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)

European Society of Gastrointestinal Endoscopy (ESGE) curricula development for postgraduate training in advanced endoscopic procedures: rationale and methodology

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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

Role of gastrointestinal endoscopy in the screening of digestive tract cancers in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

In Europe at present, but also in 2040, 1 in 3 cancer-related deaths are expected to be caused by digestive cancers. Endoscopic technologies enable diagnosis, with relatively low invasiveness, of precancerous conditions and early cancers, thereby improving patient survival. Overall, endoscopy capacity must be adjusted to facilitate both effective screening programs and rigorous control of the quality assurance and surveillance systems required

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

Efficacy of Per-oral Methylene Blue Formulation for Screening Colonoscopy

Background & aims: Topically applied methylene blue dye chromoendoscopy is effective in improving detection of colorectal neoplasia. When combined with a pH- and time-dependent multimatrix structure, a per-oral methylene blue formulation (MB-MMX) can be delivered directly to the colorectal mucosa. Methods: We performed a phase 3 study of 1205 patients scheduled for colorectal cancer screening or surveillance colonoscopies (50-75 years old) at 20 sites in Europe and the United States, from December 2013 through October 2016. Patients were randomly assigned to groups given 200 mg MB-MMX, placebo, or 100 mg MB-MMX (ratio of 2:2:1). The 100-mg MB-MMX group was included for masking purposes. MB-MMX and placebo tablets were administered with a 4-L polyethylene glycol-based bowel preparation. The patients then underwent colonoscopy by an experienced endoscopist with centralized double-reading. The primary endpoint was the proportion of patients with 1 adenoma or carcinoma (adenoma detection rate [ADR]). We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for differences in detection between the 200-mg MB-MMX and placebo groups. False-positive (resection rate for non-neoplastic polyps) and adverse events were assessed as secondary endpoints. Results: The ADR was higher for the MB-MMX group (273 of 485 patients, 56.29%) than the placebo group (229 of 479 patients, 47.81%) (OR 1.46; 95% CI 1.09-1.96). The proportion of patients with nonpolypoid lesions was higher in the MB-MMX group (213 of 485 patients, 43.92%) than the placebo group (168 of 479 patients, 35.07%) (OR 1.66; 95% CI 1.21-2.26). The proportion of patients with adenomas ≤5 mm was higher in the MB-MMX group (180 of 485 patients, 37.11%) than the placebo group (148 of 479 patients, 30.90%) (OR 1.36; 95% CI 1.01-1.83), but there was no difference between groups in detection of polypoid or larger lesions. The false-positive rate did not differ significantly between groups (83 [23.31%] of 356 patients with non-neoplastic lesions in the MB-MMX vs 97 [29.75%] of 326 patients with non-neoplastic lesions in the placebo group). Overall, 0.7% of patients had severe adverse events but there was no significant difference between groups. Conclusions: In a phase 3 trial of patients undergoing screening or surveillance colonoscopies, we found MB-MMX led to an absolute 8.5% increase in ADR, compared with placebo, without increasing the removal of non-neoplastic lesions

A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis

Background Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos. Methods 1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured. Results The AI system detected ER (UCEIS = 1) in WLE videos with 72% sensitivity, 87% specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO = 3), the sensitivity was 79 %, specificity 95%, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80% to 85%). The model s stratification of risk of flare was similar to that of physician-assessed endoscopy scores. Conclusions Our system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment