Living Labs have proven to be valuable environments for fostering innovation through user-centred approaches. However, many researchers and companies still face challenges in implementing these methodologies sustainably. Addressing these challenges requires not only structural solutions within Living Labs, but also the cultivation of expertise among researchers and practitioners. It is crucial to educate researchers and innovators in practices aligned with user-centred research, living lab practices and co-design, emphasizing societal relevance and Responsible Research and Innovation (RRI) within the research community. This paper presents and evaluates a novel training program developed within the VITALISE project, aimed at onboarding external researchers and familiarizing them with Living Lab Research Infrastructures through transnational visits and collaborations. Results The program features a modular design covering key topics such as Living Lab methodology, harmonisation of research practices, and participant recruitment and panel management. A total of 49 participants completed an evaluation questionnaire, with results indicating high satisfaction and perceived usefulness across all training modules. Post-training, most participants reported feeling confident in applying Living Lab methodologies after the training. Notably, individual differences in interest across training blocks highlighted the need for flexible, tailored programs that accommodate varying levels of prior knowledge and specific research needs. This study suggests that targeted, adaptable training initiatives are acceptable. Self-reported feedback suggests that it could help to enable researchers to integrate Living Lab methodologies into their work. However, further formal evaluation of learning gains is needed. Continued development of structured, scalable, and context-sensitive training programs, supported by international collaborations and standardized approaches, will be essential for fostering sustainable and impactful Living Lab research across disciplines and borders. Living Labs are environments where researchers, companies, and citizens work together to test and improve new ideas and technologies in real-life settings. Although these labs can help create better and more useful innovations, many researchers still struggle to use such environments (or the methods they use) effectively. To help with this, a European project called VITALISE created a training program to teach researchers about living labs and the methods they use. The training included different topics like how to involve people in research, how to manage participants, and how to make research methods more consistent across labs. Forty-nine researchers took part in an evaluation of the training and gave feedback. Most of them were very satisfied and felt more confident using Living Lab methods afterward. The training was designed to be flexible, so people with different backgrounds and needs could benefit from it. This study shows that well-designed training programs can help researchers use Living Labs more successfully, which could lead to better innovations that are more connected to society’s needs.
Cellular organelle content is fairly constant within a given cell type. This is accomplished in part by ensuring equitable organelle partitioning during division. Much of our understanding of organelle inheritance has come from investigating cells that divide in half producing two daughter cells. However, more elaborate division strategies that give rise to multiple daughters are not uncommon in nature. Here, we present the first characterization of organelle inheritance in a fungus that grows by multi-budding, producing several (2-20) daughter cells in a single cell cycle. We find that some organelles (mitochondria and ER) are evenly delivered to all growing buds, while others (vacuole and peroxisomes) are more variably inherited. We discuss the implications of even and uneven inheritance for this polyextremotolerant fungus capable of growing in dynamic, and diverse, environments.
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BackgroundSyphilis remains a significant global public health challenge, particularly in Sub-Saharan Africa. As part of the sustainable development goals, Ethiopia has aimed to eliminate vertical transmission of syphilis by 2030. Thus, this study aimed to assess the prevalence and factors associated with syphilis infection among pregnant people attending antenatal care (ANC).MethodsAn institution-based cross-sectional study was conducted and public hospitals were selected using simple random sampling, and 488 study participants were recruited via systematic random sampling. Data were collected using interviewer-administered questionnaires and patient chart reviews through the Kobo Collect mobile application. Data analysis was performed using SPSS version 23. Bivariate and multivariable logistic regression models were used to identify factors associated with syphilis infection. Statistical significance was declared at a p-value <0.05.ResultThe prevalence of syphilis among the 488 pregnant people was 4.9% (95% CI: 3.3-7.0%). Additionally, 9 (1.8%) participants had syphilis-HIV co-infection. Multivariable analysis identified three factors significantly associated with syphilis: A history of current or previous sexually transmitted infection (STI) symptoms (AOR = 7.67, 95% CI (2.7-21.6), having a partner with known or suspected extra-marital sexual contacts (AOR = 6.3, 95% CI (2.29-17.2) and history of abortion (AOR = 3.8, 95% CI (1.25-11.4).ConclusionThe prevalence of syphilis among pregnant people in the study area is high relative to the national elimination targets. It is essential to strengthen routine antenatal screening and move beyond individual treatment and should be includes partner notification and management. Targeted interventions should prioritize women with a history of STIs or prior pregnancy losses to reduce the burden of syphilis in pregnancy and prevent the devastating consequences of congenital syphilis.
