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Objective Over the last decades, psychotherapy of psychosis has increasingly gained attention. The quality of the therapeutic alliance has been shown to have an impact on therapy outcome. Yet, little is know about the influence of the therapeutic stance on the alliance. In this study, we explore psychotherapists‘ stance towards persons with psychosis with the aim of better understanding its characteristic—hindering and helpful—aspects. Method 6 semi-structured interviews with psychotherapists from three different schools (CBT, PD, ST) were analysed with Grounded Theory. Credibility was checked through external and peer-researcher-supported debriefing. Results 4 core categories were generated and interrelated in a theoretical model. Therapists‘ stance was initially characterized by uncertainty. Different ways of dealing with uncertainty yielded different stances: a monological and an open one. A helpful stance was conceived as stemming from openness and was characterized by a dialogical structure. A co-presence (or „dosing“) of you and I was conceived as its core aspect. Conclusion These findings specify the interpersonal dynamics arising from different stances and their impact on the therapeutic alliance and process. Research is still needed to further understand the characteristics of helpful and hindering therapeutic stances, which should also inform the training of psychotherapists.

Abstract Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository.

Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, detection and classification of radiological findings, and prediction of clinical outcomes, thereby paving the way for personalized medicine. The manuscript discusses ML's application in spine care, detailing supervised and unsupervised learning, regression, classification, and clustering, and highlights the importance of both internal and external validation in assessing ML model performance. Several ML algorithms such as linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks, can be used in the spine domain to analyze diverse data types (visual, tabular, omics, and multimodal).

Abstract Pathological data showed focal inflammation and regions of diffuse neuronal loss in the cortex of people with multiple sclerosis (MS). In this work, we applied a novel model (“soma and neurite density imaging (SANDI)”) to multishell diffusion-weighted MRI data acquired in healthy subjects and people with multiple sclerosis (pwMS), in order to investigate inflammation and degeneration-related changes in the cortical tissue of pwMS. We aimed to (i) establish whether SANDI is applicable in vivo clinical data; (ii) investigate inflammatory and degenerative changes using SANDI soma fraction (f soma )—a marker of cellularity—in both cortical lesions and in the normal-appearing-cortex and (iii) correlate SANDI f soma with clinical and biological measures in pwMS. We applied a simplified version of SANDI to a clinical scanners. We then provided evidence that pwMS exhibited an overall decrease in cortical SANDI f soma compared to healthy subjects, suggesting global degenerative processes compatible with neuronal loss. On the other hand, we have found that progressive pwMS showed a higher SANDI f soma in the outer part of the cortex compared to relapsing–remitting pwMS, possibly supporting current pathological knowledge of increased innate inflammatory cells in these regions. A similar finding was obtained in subpial lesions in relapsing–remitting patients, reflecting existing pathological data in these lesion types. A significant correlation was found between SANDI f soma and serum neurofilament light chain—a biomarker of inflammatory axonal damage—suggesting a relationship between SANDI soma fraction and inflammatory processes in pwMS again. Overall, our data show that SANDI f soma is a promising biomarker to monitor changes in cellularity compatible with neurodegeneration and neuroinflammation in the cortex of MS patients.

Abstract “Garbage in, garbage out” summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 “General Data Protection Regulation” (GDPR) and the 1996 United States Act of Congress “Health Insurance Portability and Accountability Act” (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation. Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology. Key points • High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging. • Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research. • Anonymisation techniques protect patient privacy during dataset preparation. • Machine learning models can accelerate image annotation, enhancing efficiency and accuracy. • Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development. Graphical Abstract

Abstract Background Intervertebral disc degeneration is frequent in dogs and can be associated with symptoms and functional impairments. The degree of disc degeneration can be assessed on T2‐weighted MRI scans using the Pfirrmann classification scheme, which was developed for the human spine. However, it could also be used to quantify the effectiveness of disc regeneration therapies. We developed and tested a deep learning tool able to automatically score the degree of disc degeneration in dog spines, starting from an existing model designed to process images of human patients. Methods MRI midsagittal scans of 5991 lumbar discs of dog patients were collected and manually evaluated with the Pfirrmann scheme and a modified scheme with transitional grades. A deep learning model was trained to classify the disc images based on the two schemes and tested by comparing its performance with the model processing human images. Results The determination of the Pfirrmann grade showed sensitivities higher than 83% for all degeneration grades, except for grade 5, which is rare in dog spines, and high specificities. In comparison, the correspondent human model had slightly higher sensitivities, on average 90% versus 85% for the canine model. The modified scheme with the fractional grades did not show significant advantages with respect to the original Pfirrmann grades. Conclusions The novel tool was able to accurately and reliably score the severity of disc degeneration in dogs, although with a performance inferior than that of the human model. The tool has potential in the clinical management of disc degeneration in canine patients as well as in longitudinal studies evaluating regenerative therapies in dogs used as animal models of human disorders.

