of Microwave Reflectometry for Plasma Position and Shape Control on Future Nuclear Fusion Devices

Providing energy from fusion and finding ways to scale up the fusion process to commercial proportions in an efficient, economical, and environmentally benign way is one of the grand challenges for engineering. Controlling the burning plasma in real-time is one of the critical issues that need to be addressed. Plasma Position Reflectometry (PPR) is expected to have an important role in next-generation fusion machines, such as DEMO, as a diagnostic to monitor the position and shape of the plasma continuously, complementing magnetic diagnostics. The reflectometry diagnostic uses radar science methods in the microwave and millimetre wave frequency ranges and is envisaged to measure the radial edge density profile at several poloidal angles providing data for the feedback control of the plasma position and shape. While significant steps have already been given to accomplish that goal, with proof of concept tested first in ASDEX-Upgrade and afterward in COMPASS, important, ground-breaking work is still ongoing. The Divertor Test Tokamak (DTT) facility presents itself as the appropriate future fusion device to implement, develop, and test a PPR system, thus contributing to building a knowledge database in plasma position reflectometry required for its application in DEMO. At DEMO, the PPR diagnostic's in-vessel antennas and waveguides, as well as the magnetic diagnostics, may be exposed to neutron irradiation fluences 5 to 50 times greater than those experienced by ITER. In the event of failure of either the magnetic or microwave diagnostics, the equilibrium control of the DEMO plasma may be jeopardized. It is, therefore, imperative to ensure that these systems are designed in such a way that they can be replaced if necessary. To perform reflectometry measurements at the 16 envisaged poloidal locations in DEMO, plasma-facing antennas and waveguides are needed to route the microwaves between the plasma through the DEMO upper ports (UPs) to the diagnostic hall. The main integration approach for this diagnostic is to incorporate these groups of antennas and waveguides into a diagnostics slim cassette (DSC), which is a dedicated complete poloidal segment specifically designed to be integrated with the water-cooled lithium lead (WCLL) breeding blanket system. This contribution presents the multiple engineering and physics challenges addressed while designing reflectometry diagnostics using radio science techniques. Namely, short-range dedicated radars for plasma position and shape control in future fusion experiments, the advances enabled by the designs for ITER and DEMO, and the future perspectives. One key development is in electronics, aiming at an advanced compact coherent fast frequency sweeping RF back-end [23-100 GHz in few μs] that is being developed at IPFN-IST using commercial Monolithic Microwave Integrated Circuits (MMIC). The compactness of this back-end design is crucial for the successful integration of many measurement channels in the reduced space available in future fusion machines. Prototype tests of these devices are foreseen to be performed in current nuclear fusion machines.

Novel patient-centred outcome in cancer care, days at home: a scoping review protocol

INTRODUCTION

Patient-centred care is valued by patients and providers. As management of cancer becomes increasingly complex, the value of providing care that incorporates an individual's values and preferences along with demographic and tumour factors is increasingly important. To improve care, patients with cancer need easily accessible information on the outcomes important to them. The patient-centred outcome, days at home (DAH), is based on a construct that measures the time a patient spends alive and out of hospitals and healthcare institutions. DAH is accurately measured from various data sources and has shown construct validity with many patient-centred outcomes. There is significant heterogeneity in terms used and definitions for DAH in cancer care. This scoping review aims to consolidate information on the outcome DAH in cancer care and to review definitions and terms used to date to guide future use of DAH as a patient-centred care, research and policy tool.

METHODS AND ANALYSIS

This scoping review protocol has been designed with joint guidance from the JBI Manual for Evidence Synthesis and the expanded framework from Arksey and O'Malley. We will systematically search MEDLINE, Embase and Scopus for studies measuring DAH, or equivalent, in the context of active adult cancer care. Broad inclusion criteria have been developed, given the recent introduction of DAH into cancer literature. Editorials, opinion pieces, case reports, abstracts, dissertations, protocols, reviews, narrative studies and grey literature will be excluded. Two authors will independently perform full-text selection. Data will be extracted, charted and summarised both qualitatively and quantitively.

