To move away from open-heart surgery towards safer transcatheter procedures, there is a growing need for improved imaging techniques and robotic solutions to enable simple, accurate tool navigation. Common imaging modalities, such as fluoroscopy and ultrasound, have limitations that can be overcome using cardioscopy, i.e., direct optical visualization inside the beating heart. We present a cardioscope designed as a steerable balloon. As a balloon, it can be collapsed to pass through the vasculature and subsequently inflated inside the heart for visualization and tool delivery through an integrated working channel. Through careful design of balloon wall thickness, a single input, balloon inflation pressure, is used to independently control two outputs, balloon diameter (corresponding to field of view diameter) and balloon bending angle (enabling precise working channel positioning). This balloon technology can be tuned to produce cardioscopes designed for a range of intracardiac tasks. To illustrate this approach, a balloon design is presented for the specific task of aortic leaflet laceration. Image-based closed-loop control of bending angle is also demonstrated as a means of enabling stable orientation control during tool insertion and removal.

Abstract--Transcatheter valve repair presents significant challenges due to the mechanical limitations and steep learning curve associated with manual catheter systems. This paper investigates the use of robotics to facilitate transcatheter procedures in the context of mitral valve edge-to-edge repair . The complex handle-based control of a clinical repair device is replaced by intuitive robotic joint-based control via a game controller . Manual versus robotic performance is analyzed by decomposing the overall device delivery task into motion-specific steps and comparing capabilities on a step-by-step basis in a phantom model of the heart and vasculature. Metrics include procedure duration and clip placement accuracy. Results demonstrate that the robotic system can reduce procedural time and motion errors while also improving accuracy of clip placement. These findings suggest that robotic assistance can address key limitations of manual systems, offering a more reliable and user-friendly platform for complex transcatheter procedures. Transcatheter valve repair procedures are complex to perform and involve substantial learning curves. For example, in transcatheter edge-to-edge repair (TEER) of mitral regurgitation (Figure 1), clinical experience demonstrates that operators improve significantly over their first 50 cases and their performance continues to improve out to their 200th case [1]. A major component of mastering a transcatheter procedure is learning how to precisely control catheter positioning.

Knowledge graphs (KGs) are increasingly used to represent biomedical information in structured, interpretable formats. However, existing biomedical KGs often focus narrowly on molecular interactions or adverse events, overlooking the rich data found in drug leaflets. In this work, we present (1) a hackable, end-to-end pipeline to create KGs from unstructured online content using a web scraper and an LLM; and (2) a curated dataset, MEDAKA, generated by applying this method to publicly available drug leaflets. The dataset captures clinically relevant attributes such as side effects, warnings, contraindications, ingredients, dosage guidelines, storage instructions and physical characteristics. We evaluate it through manual inspection and with an LLM-as-a-Judge framework, and compare its coverage with existing biomedical KGs and databases. We expect MEDAKA to support tasks such as patient safety monitoring and drug recommendation. The pipeline can also be used for constructing KGs from unstructured texts in other domains. Code and dataset are available at https://github.com/medakakg/medaka.

On 11 October, North Korea's foreign ministry accused the South of sending drones to Pyongyang at night over the course of two weeks. It said that leaflets dispersed by the drones contained "inflammatory rumours and rubbish". Kim's influential sister, Kim Yo Jong, warned Seoul of "horrible consequences" if the alleged drone flights happened again. She later said there was "clear evidence" that "military gangsters" from the South were behind the alleged provocations. North Korea has released blurry images of what it said were the drones flying in the sky, as well as pictures allegedly showing the leaflets, but there is no way of independently verifying their claims.