Disaster risk in the 21st century differs from the experience of past generations and is defined by its evolving systemic nature. The increased role of technological networks, societal interdependencies and climate change dynamics make crises more complex and unpredictable. Cascading and compounding dynamics are becoming the new “business as usual”, challenging emergency management to maintain operations in face of complex disruptions while requiring the development of good practices and strategy for facilitate the recovery process. Our special issue aimed to support the development of a paradigm shift in the understanding of complex events, utilizing a network-based, cross-disciplinary approach to resilience. This editorial introduces and summarizes 18 papers across four thematic areas: 1) Resilience Challenges; 2) Area Studies and the Sendai Framework for Disaster Risk Reduction; 3) Community and Health; 4) Enhancing New Methodologies. The conclusions highlight open research challenges for future exploration.

The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support.

Abstract Background Humans should sleep for about a third of their lifetime and the choice of the mattress is very important from a quality-of-life perspective. Therefore, the primary aim of this study was to assess the changes of lumbar angles, evaluated in a supine position using magnetic resonance imaging (MRI), on a mattress versus a rigid surface. Methods Twenty healthy subjects (10 females, 10 males), aged 32.3 ± 6.5 (mean ± standard deviation), with body mass index 22.4 ± 2.9, completed three evaluations: (i) spine MRI in supine position on a mattress (MAT); (ii) spine MRI in supine position on rigid surface (CON); and (iii) biplanar radiographic imaging in standing position. The following indexes were calculated for both MAT and CON: lumbar lordosis angles L1–L5, L1–S1, L5–S1, and the sacral slope (SS). Further, pelvic incidence (PI) was calculated from the biplanar radiographic images. Results Main findings were (i) L1–L5 and SS were greater in MAT than CON (L1:L5: +2.9°; SS: +2.0°); (ii) L5–S1 was lower in MAT than CON (−1.6°); (iii) L1–S1 was greater in MAT than CON only for male subjects (+2.0°); (iv) significant and positive correlations between PI and L1–L5, L1–S1 and SS were observed in both CON and MAT. Conclusions The use of a mattress determined small but statistically significant changes in lumbar angles. Relevance statement The use of a mattress determines small but statistically significant changes in radiological angles describing the sagittal alignment of the lumbar spine when lying in the supine position. Key points • Lordosis angle L1–L5 was greater in MAT than in CON condition (+2.9°). • Sacral slope was greater in MAT than in CON condition (+2.0°). • Lordosis angle L5–S1 was lower in MAT than in CON condition (−1.6°). Graphical Abstract

Introduction: Epimuscular fat (EF) has rarely been studied in the context of low back pain (LBP). Research question: This study aims to assess the presence and extent of EF in the lumbar muscles and its association with vertebral level in patients with low back disorders and to explore correlations between EF, demographics, BMI, and LBP. Material and methods: T2 axial MRIs from L1 to L5 were manually segmented to analyze the cross-sectional area (CSA) of EF (mm2), and fat infiltration (FI,%) of 40 patients (23 females, 17 males; mean age:65.9 years) with lumbar degenerative pathologies awaiting a surgical procedure. COMI, LBP, demographic, and clinical data were extracted from the institutional registry. Statistical analyses included Wilcoxon and Mann-Whitney tests for differences in EF between sides and sexes, the Friedman test for EF size differences among lumbar levels, and Spearman’s correlation for associations, adjusted for BMI, age, and sex. Results: EF was found in 77.5% of subjects at L1, 92.5% at L2, 100% at L3 and L4, and 95.0% at L5. EF was significantly larger at L4 (253.1 ± 183.6 mm2) and L5 (220.2 ± 194.9 mm2) than at L1 (36.1 ± 37.8 mm2) and L2 (72.2 ± 84.4 mm2). No significant EF differences were found between sides and sexes. EF correlated strongly with BMI (rs = 0.65,p

Introduction: Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. Research question: How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation? Material and methods: Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from “excellent” to “unsatisfactory,” and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests. Results: Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5. Discussion and conclusion: LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication.

