Application of Artificial Neural Network (ANN) as a predictive tool for the removal of pharmaceutical from wastewater streams using biochar: a multifunctional technology for environment sustainability

This study investigates biochar as an attractive option for removing pharmaceuticals from wastewater streams utilizing data from various literature sources and also explores the sensitivity of the characteristics and implementation of biochar. ANN 1 was designed to determine the optimal biochar characteristics (Surface Area, Pore Volume) to achieve the maximum percentage removal of pharmaceuticals in wastewater streams. ANN 2 was developed to identify the optimal biomass feedstock composition, pyrolysis conditions (temperature and time), and chemical activation (acid or base) to produce the optimal biochar from ANN 1. ANN 3 was developed to investigate the effectiveness of the biochar produced in ANN 1 and 2 in removing dye from water. Biomass feedstock with a high lignin content and high volatile matter at a high pyrolysis temperature, whether using an acid or base, achieves a high mesopore volume and high surface area. The biochar with the highest surface area and mesopore volume achieved the highest removal percentage. Regardless of hydrophobicity conditions, at low dosages (0.2), a high surface area and pore volume are required for a high percent removal. And with a higher dosage, a lower surface area and pore volume is necessary to achieve a high percent removal.

Real-world multicentre experience of the pathological features of colonic ischaemia and their relationship to symptom duration, disease distribution and clinical outcome

AIM

To determine the pathological features of colonic ischaemia (CI) and their relationship to symptom duration, disease distribution and clinical outcome in a real-world, clinical setting.

METHOD

A retrospective, multicentre chart review was performed in patients diagnosed with CI at Montefiore Medical Center (January 2005 to July 2015), and Yale-New Haven Hospital (January 2005 to June 2010). Patients were included if clinical presentation, colonoscopic findings and colonic pathology were all consistent with CI.

RESULTS

Six hundred and sixteen patients with pathologically proven CI were included. Common pathological findings included inflammation (51.1%), ulceration (38.2%), fibrosis (26.0%) and necrosis (20.4%). Infarction and ghost cells were seen in 1.6% and 0.2% of cases, respectively. There was a significant relationship between symptom duration and hyalinization of the lamina propria (P = 0.05) and cryptitis/crypt abscesses (P = 0.01). Patients with isolated right CI (IRCI) were more likely than patients with isolated left CI (ILCI) to exhibit necrosis (P < 0.01), cryptitis/crypt abscess (P < 0.01) and inflammation (P = 0.03). Patients with poor outcomes were more likely to exhibit necrosis (P < 0.01) and capillary fibrin thrombi (P < 0.01) and less likely to exhibit fibrosis (P < 0.01) and epithelial changes (P < 0.01).

CONCLUSION

CI is accompanied by a broad spectrum of pathological findings. The traditional pathognomonic findings of CI are rare and cannot be relied upon to exclude the diagnosis. Patients with IRCI and/or poor outcomes were more likely to have pathological findings of necrosis than patients who had ILCI and/or nonpoor outcomes.