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.

The secretome of macrophages has a differential impact on spinal cord injury recovery according to the polarization protocol

Introduction

The inflammatory response after spinal cord injury (SCI) is an important contributor to secondary damage. Infiltrating macrophages can acquire a spectrum of activation states, however, the microenvironment at the SCI site favors macrophage polarization into a pro-inflammatory phenotype, which is one of the reasons why macrophage transplantation has failed.

Methods

In this study, we investigated the therapeutic potential of the macrophage secretome for SCI recovery. We investigated the effect of the secretome in vitro using peripheral and CNS-derived neurons and human neural stem cells. Moreover, we perform a pre-clinical trial using a SCI compression mice model and analyzed the recovery of motor, sensory and autonomic functions. Instead of transplanting the cells, we injected the paracrine factors and extracellular vesicles that they secrete, avoiding the loss of the phenotype of the transplanted cells due to local environmental cues.

Results

We demonstrated that different macrophage phenotypes have a distinct effect on neuronal growth and survival, namely, the alternative activation with IL-10 and TGF-β1 (M(IL-10+TGF-β1)) promotes significant axonal regeneration. We also observed that systemic injection of soluble factors and extracellular vesicles derived from M(IL-10+TGF-β1) macrophages promotes significant functional recovery after compressive SCI and leads to higher survival of spinal cord neurons. Additionally, the M(IL-10+TGF-β1) secretome supported the recovery of bladder function and decreased microglial activation, astrogliosis and fibrotic scar in the spinal cord. Proteomic analysis of the M(IL-10+TGF-β1)-derived secretome identified clusters of proteins involved in axon extension, dendritic spine maintenance, cell polarity establishment, and regulation of astrocytic activation.

Discussion

Overall, our results demonstrated that macrophages-derived soluble factors and extracellular vesicles might be a promising therapy for SCI with possible clinical applications.

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.

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.

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.

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.

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.

Sustained Release of Human Adipose Tissue Stem Cell Secretome from Star-Shaped Poly(ethylene glycol) Glycosaminoglycan Hydrogels Promotes Motor Improvements after Complete Transection in Spinal Cord Injury Rat Model

Adipose tissue-derived stem cells (ASCs) have been shown to assist regenerative processes after spinal cord injury (SCI) through their secretome, which promotes several regenerative mechanisms, such as inducing axonal growth, reducing inflammation, promoting cell survival, and vascular remodeling, thus ultimately leading to functional recovery. However, while systemic delivery (e.g., i.v. [intravenous]) may cause off-target effects in different organs, the local administration has low efficiency due to fast clearance by body fluids. Herein, a delivery system for human ASCs secretome based on a hydrogel formed of star-shaped poly(ethylene glycol) (starPEG) and the glycosaminoglycan heparin (Hep) that is suitable to continuously release pro-regenerative signaling mediators such as interleukin (IL)-4, IL-6, brain-derived neurotrophic factor, glial-cell neurotrophic factor, and beta-nerve growth factor over 10 days, is reported. The released secretome is shown to induce differentiation of human neural progenitor cells and neurite outgrowth in organotypic spinal cord slices. In a complete transection SCI rat model, the secretome-loaded hydrogel significantly improves motor function by reducing the percentage of ameboid microglia and systemically elevates levels of anti-inflammatory cytokines. Delivery of ASC-derived secretome from starPEG-Hep hydrogels may therefore offer unprecedented options for regenerative therapy of SCI.

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.

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.

Differential gene co-expression network analyses reveal novel molecules associated with transcriptional dysregulation of key biological processes in osteoarthritis knee cartilage

OBJECTIVES

To compare co-expression networks of normal and osteoarthritis knee cartilage to uncover molecules associated with the transcriptional misregulation compromising biological processes (BPs) critical for cartilage homeostasis.

DESIGN

Normal and osteoarthritis human knee cartilage RNA-seq GSE114007 dataset was obtained from the Gene Expression Omnibus database. Partial Correlation and Information Theory (PCIT) algorithm was used to build co-expression networks containing all nodes connecting to at least one differentially expressed gene (DEG) in normal and osteoarthritis networks. Hub and hub centrality genes were used to perform functional enrichment analysis. Enriched BPs known to be associated with both healthy and diseased cartilage were compared in depth.

