Ovarian carcinoma is a gynecological cancer with poor long-term survival rates when detected at advanced disease stages. Early symptoms are non-specific, and currently, there are no adequate strategies to identify this disease at an early stage when much higher survival rates can be expected. Ovarian carcinoma is a heterogeneous disease, with various histotypes originating from different cells and tissues, and is characterized by distinct somatic mutations, progression profiles, and treatment responses. Our study presents a targeted metabolomics approach, characterizing seven different ovarian (cancer-) cell lines according to their extracellular, intracellular, and RNA-derived modified nucleoside profiles. Moreover, these data were correlated with transcriptomics data to elucidate the underlying mechanisms. Modified nucleosides are excreted in higher amounts in cancer cell lines due to their altered DNA/RNA metabolism. This study shows that seven different ovarian cancer cell lines, representing different molecular subtypes, can be discriminated according to their specific nucleoside pattern. We suggest modified nucleosides as strong biomarker candidates for ovarian cancer with the potential for subtype-specific discrimination. Extracellular modified nucleosides have the highest potential in the distinguishing of cell lines between control cell lines and themselves, and represent the closest to a desirable, non-invasive biomarker, since they accumulate in blood and urine.

Background: Neoadjuvant chemotherapy (NACT) for non-metastatic breast cancer is often preferred over adjuvant chemotherapy to shrink tumours and facilitate surgical removal. NACT typically comprises 8 cycles over 20–24 weeks: 4 cycles of anthracycline with cyclophosphamide followed by 4 cycles of paclitaxel. After surgery, the therapeutic response is assessed histopathologically using the TNM classification, where ypT0 indicates pathological complete remission (PCR) and residual tumour cells (> ypT0) indicate non-complete remission (non-PCR). Currently, imaging techniques such as ultrasound are used during NACT to assess clinical response. Liquid biopsy-based methods may complement imaging by enabling early response monitoring. In this study, we used serum proteomics to identify marker candidates associated with PCR as early as after the second NACT cycle. Methods: Longitudinal, retrospective serum proteomic analyses were performed on 22 breast cancer patients (11 PCR, 11 non-PCR) and 21 age-matched healthy controls. Serum samples were collected pre-therapy and after two and six NACT cycles. Proteins were analysed using liquid chromatography–tandem mass spectrometry (LC–MS/MS) following immunoaffinity depletion, trypsin digestion, tandem mass tag labelling, and fractionation. Protein quantitation was performed with MaxQuant software, and abundance analysis utilised linear models of microarray analysis. Tumour-resident expression of a candidate marker was evaluated via immunohistochemistry in an independent cohort of 37 cases. Results: Across 84 samples, >390 proteins were consistently identified and quantified. Pre-therapy serum proteomes showed no significant differences between PCR and non-PCR groups. Longitudinal analysis revealed that serum levels of c-Met and N-cadherin could distinguish responders after the second NACT cycle with high predictive value (AUC 0.93). More pronounced changes were observed after the sixth cycle, including significant alterations in centrosomal protein, sex hormone-binding globulin, and cholinesterase levels. Additionally, N-cadherin expression was elevated in therapy-naïve tumour samples from patients achieving PCR. Conclusions: This study highlights the potential of serum proteomics for identifying markers to assess NACT efficacy in breast cancer. Soluble N-cadherin and c-Met may serve as promising serum markers for PCR, particularly when combined with (immune)histochemical tumour characterisation.

Endometrial carcinoma (EC) is the most common malignancy of the female reproductive tract, with increasing incidence driven by aging populations and obesity. While molecular classification has improved diagnostic precision, the identification of clinically relevant metabolic biomarkers remains incomplete, and targeted therapies are not yet standardized. In this study, we investigated metabolic alterations in four EC cell lines (AN3-CA, EFE-184, HEC-1B and MFE-296) compared to non-malignant controls under normoxic and stress conditions (hypoxia and lactic acidosis) to identify metabolomic differences with potential clinical relevance. Untargeted gas chromatography-mass spectrometry (GC/MS) and targeted liquid chromatography-mass spectrometry (LC/MS) profiling revealed two distinct metabolic subtypes of EC. Cells of metabolic subtype 1 (AN3-CA and EFE-184) exhibited high biosynthetic and energy demands, enhanced cholesterol and hexosyl-ceramides synthesis and increased RNA stability, consistent with classical cancer-associated metabolic reprogramming. Cells of metabolic subtype 2 (HEC-1B and MFE-296) displayed a phospholipid-dominant metabolic profile and greater hypoxia tolerance, suggesting enhanced tumor aggressiveness and metastatic potential. Key metabolic findings were validated via real-time quantitative PCR. This study identifies and characterizes distinct metabolic subtypes of EC within the investigated cancer cell lines, thereby contributing to a better understanding of tumor heterogeneity. The results provide a basis for potential diagnostic differentiation based on specific metabolic profiles and may support the identification of novel therapeutic targets. Further validation in three-dimensional culture models and ultimately patient-derived samples is required to assess clinical relevance and integration with current molecular classifications.