This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the MHealth domain from English to Persian, Arabic, Turkish, Romanian, and Spanish. Two datasets comprising MHealth information from the UK National Health Service website and information leaflets from The Royal College of Psychiatrists were used. Native speakers of the target languages manually assessed the GT translations, focusing on medical terminology accuracy, comprehensibility, and critical syntactic/semantic errors. GT output analysis revealed challenges in accurately translating medical terminology, particularly in Arabic, Romanian, and Persian. Fluency issues were prevalent across various languages, affecting comprehension, mainly in Arabic and Spanish. Critical errors arose in specific contexts, such as bullet-point formatting, specifically in Persian, Turkish, and Romanian. Although improvements are seen in longer-text translations, there remains a need to enhance accuracy in medical and mental health terminology and fluency, whilst also addressing formatting issues for a more seamless user experience. The findings highlight the need to use customised translation engines for Mhealth translation and the challenges when relying solely on machine-translated medical content, emphasising the crucial role of human reviewers in multilingual healthcare communication.

In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, by Text, as well as by Image and Text. The approach "Classification by Text" uses the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. We release our code at https://github.com/ladwigd/Leaflet-Product-Classification.

Academics and researchers gathered recently to discuss the findings of a new report on algorithms and their possibilities in the judicial system. Prepared and presented by DAKSH, a research centre that works on access to justice and judicial reforms, the report has been described as a superlative introduction to the various problems that ail our courts and how the usage of algorithms and allied technologies complicates it.

A Russian teenager has been sentenced to five years in prison for allegedly planning to blow up a virtual FSB security service building in the video game Minecraft. The ruling falls into a broader pattern under President Vladimir Putin in which young Russians are put behind bars on controversial and preemptive terrorism charges. A military court in Siberia sentenced 16-year-old Nikita Uvarov to five years in a penal colony on charges of "training for terrorist activities", the rights lawyer Pavel Chikov said on the messaging service Telegram. Two other defendants were cleared of criminal charges and handed suspended sentences because they cooperated with investigators, Chikov added. The hearing was held behind closed doors. Uvarov and two other teenagers in the Siberian city of Kansk were detained in the summer of 2020 for spreading leaflets in support of a Moscow mathematician and anarchist activist who was on trial for vandalism.

Jacqui Quibbell has suffered from'crippling periods of depression and suicidal thoughts' for all her adult life. In 2003, her doctors suggested Jacqui underwent electro-convulsive therapy (ECT). This involves attaching electrodes to the patient's head and, under general anaesthetic, passing electric shocks through their brain -- which is said to'rewire' it. 'I didn't know much about ECT, I didn't have Google then,' says Jacqui, 57. 'I started suffering memory loss during the treatment and by the time it finished, my short-term memory had disappeared completely and has never come back.

