Applied Sciences

BACKGROUND: Cranioplasty is an essential procedure to restore cranial integrity, protect neural structures, and improve cosmetic outcomes. However, commercially available implants are often costly, limiting their accessibility in public healthcare systems. Three dimensional (3D) printing offers a low cost alternative for producing patient-specific solutions. METHODS: A retrospective case series of eight patients undergoing cranioplasty using customized polymethylmethacrylate (PMMA) implants fabricated with 3D printed molds was conducted. Computed tomography (CT) scans were used for segmentation and digital modeling. Patient specific molds were designed and printed preoperatively. Variables analyzed included design time, printing time, intraoperative workflow, and clinical outcomes. RESULTS: Design time ranged from approximately 1 hour for small defects to 3 hours for larger defects. Printing time ranged from 2 3 hours for smaller defects and up to 8 10 hours for larger reconstructions. Satisfactory aesthetic outcomes were achieved in 7 of 8 patients (87.5%). No major implant related complications were observed. CONCLUSION: Low cost 3D printing for PMMA cranioplasty is a feasible, accessible, and effective technique for cranial reconstruction, particularly in resource limited settings. Keywords: Cranioplasty; 3D printing; Cranial defect reconstruction; Low cost surgery; Patient specific implants; Polymethylmethacrylate; Skull reconstruction

Biomechanics studies using traditional optical motion capture have been limited by small, homogeneous sample sizes and a focus on single movements, restricting the ability to capture clinically relevant adaptations across daily tasks. These limitations are particularly consequential in heterogeneous musculoskeletal conditions such as knee osteoarthritis (OA), where variability in demographic and clinical characteristics necessitates large, representative samples to identify patient-specific biomechanical intervention targets. Markerless motion capture enables faster, high-throughput data collection and offers the potential for community-based assessments; however, its feasibility of use in clinical populations across diverse tasks remains unclear. This study evaluated the feasibility of community-based, high-throughput markerless biomechanics data collection in individuals with knee OA. Participants (n = 85) completed a series of activities of daily living using a portable markerless motion capture system deployed across two community-based and two on-campus sites. Feasibility was assessed using timing metrics related to research operations (transit, setup, calibration, breakdown), participant workflow (consent, questionnaires, motion capture), and task-specific durations. No significant differences in timing metrics were observed across sites despite logistical and operational challenges. These findings support the feasibility of using high-throughput, community-based markerless motion capture and suggest a viable pathway for addressing long-standing limitations in sample size and representativeness through scalable data collection workflows in biomechanics studies.

BackgroundInflammatory markers play an important role in the pathophysiology of Lumbar disc herniation (LDH). This study presents a comprehensive multi-assessment of the inflammatory landscape by combining serum inflammatory cytokines quantification, their diagnostic performance, associations with radiological features, and integrating the experimental findings into an in-silico protein-protein interaction network. MethodsA multifaceted study design was utilized to quantify and compare the distribution of selected inflammatory cytokines in patients with LDH and control subjects. The diagnostic ability of these cytokines was assessed using receiver operating characteristic curve analysis. The cytokines values were correlated with selected radiological findings including disc herniation subtypes (protrusion, extrusion, and sequestration), and further categorized as contained and non-contained in patients using a Spearmans rank correlation test. Additionally, computational analysis was performed to identify the central hubs and functionally enriched pathways. ResultsIn patients with LDH, IL-6 and IL-1{beta} showed statistically significant (IL-6: p < 0.001; IL-1{beta}: p = 0.001) rise, but IL-6 showed high diagnostic and discriminative power (AUC = 0.99; cut-off: 19.99 pg/mL). Further IL-1{beta} exhibited a positive correlation with non-contained disc herniation (extrusion and sequestration), while displaying a significant (p < 0.05) negative correlation with protrusion. In silico analysis identified IL-1{beta}, IL-8, TNF-, IL-6, IL-1, CSF2, CSF3, and IL-10 as central hubs, with IL-1{beta} being the top ranked hub in determining functionally enriched cytokine-cytokine receptor interaction. ConclusionsStudy confirmed IL-6 as a powerful diagnostic marker for LDH, while IL-1{beta} aids in determining contained and non-contained disc herniation. Further, IL-1{beta} was identified as the central hub, triggering functionally enriched pathways in the pathogenesis of LDH.

