Label-free in-flow detection of single DNA molecules using glass nanopipettes.

With the view of enhancing the functionality of label-free single molecule nanopore-based detection, we have designed and developed a highly robust, mechanically stable, integrated nanopipette-microfluidic device which combines the recognized advantages of microfluidic systems and the unique properties/advantages of nanopipettes. Unlike more typical planar solid-state nanopores, which have inherent geometrical constraints, nanopipettes can be easily positioned at any point within a microfluidic channel. This is highly advantageous, especially when taking into account fluid flow properties. We show that we are able to detect and discriminate between DNA molecules of varying lengths when motivated through a microfluidic channel, upon the application of appropriate voltage bias across the nanopipette. The effects of applied voltage and volumetric flow rates have been studied to ascertain translocation event frequency and capture rate. Additionally, by exploiting the advantages associated with microfluidic systems (such as flow control and concomitant control over analyte concentration/presence), we show that the technology offers a new opportunity for single molecule detection and recognition in microfluidic devices.

Nomogram for prediction of peritoneal metastasis risk in colorectal cancer.

Objective: Peritoneal metastasis is difficult to diagnose using traditional imaging techniques. The main aim of the current study was to develop and validate a nomogram for effectively predicting the risk of peritoneal metastasis in colorectal cancer (PMCC). Methods: A retrospective case-control study was conducted using clinical data from 1284 patients with colorectal cancer who underwent surgery at the First Affiliated Hospital of Guangxi Medical University from January 2010 to December 2015. Least absolute shrinkage and selection operator (LASSO) regression was applied to optimize feature selection of the PMCC risk prediction model and multivariate logistic regression analysis conducted to determine independent risk factors. Using the combined features selected in the LASSO regression model, we constructed a nomogram model and evaluated its predictive value via receiver operating characteristic (ROC) curve analysis. The bootstrap method was employed for repeated sampling for internal verification and the discrimination ability of the prediction models evaluated based on the C-index. The consistency between the predicted and actual results was assessed with the aid of calibration curves. Results: Overall, 96 cases of PMCC were confirmed via postoperative pathological diagnosis. Logistic regression analysis showed that age, tumor location, perimeter ratio, tumor size, pathological type, tumor invasion depth, CEA level, and gross tumor type were independent risk factors for PMCC. A nomogram composed of these eight factors was subsequently constructed. The calibration curve revealed good consistency between the predicted and actual probability, with a C-index of 0.882. The area under the curve (AUC) of the nomogram prediction model was 0.882 and its 95% confidence interval (CI) was 0.845-0.919. Internal validation yielded a C-index of 0.868. Conclusion: We have successfully constructed a highly sensitive nomogram that should facilitate early diagnosis of PMCC, providing a robust platform for further optimization of clinical management strategies.

Scale-up and Sustainability Evaluation of Biopolymer Production from Citrus Waste Offering Carbon Capture and Utilisation Pathway.

Invited for this month's cover picture is the group of Dr Miao Guo from Department of Chemical Engineering at the Imperial College London (UK). The cover picture shows modelling research on the co-polymerisation of waste-sourced limonene oxide with CO2 to produce poly(limonene carbonate), which offers a sustainable pathway to achieve carbon capture and utilisation. A computational approach to process design was integrated with sustainability evaluation to model this synthetic pathway and identify the environmental-damaging and performance-limiting steps for further improvement. Our research highlights the potential of closed-loop manufacturing systems with waste recovery, which is instrumental in building a sustainable circular economy. Read the full text of their Full Paper at 10.1002/open.201900015.

Scale-up and Sustainability Evaluation of Biopolymer Production from Citrus Waste Offering Carbon Capture and Utilisation Pathway.

Poly(limonene carbonate) (PLC) has been highlighted as an attractive substitute to petroleum derived plastics, due to its utilisation of CO2 and bio-based limonene as feedstocks, offering an effective carbon capture and utilisation pathway. Our study investigates the techno-economic viability and environmental sustainability of a novel process to produce PLC from citrus waste derived limonene, coupled with an anaerobic digestion process to enable energy cogeneration and waste recovery maximisation. Computational process design was integrated with a life cycle assessment to identify the sustainability improvement opportunities. PLC production was found to be economically viable, assuming sufficient citrus waste is supplied to the process, and environmentally preferable to polystyrene (PS) in various impact categories including climate change. However, it exhibited greater environmental burdens than PS across other impact categories, although the environmental performance could be improved with a waste recovery system, at the cost of a process design shift towards energy generation. Finally, our study quantified the potential contribution of PLC to mitigating the escape of atmospheric CO2 concentration from the planetary boundary. We emphasise the importance of a holistic approach to process design and highlight the potential impacts of biopolymers, which is instrumental in solving environmental problems facing the plastic industry and building a sustainable circular economy.