Breast cancer remains the most common cancer in women worldwide. Neoadjuvant chemotherapy (NACT) is often preferred to adjuvant chemotherapy to achieve tumour shrinkage, monitor response to therapy and facilitate surgical removal in the absence of metastases. In addition, there is strong evidence that pathological complete remission (pCR) is associated with prolonged survival. In this study, we sought to identify candidate markers that signal response or resistance to therapy. We present a retrospective longitudinal serum proteomic study of 22 breast cancer patients (11 with pCR and 11 with non-pCR) matched with 21 healthy controls. Serum was analysed by LC-MS/MS after depletion of abundant proteins by immunoaffinity, trypsinisation, isobaric labelling and fractionation by reversed-phase HPLC. We observed an inverse behaviour of the serum proteins c-Met and N-cadherin after the second cycle of chemotherapy with a high predictive value (AUC 0.93). More pronounced changes were observed after the 6th cycle of NACT, with significant changes in the intensity of the proteins contactin-1, centrosomal protein, sex hormone-binding globuline and cholinesterase. Our study highlights the possibility of monitoring response to NACT using serum as a liquid biopsy.

In assembly of small-volume products, tasks are still frequently executed manually. However, the lead times foreseen for these tasks, often do not take into account the actual capabilities of the employees, which in turn leads to increased workload and the associated stress among the employees. This paper investigates how a commercially available wearable low-cost sensor and two machine learning algorithms can be applied to measure and evaluate heart rate, heart rate variability and respiration rate to establish a relationship with workload. The investigated algorithms, namely Random Forest and K-Nearest-Neighbours are able to distinguish between tasks phases and rest phases as well as between easy and difficult tasks executed by the employee, which is the main novelty of this paper.

To reduce CO2 emissions, besides the automotive industry, manufacturing industries are increasingly under pressure to optimize processes and procedures for energy efficiency. These optimizations mainly involve the production processes, where most energy demand occurs. Alternatively, when most of this energy demand can be determined during the product design phase, the product designer can make energy-efficient decisions in the design phase. Most product designers are unaware of their decisions' significant impact on a product's energy demand. Therefore, this paper presents a workflow and novel method for predicting the energy demand of parts of their machining operation during the design phase. For this purpose, 29 energy consumption models for machining processes are examined, and the data published in the literature are summarized. Four resulting comprehensive process maps are derived, which enable the prediction of a part's energy consumption due to machining, specifically, the milling operation, based on the geometric features of the part. This was further verified on three machined parts. The workflow and methods presented in this paper are some of the first steps to facilitate conscious decisions already in the product design phase. The benefits of the method were demonstrated in a final survey: Both product designers and machine operators showed in this survey that their estimation of the energy consumption of the parts investigated differed by orders of magnitude.

Zusammenfassung Ziel der Studie Es soll ein empirischer Überblick über die Praxis von psychiatrischen Zwangseinweisungen gegeben werden. Methodik Anhand der Krankenakten erfolgte eine retrospektive Auswertung von 346 Fällen mit einer Zwangseinweisung auf öffentlich-rechtlicher Grundlage im Jahre 2020 (21,0% aller vollstationären Aufnahmen in diesem Zeitraum). Ergebnisse Der häufigste Grund für eine Zwangseinweisung war eine Suizidankündigung (45,4%). Diagnostisch standen Suchterkrankungen (30,1%), Belastungsstörungen (19,9%) und schizophrene Psychosen (18,8%) im Vordergrund. In nur 12,7% der Fälle führte die Zwangseinweisung zu einer anschließenden richterlichen Unterbringung, in 44,5% kam es zu einer Entlassung innerhalb von 24 Stunden. Schlussfolgerung Zwangseinweisungen stellen häufig eine fürsorgliche Maßnahme dar, um auf suizidale Krisen zu reagieren. Es bleibt abzuwarten, ob die Etablierung von alternativen Hilfsmodellen wie psychosozialer Krisendienste die Anzahl von Zwangseinweisungen verringern kann.

