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Malignant distal biliary obstructions are becoming increasingly common, especially in patients with cancers of the pancreatic head, despite progress in medical oncology research. ERCP is the current gold standard for management of such strictures, but the emergence of EC-LAMS has rendered EUS-CDS both safe and efficient. It is a “game changer”; originally intended for ERCP failure, two randomised clinical trials recently proposed EUS-CDS as a first-intent procedure in palliative settings. For resectable diseases, the absence of iatrogenic pancreatitis associated with a lower rate of postsurgical adverse events (compared with ERCP) leads us to believe that EUS-CDS might be used in first-intent as a pre-operative endoscopic biliary drainage.

Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ transmission electron microscopy (TEM) experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames from such experiments can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects. The vast amount of data also makes manual annotation very time consuming, thereby limiting the use of deep learning (DL)-based, automated image analysis and segmentation of the dislocation microstructure. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed. Even though domain scientists might dismiss synthetic images as artificial, our findings show that they can result in superior performance. Additionally, we propose an enhanced DL method optimized for segmenting overlapping or intersecting dislocation lines. Upon testing this framework on four distinct real datasets, we find that a model trained only on synthetic training data can also yield high-quality results on real images–even more so if the model is further fine-tuned on a few real images. Our approach demonstrates the potential of synthetic data in overcoming the limitations of manual annotation of TEM image data of dislocation microstructure, paving the way for more efficient and accurate analysis of dislocation microstructures. Last but not least, segmenting such thin, curvilinear structures is a task that is ubiquitous in many fields, which makes our method a potential candidate for other applications as well.

Abstract In psychiatry, recent years have seen a change of focus from a clinician- to a patient-centered perspective that emphasizes quality of life as a treatment target. As a complex construct, quality of life is composed of multiple dimensions that interact with one-another (e.g. physical and psychological well-being, relationships, autonomy, self-esteem). Here, we used data from the REHABase cohort, which includes N = 2180 patients from 15 psychosocial rehabilitation centers in France, to explore networks of quality-of-life dimensions among six psychiatric disorders: schizophrenia, neurodevelopmental, bipolar, depressive, anxiety, and personality disorders. Stronger connections (edges) involved the Self-Esteem dimension, such as Self-Esteem–Physical Well-Being, Self-Esteem–Autonomy, Self-Esteem–Psychological Well-Being, and Self-Esteem–Resilience. Self-esteem was also consistently retrieved as the most central node (the dimension with the most connections within each network). Between-group tests did not reveal any differences regarding network structure, overall connectivity, edge-weights, and nodes’ centrality. Despite presenting with different symptom profiles, various psychiatric disorders may demonstrate similar inter-relationships among quality-of-life dimensions. In particular, self-esteem may have a crucial inter-connecting role in patients’ quality of life. Our findings could support treatment programmes that specifically target self-esteem to improve patients’ quality of life in a cost-effective way.

We address two interrelated issues affecting the hinterland portion of the maritime container supply chain: reducing the movement of empty containers and reducing empty trips by trucks carrying these containers. In this paper, we show that empty container flow optimization can be implemented via a blockchain based on the proof‐of‐useful‐work concept where the proof of work requires the solution of an NP$\mathcal{NP}$‐hard optimization problem whose solution benefits the blockchain participants. Accordingly, we propose that anonymous miners compete to solve the container truck routing problem, which seeks to find the most efficient routes for trucks. We show that this problem is NP$\mathcal{NP}$‐hard. Miners must also solve the problem of optimally matching consignees and shippers, which will reduce transportation and storage costs for empty containers. In essence, the proposed framework turns blockchain into a massive optimization engine that directly benefits the hinterland container supply chain ecosystem. [ABSTRACT FROM AUTHOR]

An efficient one-step procedure for the synthesis of a new series of multifunctional olefins by condensation of α-substituted β-diketones as well as β-phosphonoesters and benzoylated β-ketoesters with formaldehyde, using potassium carbonate in refluxing THF, followed by a deacylation reaction, is herein described. In contrast, the α-substituted β-keto esters, bearing an acyl moiety, only undergo a hydroxymethylation reaction, with no metal catalysts/activating agents, as is usually required for the process, affording the corresponding α-hydroxymethyl β-keto esters in high yields. [ABSTRACT FROM AUTHOR]

Introduction: Cortico‐cortical evoked potentials (CCEPs) were described as reproducible during trains of single‐pulse electrical stimulations (SPES). Still, few studies described a variability of CCEPs that was higher within the epileptogenic zone (EZ). This study aimed at characterizing the relationship of CCEP variability with the occurrence of interictal/ictal epileptiform discharges at the temporal vicinity of the stimulation, but not during the stimulation, by effective connectivity modifications. Methods: We retrospectively included 20 patients who underwent SPES during their stereo‐electroencephalography (SEEG). We analyzed the variability of CCEPs by using the post‐stimulation time course of intertrial standard deviation (amplitude) and the timing of peak amplitude signal of CCEP epochs (latency). Values were corrected for the Euclidian distance between stimulating/recording electrodes. Receiver operating characteristics curves were used to assess the relationship with the EZ. The link between CCEP variability and interictal discharges occurrence, seizure frequency prior to the SEEG recording, and number of seizures during SEEG recording was assessed with Spearman's correlations. Results: A relationship was demonstrated between the EZ and both the distance‐corrected latency variation (area under the curve (AUC): 0.73–0.74) and the distance‐corrected amplitude variation (AUC: 0.71–0.72) and both were related with the occurrence of seizures. Conclusion: Seizures before/during SEEG impact the dynamics of effective connectivity within the epileptogenic network by reducing the variability of CCEP latency/amplitude when the seizure frequency increases. It suggests a strengthening of the epileptogenic network with the occurrence of many seizures. These findings stress the importance of early epilepsy surgery at a time when the network organization has not yet been complete. [ABSTRACT FROM AUTHOR]

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses. [ABSTRACT FROM AUTHOR]

Background The ileocecal valve (ICV) is considered to be one of the most difficult locations for endoscopic submucosal dissection (ESD). The objective of this study was to evaluate the efficacy and safety of traction-assisted ESD in this situation. Methods All patients who underwent traction-assisted ESD for an ICV lesion at three centers were identified from a prospective ESD database. En bloc and R0 rates were evaluated. Factors associated with non-R0 resection were explored. Results 106 patients with an ICV lesion were included. The median lesion size was 50 mm (interquartile range 38–60) and 58.5% (62/106) invaded the terminal ileum. The en bloc and R0 resection rates were 94.3% and 76.4%, respectively. Factors associated with non-R0 resection were lesions covering ≥75% of the ICV (odds ratio [OR] 0.21. 95%CI 0.06–0.76; P=0.02), and involving the anal lip (OR 0.36, 95%CI 0.13–0.99; P=0.04) or more than two sites on the ICV (OR 0.27, 95%CI 0.07–0.99; P=0.03). Conclusion Traction-assisted ESD for treatment of ICV lesions was a safe and feasible option. Large lesions and anal lip involvement appeared to be factors predictive of difficulty. [ABSTRACT FROM AUTHOR]