Older women experiencing homelessness (OWEH) have been a hidden or invisible population; however, with rising numbers globally, they are gaining attention. Our study examines what it means to be a woman in her 50s who is struggling financially and precariously housed in the United States. Although in their 50s, these women often experience accelerated aging and exhibit chronic health conditions comparable to those of housed women in their 70s and 80s. Lacking access to both public old-age benefits and family support, they often fall between the cracks of the nation's safety net system. While homelessness among women of reproductive age, including the role of gender-based violence in increasing their vulnerability, is now well documented, studies on the unique challenges faced by OWEH remain relatively limited. This study explores how daily life on the streets and in emergency housing shelters, a system historically designed for men (particularly younger men), affects the well-being of OWEH and their journey toward stable housing. The aim was to describe, from the perspectives of OWEH, how shelter environment, policies, and practices (including continual displacement to the streets) shape their daily lives, well-being, and pathways to stable housing. Of particular interest was gaining a deeper understanding of how navigating the traumas of homelessness and shelter living affects individuals' sense of dignity, self-worth, adaptive resources, and resilience. This qualitative study involved 15 semistructured, private individual interviews, each lasting about 60 min, conducted by an MSW social worker. Eligibility criteria included being homeless for at least 1 month, being in one's 50s, and being able to take part in an English-language interview. Using NVivo (qualitative research software), the interview audiotape transcripts were coded and analyzed using a multistep interpretative phenomenological approach to enable exploration of how OWEH make sense of what is happening to them (or the "lived experience') and their views (or "meaning making') of life and these experiences. Trauma was a universal experience, and almost all participants were coping with significant physical and/or mental health issues. Overall, participants perceived that shelter management and staff failed to fully understand the challenges women faced in rebuilding their lives, especially the interconnectedness of health struggles, societal bias against older women, and the social prejudice of people experiencing homelessness. Five superordinate themes were identified, highlighting how shelter physical and social environments contribute to OWEH's daily struggles and sense of disempowerment: (a) dehumanizing and stigmatizing treatment; (b) unsafe surroundings and hypervigilance; (c) harsh living conditions and declining physical and emotional health; (d) disempowering situations and loss of control; and (e) an absence of normalcy and stability. This study contributes to our understanding of how emergency housing shelters create or exacerbate the challenges faced by OWEH and often result in disempowering. The findings suggest the importance of transforming both the physical and social environments of shelters, using trauma- and aging-informed approaches, to better support this growing population of OWEH in their pathway to stable housing.
Phosphate (PO4) pollution in irrigated catchments and their return-flow and drainage networks threatens water quality and agricultural sustainability, particularly under conditions of intensive fertilization and shallow groundwater. This study presents a predictive approach to estimate PO4 concentration using a Generalized Additive Model (GAM) based on daily monitoring data from the Akarsu Irrigation District in Türkiye's Lower Seyhan Plain. Here, the modeled variable is PO4 in irrigation return-flow/drainage water, measured at the main drainage outlet (L4), which integrates excess irrigation water that has passed through the agricultural landscape and collected surface runoff and subsurface drainage. Downstream of L4, drainage water is conveyed by the main drainage channel; part is reused for irrigation, and the remainder flows toward lagoon and wetland areas and ultimately the Mediterranean Sea. The dataset comprised 522 daily observations from the 2022-2023 water years and included nitrate (NO3), nitrite (NO2), electrical conductivity (EC), pH (hydrogen ion activity), flow rate (Q), and precipitation (P) as predictors. Despite weak pairwise correlations of PO4 with individual variables (maximum r = 0.1293 with NO3), the GAM captured nonlinear multivariate relationships and produced good agreement between predicted and measured PO4 at the L4 outlet (mean squared error (MSE) = 0.019966; root mean squared error (RMSE) = 0.1413 mg L-1; mean error = -0.00457 mg L-1; error SD = 0.14136 mg L-1), indicating minimal bias and stable performance. In benchmark comparisons using identical inputs and the same time-structured validation design (80/20 split; random splits were used only for sensitivity analysis), the GAM substantially outperformed linear regression (LR), artificial neural network (ANN), and support vector machine (SVM), which showed very low predictive skill (R2 ≈ 0.03-0.05). Predictive performance was evaluated primarily using error-based metrics; R2 was reported only as a goodness-of-fit measure. The L4 outlet drains an intensively managed agricultural catchment dominated by irrigated cropland. Model fit, expressed as explained variance values (training R2 = 0.832; testing R2 = 0.788), indicated consistent performance without evidence