Bioengineering

BackgroundPlatelet-derived Growth factors play key roles in tissue repair and regeneration, yet conventional platelet-rich plasma (PRP) formulations release these mediators inconsistently in vivo due to variability in platelet yield and activation dynamics. To overcome this limitation, direct administration of concentrated platelet-derived growth factor preparations has gained interest, though current manufacturing approaches for human platelet lysate (hPL), growth factor concentrates (GFC), and conditioned serum remain constrained by batch variability, incomplete platelet degranulation, and reliance on anticoagulants. Here, we examine alternative platelet activation workflows to establish a standardized, efficient, and reproducible method for high-yield growth factor recovery suitable for translational and clinical applications. MethodsNine GFC production protocols were compared, employing different combinations of freeze-thaw (FT) cycling, glass bead (GB) agitation, calcium (Ca2) activation, and a novel Enriched Growth Factor (Enriched-GF) method. The objective was to identify a protocol capable of maximizing growth factor yield within a three-hour workflow. Optimal Ca2 concentrations and GB conditions were determined from prior optimization studies and integrated into the Enriched-GF processing scheme. Platelet concentrates (n = 10 per protocol) were processed under each condition, and growth factor levels were quantified using ELISA. ResultsGrowth factor yields differed significantly across protocols. The greatest and most consistent increases in growth factor release were observed with the Enriched-GF method combining GB activation, FT cycling, and Ca2 stimulation. This approach resulted in markedly elevated concentrations of key regenerative mediators, including enhanced EGF release, a 4.5-fold increase in PDGF, maximal TGF-{beta} liberation, and a four-fold increase in FGF2 relative to conventional platelet lysate or conditioned serum preparations. These results were reproducible across independent donor pools, demonstrating robustness and batch-to-batch consistency. ConclusionWe describe a rapid and reproducible method for producing highly concentrated platelet-derived growth factors using a combined GB-FT-Ca2 activation strategy. The Enriched-GF protocol consistently outperformed existing platelet lysate, conditioned serum, and conventional GFC preparation methods, yielding a standardized product with enhanced growth factor content. This Enriched-GF approach offers a clinically practicable solution for applications in regenerative medicine requiring reliable and high-yield growth factor delivery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/712883v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1f059d9org.highwire.dtl.DTLVardef@9aeffforg.highwire.dtl.DTLVardef@27cd1org.highwire.dtl.DTLVardef@150b7d1_HPS_FORMAT_FIGEXP M_FIG C_FIG Schematic overview of platelet concentrate preparation from whole blood and the generation of different platelet lysates and growth factor-enriched serum using freeze-thaw, calcium gluconate, and glass bead activation methods.

Background and ObjectivesLung cancer is one of the most frequently diagnosed cancers worldwide. While non-surgical treatment options have increased in number and efficacy, lung resection for primary cancers is still a mainstay of treatment. Lung resection has been shown to impair right ventricular function, although the mechanism for the impairment remains unclear. Wave intensity is increasingly used as a metric for increased post-operative afterload. Here, we develop a computational framework to assess the impact of simulated lung resection on wave intensity to establish that post-operative changes in wave intensity are attributable to the change in pulmonary artery morphometry. MethodsWe analyse a 48 pulmonary arterial surfaces segmented from CT images in patients with no evidence of lung disease to obtain 1D representations of the pulmonary vasculature. For each pulmonary vasculature we sequentially remove vessel branches to mimic post-operative morphometric changes to the arterial network. Using an established 1D computational flow model, we simulate pulsate blood flow in 44 pre-operative cases and 1596 post-operative cases. We compute wave intensity in the main, right, and left pulmonary arteries for all simulations. ResultsWe compare the change in computed wave intensities pre-versus post-operatively to the results of an experimental clinical study comparing pre- and post-operative wave intensity in a 27 patient cohort. We see good agreement between the changes in the parameters of wave intensity between this study and those reported in the clinical study. Further, we capture flow distribution the changes pre-versus post-operatively which indicates that the computational model behaves as expected. ConclusionsIn this preliminary study on a computational framework to capture changes in pulmonary arterial haemodynamics following lung resection, we have shown that our model and analysis pipeline is capable of capturing post-operative changes to wave intensity and flow redistribution between the pulmonary arteries following lung resection. These results motivate further research to develop and validate a patient specific model which is an area of active research for us.