BackgroundThe clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dependent physical examination tests (i.e. pivot shift) and standardized patient-reported outcomes. Unfortunately, both are unable to perceive and quantify the subtle rotational biomechanical deficiencies from an ACL tear. Although specialized laboratory-based motion capture systems may provide objective measurements, they are found in research institutions and thus, are not suitable for clinical use. In contrast, GATOR PRO is a clinic-based multimodal wearable sensor system that uses a machine learning (ML) model (ensemble deep learning) to differentiate and classify its data outputs for assessing in-vivo dynamic rotational knee stability. ObjectiveThe purpose of this study is to validate the deep machine learning model and its performance used in GATOR PRO, which integrates knee-mounted Inertial Measurement Units (IMUs) with ultrasound images to derive high-fidelity in-vivo biomechanical rotational data. Based on this data collected by the GATOR PRO, it is hypothesized that the model can effectively classify knee stability after ACL injury and reconstruction. MethodsThis prospective clinical study at Singapore General Hospital (SGH) (CIRB 2019/2766, PDPA-compliant) aimed to enroll 60 patients (30 ACL-deficient, 30 ACL-reconstructed [&ge;]6 months post-surgery). At the halfway point of the clinical trial, 29 patients (8 ACL-deficient, 21 ACL-reconstructed [&ge;]6 months post-surgery) were recruited through physician referral at SGH outpatient clinics to perform standardized chair-stand tests. An ensemble deep learning model that combines convolutional (EfficientNet) and time-series (InceptionTime) classifiers is used to output binary stability classifications (ACL-deficient/ACL-reconstructed). The models performance was evaluated using 10-fold stratified cross-validation with patient-wise splitting, repeated across 100 random seeds to assess variability. ResultsAt the halfway point of the trial, the ensemble model performance with regard to the Receiver Operating Characteristic area under the curve (ROC-AUC) was 0.8365 (SD: 0.042, p-value < 0.001), and the classification accuracy was 75.9% (SD: 3.2%) when the model was tested on the 29 CIRB-approved patients. For the ACL-reconstructed class, the performance indicators were as follows: precision 71.4%, recall 93.8%, F1-score 81.1%. For the ACL-deficient class, the indicators were: precision 87.5%, recall 53.8%, F1-score 66.7%.Against the clinical pivot shift tests low sensitivity (24-32%), the model delivers an almost 2X better sensitivity (53.8%)[2, 3], with a comparable specificity (93.8% vs. 90-98%) ConclusionThe multimodal machine learning model was able to perform at a level that was relevant to clinical classification (AUC-ROC 0.8365, accuracy 75.9%) in differentiating between ACL-deficient and ACL-reconstructed knees. Moreover, the model demonstrated far superior sensitivity than previously published estimates for manual pivot shift testing (53.8% vs. 24-32%). These findings demonstrate that rotational knee instability can be reliably differentiated in clinical settings with a ML model deployed on GATOR PRO data.

This paper investigates the way in which mitochondria distribute and align inside HeLa cells observed with serial block-face scanning electron microscopy. Four models of alignment were considered: (1) mitochondria exhibiting no discernible alignment pattern, (2) mitochondria aligned pointing towards the nucleus of the cell, (3) mitochondria aligned all in one direction when viewed from above, (4) mitochondria aligned tangent to the surface of the nucleus. These models were named (1) unaligned, (2) petals, (3) racecars, and (4) clouds. The mitochondria, nucleus and plasma membrane of 25 individual cells were segmented. A total of 12,299 mitochondria were identified and analysed. Alignment of the major axis of each mitochondrion was calculated in two ways: relative to a ray that joins it to the centroid of the nucleus, and relative to a ray that joins it to the nucleus surface. Results indicate that mitochondria tend to align tangentially to the nucleus surface, i.e., a clouds model. In addition, differences in the spatial distributions of the mitochondria were found and quantified with clearly defined metrics. The methodology here presented can be extended to other acquisition settings where the distribution and alignment of cells could be important, for instance, histopathology.

