Pilot-scale conversion of lime-treated wheat straw into bioethanol: quality assessment of bioethanol and valorization of side streams by anaerobic digestion and combustion

The limited availability of fossil fuel sources, worldwide rising energy demands and anticipated climate changes attributed to an increase of greenhouse gasses are important driving forces for finding alternative energy sources. One approach to meeting the increasing energy demands and reduction of greenhouse gas emissions is by large-scale substitution of petrochemically derived transport fuels by the use of carbon dioxide-neutral biofuels, such as ethanol derived from lignocellulosic material. Results This paper describes an integrated pilot-scale process where lime-treated wheat straw with a high dry-matter content (around 35% by weight) is converted to ethanol via simultaneous saccharification and fermentation by commercial hydrolytic enzymes and bakers' yeast (Saccharomyces cerevisiae). After 53 hours of incubation, an ethanol concentration of 21.4 g/liter was detected, corresponding to a 48% glucan-to-ethanol conversion of the theoretical maximum. The xylan fraction remained mostly in the soluble oligomeric form (52%) in the fermentation broth, probably due to the inability of this yeast to convert pentoses. A preliminary assessment of the distilled ethanol quality showed that it meets transportation ethanol fuel specifications. The distillation residue, which contained non-hydrolysable and non-fermentable (in)organic compounds, was divided into a liquid and solid fraction. The liquid fraction served as substrate for the production of biogas (methane), whereas the solid fraction functioned as fuel for thermal conversion (combustion), yielding thermal energy, which can be used for heat and power generation. Conclusion Based on the achieved experimental values, 16.7 kg of pretreated wheat straw could be converted to 1.7 kg of ethanol, 1.1 kg of methane, 4.1 kg of carbon dioxide, around 3.4 kg of compost and 6.6 kg of lignin-rich residue. The higher heating value of the lignin-rich residue was 13.4 MJ thermal energy per kilogram (dry basis)

Leaving colorectal polyps in place can be achieved with high accuracy using blue light imaging (BLI)

Objectives: A negative predictive value of more than 90% is proposed by the American Society of Gastrointestinal Endoscopy Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) statement for a new technology in order to leave distal diminutive colorectal polyps in place without resection. To our knowledge, no prior prospective study has yet evaluated the feasibility of the most recently introduced blue light imaging (BLI) system for real-time endoscopic prediction of polyp histology for the specific endpoint of leaving hyperplastic polyps in place. Aims: Prospective assessment of real-time prediction of colorectal polyps by using BLI. Material and methods: In total, 177 consecutive patients undergoing screening or surveillance colonoscopy were included. Colorectal polyps were evaluated in real-time by using high-definition endoscopy and the BLI technology without optical magnification. Before resection, the endoscopist described each polyp according to size, shape and surface characteristics (pit and vascular pattern, colour and depression), and histology was predicted with a level of confidence (high or low). Results: Histology was predicted with high confidence in 92.5% of polyps. Sensitivity of BLI for prediction of adenomatous histology was 92.68%, with a specificity and accuracy of 94.87 and 93.75%, respectively. Following the recommendation of the PIVI statement, positive and negative predictive values were calculated with values of 95 and 92.5%, respectively. Prediction of surveillance based on both US and European guidelines was correctly predicted in 91% of patients. Conclusion: The most recently introduced BLI technology is accurate enough to leave distal colorectal polyps in place without resection. BLI also allowed for assignment of postpolypectomy surveillance intervals. This approach therefore has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps

Systematic assessment with I-SCAN magnification endoscopy and acetic acid improves dysplasia detection in patients with Barrett's esophagus