Background and Objectives: We report the first intraoperative deployment of a real-time machine vision system in neurosurgery, derived from our previous anatomical detection work, automatically identifying structures during endoscopic endonasal surgery. Existing systems demonstrate promising performance in offline anatomical recognition, yet so far none have been implemented during live operations. Methods: A real-time anatomy detection model was trained using the YOLOv8 architecture (Ultralytics). Following training completion in the PyTorch environment, the model was exported to ONNX format and further optimized using the NVIDIA TensorRT engine. Deployment was carried out using the NVIDIA Holoscan SDK, the system ran on an NVIDIA Clara AGX developer kit. We used the model for real-time recognition of intraoperative anatomical structures and compared it with the same video labelled manually as reference. Model performance was reported using the average precision at an intersection-over-union threshold of 0.5 (AP50). Furthermore, end-to-end delay from frame acquisition to the display of the annotated output was measured. Results: A mean AP50 of 0.56 was achieved. The model demonstrated reliable detection of the most relevant landmarks in the transsphenoidal corridor. The mean end-to-end latency of the model was 47.81 ms (median 46.57 ms). Conclusion: For the first time, we demonstrate that clinical-grade, real-time machine-vision assistance during neurosurgery is feasible and can provide continuous, automated anatomical guidance from the surgical field. This approach may enhance intraoperative orientation, reduce cognitive load, and offer a powerful tool for surgical training. These findings represent an initial step toward integrating real-time AI support into routine neurosurgical workflows.

Quality and price of fetal bovine serum (FBS) are traditionally determined by geographical origin and parameters listed in the Certificate of Analysis (CoA). Despite its central role in cell culture, selecting suitable FBS batches remains costly and labor-intensive due to substantial batch-to-batch variation. We propose a molecular assessment strategy based on transcriptomic and cytokine profiling of cells cultured in different FBS batches to evaluate performance more reliably. Analysis of differential gene expression in three cell lines - MRC-5, Jurkat, and THP-1 - enables batch grouping and reveals pathway-specific effects, with immune-related pathways showing the most pronounced variability. Although CoA parameters can stratify batches by origin, they do not consistently correlate with cytokine secretion or gene expression across cell lines. These findings demonstrate that geographical origin is an inadequate predictor of functional FBS performance and that molecular profiling provides a more robust and informative assessment.

Conventional temporal interference stimulation (TI, TIS, or tTIS) leverages two pairs of electrodes to induce an interfering electrical field in the brain. Both computational and experimental studies show that TI can stimulate deep brain regions without significantly affecting shallow areas. While promising, optimization of the locations and dosages on these two pairs of electrodes for maximal focal modulation remains computationally challenging. We are the first to propose two arrays of electrodes instead of two or multiple pairs of electrodes to boost modulation focality. However, the optimization algorithm outputs too many electrodes with overlaps across two frequencies, making it difficult to implement in practice. Based on recent progress in developing multi-channel TI devices and computational work on TI optimization, here we again advocate two-array TI, but with solid software and hardware evidence to show the feasibility. Specifically, we show that the latest optimization algorithm for two-pair TI innately works for two-array TI with the fastest speed (under 30s) among all major algorithms. With a similar amount of electrodes, two-array TI could achieve better focality (3.03 cm) at the hippocampus even than TI using up to 16 pairs of electrodes (3.19 cm) that takes days to optimize. We also show a hardware implementation of two-array TI using 10 electrodes on our 8-channel TI device. We argue that two-pair TI is only preferred when one does not care about modulation focality and promote two-array TI for its advantages in focality and lower cost in terms of both optimization time and electrodes needed. We restate the focality-intensity tradeoff but in the context of TI and provide a first voxel-level map of achievable focality and modulation strength by TI in the MNI-152 head template. We hope this work will pave the way for future adoptions of two-array TI for more focal non-invasive deep brain stimulation.