Conductive hearing loss (CHL) with a normal otoscopic exam can be difficult to diagnose because routine clinical measures such as audiometric air-bone gaps (ABGs) can identify a conductive component but often cannot distinguish among specific underlying mechanical pathologies (e.g., stapes fixation versus superior canal dehiscence, which may produce similar audiograms). Wideband tympanometry (WBT) is a fast, noninvasive test that can provide additional mechanical information across a broad range of frequencies (200 Hz to 8 kHz). However, WBT metrics are influenced by variations in ear canal geometry and probe placement and can be challenging to interpret clinically. In this study, we extend prior WBT absorbance-based classification work by estimating the middle ear input impedance at the tympanic membrane (ZME), a WBT-derived metric intended to reduce ear canal effects. To estimate ZME, we fit an analog circuit model of the ear canal, middle ear, and inner ear to raw WBT data collected at tympanometric peak pressure (TPP). Data from 27 normal ears, 32 ears with superior canal dehiscence, and 38 ears with stapes fixation were analyzed. A multinomial logistic regression classifier was trained using principal component analysis (retaining 90% variance) and stratified 5-fold cross-validation with regularization. We compared feature sets based on ABGs alone, ABGs combined with absorbance, and ABGs combined with the magnitude of ZME. The combination of ABGs and the magnitude of ZME produced the best performance, achieving an overall accuracy of 85.6% compared to 80.4% for ABGs alone and 78.4% for ABGs combined with absorbance. These results suggest that incorporating model-derived middle ear impedance features with standard audiometric measures (ABGs) can improve automated pathology classification for stapes fixation and superior canal dehiscence.

The in vitro study aimed to evaluate linear dimensional shifts in intraoral photographs of the esthetic zone captured using two smartphone cameras--the iPhone 15 Pro Max and the Samsung Galaxy S23 Ultra--compared to a digital single-lens reflex (DSLR) camera, which is regarded as the gold standard for dental photography. Imaging was performed under controlled conditions using a custom-designed stand and stabilizer to maintain a consistent distance and angle between the dental model and the photographic devices. Standardized frontal and occlusal images of the anterior maxillary region were acquired, and point-to-point linear measurements between specified dental landmarks were performed using calibrated digital imaging software. Each measurement was conducted triple and then averaged across various samples per image to guarantee precision and dependability. Friedmans test with Bonferroni correction was applied for statistical analysis to evaluate differences among the imaging devices. The results indicated no statistically significant variations in linear measures between the DSLR and the Samsung Galaxy S23 Ultra (p > 0.05), however minor inconsistencies were noted between the DSLR and the iPhone 15 Pro Max. It is important to acknowledge that all images were obtained utilizing the stabilization system, which contrasts with the conventional handheld approach applied in clinical environments and could impact the external validity of the results. The Samsung Galaxy S23 Ultra, in telephoto mode, demonstrated measurement precision similar to that of a DSLR camera, potentially serving as a reliable choice for clinical intraoral photography. The iPhone 15 Pro Max demonstrated potential, although minor measurement discrepancies.

Skin cancer requires early detection for improved survival rates. Most existing methods rely on deep learning based image classification, which is affected by visual similarity among lesions. Fewer studies use Gene Expression (GE) analysis, which captures molecular characteristics but lacks structural and visual details. To overcome limitations of individual modalities, this paper proposes a multimodal framework integrating dermoscopic images and GE profiles for skin cancer classification. EfficientNet and logistic regression are used for image based analysis and genomic skin lesion profiling, respectively, followed by fuzzy rule based decision systems to reduce uncertainty within individual modalities. Finally, fuzzy fusion combines predictions from both modalities using uncertainty based weighting of classifier outputs. The experimental findings show that both the image based and GE based classification models individually achieved accuracies of nearly 92%. However, the integration of prediction results through the proposed fuzzy fusion strategy further enhanced the classification performance, achieving an overall accuracy of 94.25%. The results obtained outperform contemporary methods, highlighting the effectiveness of combining complementary multimodal information compared with single modality approaches.

