Trends in Neurosurgical Treatment for Chronic Subdural Hematoma in Germany: A National Survey.

Background:  Chronic subdural hematoma (cSDH) is a common neurosurgical condition of growing importance due to the aging population and increasing use of antithrombotic agents. Due to the lack of guidelines, great variability is observed in the treatment of cSDH. We conducted a multicenter, nationwide survey to assess the differences in treatment across Germany in the context of surgical practices discussed in the literature.

Beyond the Operating Room: The Reality of Gender Disparities in Neurosurgical Education Starting in Medical School.

Objective: Despite the growing number of female medical students, there remains a significant disparity between the number of female and male neurosurgeons. This study aims to determine if this trend is also evident among medical students, examine how various medical schools worldwide are addressing this issue, and propose potential solutions.

Methods: The data includes anonymous baseline information from congress registrations, the abstract submission system, and 2 surveys designed to assess student experiences before and after the congress.

Identification of a Therapeutic Window for Neurovascular Unit Repair after Experimental Spinal Cord Injury.

Traumatic spinal cord injury (SCI) is a devastating condition for which effective neuroregenerative and neuroreparative strategies are lacking. The post-traumatic disruption of the blood-spinal cord barrier (BSCB) as part of the neurovascular unit (NVU) is one major factor in the complex pathophysiology of SCI, which is associated with edema, inflammation, and cell death in the penumbra regions of the spinal cord adjacent to the lesion epicenter. Thus, the preservation of an intact NVU and vascular integrity to facilitate the regenerative capacity following SCI is a desirable therapeutic target.

Conceptualisation and Implementation of a Competency-based Multidisciplinary Course for Medical Students in Neurosurgery.

The field of medicine is quickly evolving and becoming increasingly more multidisciplinary and technologically demanding. Medical education, however, does not yet adequately reflect these developments and new challenges, which calls for a reform in the way aspiring medical professionals are taught and prepared for the workplace. The present article presents an attempt to address this shortcoming in the form of a newly conceptualized course for medical students with a focus on the current demands and trends in modern neurosurgery.

Modelling dataset bias in machine-learned theories of economic decision-making.

Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networks on a new online large-scale dataset, choices13k. Here we systematically analyse the relationships between several models and datasets using machine-learning methods and find evidence for dataset bias.

Endovascular treatment of brain aneurysms under conscious sedation: a systematic review of procedural feasibility and safety.

Over the last decades, minimally invasive techniques have revolutionized the endovascular treatment (EVT) of brain aneurysms. In parallel, the development of conscious sedation (CS), a potentially less harmful anesthetic protocol than general anesthesia (GA), has led to the course optimization of surgeries, patient outcomes, and healthcare costs. Nevertheless, the feasibility and safety of EVT of brain aneurysms under CS have yet to be assessed thoroughly.

Learning with privileged and sensitive information: a gradient-boosting approach.

We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learners, specifically gradient-boosted decision trees for learning with privileged information. Our methods use privileged features as knowledge to guide the algorithm when learning from fully observed (usable) features.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.

AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong.

In recent years, deep neural networks for strategy games have made significant progress. AlphaZero-like frameworks which combine Monte-Carlo tree search with reinforcement learning have been successfully applied to numerous games with perfect information. However, they have not been developed for domains where uncertainty and unknowns abound, and are therefore often considered unsuitable due to imperfect observations.

Validation of the Berlin Grading System for moyamoya angiopathy with the use of [O]HO PET.

The Berlin Grading System assesses clinical severity of moyamoya angiopathy (MMA) by combining MRI, DSA, and cerebrovascular reserve capacity (CVRC). Our aim was to validate this grading system using [O]HO PET for CVRC. We retrospectively identified bilateral MMA patients who underwent [O]HO PET examination and were treated surgically at our department.

Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes.

We consider the problem of modeling gestational diabetes in a clinical study and develop a domain expert-guided probabilistic model that is both interpretable and explainable. Specifically, we construct a probabilistic model based on causal independence (Noisy-Or) from a carefully chosen set of features. We validate the efficacy of the model on the clinical study and demonstrate the importance of the features and the causal independence model.

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites.

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites.

Palliative Cytoreductive Surgery With or Without Hyperthermic Intraperitoneal Chemotherapy for Peritoneal Carcinomatosis: Is It Safe and Effective?

Introduction: Palliation is a controversial indication for cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Patients with peritoneal carcinomatosis (PC) are living longer, and the roles of palliative CRS and HIPEC are increasingly challenged. The purpose of this study is to evaluate indications, morbidity, and symptom improvement from CRS/HIPEC in advanced PC.

Declarative Learning-Based Programming as an Interface to AI Systems.

Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models' output and input.

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition.

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions.

Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model.

The Moral Choice Machine.

Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? In this study, we show that applying machine learning to human texts can extract deontological ethical reasoning about "right" and "wrong" conduct. We create a template list of prompts and responses, such as "Should I [action]?", "Is it okay to [action]?", etc.

Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data.

Deep neural networks have been successfully applied in learning the board games Go, chess, and shogi without prior knowledge by making use of reinforcement learning. Although starting from zero knowledge has been shown to yield impressive results, it is associated with high computationally costs especially for complex games. With this paper, we present which is a neural network based engine solely trained in supervised manner for the chess variant crazyhouse.

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning.

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal.

Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!

Determination and characterization of resistance reactions of crops against fungal pathogens are essential to select resistant genotypes. In plant breeding, phenotyping of genotypes is realized by time consuming and expensive visual plant ratings. During resistance reactions and during pathogenesis plants initiate different structural and biochemical defence mechanisms, which partly affect the optical properties of plant organs.

Optimization of Polycistronic Anti-CCR5 Artificial microRNA Leads to Improved Accuracy of Its Lentiviral Vector Transfer and More Potent Inhibition of HIV-1 in CD4⁺ T-Cells.

C-C chemokine receptor type 5 (CCR5) is utilized by human immunodeficiency virus (HIV) as a co-receptor for cell entry. Suppression of the CCR5 gene by artificial microRNAs (amiRNAs) could confer cell resistance. In previous work, we created a lentivector that encoded the polycistron of two identical amiRNAs that could effectively suppress CCR5.

Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering.

Wound healing is a complex and dynamic process with different distinct and overlapping phases from homeostasis, inflammation and proliferation to remodelling. Monitoring the healing response of injured tissue is of high importance for basic research and clinical practice. In traditional application, biological markers characterize normal and abnormal wound healing.