IntroductionIn this study we explore how the diagnostic category of mood disorders is constructed in two handbooks of Psychopathology as an example of the mainstream construction of psychopathology. Despite the increasing criticism and lack of evidence, the debunked chemical imbalance theory of the etiology of depression still dominates the professional and pop/folk understanding and interventions.MethodsWe analysed the breadth of the inference field and the type of etiopathogenetic contents of the explanations of mood disorders using the “1to3” Coding System.ResultsOur findings show that the dominant explanations draw almost exclusively onto monadic explanations, followed by limited dyadic ones. Intrapersonal etiopathogenetic contents prevailed, and biomedical explanations were dominant in both textbooks.DiscussionWe critically discuss the underpinnings of these results and address the clinical implications of these biased representations, as well as potential alternative approaches to psychopathology.

Introduction: Modic Changes (MCs) are MRI alterations in spine vertebrae's signal intensity. This study introduces an end-to-end model to automatically detect and classify MCs in lumbar MRIs. The model's two-step process involves locating intervertebral regions and then categorizing MC types (MC0, MC1, MC2) using paired T1-and T2-weighted images. This approach offers a promising solution for efficient and standardized MC assessment. Research question: The aim is to investigate how different MRI normalization techniques affect MCs classification and how the model can be used in a clinical setting. Material and methods: A combination of Faster R–CNN and a 3D Convolutional Neural Network (CNN) is employed. The model first identifies intervertebral regions and then classifies MC types (MC0, MC1, MC2) using paired T1-and T2-weighted lumbar MRIs. Two datasets are used for model development and evaluation. Results: The detection model achieves high accuracy in identifying intervertebral areas, with Intersection over Union (IoU) values above 0.7, indicating strong localization alignment. Confidence scores above 0.9 demonstrate the model's accurate levels identification. In the classification task, standardization proves the best performances for MC type assessment, achieving mean sensitivities of 0.83 for MC0, 0.85 for MC1, and 0.78 for MC2, along with balanced accuracy of 0.80 and F1 score of 0.88. Discussion and conclusion: The study's end-to-end model shows promise in automating MC assessment, contributing to standardized diagnostics and treatment planning. Limitations include dataset size, class imbalance, and lack of external validation. Future research should focus on external validation, refining model generalization, and improving clinical applicability.

IntroductionDuring the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood.MethodsGAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure.ResultsIn the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p

Among the many inputs, nitrogen fertilizers are the main yield-limiting factor in agriculture. Liquid fractions of digestates can be a most promising substitute to synthetic nitrogen fertilizers, using little energy to turn waste into valuable fertilizers. In this study, the efficacy of five digestates from different origin (C, cow slurry; P, pig slurry; PCE, pig slurry, cow slurry, energy crops; SS, sewage sludge; W, organic fraction of municipal waste) were assessed as fertilizers for the cultivation of Lactuca sativa L., compared to traditional mineral fertilization. Digestates showed promising results as fertilizers for Lactuca sativa L., as yield and chemical parameters were overall comparable to the mineral fertilizer. Analysis of nitrogen evolution showed that most digestates showed higher nitrates in the substrates than the mineral fertilizers at earlier stages. Another topic investigated in the study is the effect of the digestates on the bacterial populations of the growth substrate, investigated through quantification and sequencing of 16S gene. These results varied based on the digestate considered, but, in general, an increase in biodiversity could be linked to use of digestates. These results suggest that digestates might become an alternative to mineral fertilizers, contributing to the circular economy and waste reduction.

This study aims to produce Fe2.9wt.%Si ferromagnetic material via laser powder bed fusion (L-PBF) for the realization of electromagnetic actuators (EMA). This study is necessary as there are no documents in scientific literature regarding the manufacturing of Iron-Silicon plungers using the L-PBF additive manufacturing (AM) technique. The microstructure, and magnetic properties were characterized using various techniques. The results indicate that the samples produced via L-PBF process exhibit good magnetic properties ( $\mu = 748, H_{C}= 87.7 [A/m]$ ) especially after annealing treatment at 1200° C for 1h ( $\mu = 3224, H_{C}= 69.1 [A/m] $ ), making it a promising material for use in electromagnetic actuators.

