Does smoking cessation affect postoperative healing following oral surgery among smokers? - a systematic review.

BACKGROUND: It is well documented that smokers suffer increased risk of postoperative complications after medical surgery, for example delayed healing and increased risk of infection. It is also known that preoperative smoking cessation can reduce the risk of these complications. Because of this there are guidelines regarding preoperative smoking cessation in non-oral medical surgery. There are however no specific guidelines regarding oral surgical procedures, such as surgical extractions, dentoalveolar surgery, periodontal surgery, or dental implantation. Nevertheless, it is common that dentists and oral surgeons recommend smoking cessation pre to oral surgical procedures. The aim with this systematic review was to see if there are any evidence in the literature, supporting preoperative smoking cessation in oral surgical procedures. METHODS: A systematic search of the electronic databases PubMed, Scopus, Web of Science, and Cochrane was conducted to identify studies addressing the effect of preoperative smoking cessation in oral surgical procedures. Included publications were subjected to preidentified inclusion criterion. Six examiners performed the eligibility and quality assessment of relevant studies. Risk of bias was assessed using ROBINS-I and RoB 2. Certainty assessment was carried out using GRADE. RESULTS: The initial search resulted in 2255 records, and after removal of 148 duplicates, 16 articles met an acceptable level of relevance. These were read in full text, whereof 12 articles were excluded, due to different intervention, outcome, or study design than stated in the review protocol. One study remained with moderate risk of bias and three were excluded due to high risk of bias. CONCLUSION: This systematic review could not determine the effect of smoking cessation pre to oral surgical procedures, in smokers. This indicates lack of knowledge in the effects of smoking cessation. We also conclude a lack of knowledge in how to design smoking cessation in the most effective way.

Adaptable control policies for variable liquid chromatography columns using deep reinforcement learning.

Controlling chromatography systems for downstream processing of biotherapeutics is challenging because of the highly nonlinear behavior of feed components and complex interactions with binding phases. This challenge is exacerbated by the highly variable binding properties of the chromatography columns. Furthermore, the inability to collect information inside chromatography columns makes real-time control even more problematic. Typical static control policies either perform sub optimally on average owing to column variability or need to be adapted for each column requiring expensive experimentation. Exploiting the recent advances in simulation-based data generation and deep reinforcement learning, we present an adaptable control policy that is learned in a data-driven manner. Our controller learns a control policy by directly manipulating the inlet and outlet flow rates to optimize a reward function that specifies the desired outcome. Training our controller on columns with high variability enables us to create a single policy that adapts to multiple variable columns. Moreover, we show that our learned policy achieves higher productivity, albeit with a somewhat lower purity, than a human-designed benchmark policy. Our study shows that deep reinforcement learning offers a promising route to develop adaptable control policies for more efficient liquid chromatography processing.

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration.

Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples.

LIVECell-A large-scale dataset for label-free live cell segmentation.

Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.

A Machine Vision Approach for Bioreactor Foam Sensing.

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.