RESULTS

Differential co-expression network analyses allowed the identification of DDX43 and USP42 as exclusively co-expressed with DEGs in normal and osteoarthritis networks, respectively. The top hub and hub centrality genes of these networks were HIST1H3A and SNHG12 (normal) and TAF9B and OTUD1 (osteoarthritis). Enrichment analysis revealed several shared BPs between the contrasting groups, which are well-known in osteoarthritis pathogenesis. Protein-protein interaction network analysis for these BPs showed a global down-regulation of transcription factors in osteoarthritis. Specific transcription factors were identified as pleiotropic mediators in articular cartilage maintenance since they take part in several BPs. In addition, chromatin organisation and modification proteins were found relevant for osteoarthritis development.

CONCLUSION

Differential gene co-expression analysis allowed the identification of novel and high priority therapeutic candidate genes that may drive modifications in the transcriptional "status" of cartilage in osteoarthritis.

Caso raro de um Schwannoma no pé – Relato de caso

Um schwannoma é uma lesão pouco frequente, sendo um tumor que deriva da bainha de mielina dos nervos periféricos; na maioria dos casos, este tumor é benigno e raramente se apresenta na região do pé e tornozelo. Os pacientes afetados por esse tipo de patologia são geralmente assintomáticos. Ainda assim, às vezes eles têm sintomas neurológicos sensoriais ou motores se o tumor for grande o suficiente para causar a compressão direta ou indireta do nervo afetado. Um paciente do gênero masculino de 55 anos se apresentou ao nosso departamento com inchaço não traumático e dor no aspecto lateral do pé direito e da perna. A ressonância magnética (RM) da perna direita revelou uma lesão bem-circunscrita, medindo 2,5 por 1 cm, mostrando hipointensidade nas sequências T1 e hiperintensidade em T2, compatível com um tumor superficial de células do nervo peronal. Foi realizada excisão cirúrgica da lesão e o exame histopatológico confirmou a suspeita inicial – Schwannoma do nervo peroneal superficial. O pós-operatório foi tranquilo, com melhora progressiva da dor e recuperação funcional completa sem déficits neurológicos. Exame clínico rigoroso associado aos exames de RM permitem diagnóstico adequado, bem como a exclusão de outras patologias com apresentação clínica semelhante. Assim, o cirurgião tem que estar atento a todos os dados para um diagnóstico e tratamento eficazes nesse tipo de patologia rara que não pode ser negligenciada.

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.

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.

EEF1A1 transcription cofactor gene polymorphism is associated with muscle gene expression and residual feed intake in Nelore cattle

Cis-acting effects of noncoding variants on gene expression and regulatory molecules constitute a significant factor for phenotypic variation in complex traits. To provide new insights into the impacts of single-nucleotide polymorphisms (SNPs) on transcription factors (TFs) and transcription cofactors (TcoF) coding genes, we carried out a multi-omic analysis to identify cis-regulatory effects of SNPs on these genes' expression in muscle and describe their association with feed efficiency-related traits in Nelore cattle. As a result, we identified one SNP, the rs137256008C > T, predicted to impact the EEF1A1 gene expression (β = 3.02; P-value = 3.51E-03) and the residual feed intake trait (β = - 3.47; P-value = 0.02). This SNP was predicted to modify transcription factor sites and overlaps with several QTL for feed efficiency traits. In addition, co-expression network analyses showed that animals containing the T allele of the rs137256008 SNP may be triggering changes in the gene network. Therefore, our analyses reinforce and contribute to a better understanding of the biological mechanisms underlying gene expression control of feed efficiency traits in bovines. The cis-regulatory SNP can be used as biomarker for feed efficiency in Nelore cattle.

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.

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.

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

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

Background

Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images.

Methods

The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing.

Results

The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec.

Conclusions

The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy

INTRODUCTION

Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. 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 PP in digital single-operator cholangioscopy images.

METHODS

A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values.

RESULTS

A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00.

DISCUSSION

Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network

OBJECTIVE

Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.

DESIGN

We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin's classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.

CONCLUSION

We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.

Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network

Introduction

Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams.

Methods

A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision.

Results

Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s).

Discussion/Conclusion

We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

Assessment of the uterine dose in digital mammography and digital breast tomosynthesis

INTRODUCTION

Digital Mammography (DM-2D) and more recently Digital Breast Tomosynthesis (DBT), are two of the most effective imaging modalities for breast cancer detection, often used in screening programmes. It may happen that exams using these two imaging modalities are inadvertently performed to pregnant women. The objective of this study is to assess the dose in the uterus due to DM-2D and DBT exams, according to two main irradiation scenarios: in the 1st scenario the exposure parameters were pre-selected directly by the imaging system, while in the 2nd scenario, the maximum exposure parameters were chosen.