BACKGROUND AND STUDY AIMS: Enhanced endoscopic imaging with chromoendoscopy may improve dysplasia recognition in patients undergoing assessment of Barrett's esophagus (BE). This may reduce the need for random biopsies to detect more dysplasia. The aim of this study was to assess the effect of magnification endoscopy with I-SCAN (Pentax, Tokyo, Japan) and acetic acid (ACA) on dysplasia detection in BE using a novel mucosal and vascular classification system. METHODS: BE segments and suspicious lesions were recorded with high definition white-light and magnification endoscopy enhanced using all I-SCAN modes in combination. We created a novel mucosal and vascular classification system based on similar previously validated classifications for narrow-band imaging (NBI). A total of 27 videos were rated before and after ACA application. Following validation, a further 20 patients had their full endoscopies recorded and analyzed to model use of the system to detect dysplasia in a routine clinical scenario. RESULTS: The accuracy of the I-SCAN classification system for BE dysplasia improved with I-SCAN magnification from 69 % to 79 % post-ACA (P = 0.01). In the routine clinical scenario model in 20 new patients, accuracy of dysplasia detection increased from 76 % using a "pull-through" alone to 83 % when ACA and magnification endoscopy were combined (P = 0.047). Overall interobserver agreement between experts for dysplasia detection was substantial (0.69). CONCLUSIONS: A new I-SCAN classification system for BE was validated against similar systems for NBI with similar outcomes. When used in combination with magnification and ACA, the classification detected BE dysplasia in clinical practice with good accuracy.Trials registered at ISRCTN (58235785)

Establishing key research questions for the implementation of artificial intelligence in colonoscopy - a modified Delphi method

Background and Aims Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers from 9 countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorised into 5 themes. Theme 1: Clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterisation, determining the optimal end-points for evaluation of AI and demonstrating impact on interval cancer rates. Theme 2: Technological Developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false positive rates and minimising latency. Theme 3: Clinical adoption/Integration (1 question) concerning effective combination of detection and characterisation into one workflow. Theme 4: Data access/annotation (1 question) concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: Regulatory Approval (1 question) related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy

A clinically interpretable convolutional neural network for the real time prediction of early squamous cell cancer of the esophagus; comparing diagnostic performance with a panel of expert European and Asian endoscopists

BACKGROUND AND AIMS: Intrapapillary capillary loops (IPCLs) are microvascular structures that correlate with invasion depth of early squamous cell neoplasia (ESCN) and allow accurate prediction of histology. Artificial intelligence may improve human recognition of IPCL patterns and prediction of histology to allow prompt access to endoscopic therapy of ESCN where appropriate METHODS: One hundred fifteen patients were recruited at 2 academic Taiwanese hospitals. ME-NBI videos of squamous mucosa were labeled as dysplastic or normal according to their histology and IPCL patterns classified by consensus of 3 experienced clinicians. A convolutional neural network (CNN) was trained to classify IPCLs, using 67742 high quality ME-NBI by 5-fold cross validation. Performance measures were calculated to give an average F1 score, accuracy, sensitivity, and specificity. A panel of 5 Asian and 4 European experts predicted the histology of a random selection of 158 images using the JES IPCL classification; accuracy, sensitivity, specificity, positive and negative predictive values were calculated. RESULTS: Expert European Union (EU) and Asian endoscopists attained F1 scores (a measure of binary classification accuracy) of 97.0% and 98%, respectively. Sensitivity and accuracy of the EU and Asian clinicians were 97%, 98% and 96.9%, 97.1% respectively. The CNN average F1 score was 94%, sensitivity 93.7% and accuracy 91.7%. Our CNN operates at video rate and generates class activation maps that can be used to visually validate CNN predictions. CONCLUSIONS: We report a clinically interpretable CNN developed to predict histology based on IPCL patterns, in real-time, using the largest reported dataset of images for this purpose. Our CNN achieved diagnostic performance comparable to an expert panel of endoscopists

Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett's Oesophagus amongst Non-expert Endoscopists