of substantial overfitting. Overall, the findings demonstrate that GAM-based estimation can reliably reproduce both peak and moderate PO4 concentrations and serve as a practical screening tool for nutrient monitoring at irrigated drainage/return-flow outlets. By leveraging routinely monitored variables, the model can reduce the frequency of laboratory PO4 assays-often requiring additional reagents, consumables, and handling time-thereby lowering analytical workload and spectrophotometric operating time while enabling near-real-time assessment of PO4 dynamics. These results support the use of data-driven estimation to inform nutrient management and reduce eutrophication risk in irrigated catchments by monitoring drainage exports. Phosphate pollution in irrigation areas, particularly in regions with shallow groundwater and intensive agriculture, poses serious environmental and agricultural risks, including eutrophication and water-quality degradation. Conventional methods for phosphate monitoring are often time-consuming, costly, and spatially limited, making them unsuitable for real-time applications. Furthermore, the complex, nonlinear interactions between phosphate concentrations and environmental variables, including nitrate, nitrite, pH, EC, flow rate, and precipitation, challenge traditional predictive approaches. While various machine learning models have been explored for phosphorus prediction, their computational demands and overfitting risks often limit their field-level applicability. Therefore, this study aimed to develop a robust, efficient, and interpretable method for predicting phosphate concentrations using a GAM and leveraging daily environmental data collected in a Mediterranean irrigation district in Türkiye. Daily water samples were collected at the outlet of the L4 agricultural catchment in the Akarsu Irrigation District (AID) on the Lower Seyhan Plain, Türkiye, during the 2022 and 2023 water years. The area is characterized by intensively managed irrigated cropland and shallow groundwater conditions. A total of 522 daily observations were compiled, including PO4, NO3, NO2, EC, pH, flow rate (Q), and precipitation (P). Laboratory analyses were performed using spectrophotometric methods for nutrients and electrochemical measurements of EC and pH, while discharge data were obtained from an on-site automatic monitoring and sampling system.A GAM was developed to represent nonlinear relationships between PO4 and the predictor variables using penalized smoothing functions. Because the dataset is a daily time series, temporal dependence was addressed by including a smoother for time (date/time index) and by fitting the model with an AR(1) residual correlation structure (GAMM). To ensure realistic model evaluation under temporal dependence, predictive skill was assessed primarily using a time-structured (blocked, contiguous) 80/20 split, with the earlier 80% of observations used for training and the later 20% for testing. To assess robustness to the choice of partition (sensitivity analysis only), we additionally repeated the split-fit-evaluate procedure over 100 independent randomized 80/20 splits. These random-split results are reported as a secondary check and are not interpreted as the main estimate of predictive skill under autocorrelation. Model predictive performance was primarily assessed using error-based metrics (MSE, RMSE, bias/mean error, and error SD), while R2 was reported only as explained variance (goodness-of-fit). Residual diagnostics, including inspection of the residual distribution and autocorrelation (ACF), were used to evaluate model assumptions, stability, and potential overfitting. This study developed a data-driven method for estimating PO4 concentrations at the L4 drainage outlet using a Generalized Additive Model. Although same-day Pearson correlations between PO4 and routinely monitored predictors (EC, pH, Q, P, NO2, NO3) were weak (maximum r = 0.1293 for NO3), the GAM captured nonlinear and conditional multivariate effects. It demonstrated strong agreement between predicted and measured PO4 values. Model performance was evaluated primarily using error-based metrics, yielding MSE = 0.019966; RMSE = 0.1413 mg L-1; mean error (bias) = -0.00457 mg L-1; and error SD = 0.14136 mg L-1. R2 was reported only as explained variance (goodness-of-fit): training R2 = 0.8319; testing R2 = 0.7875. Because the dataset is a daily time series, temporal dependence was addressed by fitting a GAMM with a smooth function of time and an AR(1) residual structure; and generalization was assessed using a time-structured (blocked or contiguous) train-test split to reduce information leakage from autocorrelation. Repeated random 80/20 splits were used only as a sensitivity analysis and showed consistent performance (mean R2 = 0.772, SD = 0.0166 across 100 trials). In benchmark comparisons, the GAM substantially outperformed traditional alternatives (LR, ANN, SVM), which showed very low predictive skill for PO4 (R2 ≈ 0.03-0.05), highlighting the need for a flexible nonlinear structure to reproduce the observed phosphate dynamics. The model reproduced the overall temporal pattern of PO4, while some underestimation remained for the highest short-duration peaks-consistent with the sparse nature of extreme events in the dataset. Overall, the results support the use of the proposed GAM/GAMM framework as an outlet-scale screening tool for near-real-time identification of periods with elevated PO4, thereby helping to prioritize laboratory sampling and monitoring efforts when direct PO4 measurements are costly or intermittent.
