Search Results (5,321)

Introduction: Clear aligner therapy has become a mainstream alternative to fixed orthodontics due to its versatility. However, the variability in thermoforming and the limited validation of digital workflows remain major barriers to reproducibility and predictability. Methods: This study addresses that gap by presenting a proof-of-concept digital workflow for clear aligner manufacturing by integrating additive manufacturing (AM), thermoforming simulation, and finite element analysis (FEA). Dental models were 3D-printed and thermoformed under clinically relevant pressures (400 kPa positive and −90 kPa negative). Results and Discussion: Geometric accuracy was quantified using CloudCompare v2.13.0, showing that positive-pressure thermoforming reduced maximum deviations from 1.06 mm to 0.4 mm, with all deviations exceeding the expanded measurement uncertainty. Thickness simulations of PETG sheets (0.5 and 0.75 mm) showed good agreement with experimental values across seven validation points, with errors <10% and overlapping 95% confidence intervals. Stress analysis indicated that force transmission was localized at the aligner–attachment interface, consistent with expected orthodontic mechanics. Conclusion: By quantifying accuracy and mechanical behavior through numerical and experimental validation, this framework demonstrates how controlled thermoforming and simulation-guided design can enhance aligner consistency, reduce adjustments, and improve treatment predictability. Full article

Breast cancer remains a leading cause of global cancer burden, with marked differences in incidence across countries. While biological risk factors are well established, understanding the broader structural and sociocultural influences has been less comprehensive. In this study, we analyzed harmonized data from 183 countries (2017–2023), encompassing 33 variables and 7 subvariables related to demographics, nutrition, environment, health, and healthcare access, drawn from open-access international databases. Spearman correlation analysis identified strong positive associations between breast cancer incidence and discontinued breastfeeding, high LDL cholesterol, out-of-pocket healthcare expenditure, and educational attainment. Conversely, poor sanitation, lack of handwashing facilities, unsafe water, and certain nutritional deficiencies exhibited robust negative correlations, likely reflecting under detection and reporting limitations in lower-resource settings rather than true protective effects. These findings were further explored using multiple linear regression, which explained approximately 73% of the variance in global breast cancer incidence. The final model highlighted discontinued breastfeeding, prevalence of cocaine use, unsafe sanitation, high out-of-pocket healthcare expenditure, limited handwashing access, and high processed meat consumption as the most influential independent predictors. Receiver operating characteristic (ROC) analysis confirmed strong predictive value for discontinued breastfeeding and out-of-pocket expenditure, with sanitation and hygiene variables showing paradoxical inverse associations. Our results emphasize that breast cancer risk is shaped not only by individual behaviors and genetics, but also by larger-scale structural, socioeconomic, and environmental factors. These patterns suggest that targeted interventions addressing both lifestyle behaviors and systemic inequities—such as promoting breastfeeding, reducing financial barriers to healthcare, and strengthening public health infrastructure—could meaningfully reduce the global burden of breast cancer. In conclusion, this study underscores the importance of multisectoral, equity-focused prevention strategies. It also highlights the value of country-level ecological analyses in uncovering upstream determinants of cancer incidence and calls for further research to disentangle individual and contextual effects in cancer epidemiology. Full article

Intestinal microbiota populations are constantly shaped by both intrinsic and extrinsic factors, including diet, environment, and host genetics. As a result, understanding how to assess, monitor, and exploit microbiome–host interplay remains an active area of investigation, especially in aquaculture. In this study, we analyzed the taxonomic structure and functional potential of the intestinal microbiota of European sea bass and rainbow trout, incorporating gilthead sea bream as a final reference. The results showed that the identified core microbiota (40 taxa for sea bass and 20 for trout) held a central role in community organization, despite taxonomic variability, and exhibited a predominant number of positive connections (>60% for both species) with the rest of the microbial community in a Bayesian network. From a functional perspective, core-associated bacterial clusters (75% for sea bass and 81% for sea bream) accounted for the majority of predicted metabolic pathways (core contribution: >75% in sea bass and >87% in trout), particularly those involved in carbohydrate, amino acid, and vitamin metabolism. Comparative analysis across ecological phenotypes highlighted distinct microbial biomarkers, with genera such as Vibrio, Pseudoalteromonas, and Paracoccus enriched in saltwater species (Dicentrarchus labrax and Sparus aurata) and Mycoplasma and Clostridium in freshwater (Oncorhynchus mykiss). Overall, this study underscores the value of integrating taxonomic, functional, and network-based approaches as practical tools to monitor intestinal health status, assess welfare, and guide the development of more sustainable production strategies in aquaculture. Full article