INTRODUCTION: Barrett’s oesophagus (BE) is a precursor to oesophageal adenocarcinoma (OAC). Endoscopic surveillance is performed to detect dysplasia arising in BE as it is likely to be amenable to curative treatment. At present, there are no guidelines on who should perform surveillance endoscopy in BE. Machine learning (ML) is a branch of artificial intelligence (AI) that generates simple rules, known as decision trees (DTs). We hypothesised that a DT generated from recognised expert endoscopists could be used to improve dysplasia detection in non-expert endoscopists. To our knowledge, ML has never been applied in this manner. METHODS: Video recordings were collected from patients with non-dysplastic (ND-BE) and dysplastic Barrett’s oesophagus (D-BE) undergoing high-definition endoscopy with i-Scan enhancement (PENTAX®). A strict protocol was used to record areas of interest after which a corresponding biopsy was taken to confirm the histological diagnosis. In a blinded manner, videos were shown to 3 experts who were asked to interpret them based on their mucosal and microvasculature patterns and presence of nodularity and ulceration as well as overall suspected diagnosis. Data generated were entered into the WEKA package to construct a DT for dysplasia prediction. Non-expert endoscopists (gastroenterology specialist registrars in training with variable experience and undergraduate medical students with no experience) were asked to score these same videos both before and after web-based training using the DT constructed from the expert opinion. Accuracy, sensitivity, and specificity values were calculated before and after training where p < 0 05 was statistically significant. RESULTS: Videos from 40 patients were collected including 12 both before and after acetic acid (ACA) application. Experts’ average accuracy for dysplasia prediction was 88%. When experts’ answers were entered into a DT, the resultant decision model had a 92% accuracy with a mean sensitivity and specificity of 97% and 88%, respectively. Addition of ACA did not improve dysplasia detection. Untrained medical students tended to have a high sensitivity but poor specificity as they “overcalled” normal areas. Gastroenterology trainees did the opposite with overall low sensitivity but high specificity. Detection improved significantly and accuracy rose in both groups after formal web-based training although it did it reach the accuracy generated by experts. For trainees, sensitivity rose significantly from 71% to 83% with minimal loss of specificity. Specificity rose sharply in students from 31% to 49% with no loss of sensitivity. CONCLUSION: ML is able to define rules learnt from expert opinion. These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. This opens the door to standardised training and assessment of competence for those who perform endoscopy in BE. It may shorten the learning curve and might also be used to compare competence of trainees with recognised experts as part of their accreditation process

Development and external validation of a model to predict complex treatment after RFA for Barrett's esophagus with early neoplasia

Background & Aims: Endoscopic eradication therapy for Barrett's esophagus (BE)-related neoplasia is safe and leads to complete eradication in the majority of patients. However, a subgroup will experience a more complex treatment course with a risk for failure or disease progression. Early identification of these patients may improve patient counseling and treatment outcomes. We aimed to develop a prognostic model for a complex treatment course. Methods: We collected data from a nationwide registry that captures outcomes for all patients undergoing endoscopic eradication therapy for early BE neoplasia. A complex treatment course was defined as neoplastic progression, treatment failure, or the need for endoscopic resection during the radiofrequency ablation treatment phase. We developed a prognostic model using logistic regression. We externally validated our model in an independent registry. Results: A total of 1386 patients were included, of whom 78 (6%) had a complex treatment course. Our model identified patients with a BE length of 9 cm or longer with a visible lesion containing high-grade dysplasia/cancer, and patients with less than 50% squamous conversion after radiofrequency ablation were identified as high risk for a complex treatment. This applied to 8% of the study population and included 93% of all treatment failures and 76% of all patients with advanced neoplastic progression. The model appeared robust in multiple sensitivity analyses and performed well in external validation (area under the curve, 0.84). Conclusions: We developed a prognostic model that identified patients with a BE length of 9 cm or longer and high-grade dysplasia/esophageal adenocarcinoma and those with poor squamous regeneration as high risk for a complex treatment course. The good performance in external validation suggests that it may be used in clinical management (Netherlands Trial Register: NL7039)