Minimum unit pricing (MUP) reduces use of cheap, high strength alcoholic beverages that drive harm, yet concerns remain about inequitable effects for structurally vulnerable groups. As part of the Consumption, Harms, Expenditures and Alcohol Prices (CHEAP) study, we linked individual-level, product-specific alcohol consumption survey data with provincial retail price data to estimate prices per standard drink (PPSD) and examine their association with alcohol-related outcomes across sociodemographic groups. A cross-sectional survey of people who consumed alcohol in the past week in British Columbia, Canada, was linked to provincial product-level alcohol sales data. The population weighted sample included 1,217 adults ≥ 19 years (716 men; mean age 49.34, SD 16.98). Participants reported product-specific consumption, which was matched to retail prices to calculate individual-level PPSD. Survey weighted quasibinomial models examined associations between PPSD and three outcomes: (1) causing harm to self or others in the past year, (2) scoring ≥ 8 on the Alcohol Use Disorder Identification Test, and (3) consuming ≥ 15 standard drinks per week. Analyses were stratified by income, education, subjective social status, and race/ethnicity. Lower price per standard drink was associated with higher odds of harm (OR 3.05, 95% CI 1.25-7.40) and an AUDIT score ≥ 8 (OR 2.34, 95% CI 1.37-3.99). Associations were generally stronger among structurally disadvantaged groups, including low-income and Indigenous participants. Lower alcohol prices are linked to risky alcohol use, with the strongest effects among structurally disadvantaged groups. MUP is likely to reduce this risk and promote health equity.
Wound healing is a dynamic process that begins immediately after tissue damage and is divided into four main stages: hemostasis, inflammation, proliferation, and remodeling. Increased intake of the trace element selenium is critical for tissue repair. Selenium plays a key role in wound healing by regulating antioxidant, anti-inflammatory, and antimicrobial status. The purpose of this review is to highlight the role of the essential trace element selenium, both in its pure form and as part of nanocomposites and selenoproteins, in regulating wound healing by analyzing and summarizing a large body of research over the past five years. This review provides a comprehensive description of recent developments in the synthesis and theranostic applications of nanocomposites containing nanoselenium, highlighting their growing importance in healthcare. This review is the first to combine studies of the wound healing properties of various selenium-containing compounds and selenoproteins that exhibit antioxidant, anti-inflammatory, antimicrobial, immunomodulatory, and other activities. Particular attention is paid to the role of nanoselenium, particularly those obtained through "green" synthesis methods, emphasizing their eco-friendliness and cost-effectiveness in biosensors, diagnostics, imaging, and therapeutic applications. The review presents evidence that the use of selenium in the treatment of diabetic wounds is proving to be a promising and effective tool in therapy and care. The data presented in the review will not only expand our understanding of the importance of selenium in regulating wound healing processes but will also help identify the most effective forms of selenium-containing compounds for wound therapy.
Growth differentiation factor 15 (GDF-15) has emerged as a potential biomarker for neurodegenerative diseases. Although elevated serum GDF-15 levels have been reported in Parkinson's disease (PD), their association with clinical features has not been fully characterized. We evaluated serum GDF-15 concentrations in 40 patients with PD and analyzed their relationships with clinical measures, including motor severity (MDS-UPDRS), quality of life (PDQ-39), sleep disturbances (PDSS-2), autonomic symptoms (SCOPA-AUT), and cognitive function (MoCA-J). Higher serum GDF-15 levels were associated with older age and greater symptom burden across multiple domains. Significant relationships were observed with MDS-UPDRS Parts I-III and total scores, PDQ-39 summary index and bodily discomfort index, two different PDSS-2 domains (motor symptoms at night and PD symptoms at night), and SCOPA-AUT total and gastrointestinal dysfunction scores. After adjusting for age, the associations between serum GDF-15 levels and MDS-UPDRS Part II, Part III, and total scores remained significant. No sex-related differences were detected. A trend toward lower MoCA-J scores with increasing GDF-15 levels was observed but did not reach statistical significance. Serum GDF-15 levels are linked to both motor and nonmotor aspects of PD and may reflect overall disease burden. Further longitudinal studies are needed to determine their value for disease monitoring and prognosis.