ETHICS AND DISSEMINATION

No ethics approval is required for this scoping review. Results will be disseminated through scientific publication and presentation at relevant conferences.

Changes in Estimated Body Composition and Physical Fitness of Adolescent Boys after One Year of Soccer Training

Sports participation is one of the most popular forms of physical activity among youngsters. This study aimed to examine the changes in the estimated body composition, strength, and flexibility of adolescent boys after 12 months of soccer training compared with those of age-matched controls with non-sports participation. We assessed 137 boys (62 soccer players and 75 controls) at baseline (TM1) and 12 months later (TM2). The differences in estimated body composition, strength, and flexibility were investigated using a repeated measure analysis of variance. The analysis revealed a significant main effect of soccer training on fat mass (F = 73.503, p ≤ 0.01, η2 = 0.59) and fat-free mass (F = 39.123, p ≤ 0.01, η2 = 0.48). Over time, the soccer group decreased their fat mass and increased their fat-free mass, while the opposite results were observed for the controls. Among physical fitness tests, a substantial effect of soccer training was evidenced for the sit-up performance (F = 16.224, p ≤ 0.01, η2 = 0.32). Regarding the time factor, significant effects were noted for height and handgrip strength. No significant differences were detected for flexibility. Overall, the benefits of soccer training were exhibited by the larger improvements in fat mass, fat-free mass, sit-ups, and handgrip strength performance, underlining the important role of soccer participation during adolescence.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network

BACKGROUND AND AIMS

capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions.

METHODS

a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values.

RESULTS

a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second.

CONCLUSIONS

we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

Comparison of single- and multistage strategies during fenestrated-branched endovascular aortic repair of thoracoabdominal aortic aneurysms

OBJECTIVE

The aim of this study was to compare outcomes of single or multistage approach during fenestrated-branched endovascular aortic repair (FB-EVAR) of extensive thoracoabdominal aortic aneurysms (TAAAs).

METHODS

We reviewed the clinical data of consecutive patients treated by FB-EVAR for extent I to III TAAAs in 24 centers (2006-2021). All patients received a single brand manufactured patient-specific or off-the-shelf fenestrated-branched stent grafts. Staging strategies included proximal thoracic aortic repair, minimally invasive segmental artery coil embolization, temporary aneurysm sac perfusion and combinations of these techniques. Endpoints were analyzed for elective repair in patients who had a single- or multistage approach before and after propensity score adjustment for baseline differences, including the composite 30-day/in-hospital mortality and/or permanent paraplegia, major adverse event, patient survival, and freedom from aortic-related mortality.

RESULTS

A total of 1947 patients (65% male; mean age, 71 ± 8 years) underwent FB-EVAR of 155 extent I (10%), 729 extent II (46%), and 713 extent III TAAAs (44%). A single-stage approach was used in 939 patients (48%) and a multistage approach in 1008 patients (52%). A multistage approach was more frequently used in patients undergoing elective compared with non-elective repair (55% vs 35%; P < .001). Staging strategies were proximal thoracic aortic repair in 743 patients (74%), temporary aneurysm sac perfusion in 128 (13%), minimally invasive segmental artery coil embolization in 10 (1%), and combinations in 127 (12%). Among patients undergoing elective repair (n = 1597), the composite endpoint of 30-day/in-hospital mortality and/or permanent paraplegia rate occurred in 14% of single-stage and 6% of multistage approach patients (P < .001). After adjustment with a propensity score, multistage approach was associated with lower rates of 30-day/in-hospital mortality and/or permanent paraplegia (odds ratio, 0.466; 95% confidence interval, 0.271-0.801; P = .006) and higher patient survival at 1 year (86.9±1.3% vs 79.6±1.7%) and 3 years (72.7±2.1% vs 64.2±2.3%; adjusted hazard ratio, 0.714; 95% confidence interval, 0.528-0.966; P = .029), compared with a single stage approach.

CONCLUSION

Staging elective FB-EVAR of extent I to III TAAAs was associated with decreased risk of mortality and/or permanent paraplegia at 30 days or within hospital stay, and with higher patient survival at 1 and 3 years.