Background/Objectives: Ocular wavefront aberrations are clinically relevant for optimizing vision correction and predicting surgical outcomes. This study aimed to establish normative reference ranges for a Korean population by quantifying wavefront aberrations using a Hartmann–Shack wavefront sensor and Zernike coefficients, and to assess correlations with age, sex, and spherical equivalent (SE). Methods: Wavefront aberrations were measured in 98 Koreans (196 eyes) using a Hartmann–Shack aberrometer without cycloplegia. Five repeated measurements per eye at a 6 mm pupil size were averaged. Parameters included Zernike coefficients (Z3–Z20), higher-order aberration (HOA) root mean square (RMS, Z6–Z20), and total RMS (Z3–Z20). Associations with age, sex, and SE were assessed using univariable and multivariable linear mixed-effects models. Second-order polynomial regression assessed nonlinear relationships. Interocular symmetry was evaluated using mirror-symmetry-adjusted Spearman’s correlation and intraclass correlation coefficients (ICCs). Results: Vertical coma (Z7, 0.208 ± 0.174 μm) and spherical aberration (Z12, 0.200 ± 0.161 μm) were the largest contributors to HOA RMS. Mean HOA RMS and total RMS were 0.51 ± 0.21 μm and 3.03 ± 2.51 μm, respectively. HOA RMS increased with age (β = 0.003 μm/year, p = 0.010), whereas total RMS decreased with SE (β = −0.678 μm/D, p < 0.001). Most Zernike coefficients showed positive interocular correlations, with ICCs of 0.75 for total RMS and 0.64 for HOA RMS. Conclusions: In normal Korean eyes, HOAs increased with age and exhibited significant interocular symmetry. Vertical coma and spherical aberration were predominant components. While the pattern was similar to that in Western populations, the absolute values were greater. These normative values may aid future wavefront-guided refractive surgery and presbyopia correction procedures. Full article

Artificial Intelligence (AI) applications in sports dentistry have the potential to improve early detection and diagnosis. We aimed to validate the diagnostic performance of AI-assisted software in detecting dental caries, periodontitis, and tooth wear using panoramic radiographs in elite athletes. This cross-sectional validation study included secondary data from 114 elite athletes from the Sports Dentistry department at Egas Moniz Dental Clinic. The AI software’s performance was compared to clinically validated assessments. Dental caries and tooth wear were inspected clinically and confirmed radiographically. Periodontitis was registered through self-reports. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as the area under the curve and respective 95% confidence intervals. Inter-rater agreement was assessed using Cohen’s kappa statistic. The AI software showed high reproducibility, with kappa values of 0.82 for caries, 0.91 for periodontitis, 0.96 for periapical lesions, and 0.76 for tooth wear. Sensitivity was highest for periodontitis (1.00; AUC = 0.84), moderate for caries (0.74; AUC = 0.69), and lower for tooth wear (0.53; AUC = 0.68). Full agreement between AI and clinical reference was achieved in 86.0% of cases. The software generated a median of 3 AI-specific suggestions per case (range: 0–16). In 21.9% of cases, AI’s interpretation of periodontal level was deemed inadequate; among these, only 2 cases were clinically confirmed as periodontitis. Of the 34 false positives for periodontitis, 32.4% were misidentified by the AI. The AI-assisted software demonstrated substantial agreement with clinical diagnosis, particularly for periodontitis and caries. The relatively high false-positive rate for periodontitis and limited sensitivity for tooth wear underscore the need for cautious clinical integration, supervision, and further model refinements. However, this software did show overall adequate performance for application in Sports Dentistry. Full article