A 7-year-old neutered male domestic shorthair cat was presented for evaluation of a large intra-abdominal mass. Contrast-enhanced CT revealed a pedunculated hepatic mass measuring 15 × 9.5 × 6.5 cm arising from the papillary process of the caudate lobe, without evidence of metastasis. A three-port laparoscopic liver lobectomy was performed. The mass, attached by a torsed pedicle, was excised using a bipolar advanced energy vessel sealing device (ENSEAL; Ethicon). Histopathology confirmed a primary hepatic fibrosarcoma with complete resection. The cat recovered uneventfully, was discharged the following day and received five cycles of adjuvant doxorubicin (Adriamycin; Pfizer), maintaining an excellent quality of life and stable disease for at least 3 years. This is the first report of laparoscopic liver lobectomy in a cat with a large torsed hepatic mass. The case demonstrates that minimally invasive liver lobectomy can be successfully performed in feline patients, even in challenging cases involving substantial or torsed lobes. Removing a part of a cat liver using a minimally invasive surgery A 7-year-old male cat was found to have a large growth on his liver. Scans showed that the growth was attached by a twisted stalk, but there was no sign that it had spread elsewhere. The cat underwent a minimally invasive surgery, called a laparoscopic liver lobectomy, to remove the affected part of the liver. The growth was successfully removed, and laboratory testing confirmed it was a type of liver tumour called fibrosarcoma. The cat recovered very well from surgery and went home the next day. He also received additional chemotherapy to reduce the chance of the tumour coming back. Three years after the surgery, the cat remained healthy, happy and showed no signs of disease. This case is important because it is the first report of minimally invasive liver surgery being used in a cat with a large, twisted liver tumour. It shows that even complex liver problems in cats can be treated safely with less invasive techniques, which can help cats recover faster and maintain a good quality of life.
Aging reshapes the cellular and molecular landscape of mammalian tissues. These changes can be progressive, preceding linearly with age, or occur as abrupt transitions of the course of lifespan. To investigate the age-dependent cellular and molecular shifts we profiled matched proteomes and transcriptomes from male and female murine spleens across eight time points, from stable adults through late life. The spleen was chosen to integrate understanding of age-dependent changes associated with immune surveillance, inflammaging, and immune-related proteostasis. Male and female mice follow distinct aging trajectories particularly in protein-RNA correlation in late life, reflecting both compositional shifts and failure of post-transcriptional buffering. To investigate whether these changes could be attributed to specific cell-types within the spleen, we developed Celestial, a machine-learning framework to identify cell-type-specific changes in bulk tissue samples. We found that age-related bulk molecular changes could be attributed in part to compositional remodeling of cell-types-expansion of GZMK+ CD8+ T cells and C1Q+ macrophages alongside naive T cell and global B cell loss. These results demonstrate that cell-type-aware interpretation can inform bulk multi-omic data for accurate mechanistic inference in heterogeneous tissues undergoing complex molecular remodeling.