OBJECTIVESDemonstrate correlations of clinic-based measures of International Classification of Functioning, Disability and Health (ICF) Body Structure and Function, capacity and performance with a school-based performance measure in children with Cerebral Palsy (CP) using a transdiagnostic approach. METHODS102 ambulatory children with CP underwent assessment of Gross Motor Function Classification System (GMFCS), Gross Motor Function Measure (GMFM), Pediatric Quality of Life Inventory Generic Core Scales (PedsQL), 3-Dimensional Gait Analysis, Gillette Functional Assessment Questionnaire (GFAQ), and Pediatric Outcomes Data Collection Instrument (PODCI) done in clinics, compared with School Function Assessment (SFA) done in schools. Here we report on SFA correlations. For this paper, Spearmans correlations were calculated. RESULTSAll measures showed some significant correlations with the SFA; greatest number of moderate to strong correlations were with PODCI, including PODCI comorbidities scales. PODCI performance questionnaire was correlated with all SFA scales. PODCI, as a performance measure, is broader, more holistic, than the capacity and BSF measures. Findings are demonstrative of a focus on the ICF approach, indicating separate domains of function and well-being, reflective of the transdiagnostic approach. CONCLUSIONSThe transdiagnostic approach, looking at a broader picture than simply diagnosis, thus paralleling concepts presented in the ICF, is beneficial in assessing functioning and well-being in children with CP.

ABSTRACT Objective: This study aimed to systematically analyze the correlation between body weight and body measurement traits and the growth pattern of female Jiangyue donkeys. Methods: A total of 484 female Jiangyue donkeys were selected to determine their body weight (Y) and 11 body measurement traits, including withers height (X1), body length (X2), chest circumference (X3), cannon circumference (X4), head length (X5), neck length (X6), chest width (X7), chest depth (X8), rump height (X9), rump length (X10) and rump width (X11). SPSS 27.0 statistical software was used to conduct descriptive statistics, correlation and regression analysis on body weight and body measurement traits, and the optimal regression equation between them was established by the stepwise method and validated. Furthermore, four growth curve models (Logistic, Gompertz, Brody and Von Bertalanffy) were used to fit the body weight of 241 female Jiangyue donkeys at different months of age. Results: The body weight of female Jiangyue donkeys was significantly positively correlated with all body measurement traits (p<0.01), with the highest correlation coefficient observed for chest circumference. The optimal regression equations for body weight established by stepwise regression yielded R2 values of 0.918 and 0.844 for growing and adult donkeys, respectively (p<0.01). Among the four growth curve models, the Von Bertalanffy model exhibited the best fitting effect (R2=0.99992), with an estimated asymptotic body weight of 184.41 kg, which was close to the measured values of growing female donkeys. Conclusion: The regression models for body weight estimation and the Von Bertalanffy growth curve model for growth pattern evaluation established in this study can serve as effective tools and production management of female Jiangyue donkeys.

ObjectiveThe objective of this study is to assess the quality, empathy, and readability of large language model (LLM) responses regarding otologic questions from patients as they compare to verified physician responses in other patient-driven forums. This study aims to predict the potential utility of LLMs in patient-centered communication. Study DesignComparative study SettingsInternet MethodsA sample of 49 otology-related questions posted on Reddit r/AskDocs1 between January 2020 and June 2025 were selected using search terms including "hearing loss," "ear infection," "tinnitus," "ear pain," and "vertigo." Posts were retrieved using Reddits "Top" filter. Each question was answered by a verified doctor on Reddit and three AI LLMs (ChatGPT-4o, ClaudeAI, Google Gemini). Responses were scored by five evaluators. ResultsCommon otologic concerns posed in patient questions were otalgia (38.7%), vertigo (28.6%), tinnitus (24.5%), hearing loss (22.4%), and aural fullness (20.4%). LLM responses were longer than physician responses (mean 145 vs 67 words; p < .05) and rated higher in quality (10.95 vs 9.58), empathy (7.26 vs 5.18), and readability (4.00 vs 3.73); (all p < .05). Evaluators correctly identified AI versus physician responses in 89.4% of cases with higher sensitivity for detecting physician responses (93.5%). By Flesch-Kincaid grade level, ChatGPT produced the most readable content (mean 7.25), while ClaudeAI responses were more complex (11.86; p < .05). ConclusionLLM responses received higher ratings in quality, empathy, and readability than those of physicians in response to a variety of otologic concerns. When appropriately implemented, such systems may enhance access to understandable otologic information and complement clinician-delivered care.