METHODS

The mammography equipment used was a Siemens Mammomat Inspiration. A physical anthropomorphic phantom, PMMA plates (simulating a breast thickness of 6 cm) and thermoluminescent dosimeters (TLDs) were used to measure entrance air kerma values on the phantom's breast and abdomen in order to successively estimate the mean glandular dose (MGD) and the dose in the uterus. For the two irradiation scenarios chosen, two-breast imaging modalities were selected: 1) DBT in Cranio-Caudal (CC) view (with 28 kV and 160 mAs as exposure parameters), 2) DBT and DM in Medio Lateral-Oblique (MLO) and CC views (with 34 kV and 250 mAs as exposure parameters).

RESULTS

In the 1st scenario, the TLD measurements did not detect significant dose values in the abdomen whereas the MGD estimated using the D.R. Dance model was in close agreement with data available in the literature. In the 2nd scenario, there was no significant difference in MGD estimation between the different views, whereas the air kerma values in the abdomen (in DBT mode, CC and MLO) were 0.049 mGy and 0.004 mGy respectively. In CC DM-2D mode the abdomen air kerma value was 0.026 mGy, with no significant detected value in MLO view.

CONCLUSIONS

For the dose in the uterus, the obtained values seem to indicate that DM-2D and DBT examinations inadvertently performed during pregnancy do not pose a significant radiological risk, even considering the case of overexposure in both breasts.

IMPLICATIONS FOR PRACTICE

The accurate knowledge of the doses in DM-2D and DBT will contribute to raise the awareness among medical practitioners involved in breast imaging empowering them to provide accurate information about dose levels in the uterus, improving their radiation risk communication skills and consequently helping to reduce the anxiety of pregnant women undergoing this type of examinations.

Artificial intelligence and colon capsule endoscopy: development of an automated diagnostic system of protruding lesions in colon capsule endoscopy

BACKGROUND

Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients unwilling to undergo conventional colonoscopy, or for whom the latter exam is contraindicated. This is particularly important in the setting of colorectal cancer screening. Nevertheless, these exams produce large numbers of images, and reading them is a monotonous and time-consuming task, with the risk of overlooking important lesions. The development of automated tools based on artificial intelligence (AI) technology may improve some of the drawbacks of this diagnostic instrument.

METHODS

A database of CCE images was used for development of a Convolutional Neural Network (CNN) model. This database included anonymized images of patients with protruding lesions in the colon or patients with normal colonic mucosa or with other pathologic findings. A total of 3,387,259 frames from 24 CCE exams were retrospectively reviewed. For CNN development, 3640 images (860 protruding lesions and 2780 with normal mucosa or other findings) were ultimately extracted. Training and validation datasets were constructed for the development and testing of the CNN.

RESULTS

The CNN detected protruding lesions with a sensitivity, specificity, positive and negative predictive values of 90.7, 92.6, 79.2 and 96.9%, respectively. The area under the receiver operating characteristic curve for detection of protruding lesions was 0.97.

CONCLUSIONS

The deep learning algorithm we developed is capable of accurately detecting protruding lesions. The application of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network

Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

Evaluation of an Image Analysis Approach to Predicting Primal Cuts and Lean in Light Lamb Carcasses

Simple Summary

The traditional way of estimating the carcass composition by complete dissection of muscle, fat and bone is an expensive, time-consuming and inconsistent process. The purpose of this study was to evaluate the accuracy of a simple video image analysis (VIA) system to predict the composition and primal cuts using light lamb carcasses. The six cuts of the carcasses were grouped according to their commercial value: high-value cuts (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). Results showed the ability of the VIA system to estimate the weight and yield of the groups of carcass joints.

Abstract

Carcass dissection is a more accurate method for determining the composition of a carcass; however, it is expensive and time-consuming. Techniques like VIA are of great interest once they are objective and able to determine carcass contents accurately. This study aims to evaluate the accuracy of a flexible VIA system to determine the weight and yield of the commercial value of carcass cuts of light lamb. Photos from 55 lamb carcasses are taken and a total of 21 VIA measurements are assessed. The half-carcasses are divided into six primal cuts, grouped according to their commercial value: high-value (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). K-folds cross-validation stepwise regression analyses are used to estimate the weights of the cuts in the groups and their lean meat yields. The models used to estimate the weight of AllC, HVC, MVC and LVC show similar results and a k-fold coefficient of determination (k-fold-R²) of 0.99 is achieved for the HVC and AllC predictions. The precision of the weight and yield of the three prediction models varies from low to moderate, with k-fold-R² results between 0.186 and 0.530, p < 0.001. The prediction models used to estimate the total lean meat weight are similar and low, with k-fold-R² results between 0.080 and 0.461, p < 0.001. The results confirm the ability of the VIA system to estimate the weights of parts and their yields. However, more research is needed on estimating lean meat yield.