Each year in the United Kingdom, approximately 6000 people are diagnosed with Multiple Myeloma (MM) and treated with targeted cancer drugs. The duration and frequency of these treatments vary and include oral, intravenous (IV), and subcutaneous administration. Patients often undergo multiple lines of treatment and live with uncertainty spanning years and decades. This study (ClinicalTrials.gov Identifier: NCT06322927) explores the experiences of people receiving treatment for MM, what matters most when making treatment decisions, and what influences their treatment preference. This was a qualitative study using semi-structured interviews. Patients were eligible if they had a confirmed MM diagnosis and received at least five lines of treatment. Interviews focused on their extensive experiences of multiple lines of oral anti-cancer and bispecific antibody treatment, or IV therapy, and were analyzed using inductive thematic analysis. Four key themes were identified from nine interviews: "Living with MM and its impact on quality of life", portrays the relentless challenges and side effects of MM; "Factors influencing treatment decision making" outlines the importance of family, shared decision-making and information needs; "Factors influencing treatment experience", including practical challenges, and self-management; and "Treatment preference" explores participants' perceptions of treatment within the context of their own circumstances. Participants showed a willingness and tolerance to accept treatments that significantly impact their everyday life, quality of life, and relationships, to achieve their goals of care. The findings highlight the need for healthcare professionals to better understand individual patient circumstances and priorities, inform them of the treatment impact on their priorities to empower patients to choose the right treatment for them and improve quality of life. More research is needed to understand how to integrate this into the clinical care pathway. Multiple Myeloma is a type of blood cancer which often requires patients to undergo multiple lines of treatment with the intent to control the disease or improve quality of life. However, the burdens and side effects of these intensive treatments and the cancer itself can have physical, social, and emotional impacts, often causing patients to change their everyday lives and rely on informal caregivers. To improve our support and experience for patients during this time, we need to better understand their priorities and preferences surrounding treatment. In this study, we have thoroughly explored the treatment experiences and preferences of nine patients with multiple myeloma. This study found that personal circumstances and goals of care influenced treatment decisions and preferences. Patients will endure significant side effects, impacts on their daily lives, and many lines of treatment if it helps them to achieve their goals of care. Findings from this study will support patients with multiple myeloma to make informed decisions about their treatment.
Detection of stunting in young children using traditional anthropometry has been a challenge at the community level especially in resource-constrained settings. We evaluated the performance of Child Growth Monitor (CGM), a smartphone-based 3D depth-imaging application, for screening of stunting, and height measurement among children (24-59 months of age) in Nepal. At four health-posts in Rautahat district, Nepal, in October 2024, we conducted cross-sectional validation study comparing CGM's stunting detection against gold standard (manual measurement) and prospective reliability assessment with repeat measurements, four days apart. Eight trained enumerators collected data using calibrated height boards and CGM app. Validity was assessed through sensitivity, specificity and accuracy, reliability through intraclass correlation coefficients (ICC) and Technical Error of Measurement (TEM). Pitman's test evaluated variance. Bias was assessed using Bland Altman statistics. 310 children were recruited, with analyses based on paired subsets (n = 261). Manual measurements identified 36% as stunted, while CGM detected 31·8%. CGM demonstrated 84·0% sensitivity, 98·2% specificity, 96·3% positive and 91·6% negative predictive values, and 93·1% accuracy. For height measurement, the ICC for intra-rater and inter-rater reliability were 0·990 (n = 76) and 0·992 (n = 75) respectively, with intra- and inter-rater TEM of 0·5 cm. Inter-method TEM was 0·7 cm. Pitman's test was significant; Bland-Altman showed mean bias of 0·3 cm. CGM demonstrated accuracy and reliability for field-based stunting detection among children belonging to 24-59 months of age in Nepal. Future research on its use in younger children (6-24 months) and diverse settings will enable its integration into routine growth monitoring. This study was funded by the German Federal Foreign Office as part of the overall funding for the development of Child Growth Monitor app.
