Sex and gender differences in the molecular etiology of Parkinson's disease: considerations for study design and data analysis

Parkinson’s disease (PD) is more prevalent in men than women, and presents with different clinical features in each sex. Despite widespread recognition of these differences, females are under-represented in clinical and experimental studies of PD, and much remains to be elucidated regarding the biological underpinnings of sex differences in PD. In this review, we summarize known contributors to sex differences in PD etiology across the life course, with a focus on neurological development and gene regulation. Sex differences that are established at conception and heightened during adolescence and midlife may partially embed future PD risk, due to the complex interactions between gonadal hormones, gene regulation, lifestyle factors, and aging. While the neuroprotective properties of estrogen are strongly implicated in reduced prevalence of PD in women, interactions with genotype and gender-biased lifestyle factors are incompletely understood. Consideration of sex and genderrelated factors in study design, data analysis, and interpretation have the power to expedite our knowledge of the etiology of PD in men and in women, and to inform prevention and therapeutic strategies tailored to each sex. Plain english summary Parkinson’s disease (PD) more commonly affects men, and is known to have different symptoms in men and women. While this is in part due to the protective effects of estrogen in women, our understanding of why there is a sex difference in PD, and how it develops in each sex, is currently incomplete. This article provides an overview of factors throughout the lifespan that contribute to the differences between men and women in brain health and risk for PD, with a focus on hormones, gene regulation, and their intersections with lifestyle factors. We also discuss how researchers can consider sex and gender in future studies to enhance our understanding of how PD develops, and potentially develop sex-tailored prevention and treatment strategies. Highlights • Genetic risk for PD is similar between men and women. • Transcriptomic and epigenetic differences in men and women with PD have been reported, particularly in substantia nigra tissue. • Emerging evidence suggests interactions between gene regulation, sex hormones, and lifestyle factors contribute to disease pathogenesis in each sex. • Statistical approaches can be used to balance sex ratios and explore sex as a contributor to PD etiology, rather than a confounder. • Increasing representation of women in PD clinical studies is a priority for future research endeavors

Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care

Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as “hallucinations”. This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations

Discovering motifs to fingerprint multi-layer networks: a case study on the connectome of C. Elegans

Motif discovery is a powerful and insightful method to quantify network structures and explore their function. As a case study, we present a comprehensive analysis of regulatory motifs in the connectome of the model organism Caenorhabditis elegans (C. elegans). Leveraging the Efficient Subgraph Counting Algorithmic PackagE (ESCAPE) algorithm, we identify network motifs in the multi-layer nervous system of C. elegans and link them to functional circuits. We further investigate motif enrichment within signal pathways and benchmark our findings with random networks of similar size and link density. Our findings provide valuable insights into the organization of the nerve net of this well-documented organism and can be easily transferred to other species and disciplines alike

A screening setup to streamline in vitro engineered living material cultures with the host

Engineered living materials (ELMs), which usually comprise bacteria, fungi, or animal cells entrapped in polymeric matrices, offer limitless possibilities in fields like drug delivery or biosensing. Determining the conditions that sustain ELM performance while ensuring compatibility with ELM hosts is essential before testing them in vivo. This is critical to reduce animal experimentation and can be achieved through in vitro investigations. Currently, there are no standards that ensure ELM compatibility with host tissues. Towards this goal, we designed a 96-well plate-based screening method to streamline ELM growth across culture conditions and determine their compatibility potential in vitro. We showed proliferation of three bacterial species encapsulated in hydrogels over time and screened six different cell culture media. We fabricated ELMs in bilayer and monolayer formats and tracked bacterial leakage as a measure of ELM biocontainment. After screening, an appropriate medium was selected that sustained growth of an ELM, and it was used to study cytocompatibility in vitro. ELM cytotoxicity on murine fibroblasts and human monocytes was studied by adding ELM supernatants and measuring cell membrane integrity and live/dead staining, respectively, proving ELM cytocompatibility. Our work illustrates a simple setup to streamline the screening of compatible environmental conditions of ELMs with the host