Abstract Background Internationally, intensive psychiatric home treatment has been increasingly implemented as a community-based alternative to inpatient admission. Since 2018, the so-called Inpatient Equivalent Home Treatment (IEHT; German: "Stationsäquivalente Behandlung", short: "StäB") has been introduced as a particularly intensive form of home treatment that provides at least one daily treatment contact in the service users’ (SU) home environment. Prior research shows that this can be challenging in rural catchment areas. Our paper investigates to which extent the location of the SU home location within the catchment area as well as the distance between the home and the clinic influence the utilisation of inpatient treatment compared to IEHT. Method Routine data of one psychiatric hospital in the federal state of Brandenburg in Germany were analysed for the observational period 07/2018–06/2021. Two comparison groups were formed: SU receiving inpatient treatment and SU receiving IEHT. The SU places of residence were respectively anonymised and converted into geo-coordinates. A geographic information system (GIS) was used to visualise the places of residence, and car travel distances as well as travel times to the clinic were determined. Spatial analyses were performed to show the differences between comparison groups. In a more in-depth analysis, the proximity of SU residences to each other was examined as an indicator of possible clustering. Results During the observational period, the location of 687 inpatient and 140 IEHT unique SU were mapped using the GIS. SU receiving treatment resided predominantly within the catchment area, and this proportion was slightly higher for SU receiving IEHT than for those treated in inpatient setting (95.3% vs. 84.7%). In the catchment area, the geographical distribution of SU place of residence was similar in the two groups. There was a general higher service provision in the more densely populated communities close to Berlin. SU with residence in peripheral communities were mainly treated within the inpatient setting. The mean travel times and distances to the place of residence only differed minimally between the two groups of SU (p ; 0.1). Conclusion In especially peripheral parts of the examined catchment area, it may be more difficult to have access to IEHT rather than to inpatient services. The results raise questions regarding health equity and the planning of health care services and have important implications for the further development of intensive home treatment. Telehealth interventions such as blended-care approaches and an increase of flexibility in treatment intensity, e.g. eliminating the daily visit requirement, could ease the implementation of intensive home treatment especially in rural areas.

Specific immune suppression types have been associated with a greater risk of severe COVID-19 disease and death. We analyzed data from patients >17 years that were hospitalized for COVID-19 at the “Fondazione IRCCS Ca′ Granda Ospedale Maggiore Policlinico” in Milan (Lombardy, Northern Italy). The study included 1727 SARS-CoV-2-positive patients (1,131 males, median age of 65 years) hospitalized between February 2020 and November 2022. Of these, 321 (18.6%, CI: 16.8–20.4%) had at least one condition defining immune suppression. Immune suppressed subjects were more likely to have other co-morbidities (80.4% vs. 69.8%, p

IntroductionIn the tradition of phenomenological psychiatry, schizophrenia is described as a disturbance of the minimal self, i.e. the most basic form of self-awareness. This disturbance of the minimal self at the individual level is assumed to precede the intersubjective disturbances such as boundary weakening. However, the role of intersubjective disturbances in the emergence and recovery of schizophrenic experience still remains an open question. This phenomenological study focuses on how encounters with others shape self-experience during from psychosis by analyzing this process from the perspective of cultural differences, which in current research is especially under-researched. While most phenomenological accounts are based on first person-accounts from Western, individualist cultures where the self is conceived and experienced as separate to others, the present study qualitatively investigates psychotic experiences of patients from Jaffna, Sri Lanka.MethodSemi-structured interviews were conducted with three participants with a diagnosis of schizophrenia or first episode psychosis. The interviews were transcribed and analyzed using interpretative phenomenological analysis (IPA). Eight group experiential themes were identified across interviews.ResultsThe data suggest that intersubjective processes of boundary weakening such as invasiveness and hyperattunement may shape minimal self-experience and more specifically contribute to a mistrust of the own senses and to hyper-reflexivity. Interestingly, boundary weakening yields pervasive emotions and can be experienced as a threat to the whole social unit. On the one hand, the strengthening of self-other-boundary was achieved through opposition, closedness and withdrawal from others. On the other hand, this study suggests that the re-opening of self-other-boundaries in response to the crisis may help establish connectedness and may lead to recovery.