Routine data from healthcare are gaining importance for evidence generation. In Germany, new structures have been established in recent years to support their use. As part of the Medical Informatics Initiative (MII), data integration centres (DIZ) have been set up at all university hospitals, where patient data are made available in a pseudonymized, standardized and operationalized form. Since routine data, unlike primary data, are collected for healthcare purposes, aspects of the original data collection must be considered when formulating the research question, planning and analysing the study, and interpreting the results.The EVAluation research based on data from routine clinical care 4 the MII (EVA4MII) project supports researchers in analysing nationwide clinical routine data, for example through training programs and a central advisory service. This support is provided by an interdisciplinary team with methodological-statistical, data-technical, and clinical-epidemiological expertise in close coordination with data-providing institutions. The service covers the entire research process, from study planning and formal requirements to implementation, analysis, evaluation, and publication, and is available to projects within the MII and beyond.The aim of this article is to highlight the importance of methodological support in the analysis of clinical routine data and to identify key stages in the research process where such support is particularly relevant. Finally, an outlook on future advisory needs is provided, including the potential role of artificial intelligence as a supportive tool. Routinedaten aus der medizinischen Versorgung gewinnen für die Evidenzgenerierung an Bedeutung. In Deutschland wurden hierfür in den letzten Jahren neue Strukturen geschaffen. Beispielsweise wurden im Zuge der Medizininformatik-Initiative (MII) an allen Universitätskliniken Datenintegrationszentren (DIZ) aufgebaut, in denen Patient*innendaten pseudonymisiert, standardisiert und operationalisiert vorgehalten werden. Da Routinedaten, anders als Primärdaten, für Versorgungszwecke erhoben werden, müssen Aspekte der ursprünglichen Datenerhebung bei der Forschungsfrage, Studienplanung und -auswertung sowie bei der Interpretation der Ergebnisse berücksichtigt werden.Das Projekt EVA4MII (EVAluationsforschung auf der Grundlage von Daten aus der klinischen Routineversorgung 4 MII) unterstützt Forschende bei der Analyse deutschlandweiter klinischer Routinedaten unter anderem durch Weiterbildungsangebote und eine zentrale Beratungsplattform. Die Beratung erfolgt durch ein interdisziplinäres Team mit methodisch-statistischer, datentechnischer und klinisch-epidemiologischer Expertise in enger Abstimmung mit datenbereitstellenden Einrichtungen. Das Angebot umfasst den gesamten Forschungsprozess – von der Studienplanung und den Formalitäten über Durchführung, Auswertung und Bewertung bis hin zur Veröffentlichung – und richtet sich an Projekte der MII und darüber hinaus.Ziel des Artikels ist es, die Bedeutung methodischer Unterstützung bei der Analyse klinischer Routinedaten darzustellen und zentrale Punkte im Forschungsprozess zu identifizieren, an denen diese besonders relevant ist. Abschließend wird ein Ausblick auf zukünftigen Beratungsbedarf gegeben, wobei auch der Einsatz künstlicher Intelligenz als unterstützendes Werkzeug berücksichtigt wird.
Neuromyelitis optica spectrum disorder (NMOSD) constitutes a demyelinating condition of the central nervous system driven by autoimmune inflammation. A hallmark of its pathogenesis is the antibody-mediated injury of astrocytes, primarily targeting the water channel aquaporin-4 (AQP4). Animal models are indispensable for dissecting disease mechanisms and accelerating the development of new therapies. However, creating models that closely mirror NMOSD remains difficult, in part because immune tolerance limits the induction of the autoreactive responses central to the disease. This review follows NMOSD over time and surveys experimental systems across four interconnected mechanistic themes: breakdown of immune tolerance, T-B cell collaboration, antibody-mediated effector injury, and the formation of pro-inflammatory tissue milieus that sustain pathology. For each theme, we outline the rationale for model design, evaluate how well key pathological features are produced, and discuss the limitations that shape interpretation. Rather than offering a single continuous reconstruction of human NMOSD, these platforms capture complementary and only partially overlapping aspects of pathogenesis. Taken together, they provide a mechanism-oriented framework for understanding how upstream immune dysregulation, humoral immunity, and tissue-level permissive factors shape disease expression. At the same time, they also highlight the distance between experimental systems and the heterogeneous, dynamic course seen in patients. We further discuss how findings from animal studies are informing therapeutic target discovery and outline priorities for the development of next-generation models with greater translational relevance.
Rapid identification of bacterial species from patient samples is crucial for clinical decision-making. In severe infections, such as bloodstream infections, the early start of an effective treatment is directly associated with reduced mortality rates. Current rapid species identification methods, such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) or multiplex PCR, require specialized hardware and extensive technical support that prevents application in resource-limited settings. Here, we present a staining and imaging procedure for bacterial smears using fluorescent dyes directed against intracellular structures and cell wall components. Data on relevant features were extracted from segmented images and used to train a machine learning (ML) model for species classification. The method was tested on clinical isolates from 126 patients. For the seven most common bacteria, the classification performance, indicated by area under the receiver operating characteristic (ROC) curve, ranged from 0.8 (Klebsiella pneumoniae) to 1 (Pseudomonas aeruginosa). Species that were not part of the training dataset, were reliably classified as unknown species. These results hold promise for the identification of further species, particularly Enterobacterales, and clinical application.
