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Radiology Radiology

Study protocol of the Bergen brain-gut-microbiota-axis study: A prospective case-report characterization and dietary intervention study to evaluate the effects of microbiota alterations on cognition and anatomical and functional brain connectivity in patients with irritable bowel syndrome.

In Medicine

INTRODUCTION : Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained gastrointestinal (GI) symptoms, recently described as a disturbance of the brain-gut-microbiota (BGM) axis. To gain a better understanding of the mechanisms underlying the poorly understood etiology of IBS, we have designed a multifaceted study that aim to stratify the complex interaction and dysfunction between the brain, the gut, and the microbiota in patients with IBS.

METHODS : Deep phenotyping data from patients with IBS (n = 100) and healthy age- (between 18 and 65) and gender-matched controls (n = 40) will be collected between May 2019 and December 2021. Psychometric tests, questionnaires, human biological tissue/samples (blood, faeces, saliva, and GI biopsies from antrum, duodenum, and sigmoid colon), assessment of gastric accommodation and emptying using transabdominal ultrasound, vagal activity, and functional and structural magnetic resonance imaging (MRI) of the brain, are included in the investigation of each participant. A subgroup of 60 patients with IBS-D will be further included in a 12-week low FODMAP dietary intervention-study to determine short and long-term effects of diet on GI symptoms, microbiota composition and functions, molecular GI signatures, cognitive, emotional and social functions, and structural and functional brain signatures. Deep machine learning, prediction tools, and big data analyses will be used for multivariate analyses allowing disease stratification and diagnostic biomarker detection.

DISCUSSION : To our knowledge, this is the first study to employ unsupervised machine learning techniques and incorporate systems-based interactions between the central and the peripheral components of the brain-gut-microbiota axis at the levels of the multiomics, microbiota profiles, and brain connectome of a cohort of 100 patients with IBS and matched controls; study long-term safety and efficacy of the low-FODMAP diet on changes in nutritional status, gut microbiota composition, and metabolites; and to investigate changes in the brain and gut connectome after 12 weeks strict low-FODMAP-diet in patients with IBS. However, there are also limitations to the study. As a restrictive diet, the low-FODMAP diet carries risks of nutritional inadequacy and may foster disordered eating patterns. Strict FODMAP restriction induces a potentially unfavourable gut microbiota, although the health effects are unknown.


Berentsen Birgitte, Nagaraja Bharath Halandur, Teige Erica Pearson, Lied Gülen Arslan, Lundervold Astri J, Lundervold Katarina, Steinsvik Elisabeth Kjelsvik, Hillestad Eline Randulff, Valeur Jørgen, Brønstad Ingeborg, Gilja Odd Helge, Osnes Berge, Hatlebakk Jan Gunnar, Haász Judit, Labus Jennifer, Gupta Arpana, Mayer Emeran A, Benitez-Páez Alfonso, Sanz Yolanda, Lundervold Arvid, Hausken Trygve


Public Health Public Health

Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach.

In Medical care

OBJECTIVE : The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowable secondary diagnoses in January 2011 and its association with financial penalties under the Hospital Readmission Reduction Program (HRRP).

DATA SOURCES/STUDY SETTING : Medicare administrative claims for discharges between July 2008 and June 2011 (N=3102 hospitals).

RESEARCH DESIGN : We examined hospital variation in response to the expansion of secondary diagnoses by describing changes in comorbidity coding before and after the policy change. We used random forest machine learning regression to examine hospital characteristics associated with coded severity. We then used a 2-part model to assess whether variation in coded severity was associated with readmission penalties.

RESULTS : Changes in severity coding varied considerably across hospitals. Random forest models indicated that greater baseline levels of condition categories, case-mix index, and hospital size were associated with larger changes in condition categories. Hospital coding of an additional condition category was associated with a nonsignificant 3.8 percentage point increase in the probability for penalties under the HRRP (SE=2.2) and a nonsignificant 0.016 percentage point increase in penalty amount (SE=0.016).

CONCLUSION : Changes in patient coded severity did not affect readmission penalties.

Li Jun, Sukul Devraj, Nuliyalu Ushapoorna, Ryan Andrew M


Surgery Surgery

Machine learning in the optimization of robotics in the operative field.

In Current opinion in urology

PURPOSE OF REVIEW : The increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery.

RECENT FINDINGS : Machine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy.

SUMMARY : Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.

Ma Runzhuo, Vanstrum Erik B, Lee Ryan, Chen Jian, Hung Andrew J


General General

Application of Artificial Intelligence in Gastrointestinal Endoscopy.

In Journal of clinical gastroenterology

Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.

Wu Jia, Chen Jiamin, Cai Jianting


Ophthalmology Ophthalmology

Retinopathy of Prematurity: How to Prevent the Third Epidemics in Developing Countries.

In Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

Retinopathy of prematurity (ROP) is vasoproliferative disease affecting preterm infants and is a leading cause of avoidable childhood blindness worldwide. The world is currently experiencing the third epidemic of ROP, where majority of the cases are from middle-income countries. Over 40% of the world's premature infants were born in India, China, Bangladesh, Pakistan, and Indonesia. Together with other neighboring nations, this region has unique challenges in ROP management. Key aspects of the challenges including heavier and more mature infants developing severe ROP. Current strategies include adoption of national screening guidelines, telemedicine, integrating vision rehabilitation and software innovations in the form of artificial intelligence. This review overviews some of these aspects.

Azad Rajvardhan, Gilbert Claire, Gangwe Anil B, Zhao Peiquan, Wu Wei-Chi, Sarbajna Puja, Vinekar Anand


Internal Medicine Internal Medicine

Gastric vascular abnormalities: diagnosis and management.

In Current opinion in gastroenterology

PURPOSE OF REVIEW : Gastric vascular abnormalities are a well known cause of gastrointestinal bleeding. Due to their recurrent bleeding tendency and potential to cause life-threatening blood loss, gastric vascular abnormalities can result in significant morbidity and cost.

RECENT FINDINGS : There have been novel advances in medical and endoscopic management of gastric vascular lesions. New data suggest that endoscopic band ligation and ablation may be comparable, or even superior, to argon plasma coagulation (APC) for management of gastric antral vascular ectasia (GAVE). A creative, highly sensitive and specific computer-assisted tool has been developed to facilitate reading video capsule endoscopies for the detection of angiodysplasias, paving the way for artificial intelligence incorporation in vascular lesions diagnostics. Over-the-scope clipping is a relatively new technology that shows promising results in controlling bleeding from Dieulafoy's lesions.

SUMMARY : In this article, we will broadly review the management of the most prevalent gastric vascular lesions, focusing on the most recent areas of research.

Awadalla Mohanad, Mahmoud Mohamed, McNamara Philip, Wassef Wahid