The information held by visual representations is typically opaque to information processing systems and able to be interpreted only by human viewers. We introduce the Graphics Descriptor Ontology (GDO) to serve as an ontology for domain-independent annotation and description of graphics and their elements. Our goal is to represent information about graphics that corresponds to what a human observer could conclude from viewing a graphic or that would help to inform a viewer about a graphic. This work builds upon ontological modeling of information content entities and uses theories and vocabularies from the fields of semiotics, visual arts, technical communication, and computer graphics. We define a graphic as a spatial composition composed of graphical marks. The three types of graphical marks are line mark, point mark, and region mark. We present an approach to representing roles and qualities for information content entities, including graphical marks and graphics. Anatomical graphics serve as our use cases, and we provide an anatomy extension for the GDO to model anatomy-specific content. We introduce our work as an illustrated ontology available through a web browser, accompanied by over 100 explanatory graphics.
Generative artificial intelligence (AI) has emerged as a transformative tool for creating high-quality visual materials in medical research and education. In pediatric neurosurgery, where ethical and legal constraints limit the use of real patient photographs, AI-assisted illustrations offer significant potential. However, concerns regarding clinical accuracy, intellectual property, and the protection of vulnerable pediatric patients necessitate rigorous oversight. We present a human-in-the-loop workflow that integrates generative AI with vector-based digital editing to produce scientifically accurate and ethically grounded medical illustrations. We reviewed current AI usage policies from major medical journals, including the International Committee of Medical Journal Editors (ICMJE) and the Journal of Korean Neurosurgical Society (JKNS). To demonstrate practical application, we developed illustrative examples for conditions such as sacral dimple, Crouzon syndrome, and Down syndrome using clinician-led sketches and AI-assisted refinement. Vector-based workflows facilitate the transformation of AI-generated raster drafts into editable, high-resolution graphics, allowing clinicians to correct "hallucinations" and ensure anatomical precision. While most journals prohibit listing AI as an author, they permit its use for conceptual figures provided there is transparent disclosure of the tools and prompts used. Our proposed workflow emphasizes that AI should function as a "constrained assistant" rather than an autonomous creator, ensuring that the final output remains non-identifiable and respectful of pediatric patients' dignity. Generative AI tools can significantly enhance visualization in pediatric neurosurgery when governed by strict ethical and technical safeguards. Adherence to journal policies and the maintenance of human-directed validation are essential to uphold scientific integrity and patient privacy in the era of AI-assisted publishing.
Dense reconstruction and differentiable rendering are fundamental tightly connected operations in 3D vision and computer graphics. Recent neural implicit representations demonstrate compelling advantages in reconstruction fidelity and differentiability over conventional discrete representations such as meshes, point clouds, and voxels. However, many neural implicit models, such as neural radiance fields (NeRF) and signed distance function (SDF) networks, are inefficient in rendering due to the need to perform multiple queries along each camera ray. Moreover, NeRF and Gaussian Splatting methods offer impressive photometric reconstruction but often require careful supervision to achieve accurate geometric reconstruction. To address these challenges, we propose a novel representation called signed directional distance function (SDDF). Unlike SDF and similar to NeRF, SDDF has a position and viewing direction as input. Like SDF and unlike NeRF, SDDF directly provides distance to the observed surface rather than integrating along the view ray. As a result, SDDF achieves accurate geometric reconstruction and efficient differentiable directional distance prediction. To learn and predict scene-level SDDF efficiently, we develop a differentiable hybrid representation that combines explicit ellipsoid priors and implicit neural residuals. This allows the model to handle distance discontinuities around obstacle boundaries effectively while preserving the ability for dense high f idelity distance prediction. Through extensive evaluation against state-of-the-art representations, we show that SDDF achieves (i) competitive SDDF prediction accuracy, (ii) faster prediction speed than SDF and NeRF, and (iii) superior geometric consistency compared to NeRF and Gaussian Splatting.