Artificial intelligence and capsule endoscopy: unravelling the future

The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.

Bilateral Luxatio Erecta: An Atypical Presentation at the Emergency Department

Bilateral luxatio erecta remains a rare condition, with less than 30 cases currently described in the literature. The authors present a case of bilateral inferior glenohumeral dislocation after a fall with low-energy trauma, treated with closed reduction and immobilization for 3 weeks followed by a physiotherapy program for functional rehabilitation. After 1 year of follow-up, the patient presented satisfactory results with a complete recovery of the previous mobility arch of both shoulders.

Utilization of Bioelectrical Impedance to Predict Intramuscular Fat and Physicochemical Traits of the Beef Longissimus Thoracis et Lumborum Muscle

The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% ≤ CV ≤ 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (−0.50 ≤ r ≤ 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Low Golimumab Trough Levels at Week 6 Are Associated With Poor Clinical, Endoscopic and Histological Outcomes in Ulcerative Colitis Patients: Pharmacokinetic and Pharmacodynamic Sub-analysis of the Evolution Study

BACKGROUND AND AIMS

Golimumab has an established exposure-response relationship in patients with ulcerative colitis [UC]. However, the association of serum golimumab trough levels [TL] with objective markers of disease activity, such as endoscopic and histological activity scores and concentrations of biomarkers, remains less understood. This report describes the relationship of serum golimumab TL at the end of the induction period [Week 6] with clinical, endoscopic, histological, and biomarker parameters.

METHODS

This was an open-label, uncontrolled, prospective and interventional study. Moderate to severely active UC patients naïve to biologic therapy were treated with golimumab. Serum golimumab TL and faecal calprotectin levels were measured at baseline [Week 0 of induction] and Week 6.

RESULTS

A total of 34 patients completed the induction phase [Week 6] and were included in this analysis. Overall, 47.1% and 14.7% of patients achieved clinical response and remission with significantly higher serum golimumab TL in patients with early response or remission [3.7 μg/mL vs 1.3 μg/mL, p = 0.0013; and 3.1 μg/mL vs 1.7 μg/mL, p = 0.0164, respectively]. In addition, golimumab TL were significantly higher in patients achieving histological remission [4.2 μg/mL vs 1.7 μg/mL, p = 0.0049]. Week 6 golimumab TL were inversely correlated with the total Mayo score [rs = -0.546; p = 0.0008], the Mayo endoscopic subscore [rs = -0.381; p = 0.0262], the Geboes histological activity score [rs = -0.464; p = 0.0057], and faecal calprotectin levels [rs = -0.497; p = 0.0044].

CONCLUSIONS

A higher early exposure to golimumab is associated with a better objective response in active UC patients and appears to drive the outcome at Week 6.

The performance of Remicade®-optimized quantification assays in the assessment of Flixabi® levels

BACKGROUND

The advent of Remicade® biosimilars, Remsima®, Inflectra® and, more recently, Flixabi®, has brought along the potential to decrease the costs associated with this therapy, therefore increasing its access to a larger group of patients. However, and in order to assure a soft transition, one must make sure the assays and algorithms previously developed and optimized for Remicade perform equally well with its biosimilars. This study aimed to: (a) validate the utilization of Remicade-optimized therapeutic drug monitoring assays for the quantification of Flixabi; and (b) determine the existence of Remicade, Remsima and Flixabi cross-immunogenicity.

METHODS

Healthy donors' sera spiked with Remicade, Remsima and Flixabi were quantified using three different Remicade-quantification assays, and the reactivity of anti-Remicade and anti-Remsima sera to Remicade and to its biosimilars was assessed.

RESULTS

The results show that all tested Remicade-infliximab-optimized assays measure Flixabi as accurately as they measure Remicade and Remsima: the intraclass correlation coefficients between theoretical and measured concentrations varied from 0.920 to 0.990. Moreover, the interassay agreement values for the same compounds were high (intraclass correlation coefficients varied from 0.936 to 0.995). Finally, the anti-Remicade and anti-Remsima sera reacted to the different drugs in a similar fashion.

CONCLUSIONS

The tested assays can be used to monitor Flixabi levels. Moreover, Remicade, Remsima and Flixabi were shown to have a high cross-immunogenicity, which supports their high similarity but prevents their switching in nonresponders with antidrug antibodies.