Objective: The material properties of craniofacial sutures significantly influence the outcomes of orthodontic treatment, particularly with newer appliances. This study specifically investigates how the Young’s modulus of craniofacial sutures impacts the anterosuperior protraction achieved using a recently developed extraoral appliance. Our goal is to identify the patterns by which suture properties affect skull deformation induced by this device. Materials and Methods: We conducted four finite element (FE) simulations to evaluate the Right Angle Maxillary Protraction Appliance (RAMPA) when integrated with an intraoral device (gHu-1). We tested Young’s moduli of 30 MPa, 50 MPa, and 80 MPa for the sutures, drawing on values reported in previous research. To isolate RAMPA’s effects on craniofacial deformation, we also performed an additional simulation with rigid sutures and a separate model that included only the intraoral device. Results: Simulations with flexible sutures showed consistent displacement and stress patterns. In contrast, the rigid suture model exhibited substantial deviations, ranging from 32% to 76%, especially in the maxillary palatine suture and orbital cavity. Both displacements and von Mises stresses were proportional to the Young’s modulus, with linear variations of approximately 15%. Conclusions: Our findings demonstrate that RAMPA effectively achieves anterosuperior protraction across a broad spectrum of suture material properties. This positions RAMPA as a promising treatment option for patients with long-face syndrome. Furthermore, the observed linear relationship (with a fixed slope) between craniofacial deformation and the Young’s modulus of sutures provides a crucial foundation for predicting treatment outcomes in various patients. Full article

Vasconcellea pubescens (mountain papaya) is an underutilized Andean fruit with distinctive nutritional and functional properties, yet its rapid softening and short shelf-life result in significant postharvest losses. This review summarizes current knowledge on the physiology of fruit development and ripening, with emphasis on cell wall disassembly, color changes, and ethylene regulation as determinants of postharvest quality. Advances in postharvest management strategies, including temperature control, packaging, and ethylene-modulating treatments (such as 1-MCP), are discussed in the context of preserving fruit firmness, extending shelf life, and reducing food waste. Furthermore, the high content of bioactive compounds—such as papain, phenolics, and flavonoids—underscores the potential of valorizing by-products through sustainable biotechnological applications. Despite recent progress, critical gaps remain in genomic resources, predictive quality monitoring, and large-scale implementation of preservation techniques. Addressing these challenges could enhance the economic and ecological value of V. pubescens, positioning it as both a model species for postharvest research and a promising fruit for reducing food losses in horticultural supply chains. Full article

Background: Migraine is often not diagnosed or treated properly, despite being a common condition. The ID-migraine is a brief, self-administered test developed as a valuable tool for screening and diagnosing migraine in primary care settings. The objective of the present study was to produce a Persian translation of the original ID-migraine, perform cultural adaptation, and evaluate its validity. Methods: Consecutive patients who attended two neurology clinics for headache were enrolled in the study. Diagnoses were established by headache specialists and were compared with the Persian ID-migraine results. Results: Among the 657 participants included, 470 (71.5%) were clinically diagnosed with migraine, 120 (18.2%) with tension-type headache, and 40 (6.1%) with cluster headache. The validity attributes of the Persian ID-migraine were as follows: sensitivity, 0.96 (95% CI, 0.93–0.98); specificity, 0.46 (95% CI, 0.35–0.56); positive predictive value, 0.86 (95% CI, 0.82–0.89); negative predictive value, 0.76 (95% CI, 0.63–0.87), and a misclassification error of 14.9%. The questionnaire’s Kappa coefficient was 0.78. Conclusions: The Persian version of the ID-migraine questionnaire exhibited sufficient sensitivity and positive predictive value, along with an acceptable misclassification error. However, it demonstrated a deficient level of specificity and a considerably reduced negative predictive value. Full article

Background/Objectives: Understanding the psychological determinants of vaccine uptake is critical for effective public health strategies, particularly during prolonged pandemics. The Health Belief Model is widely used to examine vaccine behavior, yet its applicability in longitudinal and policy-intensive contexts remains underexplored. This study assessed how two core Health Belief Model constructs—perceived severity of and susceptibility to COVID-19—related to vaccine intentions and uptake over time, and how these perceptions varied by demographic characteristics. Methods: Data came from Optimise, a longitudinal cohort study of adults in Victoria, Australia, conducted between September 2020 and August 2022. Perceived severity of and susceptibility to COVID-19 were measured monthly, alongside COVID-19 vaccine intentions and uptake. Generalized Estimating Equations evaluated associations between these two Health Belief Model constructs and vaccine outcomes over time. Separate models identified demographic predictors of perceived severity and susceptibility. Results: Perceived severity of COVID-19 was positively associated with intention to receive further COVID-19 vaccine doses (OR = 2.53, 95% CI: 1.26–5.07) and the total vaccine doses received (OR = 2.74, 95% CI: 1.58–4.76), with these associations changing over time as vaccine mandates were lifted and the pandemic context evolved. Perceived susceptibility to COVID-19 showed no significant associations with vaccine outcomes. Older age, presence of a chronic health condition, and lower employment status was associated with higher perceived severity. In contrast, perceived susceptibility was higher among high-income earners but lower among older adults and the unemployed. Conclusions: The predictive value of two Health Belief Model constructs was context- and time-dependent. Perceived severity consistently predicted vaccine uptake once mandates were lifted, while susceptibility did not. Our findings highlight the importance of context-sensitive behavioral frameworks when designing vaccine promotion strategies during extended public health crises. Full article

