Extraction and recovery of nitric acid and copper from leach liquor of waste PCBs

The disposal of large quantities of electronic scrap world wide is causing an enormous harm to environment as well as to mankind. Therefore, efforts have been made to develop a suitable hydrometallurgical process for the extraction of metals from electronic scraps.The leach liquor of waste PCBs was generated containing 18.78g/LCu, 0.38g/LFe, 0.13g/LNi, 1.34g/L Pb and 6.3 M HNO3. Initially, HNO3 was extracted from the leach liquor using TBP as an extractant. Various process parameters such as time, concentration of extractant, O/A ratio etc were studied for the extraction of HNO3. It was observed that the extraction of HNO3 increased from 8.1–39.6% with increase in TBP concentration from 10 to 100%. The plot of log D vs. log[TBP] gives a straight line with slope ~ 1indicated that the 1 mole of TBP used for the extraction of 1mole of HNO3. The McCabe – Thiele Plot was drawn to investigate the stage required for maximum acid extraction. After extraction of HNO3 from leach liquor, extraction of copper was investigated using LIX84IC. Various parameters such as effect of pH, phaseratio, strippingetc. Were studied to investigate the optimum experimental condition for the extraction of copper. The extraction of Cu increased from 37 to 88% with the increase in the Ph range from 0.7 to 2.0. The optimum equilibrium pH for Cu extraction was found to be ~2.0. The McCabe– Thiele Plot for Cu extraction indicated that 2 counter current stages is enough for its complete removal from acid free leach liquor at O/A=1.2/1 maintaining equilibrium pH~2.0. The present study reports removal of acid and Cu from the leach liquor of waste PCBs in an eco-friendly manner

An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

Common variants in CLDN2 and MORC4 genes confer disease susceptibility in patients with chronic pancreatitis

A recent Genome-wide Association Study (GWAS) identified association with variants in X-linked CLDN2 and MORC4 and PRSS1-PRSS2 loci with Chronic Pancreatitis (CP) in North American patients of European ancestry. We selected 9 variants from the reported GWAS and replicated the association with CP in Indian patients by genotyping 1807 unrelated Indians of Indo-European ethnicity, including 519 patients with CP and 1288 controls. The etiology of CP was idiopathic in 83.62% and alcoholic in 16.38% of 519 patients. Our study confirmed a significant association of 2 variants in CLDN2 gene (rs4409525—OR 1.71, P = 1.38 x 10-09; rs12008279—OR 1.56, P = 1.53 x 10-04) and 2 variants in MORC4 gene (rs12688220—OR 1.72, P = 9.20 x 10-09; rs6622126—OR 1.75, P = 4.04x10-05) in Indian patients with CP. We also found significant association at PRSS1-PRSS2 locus (OR 0.60; P = 9.92 x 10-06) and SAMD12-TNFRSF11B (OR 0.49, 95% CI [0.31–0.78], P = 0.0027). A variant in the gene MORC4 (rs12688220) showed significant interaction with alcohol (OR for homozygous and heterozygous risk allele -14.62 and 1.51 respectively, P = 0.0068) suggesting gene-environment interaction. A combined analysis of the genes CLDN2 and MORC4 based on an effective risk allele score revealed a higher percentage of individuals homozygous for the risk allele in CP cases with 5.09 fold enhanced risk in individuals with 7 or more effective risk alleles compared with individuals with 3 or less risk alleles (P = 1.88 x 10-14). Genetic variants in CLDN2 and MORC4 genes were associated with CP in Indian patients