Chemicals released by male sea cucumber mediate aggregation and spawning behaviours

The importance of chemical communication in reproduction has been demonstrated in many marine broadcast spawners. However, little is known about the use of chemical communication by echinoderms, the nature of the compounds involved and their mechanism(s) of action. Here, the hypothesis that the sea cucumber Holothuria arguinensis uses chemical communication for aggregation and spawning was tested. Water conditioned by males, but not females, attracted both males and females; gonad homogenates and coelomic fluid had no effect on attraction. Male spawning water, but not female spawning water, stimulated males and females to release their gametes; the spermatozoa alone did not induce spawning. H. arguinensis male spawning water also induced spawning in the phylogenetically related H. mammata. This indicates that males release pheromones together with their gametes that induce spawning in conspecifics and possibly sympatric species. Finally, the male pheromone seems to be a mixture with at least one labile compound (biological activity is lost after four hours at ambient temperature) possibly including phosphatidylcholines. The identification of pheromones in sea cucumbers offers a new ecological perspective and may have practical applications for their aquaculture.

Histological Outcomes and Predictive Value of Faecal Markers in Moderately to Severely Active Ulcerative Colitis Patients Receiving Infliximab

BACKGROUND AND AIMS

Histological healing has emerged as a promising therapeutic goal in ulcerative colitis. This is especially important in the context of biological therapies. The objectives of the present study were to investigate the ability of infliximab to induce histological remission in ulcerative colitis [UC] patients and to explore the utility of faecal calprotectin and lactoferrin in predicting histological activity.

METHODS

Multi-centre, single-cohort, open-label, 52-week trial including moderately to severely biological-naïve UC patients receiving intravenous infliximab [5mg/kg]. The primary outcome was the proportion of patients with histological remission [Geboes index ≤ 3.0] after 8 weeks of treatment, scored by two independent pathologists.

RESULTS

Twenty patients were included. The rate of histological remission increased from 5% at baseline to 15% and 35% at Week 8 and Week 52, respectively. At Week 8, 40% of patients were in clinical remission [Mayo ≤ 2] and 45% achieved mucosal healing [Mayo endoscopy subscore 0-1]. At Week 52, 25% of patients had clinical, endoscopic and histological remission. Faecal calprotectin and lactoferrin showed the highest correlation with histological activity at Week 8 (area under the curve [AUC] 94%, p = 0.017; and 96%, p = 0.013, respectively) and both markers revealed an excellent positive predictive value for this outcome at this time point [100%, p = 0.017; and 94%, p = 0.013, respectively].

CONCLUSIONS

Infliximab was able to induce histological remission. There was a good agreement between histology and faecal biomarkers. Faecal calprotectin and lactoferrin were good predictors of histological remission. Our data support inclusion of histology as a treatment target complementary to endoscopy in clinical trials when evaluating therapeutic response in UC.

Proactive therapeutic drug monitoring of infliximab: a comparative study of a new point-of-care quantitative test with two established ELISA assays

BACKGROUND

Therapeutic drug monitoring is a powerful strategy known to improve the clinical outcomes and to optimise the healthcare resources in the treatment of autoimmune diseases. Currently, most of the methods commercially available for the quantification of infliximab (IFX) are ELISA-based, with a turnaround time of approximately 8 h, and delaying the target dosage adjustment to the following infusion.

AIM

To validate the first point-of-care IFX quantification device available in the market - the Quantum Blue Infliximab assay (Buhlmann, Schonenbuch, Switzerland) - by comparing it with two well-established methods.

METHODS

The three methods were used to assay the IFX concentration of spiked samples and of the serum of 299 inflammatory bowel diseases (IBD) patients undergoing IFX therapy.

RESULTS

The point-of-care assay had an average IFX recovery of 92%, being the most precise among the tested methods. The Intraclass Correlation Coefficients of the point-of-care IFX assay vs. the two ELISA-based established methods were 0.889 and 0.939. Moreover, the accuracy of the point-of-care IFX compared with each of the two reference methods was 77% and 83%, and the kappa statistics revealed a substantial agreement (0.648 and 0.738).

CONCLUSIONS

The Quantum Blue IFX assay can successfully replace the commonly used ELISA-based IFX quantification kits. This point-of-care IFX assay is able to deliver the results within 15 min makes it ideal for an immediate target concentration adjusted dosing. Moreover, it is a user-friendly desktop device that does not require specific laboratory facilities or highly specialised personnel.