Maintaining healthy breathing is important to health and preventing serious respiratory complications, many of which can result in death. Individuals living with ongoing chronic respiratory disease (asthma, chronic obstructive pulmonary disease, sleep apnea) need ongoing personal patient-centred continuous non-hospital-based monitoring for diagnosis and appropriate treatment. Many traditional existing patient-centred physical health monitoring systems are insufficient due to their limited flexibility, combining multiple sources of sensor data that monitor patients continuously and act in real time to document health states are yet to be invented. This paper proposes a respiratory monitoring system that utilizes a cyber twin (CT) model, and a graph neural network (GNN) function for relational reasoning of the individual based on multi-source data, with a transformer architecture for long-range behaviour modifications based on time series data. The cyber twin continuously updates the physiological state of each individual and predicts what will happen if treatment occurs. This work provides complete results for the comparison of the proposed transformer GNN versus LSTM-CNN being used as evaluation frameworks for established experimental effectiveness, achieving better comparisons for the performance of the model direct interfaces. The proposed approaches provided superior performance over traditional networks with absolute error reductions of 8-13% on respiratory rate (RR) estimates, and F1 score improvements of + 3.1-6.8 points between multimodal respiratory ensembles relative to each other. A graphical figure and table show all the experimental validity and comparisons.
Gastrointestinal cancers represent a major global health burden and are associated with high morbidity and mortality worldwide. Despite advances in conventional therapies, treatment outcomes remain limited due to therapy resistance, toxicity, and poor long-term efficacy, highlighting the need for novel and complementary therapeutic strategies. Magnolol, a biphenolic lignan isolated from Magnolia officinalis, has attracted considerable attention due to its reported anticancer activity in multiple gastrointestinal malignancies. This review systematically summarizes current preclinical evidence on the anticancer effects of magnolol in oral, esophageal, gastric, colorectal, pancreatic, and liver cancers. Available studies demonstrate that magnolol inhibits cancer cell proliferation, migration, invasion, angiogenesis, and epithelial-mesenchymal transition, while inducing cell cycle arrest and apoptosis. These effects are mediated through modulation of key cancer-associated signaling pathways, including PI3K/Akt/NF-κB, MAPK/JNK, ERK, TGF-β/Smad, and caspase-dependent apoptotic pathways. The review further highlights recent insights into the structure-activity relationship of magnolol and its semi-synthetic derivatives, emphasizing how targeted chemical modifications influence anticancer potency and mechanistic specificity. In addition, challenges related to magnolol's poor aqueous solubility, rapid metabolism, and limited bioavailability are discussed, with particular focus on nanotechnology-based delivery systems developed to improve its pharmacokinetic profile and therapeutic performance. This current review provides an integrated overview of magnolol's molecular mechanisms, chemical optimization strategies, and translational limitations in gastrointestinal cancer therapy, and outlines future research directions necessary to support its progression toward clinical application.
Nutraceuticals represent promising strategies for preventing, delaying and addressing premature aging of the skin, especially as women advance in years (particularly after 30 years of age, when estrogen levels begin to decline, and remarkably after menopause when estrogen production ceases from the ovaries). This review is part of a larger project, and we present this companion review, which provides a detailed examination of the literature beyond polyphenols and/or phytoestrogens for estrogen-deficient skin. This narrative review covers the top-selling nutraceutical, collagen, along with the antioxidants, curcumin and glutathione, in women between 30 to over 65 years of age regarding their effectiveness in enhancing dermal health parameters. There were 23 clinical studies published between 2020 and 2025 that support collagen as an effective nutraceutical treatment. These studies showed improvement in various skin attributes, but investigations are lacking on collagen's effectiveness on scalp hair and nail health, which warrants further examination. Curcumin and glutathione, while these remain popular nutraceutical applications to improve skin health, had only a few clinical studies published; thus, more studies are needed to establish optimal dosing regimens and identify which combination approaches provide the most meaningful dermal benefits, especially in aging women. Trends and future directions in nutraceutical skin applications include the use of collagen (where many clinical studies have been reported), along with antioxidants and bioactives. Therefore, nutraceutical skin applications using collagen and antioxidants such as curcumin and glutathione demonstrate hopeful results for dermal antiaging effects, especially in estrogen-deficient skin. However, more investigations are warranted to expand their applications to meet the evolving dermatological challenges. Finally, there is a need to balance the psychological and ethical considerations in aesthetic medicine, distinguishing between objective beauty and subjective attractiveness, while emphasizing the importance of a patient's self-confidence in ethical practice.Graphical Abstract available for this article.