PURPOSE:Ovarian cancer is the seventh most frequent form of malignant diseases in women worldwide and over 150,000 women die from it every year. More than 70 percent of all ovarian cancer patients are diagnosed at a late-stage disease with poor prognosis necessitating the development of sufficient screening biomarkers. MicroRNAs displayed promising potential as early diagnostics in various malignant diseases including ovarian cancer. The presented study aimed at identifying single microRNAs and microRNA combinations detecting ovarian cancer in vitro and in vivo.METHODS:Intracellular, extracellular and urinary microRNA expression levels of twelve microRNAs (let-7a, let-7d, miR-10a, miR-15a, miR-15b, miR-19b, miR-20a, miR-21, miR-100, miR-125b, miR-155, miR-222) were quantified performing quantitative real-time-PCR. Therefore, the three ovarian cancer cell lines SK-OV-3, OAW-42, EFO-27 as well as urine samples of ovarian cancer patients and healthy controls were analyzed.RESULTS:MiR-15a, miR-20a and miR-222 showed expression level alterations extracellularly, whereas miR-125b did intracellularly across the analyzed cell lines. MicroRNA expression alterations in single cell lines suggest subtype specificity in both compartments. Hypoxia and acidosis showed scarce effects on single miRNA expression levels only. Furthermore, we were able to demonstrate the feasibility to clearly detect the 12 miRNAs in urine samples. In urine, miR-15a was upregulated whereas let-7a was down-regulated in ovarian cancer patients.CONCLUSION:Intracellular, extracellular and urinary microRNA expression alterations emphasize their great potential as biomarkers in liquid biopsies. Especially, miR-15a and let-7a qualify for possible circulating biomarkers in liquid biopsies of ovarian cancer patients.

The reduction of CO2 by moving from fossil to renewable energy sources is currently high on the agenda of many governments. Simultaneously these governments are also forcing the reduction of energy consumption. The primary focus of these agendas is on mobility, building, and industrial sectors. For the latter, energy efficient shop floors and machining processes assist the reduction of energy consumption. Previous research has focused on energy-efficient machining strategies during machining processes. However, an energy-efficient start-up of these machines or their spindle axis start-up has been neglected until now. This paper focuses on this neglected issue by comparing the energy-efficiency, production time, and cost-efficiency of the CNC (computer numeric control) machine by varying the power input at the spindle axis. This is done by analysing the high frequency data (500Hz) of the machine from machining operations that is retrieved via the edge device. Concepts of data analytics and especially EDA (exploratory data analytics) were used to interactively visualize the inter-dependencies and develop results. It is shown that optimized reduction of spindle power input value leads to both: peak power smoothing from 20kW to 10kW and lowering of overall energy consumption by approximately 1.4%. Moreover, the costs and production time are marginally affected (0.518% and 0.523% respectively) by this optimized reduction of spindle power input value. Thus, this paper highlights a novel method from data acquisition to process improvement towards energy-efficient and sustainable machining.

Simulations can be represented in the form of a graph structure of components. Placeholders, where components can be added to the simulation, contain dependency constraint knowledge which is stored in a graph database. In this paper an application for automatic guided simulation creation is presented in the form of a three-step process to evaluate constraints of placeholders and therefore suggest suitable components.

A well-designed generic software system can be reused in many contexts by efficiently handling variability. In this paper, data structures and their application in a software architecture is presented that apply the basic idea of the Composite design pattern to keep it as simple as possible, but also as generic as possible. The application of these data structures is shown throughout a layered architecture so that software developers can follow and apply the concepts. The benefit of such an architecture is that it (1) only makes use of familiar concepts, and it is easy to read and understand by knowing especially one design pattern and (2) results in a generic software system, usable in different domains.

