Artificial intelligence to predict outcomes of head and neck radiotherapy

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Artificial Intelligence in Hepatology- Ready for the Primetime

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification

Objective

To demonstrate that deep learning (DL) methods can produce robust prediction of gene expression profile (GEP) in uveal melanoma (UM) based on digital cytopathology images.

Design

Evaluation of a diagnostic test or technology.

Subjects Participants and Controls

Deidentified smeared cytology slides stained with hematoxylin and eosin obtained from a fine needle aspirated from UM.

Methods

Digital whole-slide images were generated by fine-needle aspiration biopsies of UM tumors that underwent GEP testing. A multistage DL system was developed with automatic region-of-interest (ROI) extraction from digital cytopathology images, an attention-based neural network, ROI feature aggregation, and slide-level data augmentation.

Main Outcome Measures

The ability of our DL system in predicting GEP on a slide (patient) level. Data were partitioned at the patient level (73% training; 27% testing).

Results

In total, our study included 89 whole-slide images from 82 patients and 121 388 unique ROIs. The testing set included 24 slides from 24 patients (12 class 1 tumors; 12 class 2 tumors; 1 slide per patient). Our DL system for GEP prediction achieved an area under the receiver operating characteristic curve of 0.944, an accuracy of 91.7%, a sensitivity of 91.7%, and a specificity of 91.7% on a slide-level analysis. The incorporation of slide-level feature aggregation and data augmentation produced a more predictive DL model (P = 0.0031).

Conclusions

Our current work established a complete pipeline for GEP prediction in UM tumors: from automatic ROI extraction from digital cytopathology whole-slide images to slide-level predictions. Our DL system demonstrated robust performance and, if validated prospectively, could serve as an image-based alternative to GEP testing.

The Use of Machine Learning for the Care of Hypertension and Heart Failure

Machine learning (ML) is a branch of artificial intelligence that combines computer science, statistics, and decision theory to learn complex patterns from voluminous data. In the last decade, accumulating evidence has shown the utility of ML for prediction, diagnosis, and classification of hypertension and heart failure (HF). In addition, ML-enabled image analysis has potential value in assessing cardiac structure and function in an accurate, scalable, and efficient way. Considering the high burden of hypertension and HF in China and worldwide, ML may help address these challenges from different aspects. Indeed, prior studies have shown that ML can enhance each stage of patient care, from research and development, to daily clinical practice and population health. Through reviewing the published literature, the aims of the current systemic review are to summarize the utilities of ML for the care of those with hypertension and HF.

In vitro and in vivo correlation for lipid-based formulations: Current status and future perspectives

Lipid-based formulations (LBFs) have demonstrated a great potential in enhancing the oral absorption of poorly water-soluble drugs. However, construction of in vitro and in vivo correlations (IVIVCs) for LBFs is quite challenging, owing to a complex in vivo processing of these formulations. In this paper, we start with a brief introduction on the gastrointestinal digestion of lipid/LBFs and its relation to enhanced oral drug absorption; based on the concept of IVIVCs, the current status of in vitro models to establish IVIVCs for LBFs is reviewed, while future perspectives in this field are discussed. In vitro tests, which facilitate the understanding and prediction of the in vivo performance of solid dosage forms, frequently fail to mimic the in vivo processing of LBFs, leading to inconsistent results. In vitro digestion models, which more closely simulate gastrointestinal physiology, are a more promising option. Despite some successes in IVIVC modeling, the accuracy and consistency of these models are yet to be validated, particularly for human data. A reliable IVIVC model can not only reduce the risk, time, and cost of formulation development but can also contribute to the formulation design and optimization, thus promoting the clinical translation of LBFs.