Analog gauges are fundamental for data acquisition in numerous industrial sectors, yet the automated reading of these devices is significantly hampered by a scarcity of large-scale, comprehensively annotated datasets. This data bottleneck impedes the development of robust and accurate computer vision algorithms. Here we introduce SyncG, a large-scale synthetic benchmark created to address this critical gap. We developed a generative framework using Blender and Python to produce 20,000 high-resolution, photorealistic gauge images across 145 diverse and challenging environmental settings. Our method allows for precise parametric control over both measurement attributes and visual appearances, ensuring a high degree of diversity and realism. Crucially, we present a fully automated annotation pipeline that generates detailed and accurate ground-truth data for a range of tasks, including object detection, keypoint localization, semantic segmentation, and optical character recognition. By providing this extensive and meticulously annotated benchmark, SyncG facilitates the training and evaluation of sophisticated gauge-reading models and supports the exploration of broader computer vision challenges, such as the interpretation of clock-like graphical representations by multimodal large models.
Combination therapies with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) have revolutionized the landscape of cancer treatment, improving the quality of life and overall survival of patients. A deep knowledge of the side effects of ICIs and TKIs combination therapy is mandatory to ensure patient compliance and improve clinical outcomes. Both ICIs and TKIs may cause endocrinopathies such as thyroid dysfunction, adrenal insufficiency, hypophysitis, and diabetes mellitus. To avoid life-threatening conditions and improve patient’s compliance and outcomes, an early diagnosis of endocrine toxicity should be achieved and a multidisciplinary approach involving both endocrinologists and oncologists may be beneficial. This review specifically examines the endocrine adverse events reported in the clinical trials of ICI plus TKI combined treatment, their underlying mechanisms, and practical management guidelines. [Image: see text]
Our research endeavored to formulate a patient-specific prognostic algorithm and elucidate the interconnection between critical genetic polymorphisms at PTPN1 loci and the predisposition to small vessel pathologies in Han Chinese subjects presenting with T2DM. From January 1, 2019, to June 30, 2024, a total of 3,847 patients with T2DM were enrolled in this cross-sectional case-control study. They were grouped into four groups by means of fundus examination and renal function assessment: the T2DM alone group (T2DM group), the T2DM combined with diabetic retinopathy (DR) group (T2DM + DR group), the T2DM combined with diabetic nephropathy (DN) group (T2DM + DN group), and the T2DM combined with DR + DN group (T2DM + DR +DN group). The genotypes of four SNP loci (rs968289, rs6067484, rs2206521, rs754118) of the PTPN1 gene were detected by PCR-RFLP. To evaluate the association between SNP loci and microvascular complications, multivariate logistic regression analysis was employed, followed by LASSO regression for variable selection to develop a nomogram prediction model. The rs968289-GG genotype demonstrated a statistically significant link to the risk of DR (adjusted OR = 1.47, 95%CI: 1.15-1.88, P = 0.002); the rs6067484-CC genotype exhibited a significant relationship with the risk of DN (adjusted OR = 1.58, 95%CI: 1.21-2.06, P < 0.001); The rs2206521-AA genotype significantly correlated with the risk of DR + DN co-morbidity (adjusted OR = 1.69, 95%CI: 1.28-2.24, P < 0.001). The column-line graphical model constructed based on nine independent predictors had AUCs of 0.823 and 0.808 in the training and validation sets, with sensitivity and specificity of 76.4%/78.9% and 74.2%/80.1%, respectively. Significant associations were observed between specific genotypic variants at the PTPN1 gene's rs968289, rs6067484 and rs2206521 loci and microvascular complication risk in Chinese Han T2DM patients. The column-line graph prediction model integrating genetic markers and clinical indicators has good discriminative ability and clinical utility, providing an important tool for individualized risk assessment and precise prevention of diabetic microvascular complications.