The human auditory system decomposes complex sounds into distinct components via a collection of processing steps. Knowing whether Spiral Ganglion Cells (SGCs) play an active role in the decoding of complex sounds can facilitate the development of Cochlear Implant (Cl) coding strategies and clinical assessment tools. Early animal studies reported SGCs being similar across different characteristic frequencies (CFs). In this study, human electrically evoked compound action potentials (eCAPs) were analysed to probe the relationship between the reciprocal of CF and the duration of the eCAP. A significant relationship could indicate that SGCs may not simply be passive cables. eCAP datasets from 6 published studies (175 Cl users, 1243 recordings) were analysed and their peaks were automatically labelled. The nlp2 latency was derived for each recording as a proxy of the action potential duration. The CF of each recording was estimated by mapping the average insertion angle of the electrode to the human SGC map. A weak but statistically significant relationship was observed between the n1p2 latency and the reciprocal of CF (random-effects model with random intercepts for subject, r = 0.09, p = 0.024, n= 450) supporting the hypothesis that lower CF is associated with slower repolarisation (longer n1p2 latency) in human spiral ganglion cells.

Introduction: Early exposure to otolaryngology (ENT) procedural skills in undergraduate medical education is limited by patient safety concerns, restricted clinical opportunities, and the cost of commercial simulators. As a result, essential ENT skills are often underrepresented in structured, hands-on curricula for medical students. Methods: We developed a low-cost, multistation ENT simulation curriculum consisting of five reproducible task trainers: ear examination and otologic procedures, mirror laryngoscopy, rigid and flexible endoscopic navigation, introductory mastoid drilling, and emergency cricothyrotomy. The curriculum was delivered as a 2-hour, faculty-led workshop during a third-year medical student otolaryngology rotation. Learners rotated through stations in small groups. Pre- and post-workshop surveys assessed self-reported anatomical familiarity, procedural confidence, and educational value using a 5-point Likert scale, with additional qualitative feedback collected. Results: All participants completed pre- and post-workshop evaluations. Learners demonstrated increased confidence across all assessed anatomical and procedural domains, including otoscopy, endoscopy, mirror laryngoscopy, mastoid drilling orientation, and cricothyroid membrane identification. Educational value ratings were high across all stations, with mean scores ranging from 4.33 to 5.00. Qualitative feedback emphasized the realism, accessibility, and benefit of hands-on practice in a low-stakes learning environment. Conclusion: This low-cost, multistation ENT simulation curriculum provides a feasible and reproducible approach for introducing foundational otolaryngology skills to medical students. The structured format and affordable models support early procedural exposure and may enhance learner preparedness prior to supervised clinical encounters, particularly in settings with limited simulation resources.

ObjectiveOnly preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study DesignRetrospective video-based study for classification development and reliability validation. MethodsVideo recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. ResultsThe final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7/65) of cases. Of those incorrect, 57% (4/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. ConclusionTissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.

OBJECTIVES: Compare results of the Pediatric Outcomes Data Collection Instrument (PODCI) in children ages 2-18 years with cerebral palsy (CP) across all severity levels of the Gross Motor Function Classification System (GMFCS) with children in the General Population, confirming discriminant validity as a performance assessment tool and health-related quality of life (HRQOL) measure. METHODS: Cross-sectional study: single response PODCI proxy survey databases of 5238 children ages 2-18 years in GP and 2470 in the Population with CP were analyzed. Statistical methods included Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), Linear Trend Test, and Standard Error Assessment. RESULTS: A statistically significant difference exists between PODCI subscales in General Population and Population with CP across age groups and GMFCS levels. Motor scales and Global Functioning increase with age in both populations and are inversely proportional to GMFCS level in the Population with CP. HRQOL measures decrease with age in both populations with Happiness decreasing more in the General Population than those with CP as age increases. CONCLUSIONS: PODCI demonstrates a statistically significant difference in motor performance and HRQOL in children ages 2-18, between the General Population and the population with CP. PODCI is a valid performance assessment tool for use in CP ages 2-18 across all GMFCS levels. KEYWORDS: Cerebral Palsy, General Population, PODCI, ICF, Performance