The creatinine-to-cystatin C ratio (CCR), a surrogate for skeletal muscle mass, may also be associated with liver fibrosis due to the strong link between sarcopenia and liver disease progression. We aimed to evaluate the utility of CCR as a noninvasive marker of liver fibrosis in metabolic-dysfunction-associated steatotic liver disease (MASLD). This retrospective study included 104 patients with biopsy-proven MASLD. CCR was calculated using serum creatinine and cystatin C levels. Liver fibrosis was staged histologically (F0–F4), and skeletal muscle mass was assessed using the skeletal muscle index (SMI) on computed tomography. Associations between CCR and liver fibrosis, SMI, and nonalcoholic fatty liver disease activity score were analyzed. ROC analysis evaluated CCR performance alone and in combination with FIB-4 and enhanced liver fibrosis (ELF) scores. CCR values were significantly lower in patients with significant fibrosis (≥F2). The AUROC of CCR for detecting ≥F2 fibrosis was 0.621 (95% CI: 0.509–0.733), with an optimal cutoff of 0.664. CCR alone yielded an AUC of 0.815 for predicting ≥F2 fibrosis. Combining CCR with FIB-4 and ELF substantially improved diagnostic accuracy, increasing the AUROC from 0.621 (CCR alone) to 0.820 for the combined model. CCR correlated positively with SMI (r = 0.451, p < 0.001). CCR is a simple, cost-effective biomarker reflecting muscle mass and liver fibrosis in MASLD. Combining CCR with established markers may enhance risk stratification and reduce the need for liver biopsy in selected cases. Full article

Background: Chimeric antigen receptor (CAR) T-cell therapy has revolutionized treatment of relapsed/refractory large B-cell lymphoma (LBCL), but its administration is often complicated by cytokine release syndrome (CRS). Interleukin-6 (IL-6) is widely used to monitor CRS, though its clinical value diminishes after tocilizumab administration. We aimed to evaluate serum amyloid A (SAA), a dynamic acute-phase reactant, as a treatment-independent biomarker of inflammation and toxicity in CAR-T recipients. Methods: This retrospective study included 43 adults with LBCL treated with axicabtagene ciloleucel. SAA and other inflammatory markers were assessed from lymphodepletion through day +11 post-infusion. CRS and ICANS were graded per ASTCT criteria. Statistical analyses included Mann–Whitney U tests, Spearman’s correlation, and ROC curve analysis to evaluate predictive performance. Results: SAA levels peaked at day +4 and normalized by day +11, displaying wave-like kinetics. Levels were significantly higher in patients with any-grade CRS at early timepoints but showed no association with ICANS. SAA correlated strongly with CRP, suPAR, sST2, fibrinogen, ferritin, procalcitonin, and IL-6. Compared to IL-6, SAA was more predictive of CRS at day +2 and +4, and unaffected by tocilizumab. Baseline SAA also correlated with the mEASIX score, suggesting linkage to endothelial stress. Non-responders at 3-month PET had higher baseline SAA than responders (196.0 vs. 17.7 mg/L, p = 0.036), with ROC analysis yielding an AUC of 0.74 and an optimal threshold of 79.8 mg/L. Conclusions: SAA is a robust and dynamic marker of systemic inflammation, with potential utility in both toxicity monitoring and response prediction in the CAR-T setting. Its independence from IL-6 modulation positions it as a promising biomarker for future integration into clinical algorithms. Full article