Preclinical small animal experiments play an indispensable role in proton therapy research. However, accurate dose calculation poses a significant challenge because of the low beam energy and the requirement for submillimeter spatial resolution. Although the Monte Carlo method offers the necessary precision, its high computational cost hinders efficient implementation. This study aims to develop a GPU-accelerated radiation dose engine for proton radiotherapy (pGARDEN) based on the Monte Carlo method, specifically designed for fast and accurate dose calculation in small animal irradiation. In pGARDEN, we optimized the particle transport algorithm to better align with the GPU architecture. Moreover, various acceleration techniques were implemented to boost computational efficiency. To enhance precision, physical parameters, such as energy cutoffs for proton and electron, were tuned to better suit small animal conditions. The performance of pGARDEN was validated against Geant4 simulations and measurements across various beams and phantoms. To demonstrate its practical utility, pGARDEN was applied to calculate a multi-beam proton treatment plan for a lung tumor-bearing mouse model. Compared to Geant4, the engine achieved a > 1000-fold speedup and a 3D gamma passing rate of > 97% with a strict 1%/0.15 mm criterion in all phantom testing scenarios. The integrated depth dose curves and dose profiles showed good agreement with measurements. In the in vivo validation, the 2D gamma passing rates with a 2%/0.3 mm criterion were 95.52% ± 0.74% for the abdomen and 94.18% ± 1.08% for the thorax. Furthermore, pGARDEN calculated the treatment plan with < 1% statistical uncertainty in 4.3 s on an NVIDIA GeForce RTX 4070 Ti GPU, achieving a 100% 3D gamma passing rate with a 2%/0.3 mm criterion. pGARDEN can calculate proton dose distribution rapidly and accurately at submillimeter resolution for small animal. It provides a valuable tool for supporting small animal proton radiation experiments, such as the investigation of relative biological effectiveness (RBE) and new therapeutic strategies.
The multifunctional potential of Angelica decursiva ethanol extract (Ad-EE) was evaluated to address the safety and efficacy limitations of conventional whitening agents. Experimental results indicate that Ad-EE suppresses melanin secretion in B16F10 cells in a dose-dependent manner. Furthermore, under 20 mJ/cm2 UVB irradiation, Ad-EE reduces the mRNA and protein expression levels of Tyrosinase and TRP1, thereby mitigating both hormonal and environmental triggers. Comprehensive chemical profiling using gas chromatography-mass spectrometry (GC-MS) and high-resolution liquid chromatography-mass spectrometry (HR- LC-MS) revealed a complex phytochemical matrix extending beyond simple volatile compounds. Molecular docking analysis demonstrated that key metabolites, including 1- methylinosine and adhyperforin, possess high binding affinities for the TRP1 catalytic pocket. Ad-EE also exhibits significant antioxidant and anti-inflammatory activities. Multiple assays, including ABTS and FRAP, showed a dose-dependent increase in radical scavenging and ferric-reducing capacities. In HaCaT and RAW264.7 cells, Ad-EE improved cellular redox status by upregulating endogenous antioxidant genes, including HO-1 and NQO-1, while suppressing inflammatory mediators. By simultaneously inhibiting key enzymatic pathways and enhancing cellular defense signaling, Ad-EE disrupts melanogenesis in response to diverse stimuli. These findings provide a strong molecular basis for considering Ad-EE as a versatile, natural candidate for advanced cosmeceutical applications. Angelica decursiva ethanol extract (Ad-EE) reduces melanin production showing suppression of tyrosinase, TRP1, and TRP2, induces the expression of anti-oxidative genes such as HO-1, and inhibits LPS-induced inflammatory response via down-regulation of Src activity.