Contributions to boundary control of distributed-parameter systems

Eine neuartige Entwurfsmethodik wird für die Regelung von linearen hyperbolischen oder parabolischen partiellen Differentialgleichungen (PDEs) vorgestellt, welche an einem Rand aktuiert und am anderen Rand mit nichtlinearen gewöhnlichen Differentialgleichungen (ODEs) gekoppelt sind. In Anlehnung an die wohlbekannte Backstepping-Methode für lineare Systeme wird eine nichtlineare Zustandstransformation herangezogen, um das System in eine für den Reglerentwurf besonders geeignete Form zu bringen. Das zentrale Ergebnis der vorliegenden Arbeit ist die Konstruktion dieser Zustandstransformation mithilfe der Lösung eines angemessen formulierten Cauchy-Problems. Die Verwendung einer flachheitsbasierten Parametrierung der entsprechenden PDE-Teilsysteme erleichtert diesen Entwurfsschritt. Zudem gestattet die Kombination von Backstepping und flachheitsbasierten Parametrierungen, auf bekannte Ergebnisse aus der bestehenden Literatur aufzubauen. Des Weiteren wird für den Spezialfall linearer Systeme gezeigt, dass der vorgestellte Ansatz sowohl für hyperbolische als auch für parabolische PDE-ODE-Systeme äquivalent zur Backstepping-Methode ist. Die in dieser Arbeit für den Reglerentwurf präsentierte Vorgehensweise bewältigt zuvor ungelöste Probleme und lässt sich aufgrund ihrer Systematik auf eine breitere Systemklasse erweitern. Allerdings sind im Rahmen dessen auch eine Vielzahl an Herausforderungen und interessanten Fragestellungen für weiterführende Untersuchungen entstanden.A novel framework is presented for the late-lumping boundary control of linear hyperbolic or parabolic partial differential equations (PDEs) that are interconnected with nonlinear ordinary differential equations (ODEs) at the unactuated boundary. Inspired by the well-established backstepping method for linear systems, a nonlinear state transformation is utilized to map the plant into a desired target system. The central result of this thesis is the construction of the state transformation through the solution of an appropriately formulated Cauchy problem. This is facilitated by the use of flatness-based parameterizations of the corresponding PDE subsystems. In fact, the combination of backstepping and flatness-based parameterizations allows this work to build upon a substantial body of existing literature. Furthermore, for the special case of linear systems, the approach is shown to be equivalent to the backstepping method for both hyperbolic and parabolic PDE-ODE systems. Although the presented control strategy overcomes previously unsolved problems and enables the systematic development of advanced designs for a broader system class, it has also given rise to numerous challenges and interesting problems for future research

Oligodendrocyte precursor cells facilitate neuronal lysosome release

Oligodendrocyte precursor cells (OPCs) shape brain function through many non-canonical regulatory mechanisms beyond myelination. Here we show that OPCs form contacts with their processes on neuronal somata in a neuronal activity-dependent manner. These contacts facilitate exocytosis of neuronal lysosomes. A reduction in the number or branching of OPCs reduces these contacts, which is associated with lysosome accumulation and altered metabolism in neurons and more senescent neurons with age. A similar reduction in OPC branching and neuronal lysosome accumulation is seen in an early-stage mouse model of Alzheimer’s disease. Our findings have implications for the prevention of age-related pathologies and the treatment of neurodegenerative diseases

Decreased PAX6 and DSG1 Protein Expression in Corneal Epithelium of Patients with Epithelial Basal Membrane Dystrophy, Salzmann Nodular Degeneration, and Pterygium

Background/Objectives: Evaluation of stem cell, keratin, retinoic acid metabolism markers and non-coding micro-RNAs (miRNAs) in conjunctival and corneal samples of patients with epithelial basal membrane dystrophy (EBMD), Salzmann nodular degeneration (SND), pterygium and congenital aniridia (CA), to detect similarities and differences in their pathogenesis. Methods: Impression cytology (IC) samples and corneal epithelial samples (CEs) of patients with EBMD, SND, pterygium, congenital aniridia, and healthy control subjects have been analyzed. The IC samples were subjected to qPCR, and the epithelial samples were subjected to qPCR and WB. Limbal epithelial stem cell markers, keratins, retinoic acid metabolism markers, and miRNAs were analyzed. Results: In conjunctival IC samples, PAX6 mRNA expression was significantly lower in EBMD, SND, pterygium, and CA compared to healthy controls (p ≤ 0.02). KRT13 mRNA expression was significantly higher in EBMD, SND, and pterygium (p ≤ 0.018), and FABP5 was increased in pterygium samples (p = 0.007). MiRNA-138-5p was significantly higher in aniridia samples than in normal controls (p = 0.037). In corneal epithelial samples, PAX6 protein, DSG1 mRNA and protein, miRNA-138-5p, and miR-204-5p expression were significantly lower in EBMD, SND, and pterygium samples than in controls (p ≤ 0.02). ALDHA1 mRNA expression was significantly lower (p < 0.0001), and FABP5 mRNA expression was significantly higher (p = 0.014) in pterygium samples than in controls. Conclusions: PAX6, DSG1, miR-138-5p, and miR-204-5p expression is decreased in the corneal epithelium of epithelial basal membrane dystrophy, Salzmann nodular degeneration, and pterygium subjects. In addition, there is a dysregulation of markers of the retinoic acid signaling pathway, such as ADH1A1 and FABP5, in the corneal epithelium of pterygium subjects. These changes may offer therapeutic targets in the treatment of these ocular surface diseases

Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems

Detecting distribution and domain shifts is critical in decision-sensitive applications, such as industrial monitoring systems. This paper introduces a novel, robust multi-sensor ensemble framework that integrates principles of automated machine learning (AutoML) to address the challenges of domain shifts and variability in sensor data. By leveraging diverse model architectures, hyperparameters (HPs), and decision aggregation strategies, the proposed framework enhances adaptability to unnoticed distribution shifts. The method effectively handles tasks with various data properties, such as the number of sensors, data length, and information domains. Additionally, the integration of HP optimization and model selection significantly reduces the training cost of ensemble models. Extensive evaluations on five publicly available datasets demonstrate the effectiveness of the proposed framework in both targeted supervised tasks and unsupervised distribution shift detection. The proposed method significantly improves common evaluation metrics compared to single-model baselines. Across the selected datasets, the framework achieves near-perfect test accuracy for classification tasks, leveraging the AutoML approach. Additionally, it effectively identifies distribution shifts in the same scenarios, with an average AUROC of 90% and an FPR95 of 20%. This study represents a practical step toward a distribution-aware front-end approach for addressing challenges in industrial applications under real-world scenarios using AutoML, highlighting the novelty of the method

Methodology for the Automatic Generation of Optimization Models of Systems of Flexible Energy Resources

The integration of increasing shares of intermittent renewable energy necessitates flexibility in both energy generation and consumption. Typically, the operation of flexible energy resources is orchestrated through optimization models. However, the manual creation of these models is a complex and error-prone task, often requiring the expertise of domain specialists. This work introduces a methodology for the automatic generation of optimization models for systems of flexible energy resources to simplify the modeling process and increase the use of energy flexibility. This methodology utilizes a modular, generic model structure designed to depict systems of flexible energy resources. It incorporates algorithms for model parameter derivation from operational data and an information model that represents the system’s structure and dependencies of resources. The efficacy of this methodology is demonstrated in two case studies, highlighting its relevance and ability to significantly streamline the optimization modeling process by minimizing the need for manual intervention

Relationship between serum estradiol level, ultrasound follicle count, number of oocytes retrieved and their influence on IVF/ICSI treatment outcomes: a retrospective cross-sectional study

This retrospective cross-sectional study investigates determinants on follicular development, oocyte retrieval and pregnancy outcome. It assessed the clinical practicability of monitoring parameters in relation to predict a successful treatment. Analysis of serum-estradiol (E2), sonographic follicle count, number of oocytes and optimizable parameters have therefore been carried out based on patient files from the IVF outpatient clinic at the Department of Gynecology, Obstetrics and Reproductive Medicine, Homburg/Saar, Germany. Equidirectional connection occurred between serum-E2, sonographic follicle count and number of oocytes (p < 0.001). There was no significant difference between sonographic and punctured follicle count (p = 0.428), but between sonographic/punctured follicle count and number of oocytes obtained (p < 0.01). Increasing endometrial thickness was associated with increasing serum-E2 (p = 0.003) and number of oocytes (p 0.05). Additionally, age was inversely associated with sonographic follicle count and number of oocytes (p 0.05). BMI, nicotine and stimulation protocol had no association with the observed parameters (p > 0.05). Mean differences in follicle numbers can be used for predicting expectable numbers of oocytes. Due to comparable numbers of follicles visualized on the day of ovulation induction and the number of follicles punctured, more emphasis should be placed in optimizing oocyte retrieval procedures