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Introduction: Striated duct adenoma (SDA) is a rare benign salivary gland tumor with a recently described genetic signature. Recurrent oncogenic mutations affecting the IDH2 gene differentiate SDA from its primary differential diagnosis of canalicular adenoma. Here, we report a case of SDA affecting the parotid gland with IDH1/2 mutation-specific immunohistochemical positivity. Additionally, we provide a scoping review developed according to the Cochrane Methodology and reported following the Joana Briggs Institute (JBI) checklist to synthesize all previously published cases of SDA. The review protocol was registered on the Open Science Framework (OSF) platform (https://osf.io/7mztg). The searches were performed using Medline, Embase, Web of Science, and LILACS, with no date or language limit. Studies were evaluated for eligibility, extracted, and compiled in a narrative form. Seven studies with 20 patients with SDA, including ours, were analyzed. The tumors mainly affected the parotid gland (13/20) in patients with a mean age of 62 years and did not display sex predilection. Swelling was the leading clinical symptom. The mean follow-up duration was 26 months with no recurrence or metastasis after resection. Conclusion: Awareness of the clinicopathological features and the use of IDH1/2 mutation-specific immunohistochemistry are pivotal for the consistent identification of SDA, and assessment for true biological potential will require increased follow-up and scrutiny. [ABSTRACT FROM AUTHOR]

Understanding the impact of variables involved in soluble-extracellular polymeric substance (S-EPS) production processes is crucial for reducing production costs and enhancing sustainability. Response surface methodology (RSM) provides essential tools that assist in developing predicted interactions among process variables for both industrial and non-industrial applications. The present study offers a simple and systematic demonstration of RSM capabilities, focusing on maximizing efficiency and minimizing production costs of S-EPS produced by Cyanocohniella rudolphia. RSM was employed to (1) design the production setup; (2) fit the collected data into a second-order polynomial model; (3) statistically evaluate the model's validity and the significance of the involved variables; and (4) identify and optimize production variables to enhance output and reduce costs. Focused on four key variables, each at three levels, RSM designed 25 distinct S-EPS production conditions, each with three replicates. Statistical analysis identified the most significant variables affecting S-EPS production as the culture medium/wet biomass ratio, production days, and nitrogen concentration. The model's validation demonstrated a strong correlation between the predicted and experimental values, with S-EPS production ranging from 70.46 to 228.65 mg/L and a maximum variation of 11.6%. This study demonstrates the effectiveness of RSM in optimizing S-EPS production, with the developed model showing a strong correlation between the variables and the response. The RSM model offers a promising approach for the bioprocessing industry, enhancing productivity and efficiency, minimizing costs, and leading to sustainable, cost-effective practices. [ABSTRACT FROM AUTHOR]

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The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel disease (IBD), represented by ulcerative colitis and Crohn's disease, is an idiopathic condition caused by a dysregulated immune response to host intestinal microflora, leading to chronic relapsing intestinal inflammation. Individuals with IBD are more prone to die from several diseases, including cancer. Methods: An extensive search was conducted of PubMed using the following medical subject heading-"inflammatory bowel disease" OR "Crohn's disease" OR "ulcerative colitis" AND "cancer." Results: In this review article, we discuss the oncogenic mechanisms and genomics of colitis-associated colorectal cancer. Beyond this, we describe the multiple other malignancies that IBD patients are at risk for, discuss caveats in the screening and diagnosis of those cancers, and shed light on pitfalls on the management and treatment of cancer in IBD patients. Conclusion: Patients, caregivers, and health professionals who deal with IBD must be educated on how to identify warning signs so that cancers can be diagnosed and treated as early as possible. [ABSTRACT FROM AUTHOR]

Three dimensional (3D) printed elastomers is a challenge which can be overcome using fillers. With that in perspective, cork was chemically modified via a two‐step reaction, aiming to achieve better compatibility with styrene‐ethylene‐butylene‐styrene (SEBS). In the first step, the OH groups of cork surface were grafted with succinic acid, while in the second step the free acid groups were reacted with a polyol to produce ester moieties. Spectroscopy analysis confirmed the occurrence of the chemical reaction which was supported by contact angle (CA) measurements. The unmodified cork presents a CA of 117.7°, while the modified cork presents a CA of 92.4° which is closer to that of SEBS (102.8°), resulting in better compatibility between the modified filler with the matrix. In fact, the enhancement of compatibility between polymer and filler was observed by inspecting the layer‐to‐layer bonding and was confirmed by the mechanical properties of the 3D printed composites, since the modified cork‐based composite presented higher Young modulus, maximum stress and elongation at break when compared to the untreated counterparts. Additionally, the advantage of the chemical treatment was evaluated by analyzing the processability of composites. The modified filler‐based composite, presents lower glass transition temperature (Tg), higher melt flow index (MFI) and higher thermal stability. Overall, in this work, a simple chemical route to modify lignocellulosic biomass and its benefit on the properties of 3D printing objects is discussed. The improved performance and sustainability of the 3D printed objects produced to be used for example in shoe applications was presented. [ABSTRACT FROM AUTHOR]

In this study, aiming to reduce the environmental impact of plastics, new composite materials containing recycled fibers from used paper coffee cups and oil extracted from spent coffee grounds (SCG) were produced. Accordingly, the cellulose fibers were chemically modified using lactic acid to be used as poly(lactic acid) filler reinforcement. The obtained results revealed that its addition improved the mechanical properties of the ensuing composites. The addition of 30% wt/wt of unmodified and modified fibers increased the Young modulus by 53% and 72%, respectively. In addition, enhanced biodegradability was obtained due to higher water absorption (up to 4% wt/wt). In parallel, coffee oil was used as a plasticizer and the affinity between the plasticizer and polymer matrix was confirmed by the lack of phase separation, by the improved flexibility of derived materials (the addition of 40% wt/wt of plasticizer increased the elongation at break by 86%) and by the better processability of the ensuing materials (the addition of 30% wt/wt of plasticizer increased the MFI by 205%). Overall, the results suggest that the use of modified cellulose fibers and the addition of SCG oil‐based plasticizer can be promising routes to produce eco‐friendly plastics. [ABSTRACT FROM AUTHOR]

In the healthcare domain, datasets are often private and lack large amounts of samples, making it difficult to cope with the inherent patient data heterogeneity. As an attempt to mitigate data scarcity, generative models are being used due to their ability to produce new data, using a dataset as a reference. However, synthesis studies often rely on a 2D representation of data, a seriously limited form of information when it comes to lung computed tomography scans where, for example, pathologies like nodules can manifest anywhere in the organ. Here, we develop a 3D Progressive Growing Generative Adversarial Network capable of generating thoracic CT volumes at a resolution of 128 3 , and analyze the model outputs through a quantitative metric (3D Muli-Scale Structural Similarity) and a Visual Turing Test. Clinical relevance - This paper is a novel application of the 3D PGGAN model to synthesize CT lung scans. This preliminary study focuses on synthesizing the entire volume of the lung rather than just the lung nodules. The synthesized data represent an attempt to mitigate data scarcity which is one of the major limitations to create learning models with good generalization in healthcare.