Small-molecule screens aimed at identifying therapeutic candidates traditionally search for molecules that affect one to several outputs at most, limiting discovery of true disease-modifying drugs. Theodoris et al. developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell disease model of a common form of heart disease involving the aortic valve. Gene network correction by the most efficacious therapeutic candidate generalized to primary aortic valve cells derived from more than 20 patients with sporadic aortic valve disease and prevented aortic valve disease in vivo in a mouse model. Science , this issue p. [eabd0724][1] ### INTRODUCTION Determining the gene-regulatory networks that drive human disease allows the design of therapies that target the core disease mechanism rather than merely managing symptoms. However, small molecules used as therapeutic agents are traditionally screened for their effects on only one to several outputs at most, from which their predicted efficacy on the disease as a whole is extrapolated. In silico correlation of disease network dysregulation with pathways affected by molecules in surrogate cell types is limited by the relevance of the cell types used and by not directly testing compounds in patient cells. ### RATIONALE In principle, mapping the architecture of the dysregulated network in disease-relevant cells differentiated from patient-derived induced pluripotent stem cells (iPSCs) and subsequent screening for small molecules that broadly correct the abnormal gene network could overcome this obstacle. Specifically, targeting normalization of the core regulatory elements that drive the disease process, rather than correction of peripheral downstream effectors that may not be disease modifying, would have the greatest likelihood of therapeutic success. We previously demonstrated that haploinsufficiency of NOTCH1 can cause calcific aortic valve disease (CAVD), the third most common form of heart disease, and that the underlying mechanism involves derepression of osteoblast-like gene networks in cardiac valve cells. There is no medical therapy for CAVD, and in the United States alone, >100,000 surgical valve replacements are performed annually to relieve obstruction of blood flow from the heart. Many of these occur in the setting of a congenital aortic valve anomaly present in 1 to 2% of the population in which the aortic valve has two leaflets (bicuspid) rather than the normal three leaflets (tricuspid). Bicuspid valves in humans can also be caused by NOTCH1 mutations and predispose to early and more aggressive calcification in adulthood. Given that valve calcification progresses with age, a medical therapy that could slow or even arrest progression would have tremendous impact. ### RESULTS We developed a machine-learning approach to identify small molecules that sufficiently corrected gene network dysregulation in NOTCH1-haploinsufficient human iPSC-derived endothelial cells (ECs) such that they classified similar to NOTCH1 +/+ ECs derived from gene-corrected isogenic iPSCs. We screened 1595 small molecules for their effect on a signature of 119 genes representative of key regulatory nodes and peripheral genes from varied regions of the inferred NOTCH1-dependent network, assayed by targeted RNA sequencing (RNA-seq). Overall, eight molecules were validated to sufficiently correct the network signature such that NOTCH1 +/– ECs classified as NOTCH1 +/+ by the trained machine-learning algorithm. Of these, XCT790, an inverse agonist of estrogen-related receptor α (ERRα), had the strongest restorative effect on the key regulatory nodes SOX7 and TCF4 and on the network as a whole, as shown by full transcriptome RNA-seq. Gene network correction by XCT790 generalized to human primary aortic valve ECs derived from explanted valves from >20 patients with nonfamilial CAVD. XCT790 was effective in broadly restoring dysregulated genes toward the normal state in both calcified tricuspid and bicuspid valves, including the key regulatory nodes SOX7 and TCF4 . Furthermore, XCT790 was sufficient to prevent as well as treat already established aortic valve disease in vivo in a mouse model of Notch1 haploinsufficiency on a telomere-shortened background. XCT790 significantly reduced aortic valve thickness, the extent of calcification, and echocardiographic signs of valve stenosis in vivo. XCT790 also reduced the percentage of aortic valve cells expressing the osteoblast transcriptional regulator RUNX2, indicating a reduction in the osteogenic cell fate switch underlying CAVD. Whole-transcriptome RNA-seq in treated aortic valves showed that XCT790 broadly corrected the genes dysregulated in Notch1-haploinsufficient mice with shortened telomeres, and that treatment of diseased aortic valves promoted clustering of the transcriptome with that of healthy aortic valves. ### CONCLUSION Network-based screening that leverages iPSC and machine-learning technologies is an effective strategy to discover molecules with broadly restorative effects on gene networks dysregulated in human disease that can be validated in vivo. XCT790 represents an entry point for developing a much-needed medical therapy for calcification of the aortic valve, which may also affect the highly related and associated calcification of blood vessels. Given the efficacy of XCT790 in limiting valve thickening, the potential for XCT790 to alter the progression of childhood, and perhaps even fetal, valve stenosis also warrants further study. Application of this strategy to other human models of disease may increase the likelihood of identifying disease-modifying candidate therapies that are successful in vivo. ![Figure][2] Network-correcting therapeutic candidate for heart disease. A gene network–based screening approach leveraging human disease-specific iPSCs and machine learning identified a therapeutic candidate, XCT790, which corrected the network dysregulation in genetically defined iPSC-derived endothelial cells and primary aortic valve endothelial cells from >20 patients with sporadic aortic valve disease. XCT790 was also effective in preventing and treating a mouse model of aortic valve disease. ILLUSTRATION: CHRISTINA V. THEODORIS Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery. [1]: /lookup/doi/10.1126/science.abd0724 [2]: pending:yes