Phosphatidylcholine (PC) is the most abundant phospholipid in eukaryotic membranes and is synthesized in part via the rate-limiting enzyme PCYT1A. In humans, hypomorphic PCYT1A variants cause diverse disorders, including retinal dystrophy, lipodystrophy with fatty liver, and spondylometaphyseal dysplasia. To define how graded reductions in PC synthesis affect organismal physiology, we generated and characterized a series of mutant alleles in the Caenorhabditis elegans homolog pcyt-1 , including variants corresponding to disease-causing human mutations, as well as an auxin-inducible degradation (AID) allele. We identify a clear allelic hierarchy. The V146M variant is embryonic lethal, whereas A97T is largely benign. P154A is temperature-sensitive, and C211Y causes growth delay, reduced brood size, sterility, and lengthened lifespan at standard temperature. Phenotypes of C211Y are rescued by choline, CDP-choline, or phosphatidylcholine supplementation, supporting reduced enzymatic function. Lipidomic profiling reveals that decreased PC synthesis consistently increases long-chain polyunsaturated fatty acids (LCPUFAs) in both PCs and PEs at the expense of shorter saturated species, without markedly altering the PC/PE ratio at 20°C. At elevated temperature, the P154A variant exhibits protein instability and a decreased PC/PE ratio. Despite significant lipid remodeling, canonical ER, mitochondrial, and metabolic stress GFP-based reporters are not activated; only the oxidative stress response is elevated, consistent with increased peroxidation-prone LCPUFAs in the pcyt-1 mutant. Acute auxin-induced degradation of PCYT-1 in larvae causes developmental arrest, while acute PCYT-1 degradation in adults disrupts oogenesis, demonstrating a continuous requirement for PC synthesis. Together, these findings establish a functional pcyt-1 allelic series and show that limiting PC synthesis drives compensatory remodeling toward LCPUFA-enriched membranes while rendering the germline particularly vulnerable.
Nurses play a crucial role as caregivers for cancer patients and providing spiritual care has become an integral part of their responsibilities. However, cancer patients' spiritual needs are often overlooked in clinical practice. This study aimed to develop and validate a spiritual care e-book for women with breast cancer undergoing chemotherapy. We developed a spiritual care e-book, Healing Light, for women with breast cancer undergoing chemotherapy and used the Fuzzy Delphi method for content validation. Two experts in clinical, two experts in nursing, one expert in education, and three experts in the spiritual care field participated in the study and evaluated the consensus of the e-book content using a standard questionnaire. The obtained data was analyzed using Microsoft Excel, including calculations of averages and formula-based computations. Healing Light comprises six sections, each covering specific topics on spiritual theory, practice, and reflection. The content validation showed that the spiritual care e-book has gained expert consensus with all items meeting the threshold values (d) ≤ 0.2, experts' agreement rate above 75%, and the fuzzy score (Amax) ≥ 0.5. Spiritual care faces many challenges in breast cancer practice, including unmet needs, limited resources, and a lack of knowledge about spiritual practices. Healing Light was designed to address these gaps in the Chinese cultural context. As a user-friendly, bilingual, and expert-validated self-management tool, it is intended to serve as a resource that may provide breast cancer women with spiritual care guidance and support during a challenging period in their treatment journey, potentially promoting spiritual health and quality of life.
The Dietary Approaches to Stop Hypertension (DASH) diet is effective in lowering blood pressure yet adherence to DASH remains low. Intuitive eating, a behavior that emphasizes responsiveness to hunger and satiety cues, may influence DASH adherence but has not been well studied among adults with hypertension. This study examined DASH adherence subgroups and their association with dietary behaviors using data from the Nourish U.S. based randomized controlled trial. A cross-sectional secondary analysis of baseline data from 301 participants was conducted. DASH adherence was assessed using the Mellen Index. Dietary behaviors were measured with the Intuitive Eating Scale-2 (IES-2). Latent class analysis (LCA) was used to identify DASH adherence subgroups, and subgroup differences in dietary behaviors and clinical outcomes were analyzed. Overall, DASH adherence was low (mean = 3.13 ± 1.35). LCA identified three DASH adherence subgroups: low adherence (44.2%), high adherence (41.2%), and mixed-pattern adherence (14.6%). Subgroups significantly differed in IES-2 subscales, including unconditional permission to eat (p < 0.0001), reliance on hunger and satiety cues (p = 0.0487), and body-food choice congruence (p < 0.0001). The low-adherence group scored higher on hunger cues, while the high-adherence group scored higher on body-food choice congruence. These findings highlight the value of examining overall dietary patterns rather than a single DASH score, as patterns better capture interactions among foods and nutrients that influence diet quality and health. Each subgroup's distinct demographic and behavioral characteristics further emphasize tailored DASH diet guidance which could lead to better cardiovascular outcomes. PATIENT OR PUBLIC CONTRIBUTION: 301 adults with hypertension contributed to this study by providing baseline data as part of the Nourish randomized controlled trial (ClinicalTrials.gov: NCT03875).