A central enigma in crop improvement lies in introducing beneficial traits without fitness trade-offs. Rice, the cornerstone of global food security, demands multifaceted genetic innovation to sustain yield, quality, and resilience in the face of mounting climatic constraints. With the global population projected to surpass ten billion, functional master regulators such as miRNAs stand out as transformative molecular tools, capable of orchestrating complex trait networks and offering a tangible path toward the next green revolution. These are robust fine-tuners that orchestrate a myriad of functional processes and provide a value addition in emerging technologies such as assisted breeding, genome editing, and genomic selection to make rice production feasible. Herein, we have provided a comprehensive synthesis and updates on functional miRNA-mediated agronomically advantageous trait improvement exclusively for rice. It represents the latest functional understanding of miRNAs and their involvement as signatures of domestication and divergence processes, in support of the previously established notion and recent updates on emerging miRNA-assisted resources and technologies, such as their application as artificial solutions for improving genotypes, coding, and dietary potentialities for environmental safeguards and innovative biotherapeutics. Recent updates signify their robust cross-kingdom communicators' potential for multifaceted non-host dialoguing, and their integrative action relies on the coordination with other non-coding regulatory elements for various downstream trait regulation. Moreover, specific highlights refer to the application of miRNAs for rice agronomical trait improvements, broadly classified into three functional domains, viz., biotic and abiotic stresses and yield and quality traits. Such updated functional aspects of different miRNA modules would strengthen rice improvement by facilitating a foundation and future roadmap for miRNA-mediated trait discovery and improvement.
Understanding the kinetics of nanoparticle vaccine drainage to the lymph nodes and their residence time in these tissues is critical in vaccine development. Evaluating these parameters using conventional techniques is expensive, time-consuming, and often leads to variable data, as several animals at different time points need to be sacrificed, and lymph nodes need to be harvested for characterization. Here, we adapted an abdominal window chamber for intravital imaging of the lymph nodes, enabling real-time tracking of ionizable lipid nanoparticles (LNPs) and polymeric nanoparticles in live mice following intramuscular administration. To mimic delivery of vaccine payloads, these nanoparticles were encapsulated with luciferase mRNA or a small molecule fluorophore, indocyanine green, and tracked for bioluminescence and fluorescence, respectively, in the lymph nodes. The results demonstrated differential lymph node drainage kinetics and retention for LNPs and polymeric nanoparticles. Notably, variations in the ionizable lipid components of LNPs resulted in changes in the mRNA translation ability within the lymph nodes. Taken together, this study introduces a valuable tool to optimize nanoparticle vaccine design in preclinical studies. Real-time in vivo tracking of the lymph node drainage kinetics of nanoparticles encapsulating mRNA or small molecule fluorescent dye, facilitated by the use of a lymph node window chamber. Created in BioRender. https://BioRender.com/786dnai. The online version contains supplementary material available at 10.1186/s41120-026-00163-5.