Present cum future of SARS-CoV-2 virus and its associated control of virus-laden air pollutants leading to potential environmental threat - A global review

The world is presently infected by the biological fever of COVID-19 caused by SARS-CoV-2 virus. The present study is mainly related to the airborne transmission of novel coronavirus through airway. Similarly, our mother planet is suffering from drastic effects of air pollution. There are sufficient probabilities or evidences proven for contagious virus transmission through polluted airborne-pathway in formed aerosol molecules. The pathways and sources of spread are detailed along with the best possible green control technologies or ideas to hinder further transmission. The combined effects of such root causes and unwanted outcomes are similar in nature leading to acute cardiac arrest of our planet. To maintain environmental sustainability, the prior future of such emerging unknown biological hazardous air emissions is to be thoroughly researched. So it is high time to deal with the future of hazardous air pollution and work on its preventive measures. The lifetime of such an airborne virus continues for several hours, thus imposing severe threat even during post-lockdown phase. The world waits eagerly for the development of successful vaccination or medication but the possible outcome is quite uncertain in terms of equivalent economy distribution and biomedical availability. Thus, risk assessments are to be carried out even during the post-vaccination period with proper environmental surveillance and monitoring. The skilled techniques of disinfection, sanitization, and other viable wayouts are to be modified with time, place, and prevailing climatic conditions, handling the pandemic efficiently. A healthy atmosphere makes the earth a better place to dwell, ensuring its future lifecycle.

Artificial intelligence in gastrointestinal endoscopy. VideoGIE. 2020;5:598-613. [PMID: 33319126 PMCID: PMC7732722 DOI: 10.1016/j.vgie.2020.08.013]

Background and Aims

Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.

Methods

The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.

Results

Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images.

Conclusions

The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26^th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26^th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26^th week of gestation.

Unraveling the Molecular Mechanism of Action of Empagliflozin in Heart Failure With Reduced Ejection Fraction With or Without Diabetes

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Using artificial intelligence, followed by in vivo validation, this study identified the key cardiac mechanism of action of empagliflozin in heart failure in patients with or without diabetes mellitus.

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The most robust mechanism of action involved the NHE-1 co-transporter with 94.7% accuracy.

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NHE-1 blockade by empagliflozin administration in rats restored the antiapoptotic activity of XIAP and BIRC5.

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The beneficial reduction in cardiomyocyte cell death after empagliflozin treatment is independent of the presence of diabetes mellitus.

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Empagliflozin could emerge as a new treatment for heart failure patients regardless of their glycemic status.

The mechanism of action of empagliflozin in heart failure with reduced ejection fraction (HFrEF) was deciphered using deep learning in silico analyses together with in vivo validation. The most robust mechanism of action involved the sodium-hydrogen exchanger (NHE)-1 co-transporter with 94.7% accuracy, which was similar for diabetics and nondiabetics. Notably, direct NHE1 blockade by empagliflozin ameliorated cardiomyocyte cell death by restoring expression of X-linked inhibitor of apoptosis (XIAP) and baculoviral IAP repeat-containing protein 5 (BIRC5). These results were independent of diabetes mellitus comorbidity, suggesting that empagliflozin may emerge as a new treatment in HFrEF.

An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

Medium optimization for pyrroloquinoline quinone (PQQ) production by Methylobacillus sp. zju323 using response surface methodology and artificial neural network-genetic algorithm

Methylobacillus sp. zju323 was adopted to improve the biosynthesis of pyrroloquinoline quinone (PQQ) by systematic optimization of the fermentation medium. The Plackett-Burman design was implemented to screen for the key medium components for the PQQ production. CoCl₂ · 6H₂O, ρ-amino benzoic acid, and MgSO₄ · 7H₂O were found capable of enhancing the PQQ production most significantly. A five-level three-factor central composite design was used to investigate the direct and interactive effects of these variables. Both response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) were used to predict the PQQ production and to optimize the medium composition. The results showed that the medium optimized by ANN-GA was better than that by RSM in maximizing PQQ production and the experimental PQQ concentration in the ANN-GA-optimized medium was improved by 44.3% compared with that in the unoptimized medium. Further study showed that this ANN-GA-optimized medium was also effective in improving PQQ production by fed-batch mode, reaching the highest PQQ accumulation of 232.0 mg/L, which was about 47.6% increase relative to that in the original medium. The present work provided an optimized medium and developed a fed-batch strategy which might be potentially applicable in industrial PQQ production.