Background: Cardiac catheterization may be required in the management of congenital heart diseases. The use of risk scoring or grading systems in these procedures can assist in planning the intervention and predicting potential complications. This study aimed to evaluate the use of risk scores in grading cardiac catheterization procedures in pediatric patients and to investigate their predictive value for serious adverse events (SAEs). Material and Methods: A total of 700 pediatric patients (350 male; median age 1 year [IQR 6 months–2 years]) who underwent cardiac catheterization in our catheterization laboratory between 1 January 2023 and 1 January 2025 were retrospectively analyzed. Demographic and clinical data of the patients, including procedure duration, anesthesia management, Catheterization Risk in Pediatrics Score (CRISP), and serious adverse events related to the procedure, were recorded. The results were analyzed statistically. Results: In total, 50% of the patients were male (n = 350), and 58% (n = 406) had single-ventricle physiology. Interventional procedures were performed in 72% of the cases. The median CRISP score was 8 (IQR 6–10). SAEs occurred in 7.7% of the patients (n = 54), most of which were arrhythmia-related. The incidence of SAEs was analyzed according to CRISP score categories. The rates of SAEs in patients with CRISP Categories 1 through 5 were 2.9%, 4.3%, 11%, 17.3%, and 41%, respectively. As the CRISP score and category increased, the incidence of SAEs also increased [area under the curve of 0.84 (95% confidence interval, 0.76–0.92; p < 0.05)]. Conclusions: CRISP may serve as an effective benchmarking and risk classification tool in pediatric cardiac catheterization procedures and can predict SAE occurrence. Therefore, it may have a positive impact on patient care by assisting in the planning of pre- and post-catheterization care. Full article

Fibroblast growth factor 21 (FGF21), a pleiotropic hormone, is a significant modulator of energy homeostasis. We evaluated serum FGF21 levels in patients with a deficiency of mitochondrial aminoacyl-tRNA synthetase (mt-aARSs). Six patients with mitochondrial aminoacyl tRNA synthetase deficiency and twelve healthy volunteers were included in this study. Whole-exome sequencing was used for molecular diagnosis. Serum FGF21 levels in the case group and healthy volunteers were analyzed using the enzyme-linked immunosorbent assay. Exome sequencing test revealed nine different pathogenic variants in the AARS2, EARS2, DARS2, SARS2, and WARS2 genes. A statistically significant difference was found between the serum FGF21 levels of the case and control groups: case group (n = 6), 882.49 ± 923.60 pg/mL; control group (n = 12), 20.89 ± 2.63 pg/mL (p < 0.001). The area under the ROC curve for FGF21 in the differential diagnosis of mitochondrial aminoacyl-tRNA synthetase deficiency was 1.000 (0.813–1.000). Sensitivity and specificity were 100%, and positive and negative predictive values were also 100% for an FGF21 cut-off value > 27.4 pg/mL. Assessment of FGF 21 levels as an indicator of mitochondrial damage in mt-aARSs deficiency may provide insight into the level of damage. Investigation of the biochemical mechanisms underlying the different levels of damage caused by different aminoacyl tRNA synthetases will be important in terms of elucidating clinical heterogeneity. Full article

Contemporary discussions of AI are often framed according to generally held assumptions which have largely escaped serious critical analysis. For instance, those who promote AI tout its predictive prowess: powerful algorithms fed massive amounts of data are able to discover knowable patterns that can accurately forecast the behaviors in anything from individual movie preferences to financial markets. Armed with this type of knowledge we can then use AI, the hope goes, to be more objective in our ethical practices. And most seriously, we must extend this to the way we develop AI, not only do we want AI to function ethically, but we caution ourselves that if Artificial General Intelligence (AGI), superintelligence, or anything like the “singularity” is ever developed, it should be positively aligned with human values. Reflecting on these positions from the perspective of classical Daoism gives us reason to pause. While Daoist texts also assume there are patterns in the world which one can successfully go along with, they are not enthusiastic about the rational or knowable nature of these patterns—rather, they encourage us to appreciate them as fundamentally complex and mysterious. In this article, some Daoist attitudes are also concretely applied to ethical considerations, which cannot easily be controlled or known, much less put into code. Inspired by Daoist texts, we might cultivate an attitude less filled with hubris than humility, where we are allowed more space from which we can reflect on how we think about AI. Many of the most pressing issues associated with AI could, in fact, be significantly alleviated simply by shifting the way we think about, use, and develop these technologies. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article