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The increasing use of electronic health records (EHRs) for real-world evidence (RWE) studies is hindered by substantial heterogeneity in data collection practices and local coding schemes across healthcare providers. Data standardization-particularly the mapping of locally defined medical concepts to standardized vocabularies-is therefore a critical but labour-intensive step, traditionally relying on extensive manual review by clinical experts. While a range of machine-learning (ML) approaches have been proposed to support medical concept mapping, their integration into practical, end-to-end workflows and their performance under real-world conditions remain insufficiently studied. In this work, we present ArcMAP, an end-to-end application that integrates a state-of-the-art biomedical representation model (BioLORD) into a human-in-the-loop workflow designed to streamline and accelerate medical concept mapping. ArcMAP provides a graphical user interface that enables clinical experts to efficiently review, validate, and correct automated mapping suggestions. A core component of the system is a continuous learning pipeline, in which expert feedback is systematically captured and used to update the underlying model, allowing ArcMAP to adapt to evolving coding practices and newly onboarded data sources. We conduct a comprehensive evaluation of ArcMAP across multiple deployment scenarios, including the impact of continuous fine-tuning, the onboarding of a new hospital, and a longitudinal real-world evaluation conducted over a two-month period using medication and laboratory test data from five UK-based NHS hospitals. Our results demonstrate the importance of domain-specific fine-tuning, with top-1 accuracy for laboratory test names increasing from 37.0% to 91.6%. However, when simulating the onboarding of a new hospital, the system achieves a weighted average top-1 accuracy of only 73.5%, indicating substantial variability across NHS hospital systems. In real-world use, the use of ArcMAP indicates an increased mapping efficiency compared to manual workflows, while also revealing considerable variation across individual data-mapping sessions.
Data security is a fundamental objective of all information systems, especially those handling financial data, which involves technologies, strategies, and measures to defend sensitive financial data. Consequently, data compression is regarded as an effective technique for reducing storage requirements for all generated and stored data. Therefore, it has become necessary to design an integrated system that provides security for financial data in addition to preserving the required storage space as much as possible. Accordingly, the proposed approach consists of three main components: the sender, the receiver, and the graphical user interface (GUI). The system accepts two types of input documents (i.e., images and PDF files), extracts their data into a structured format, and then classifies the processed data into sensitive and non-sensitive categories. Watermarking and encryption are subsequently applied only to the sensitive data to ensure privacy preservation and enhance overall security. The most suitable compression algorithm was selected based on the results obtained when comparing standard algorithms (Zstd, LZMA, and Brotli). The results showed that the proposed system, Autoencoder and ZSTD, which achieved compression efficiency improvements ranging from 26.4% to 52.8% compared to the traditional ZSTD algorithm across three different-sized datasets (6,784 bytes, 169,668 bytes, and about 327,000 bytes), while maintaining high entropy values ​​ranging from 5.83 to 5.99 bits/byte. Furthermore, chi-square tests showed high values, reaching 11,015, confirming the high level of statistical randomness and security of the resulting data.
MRI sequence prototyping increasingly relies on graphical design environments and numerical simulators to accelerate development and validation. While several platforms support interactive sequence construction, fully web-based solutions that combine integrated phantom management, high-fidelity Bloch simulation, and scalable multi-user deployment remain limited. We present MRSeqStudio, a web-based platform for interactive MR sequence design and simulation. The tool adopts a block-based representation model with real-time visualization and native JSON/Pulseq export. Simulations are performed using the GPU-enabled Bloch simulator KomaMRI, which enables accurate modeling of arbitrary pulse sequences and phantoms within an installation-free architecture. The system separates front-end interaction from back-end simulation services to support concurrent multi-user access. Sequence validity was assessed by comparing GRE and bSSFP implementations against equivalent sequences designed in mtrk and gammaSTAR. The resulting images showed minimal absolute differences and high mean structural similarity indices (SSIM). Stress testing under burst-request conditions demonstrated stable performance with up to 100 concurrent users on a high-performance desktop deployment. A comparative workflow analysis with mtrk and gammaSTAR further examined differences in representation models, parameter propagation strategies, and integration levels across platforms, highlighting the relative strengths and limitations of each tool. Results indicate that MRSeqStudio provides a reliable and accessible environment for MR sequence prototyping, combining web-native deployment with Bloch-level simulation fidelity and integrated phantom visualization. The online version contains supplementary material available at 10.1007/s10916-026-02394-1.
