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Pathology Pathology

Diet Quality, Food Groups and Nutrients Associated with the Gut Microbiota in a Nonwestern Population.

In Nutrients ; h5-index 86.0

Diet plays an important role in shaping gut microbiota. However, much remains to be learned regarding this association. We analyzed dietary intake and gut microbiota in a community-dwelling cohort of 441 Colombians. Diet quality, intake of food groups and nutrient consumption were paired with microbial diversity and composition using linear regressions, Procrustes analyses and a random-forest machine-learning algorithm. Analyses were adjusted for potential confounders, including the five cities from where the participants originated, sex (male, female), age group (18-40 and 41-62 years), BMI (lean, overweight, obese) and socioeconomic status. Microbial diversity was higher in individuals with increased intake of nutrients obtained from plant-food sources, whereas the intake of food groups and nutrients correlated with microbiota structure. Random-forest regressions identified microbial communities associated with different diet components. Two remarkable results confirmed previous expectations regarding the link between diet and microbiota: communities composed of short-chain fatty acid (SCFA) producers were more prevalent in the microbiota of individuals consuming diets rich in fiber and plant-food sources, such as fruits, vegetables and beans. In contrast, an inflammatory microbiota composed of bile-tolerant and putrefactive microorganisms along with opportunistic pathogens thrived in individuals consuming diets enriched in animal-food sources and of low quality, i.e., enriched in ultraprocessed foods and depleted in dietary fiber. This study expands our understanding of the relationship between dietary intake and gut microbiota. We provide evidence that diet is strongly associated with the gut microbial community and highlight generalizable connections between them.

García-Vega Ángela S, Corrales-Agudelo Vanessa, Reyes Alejandro, Escobar Juan S


16S rRNA, 24-h dietary recall, Colombians, community dwellers, food consumption, gut microbiome, short-chain fatty acids

General General

Tailoring Time Series Models For Forecasting Coronavirus Spread: Case Studies of 187 Countries.

In Computational and structural biotechnology journal

When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.

Ismail Leila, Materwala Huned, Znati Taieb, Turaev Sherzod, Khan Moien A B


COVID-19, Coronavirus, Epidemic transmission, Forecasting models, Machine learning models, Pandemic, Time series models

General General

When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey

ArXiv Preprint

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. In a few decades, computers may be capable of formulating diagnoses and choosing the correct treatment, while robots may perform surgical operations, and conversational agents could interact with patients as virtual coaches. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. In this scenario, important decisions will be controlled by standalone machines that have learned predictive models from provided data. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity in Python and Matlab libraries, just to name two, but to exploit all their possibilities, it is essential to fully understand how models are interpreted and which models are more interpretable than others. In this survey, we analyse current machine learning models, frameworks, databases and other related tools as applied to medicine - specifically, to cancer research - and we discuss their interpretability, performance and the necessary input data. From the evidence available, ANN, LR and SVM have been observed to be the preferred models. Besides, CNNs, supported by the rapid development of GPUs and tensor-oriented programming libraries, are gaining in importance. However, the interpretability of results by doctors is rarely considered which is a factor that needs to be improved. We therefore consider this study to be a timely contribution to the issue.

Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, Horacio Pérez-Sánchez


Pathology Pathology

Mapping out the philosophical questions of AI and clinical practice in diagnosing and treating mental disorders.

In Journal of evaluation in clinical practice

How to classify the human condition? This is one of the main problems psychiatry has struggled with since the first diagnostic systems. The furore over the recent editions of the diagnostic systems DSM-5 and ICD-11 has evidenced it to still pose a wicked problem. Recent advances in techniques and methods of artificial intelligence and computing power which allows for the analysis of large data sets have been proposed as a possible solution for this and other problems in classification, diagnosing, and treating mental disorders. However, mental disorders contain some specific inherent features, which require critical consideration and analysis. The promises of AI for mental disorders are threatened by the unmeasurable aspects of mental disorders, and for this reason the use of AI may lead to ethically and practically undesirable consequences in its effective processing. We consider such novel and unique questions AI presents for mental health disorders in detail and evaluate potential novel, AI-specific, ethical implications.

Uusitalo Susanne, Tuominen Jarno, Arstila Valtteri


diagnosis, medical ethics, philosophy of medicine, progress

General General

Diagnostic accuracy of a novel third generation esophageal capsule as a noninvasive detection method for Barrett's Esophagus: A pilot study.

In Journal of gastroenterology and hepatology ; h5-index 51.0

BACKGROUND AND AIM : Previous 2 generations of esophageal capsule did not show adequate detection rates for Barrett's esophagus (BE). We assessed the diagnostic accuracy of a novel third generation capsule with an improved frame rate of 35 frames per second for the detection of BE in a pilot study.

METHODS : This was a blinded prospective pilot study conducted at a tertiary medical center. Patients with known BE (at least C0M>1) who presented for endoscopic surveillance (May to October 2017) were included. All patients underwent novel esophageal capsule (PillCamTM UGI; Medtronic) ingestion using the simplified ingestion protocol followed by standard high definition upper endoscopy (EGD). Capsule endoscopy findings were interpreted by examiners blinded to endoscopy results and compared to endoscopic findings (gold standard). Following completion of both tests, a subjective questionnaire was provided to all patients regarding their experience.

RESULTS : Twenty patients [95%males, mean age 66.3 (+/-7.9)years] with BE undergoing surveillance EGD were eligible. The mean BE length was 3.5 (+/-2.7)cm. Novel esophageal capsule detected BE in 75% patients when images were compared to endoscopy. Novel capsule detected BE in 82% patients when the BE length was >=2cm. The mean esophageal transit time was 0.59 sec. On a subjective questionnaire, all 20 patients reported novel capsule as being more convenient compared to EGD.

CONCLUSIONS : In this pilot, single center study, novel esophageal capsule was shown to be not ready for population screening of BE. Studies integrating artificial intelligence into improved quality novel esophageal capsule should be performed for BE screening.

Duvvuri Abhiram, Desai Madhav, Vennelaganti Sreekar, Higbee April, Gorrepati Venkat Subhash, Dasari Chandra, Chandrasekar Viveksandeep Thoguluva, Vennalaganti Prashanth, Kohli Divyanshoo, Sathyamurthy Anjana, Rai Tarun, Sharma Prateek


General General

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.

In Journal of primary care & community health

BACKGROUND : In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems.

METHODS : The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR).

RESULTS : Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare.

CONCLUSION : AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

Naseem Maleeha, Akhund Ramsha, Arshad Hajra, Ibrahim Muhammad Talal

COVID-19, artificial intelligence, low middle-income countries, machine learning, pandemic