Early diagnosis and therapy in the first stages of a malignant disease is the most crucial factor for successful cancer treatment and recovery. Currently, there is a high demand for novel diagnostic tools that indicate neoplasms in the first or pre-malignant stages. MicroRNAs (miRNA or miR) are small non-coding RNAs that may act as oncogenes and downregulate tumor-suppressor genes. The detection and mutual discrimination of the three common female malignant neoplasia types breast (BC), ovarian (OC) and endometrial cancer (EC) could be enabled by identification of tumor entity-specific miRNA expression differences. In the present study, the relative expression levels of 25 BC, EC and OC-related miRNAs were assessed by reverse transcription-quantitative PCR and determined using the 2(-Delta Delta Cq)method for normalization against the mean of four housekeeping genes. Expression levels of all miRNAs were analyzed by regression against cell line as a factor. An expression level-based discrimination between BC and OC cell types was obtained for a subgroup of ten different miRNA types. miR-30 family genes, as well as three other miRNAs, were found to be uniformly upregulated in OC cells compared with BC cells. BC and EC cells could be distinguished by the expression profiles of six specific miRNAs. In addition, four miRNAs were differentially expressed between EC and OC cells. In conclusion, miRNAs were identified as a potential novel tool to detect and mutually discriminate between BC, OC and EC. Based on a subset of 25 clinically relevant human miRNA types, the present study could significantly discriminate between these three female cancer types by means of their expression levels. For further verification and validation of miRNA-based biomarker expression signatures that enable valuable tumor detection and characterization in routine screening or potential therapy monitoring, additional and extendedin vitroanalyses, followed by translational studies utilizing patients' tissue and liquid biopsy materials, are required.

Breast cancer (BC) is the most frequent malignant disease in women worldwide and the leading cause of cancer associated death as well. As especially advanced stages are challenging for the healthcare system, early BC detection is crucial striving for improved overall outcome and disease management. Established screening procedures show limited success in the early diagnosis of the disease and inherit several limitations. Aiming at a faster, easier and non-invasive BC screening, liquid biopsy biomarkers bear great potential to complete the current state-of-the-art diagnostics. This study explores the diagnostic potential of BC specific urinary microRNAs as a potential non-invasive BC screening method. Based on a case-control-study (69 BC patients vs. 40 healthy controls), expression level quantification and subsequent biostatistical computation of thirteen urine-derived microRNAs was performed to evaluate their diagnostic relevance in BC. Multilateral statistical assessment determined and repeatedly confirmed a specific panel of four urinary microRNAs (miR-424/miR-423/mir-660/let7-i) as highly specific combinatory biomarker tool. Herewith, it is possible to discriminate BC patients from healthy controls with 98.6 % sensitivity and 100 % specificity. Smaller feasibility studies have proven this method as highly potent already years ago. The given study finally provides large scale observations, sufficient statistic analysis, more refined methods and highly satisfying sensitivity and specificity. Therefore, it serves the implementation of urinary BC detection in the routine screening and offers a promising non-invasive alternative.

Purpose For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks. Design/methodology/approach We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files' timestamp information. In the last part, we try to improve one promising looking model. Findings We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries. Research limitations/implications Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results. Originality/value In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.

ABSTRACT Sintering is a complex production process where the process stability and product quality depend on various parameters. Building a forecasting model improves this process. Artificial intelligence (AI) approaches show promising results in comparison to current physical models. They are mostly considered black box models because of their hidden layers. Due to their complexity and limited traceability it is difficult to draw conclusions for real sinter processes and improving the physical models in a running plant. This challenge is addressed by focusing on detecting causal links from AI-based forecasting models in order to improve the understanding of sintering and optimizing existing physical models.

Cyber-physical systems deeply connect artefacts, systems and people. As we will use these systems on a daily basis, the question arises to what extent we can trust them, and even more, whether and how we can develop computational models for trust in such systems. Trust has been explored in a wide range of domains, starting with philosophy via psychology, sociology, and economy. Also in automation, computing and networking definitions, models and computations have emerged. In this paper we discuss our initial considerations on linking concepts and models of trust and cyber-physical systems. After structuring and selecting basic approaches in both areas, we propose appropriate links, which can be used as action areas for future development of trust models and computations in cyber-physical systems.

Studying a technical program in the context of mechatronics, the application of the lessons learned by building a robot, is one educational goal. As the students are already aware of some parts, which are needed to build a self-driving robot, a project work has to be executed in the fourth semester. In this paper, the structure for such a project is described by the four named areas - each presenting the requirements and expected output to fulfill the project goal.