Objective: This analysis assessed the acceptability and recruitment implications of a high-throughput, community-based biomechanics protocol among individuals with knee osteoarthritis (OA). Design: During the Shared Strides Study, high-throughput markerless biomechanics assessment was conducted at community sites to help facilitate research engagement in the OA population. In this cross-sectional study, biomechanics data during a set of activities of daily living (ADLs) and questionnaire data were collected. Adults aged 40 years or older with knee OA participated at one of four sites across Gainesville, FL--two on-campus and two community-based. Eligible individuals were either screened over the phone and scheduled for a specific date and time or screened on site for potential same-day participation. Participant acceptability of the community-based biomechanics data collection approach was assessed using a 15-item custom questionnaire. Recruitment characteristics and participant preferences were compared across sites. Results: The high-throughput community-based data collection approach was well received. Compared with on-campus sites, community-based sites had higher engagement from walk-in participants and new research participants (40% of the sample). Familiarity with, and distance to, a data collection site were important factors in research engagement in this population. No differences in demographic characteristics existed between sites (p > 0.05), but recruitment resulted in a large sample size (n = 85) likely representative of the communities surrounding the selected sites. Conclusions: Integrating markerless motion capture with a community-based research approach may enhance the participant experience and facilitate larger, more heterogeneous sample sizes, ultimately reducing bias and homogeneity in current OA biomechanics research.

Mosquito-borne diseases such as dengue, chikungunya, and malaria remain major public health challenges in South Asia, particularly in Bangladesh, where mosquito repellents are widely used as primary preventive tools. This study presents a comparative evaluation of commonly used repellent products, including mosquito coils, liquid vaporizers and aerosols, DEET-based creams, and natural formulations, focusing on their efficacy, protection duration, and potential health risks. Efficacy was assessed using controlled laboratory methods, including chamber-based exposure and arm-in-cage tests against Aedes aegypti (Barnard & Xue, n.d.). Safety was evaluated through in vivo toxicological analysis in a rat model, incorporating clinical observations, hematological and biochemical profiling, and histopathological examination. The results indicated an overall mean effectiveness of 85%, with DEET-based creams demonstrating the highest efficacy (95%), while natural repellents showed the lowest (70%). Mosquito coils provided the longest protection duration ([~]10 hours) but were associated with the highest health risk due to combustion-related emissions. Vaporizers and aerosols offered moderate efficacy with moderate health risk. Toxicological findings revealed that coil exposure induced significant respiratory stress, elevated liver enzymes (ALT, AST), increased leukocyte count, and notable lung tissue damage. Vaporizer and aerosol exposure resulted in mild physiological changes, whereas DEET-based and natural repellents showed minimal systemic toxicity. Overall, the study highlights a clear trade-off between efficacy and safety across repellent types. These findings emphasize the need for informed product selection, stronger regulatory control, and the development of safer, cost-effective, and sustainable mosquito repellent technologies for effective vector control in endemic regions.