Mechanical grinding is increasingly used in samples preparation for stable isotope analysis. However, plastic abrasion could contaminate samples and bias the isotopic analysis. Previous studies on plant material and large quantity of marine animal samples showed that this bias is limited. However, there is no evaluation of this potential contamination on smaller animal samples. We evaluate potential plastic contamination by analysing the C and N elemental composition and stable isotope values of ∼50 mg (dry weight) of samples of a freshwater crustacean hand-ground versus mill-ground for durations of 1, 2 and 5 min. We found no differences in %N among treatment groups, but an increase in the %C with ball milling time, with significant differences between samples hand ground and mill-ground for 2 min, hand ground and mill-ground for 5 min, and mill-ground for 1 min and mill-ground for 5 min. No significant differences in neither the δ15N nor δ13C were found among the four grinding methods. We estimated plastic abrasion to be 1.65 mg. Our results show that the use of ball milling for homogenising samples for C and N stable isotope analysis does not affect animal muscle samples with a commonly used amount of material (∼50 mg). Finally, we provided a graphical recommendation on the minimum amount of sample to be ground by milling without incurring in plastic contamination bias.
Open Dialogue has been linked to better outcomes and reduced hospital admissions amongst patients with mental health problems. Yet, information on associated health care costs is scarce. To conduct an evaluation of downstream health care costs of Open Dialogue provided to young patients in acute psychiatric crises and compared with treatment as usual. Matched case-cohort study based on clinical and register data. Open Dialogue was offered between 2000 and 2019 as standard care to adolescents in acute psychiatric crisis in four municipalities in Region Southern Denmark. 355 individuals between 14 and 19 years received treatment with Open Dialogue and were compared to 979 peers who had received standard acute psychiatric treatment in two other Danish Regions (Central Denmark Region and North Denmark Region) where Open Dialogue was not implemented. Health care cost data (including primary care, psychiatric and somatic care) was available during 2005-2018. We matched controls to the cases based on a X-factor propensity score and a 3:1 ratio. The statistical analysis took a double-robust approach combining matching with Difference-in-Difference analysis over 12-year follow-up. Graphical inspection and placebo tests were used to test parallel trends assumption, and generalized estimation equations were applied as a robustness check to validate the results. In the intervention group, the unadjusted yearly mean health care costs were €299 the year before receiving Open Dialogue. In the subsequent year, it was €1523, equivalent of a €1224 increase. In corresponding years, the respective health care costs were €208 and €1813 for members of the control group, implying an increase of €1605. The increase in health care costs was driven by psychiatric costs in both groups. Follow-up up to 12 years showed a decrease in total health care costs to €457 in the Open Dialogue group and €938 in the control group. The difference between the groups was not statistically significant. This evaluation did not find statistically significant differences in total health care costs between patients receiving Open Dialogue and controls over 12-year follow-up. Young patients in treatment with Open Dialogue during acute psychiatric crisis did not have higher total health care costs up to 12-year follow-up compared to controls.
To synthesize current evidence (2010-2025) on ketogenic, intermittent fasting, and low-calorie diets' effects on bone health, inflammation, osseointegration and identifying research gaps relevant to dental implantology. A systematic literature search was conducted across PubMed, Scopus, and Web of Science for human and animal studies, mechanistic work, and systematic reviews. Evidence was extracted and summarized, prioritizing studies on diet patterns, bone biology, and dental implant outcomes. Mechanistic and animal studies suggest dietary influence on bone remodeling, inflammation, and angiogenesis, crucial for osseointegration. Vitamin D level correlates with implant outcomes, and moderate intermittent fasting appears not to harm systemic bone markers short-term. Major gaps include peri-implant mechanistic data, comparative trials, and optimal dietary timing. Current evidence indicates a potential influence of different diets on dental implant success factors. Definitive dietary recommendations for implant patients are premature due to insufficient human clinical data. Comprehensive, implant-specific research is still needed to establish guidelines and integrate nutrition into implant practice.