Relationship between Objective and Subjective Fatigue Monitoring Tests in Professional Soccer

Studying fatigue is challenging because it is influenced by physiological, psychological, and sociological states. Fatigue can be assessed objectively or subjectively, but the literature has difficulty understanding how an analytical test relates to a response via a questionnaire. Thus, the purpose of this study was to evaluate the relationships between objective fatigue variables (Squat Jump (SJ) and Countermovement Jump (CMJ)) measured on day-2 to the game and subjective fatigue (Rating Perceived Exertion (RPE) measured on day-3 to the game and Hooper Index (HI) measured on day-2). The sample comprised 32 professional football players from the First Portuguese League aged 25.86 ± 3.15 years. The Spearman correlations and regression analyses were used to study the relationships between the variables. The results showed statistically significant (p < 0.05) but small correlations (0.113−0.172) between several objective metrics and the subjective metrics evaluated. In addition, we found two weak models with statistical significance (p < 0.05) between the dependent objective variables (contact time, height, and elasticity index) and the HI (R2 = 3.7%) and RPE (R2 = 1.6%). Also, nine statistically significant (p < 0.05) but weak models were observed between the subjective dependent variables (HI and RPE) and contact time (R2 = 1.8−2.7%), flight time (R2 = 1.1−1.9%), height (R2 = 1.2−2.3%), power (R2 = 1.4%), pace (R2 = 1.2−2.1%), and elasticity index (R2 = 1.6%). In conclusion, objective and subjective fatigue-monitoring tests in professional soccer do not measure identical but rather complementary aspects of fatigue, and therefore, both need to be considered to gain a holistic perspective.

Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction

In this work, a large-scale tactile detection system is proposed, whose development is based on a soft structure using Machine Learning and Computer Vision algorithms to map the surface of a forearm sleeve. The current application has a cylindrical design, whose dimensions intend to be like a human forearm or bicep. The model was developed assuming that deformations occur only at one section at a time. The goal for this system is to be coupled with the CHARMIE robot, a collaborative robot for domestic and medical environments. This system allows the contact detection of the entire forearm surface enabling interaction between a Human Being and a robot. A matrix with sections can be configured to present certain functionalities, allowing CHARMIE to detect contact in a particular section, and thus perform a specific behaviour. After building the dataset, an Artificial Neural Network (ANN) was created. This network was called Section Detection Network (SDN), and through Supervised Learning, a model was created to predict the contact location. Furthermore, Stratified K-Fold Cross Validation (SKFCV) was used to divide the dataset. All these steps resulted in Neural Network with a test data accuracy higher than 80%. Regarding the real-time evaluation, a graphical interface was structured to demonstrate the predicted class and the corresponding probability. This research concluded that the method described has enormous potential to be used as a tool for service robots allowing enhanced human-robot interaction.

Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network

BACKGROUND AND AIM

Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images.

METHODS

A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve.

RESULTS

The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second.

CONCLUSIONS

The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

Endoscopic Ultrasound Through-The-Needle Biopsy of Pancreatic Cysts: Toward Procedure Standardization

BACKGROUND AND AIMS

EUS-guided through-the-needle microforceps biopsy (EUS-TTNB) was introduced as a new diagnostic tool to establish pancreatic cyst histotype and help to better risk stratify the patients. The aim of this study was to describe the technical success, diagnostic yield, and adverse events of through-the-needle biopsy and discuss the technique variations, focusing on future procedure standardization.

METHODS

We performed a prospective single-center study including patients with presumed mucinous cysts harboring worrisome features or indeterminate cyst type on imaging, submitted to EUS-TTNB using Moray® microforceps between March 2018 and September 2021. Specimens were processed as a cell-block.

RESULTS

We included 40 patients. Technical success was 97.5%. The diagnostic yield was 72.5% for TTNB whereas for cyst fluid cytology/analysis it was 27.5%. Moreover, without TTNB 5 mucinous lesions would not have been diagnosed. TTNB had a sensitivity of 76% and a specificity of 91%, while FNA cytology had a sensitivity and specificity of 35% and 91%, respectively. Moreover for IPMN lesions, subtyping was possible in 63% of cases. TTNB resulted in change in clinical management in 20% of patients. We registered three adverse events: 2 self-limited intracystic bleeding and 1 patient with abdominal pain not associated with pancreatitis.

CONCLUSION

TTNB proved superior to cyst fluid analysis and cytology for the definition of cyst histotype and mucinous cyst diagnosis with acceptable risk profile. Further studies should explore the best steps for procedure standardization.

Artificial Intelligence and Device-Assisted Enteroscopy: Automatic Detection of Enteric Protruding Lesions Using a Convolutional Neural Network

INTRODUCTION

Device-assisted enteroscopy (DAE) plays a major role in the investigation and endoscopic treatment of small bowel diseases. Recently, the implementation of artificial intelligence (AI) algorithms to gastroenterology has been the focus of great interest. Our aim was to develop an AI model for the automatic detection of protruding lesions in DAE images.

METHODS

A deep learning algorithm based on a convolutional neural network was designed. Each frame was evaluated for the presence of enteric protruding lesions. The area under the curve, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of the convolutional neural network.

RESULTS

A total of 7,925 images from 72 patients were included. Our model had a sensitivity and specificity of 97.0% and 97.4%, respectively. The area under the curve was 1.00.

DISCUSSION

Our model was able to efficiently detect enteric protruding lesions. The development of AI tools may enhance the diagnostic capacity of deep enteroscopy techniques.

The Attitude of Portuguese Physical Education Teachers toward Physical Fitness

In this study, we aimed to examine how Portuguese physical education teachers perceive the development of physical fitness through fitness tests in schools. The participants were 764 Portuguese teachers teaching at middle-school and high-school levels. The Physical Education Teacher Attitudes Toward Fitness Tests Scale (PETAFTS) was used to collect the data. The means and confidence intervals for each attitude subdomain and the overall attitude were computed. A one-way ANOVA was used to examine the group differences in three subdomains of the attitudes of teachers by different variables. The overall attitude of teachers toward fitness tests was slightly positive according to a 7-point Likert scale (5.52, 95% CI: 5.47, 5.58). The results suggested that female teachers found fitness tests more useful, but male teachers significantly enjoyed implementing them. The data collected also showed that younger teachers found the implementation of fitness tests significantly more enjoyable than older teachers. In conclusion, future research should prioritise specific intervention content considering gender and the age of teachers to reinforce the development of physical fitness through fitness tests in schools.

Rheological evolution of straw-cattle manure (SCM) treated by dry anaerobic digestion in batch and in continuous pilot reactors

Knowledge of rheological evolution of biomass during dry anaerobic digestion (D-AD) is important in the engineering design, modeling, and operation of D-AD reactors. In this work, two methods of rheological analysis, the slump test and the shear-box, were used to measure the evolution of the yield stress, cohesion and friction angle of the straw-cattle manure (SCM) during the D-AD. Firstly, four 60 L batch leach-bed reactors (LBR) were started in parallel and stopped at different stages of the D-AD process on days 0, 10, 21 and 31. Secondly, a 500 L and 2 m length plug flow reactor (PFR) was operated with 40 days of solid retention time and samples were recovered at different positions. The solid degradation during D-AD process was monitored by analysis of the degradation of volatile solids, the fiber content and the Flash BMP. Similar degradation patterns of SCM and rheological evolution were observed in both reactors type. VS content decreased of 10.7% and 10.2% in 30 days in PFR and LBR respectively. VS degradation in both cases was well explained by hemicellulose and cellulose consuming in D-AD process. Considering the rheological analysis, the results showed that D-AD induced a reduction of the yield stress of 28.1 and 24.2% in 30 days in PFR and LBR respectively. Moreover, a similar evolution of cohesion and friction angle value for samples from both reactors was observed. This study demonstrates the close relationship between the state of degradation of the solid biomass and its rheological properties.

Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy

Background and study aims  Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods  A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results  The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions  Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

Satellite DNA probes of Alstroemeria longistaminea (Alstroemeriaceae) paint the heterochromatin and the B chromosome, reveal a G-like banding pattern, and point to a strong structural karyotype conservation

Alstroemeria species present a well-conserved and asymmetric karyotype. The genus is divided into a Chilean clade, rich in heterochromatin, and a Brazilian clade, poor in heterochromatin. We investigated the distribution of the main repetitive sequences in the chromosomes of the Brazilian species A. longistaminea (2n = 16 + 0-6B) aiming to evaluate the role played by these sequences on the structural organization of the karyotype. In situ hybridization of the three most abundant retrotransposons, corresponding to ~ 45% of the genome, was uniformly distributed. Three satellite DNA sequences, representing near half of the whole satellite fraction (1.93% of the genome), were mainly concentrated on the heterochromatin and one of them painted the whole B chromosome. Noteworthy, some satellites were located on euchromatin, either dispersed or concentrated in clusters along the chromosomes, revealing a G-band-like pattern. The two satellites that presented more C-band- and G-band-like labeling were also hybridized in situ in two other Alstroemeria species. They revealed astonishing similar patterns of distribution, indicating an unusually structural karyotype conservation among Brazilian species.

A10 EARLY LIFE SENSITIZATION TO GLUTEN INDUCES SUSTAINED IMMUNOPATHOLOGY IN DR3-DQ2 MICE

Background

Celiac disease (CeD) is an autoimmune T-cell mediated enteropathy, triggered by gluten, a group of proteins found in wheat, barley, and rye. The defined role of gluten as a dietary trigger, necessary genes (HLA-DQ2 and/or DQ8), and tissue transglutaminase (TG2) as the autoantigen together, are unique features of CeD. Although CeD onset can occur at any age, first dietary introduction of gluten during infancy is a critical window of exposure, especially in infants homozygous for the HLA DQ2.5 allele. While adult sensitization studies have been recently performed in DR3-DQ2 mice, the consequences of early life gluten sensitization timing remain unexplored.

Aims

Our aim was to characterize gluten-immunopathology and CeD-specific serology in specific pathogen free (SPF) DR3-DQ2 transgenic mice sensitized to gluten, 1 week before weaning.

Methods

Seven-week-old SPF DR3-DQ2 transgenic mice, kept on a gluten-free diet (GFD), were paired for breeding. At post-natal day 3, pups were standardized to 4 per litter (n=2 male, n=2 female) to ensure equal nutrition across litters. At 14 days of age, pups were sensitized with pepsin-trypsin digested gliadin and cholera toxin (CT) three times in one week (n=15). At 21 days of age, pups were weaned and placed either on a gluten-containing diet (n=7), (equivalent of 20g/d of gluten in a human diet -high dose-) or an isocaloric GFD (n=8) until 10 weeks of age. Non-sensitized controls (n=7) received only CT and were kept on the GFD. At sacrifice, serum was collected for anti-TG2 and anti-gliadin antibodies (AGA). Jejunal tissue was collected for histological analysis using villus-to-crypt (V/C) ratios and CD3+ intraepithelial lymphocytes (IEL) counts.

Results

Gluten-sensitized mice placed on a gluten-containing diet post-weaning had lower V/C ratios and higher CD3+ IEL counts compared with controls (p&lt;0.01). Pre-weaning sensitized mice that were kept on a GFD post-weaning had sustained decreases in V/C ratios and higher CD3+ IEL counts (p&lt;0.01). Out of 15 sensitized mice, 7 developed positive anti-gliadin IgA (p=0.02) and 4 had positive anti-TG2 IgA antibodies (p=0.01) in intestinal contents, irrespective of gluten in the diet. None of the controls had detectable AGA or anti-TG2 antibodies.

Conclusions

Pre-weaning gluten sensitization of DR3-DQ2 mice induced prolonged gluten immunopathology that did not reverse after 5 weeks on a GFD. Our results indicate that young DR3-DQ2 mice are susceptible to gluten sensitization, with sustained immunopathology, suggesting a critical window of vulnerability in familial carriers of DQ2.5. This novel model will be useful to investigate environmental cofactors at the first time of gluten introduction to the diet.

Funding Agencies

CIHR

Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network

Background and study aims  Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods  A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results  The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions  This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study

BACKGROUND AND AIMS

The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images.

METHODS

We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values.

RESULTS

A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame.

CONCLUSIONS

The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.