Background: Olfactory dysfunction (OD) in children remains underdiagnosed and poorly characterised. Despite its known impacts on nutrition, quality of life, safety awareness, and psychosocial development, no standardised diagnostic or management pathway currently exists for paediatric OD. This study aimed to characterise global practice patterns and identify diagnostic and therapeutic challenges unique to paediatric care. Methodology/Principal: A 44-item cross-sectional online survey was distributed to a verified international network of paediatric otolaryngologists across 36 countries via a closed professional platform. The survey assessed five domains: diagnostic practices, management protocols, technology and innovation, education and training, and barriers to effective care. Regional grouping was used to facilitate meaningful statistical comparisons. Categorical variables were evaluated using chi-square tests, with odds ratios and 95% confidence intervals reported for significant findings. Results: Of 351 potential participants, 167 responded (47.6% response rate). Most respondents (83%) reported seeing children with OD, yet 95% saw fewer than ten such patients annually. Psychophysical testing was never performed by 54.8% of respondents, while 88.4% routinely ordered cross-sectional imaging. Testing frequency increased significantly with patient age (Cochran's Q p<0.001). The most common barriers to objective testing were insufficient training (44.3%), time constraints (29.9%), and funding limitations (28.1%). Multidisciplinary collaboration was negligible. Significant regional variation was observed across most practice domains. Conclusions: Paediatric OD care is characterised by functional underinvestigation, fragmented multidisciplinary collaboration, and systemic educational gaps. These findings support urgent development of standardised clinical guidelines, age-appropriate validated assessment tools, and formal interdisciplinary care pathways.

Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk assessment and its linkage to insurance-based decision-making. There is a growing need for scalable models that can connect clinical oral health data with digital health systems and support future artificial intelligence (AI)-driven applications. Aim: To develop and pilot test the ABHA-O-SHINE framework for oral health risk prediction and insurance prioritization, with a future scope for AI integration within the Ayushman Bharat Health Account (ABHA) ecosystem. Materials and Methods: A cross-sectional pilot study was conducted among 126 participants attending the outpatient department of Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur. Participants were selected based on predefined inclusion and exclusion criteria. Data collection included a structured questionnaire and clinical examination using the WHO Oral Health Assessment Form (2013). A composite risk score (0 to 14) was developed incorporating behavioral and clinical parameters. Participants were categorized into low, moderate, and high-risk groups, and corresponding insurance priority levels were assigned. Statistical analysis included descriptive statistics, Chi-square test, Spearman correlation, and binary logistic regression. Results: The majority of participants were categorized under moderate to high-risk groups. Tobacco use showed a statistically significant association with higher risk levels (p less than 0.05). Positive correlations were observed between total risk score and clinical indicators such as DMFT and CPI. Logistic regression analysis identified tobacco use and clinical scores as significant predictors of high-risk categorization. Conclusion: The ABHA-O-SHINE framework demonstrates feasibility in integrating oral health risk assessment with an insurance prioritization model. The framework is designed to be AI-compatible, enabling future automation through machine learning and image-based analysis within the ABDM ecosystem. Keywords: ABHA, ABDM, Oral Health, Risk Assessment, Insurance, Artificial Intelligence.

Spinal fractures are an important contributor to disability worldwide, particularly in aging populations. However, comprehensive long-term comparisons between China and other major economies remain limited. Using data from the Global Burden of Disease (GBD) 2021 study, we analyzed temporal trends in the incidence, prevalence, and years lived with disability (YLDs) of spinal fractures in China and the overall G20 from 1990 to 2021. Age-standardized rates were assessed using Joinpoint regression and age-period-cohort analysis. Future burden through 2050 was projected using autoregressive integrated moving average modeling, and decomposition analysis was performed to quantify the contributions of demographic and epidemiological factors. Between 1990 and 2021, China experienced substantial increases in absolute burden. Incident cases increased by 52.27%, prevalent cases by 113.66%, and YLDs by 107.21%. The age-standardized prevalence rate (ASPR) and age-standardized YLD rate (ASYR) increased significantly, whereas the age-standardized incidence rate (ASIR) showed a non-significant upward trend. In contrast, the overall G20 aggregate showed increasing absolute case numbers but significantly declining age-standardized rates. Age-period-cohort and age-specific analyses indicated that older adults represented the main burden-bearing population. Projections suggested that Chinas ASIR may decline by 2050, whereas prevalence and YLD burden, particularly among males, may remain relatively high compared with the overall G20 level. Decomposition analysis identified population aging as the major driver of burden growth. China experienced a rising burden of spinal fractures over the past three decades, in contrast to declining age-standardized trends in the overall G20 aggregate. These findings highlight the substantial role of population aging in shaping spinal fracture burden and provide epidemiological evidence for prevention planning and aging-related health policy.