Abstract The ISBAR framework is used to standardize clinical handovers and enhance patient safety. Observational tools based on ISBAR have been developed to assess the completeness of information transfer. However, these instruments have primarily been developed in non-Chinese contexts, and validated Chinese-language observational tools suitable for clinical practice remain limited. In this study, a cross-cultural adaptation and psychometric validation of the ISBAR Structured Handover Observation Tool was conducted, examining its reliability and discriminant validity in Chinese clinical settings. The study was conducted in two phases: cross-cultural adaptation and psychometric evaluation in real-world clinical settings. Content validity was assessed using the Content Validity Index (CVI), and inter-rater reliability was evaluated using the Intraclass Correlation Coefficient (ICC) based on a two-way mixed-effects model with absolute agreement. Discriminant validity was examined using the Mann-Whitney U test to compare scores across nurses with varying levels of clinical experience. A total of 233 handover cases involving patient transfers from the intensive care unit (ICU) to general wards were collected, involving 84 nurses. The scale demonstrated good content validity, with item-level content validity indices (CVI) ranging from 0.88 to 1.00 and a scale-level CVI/Ave of 0.98. The inter-rater reliability, assessed using fifty randomly selected cases, was high, with an intraclass correlation coefficient (ICC) of 0.885 for single-rater assessments and 0.939 for average-rater assessments. Discriminant validity analysis showed that nurses with more clinical experience had significantly higher total scores than those with less experience (Z = -4.772, p < 0.001). The Chinese version of the ISBAR Structured Handover Observation Tool demonstrates good content validity, high inter-rater reliability, and acceptable discriminant validity. This tool provides a standardized and practical method for assessing the completeness of information transfer and is expected to support quality improvement in patient handover from the ICU to general wards in Chinese clinical settings.

Thoracic Endovascular Aortic Repair (TEVAR) is a minimally invasive procedure for the treatment of thoracic aortic pathologies, such as Thoracic Aortic Aneurysm (TAA). Computational simulations can provide valuable insights into TEVAR outcomes and complications prior to surgery, making them a useful tool in the procedural planning. In this work, Fluid-Structure Interaction (FSI) computational simulations are carried out in ten pre-TEVAR patient-specific TAA cases, for which post-TEVAR outcomes are known, to quantify the hemodynamic drag forces acting on the aortic wall. Based on these results, this study proposes a new risk factor R to predict the occurrence of type I and III endoleaks. The patient cohort is divided in a calibration set, used to associate specific R values with three different risk levels, and a validation set, to test the risk factor efficacy. Based on the risk factor values obtained for the calibration set, R[&le;] 0.33 is associated with low risk of endoleak formation, 0.33 < R[&le;] 0.67 with moderate risk, and R > 0.67 with high risk. Once it is applied to the validation set,the risk factor is able to predict the formation of a type Ia endoleak. The risk factor proposed in this work is capable of identifying all the endoleak cases analysed, as well as conditions known to increase the risk of TEVAR complications. This study represents a preliminary attempt to determine whether pre-TEVAR hemodynamics can effectively predict post-TEVAR complications and thereby aid clinicians in the pre-operative planning.

Boar semen contains spermatozoa and seminal plasma (SP) that carries extracellular vesicles (EVs) among other components. However, artificial insemination (AI) doses produced by AI companies are highly diluted based solely on sperm concentration. The aim of this study was to evaluate the integrity of EVs isolated from AI doses, characterize the protein and miRNA content from high-fertility (HF) and reduced-fertility (RF) boars, and evaluate their functional impact on spermatozoa after dilution by a coincubation up to 24 hours at 38 {degrees}C. Proteomics identified 108 differentially expressed proteins between HF and RF EVs (97 upregulated in HF, 11 in RF), and transcriptomics revealed 80 differentially expressed miRNAs (DEMs) in EVs, 52 in SP, and 3 in spermatozoa, showing inverse expression in various shared DEMs between fertility rates, suggesting compartment-specific regulation. Functional coincubation demonstrated that EVs remain biologically active after dilution. HF EVs improved sperm quality parameters and reduced oxidative stress, while RF EVs increased total and progressive motility. Overall, our findings show that EVs from AI doses retain structural integrity, carry fertility-associated protein and miRNA signatures, and functionally modulate sperm quality in vitro. These features highlight porcine EVs as promising biomarkers and potential tools to optimize reproductive performance in swine production.

BackgroundSnakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem for rural communities of low and middle-income countries, India accounts for the largest proportion of snakebite deaths globally. Timely identification of venomous snakebite and its syndromic pattern is essential for effective administration of antivenom and supportive treatment. Expert identification of snake species and syndromes is not always available in peripheral healthcare settings. This leads to delays, unnecessary referrals, or improper treatment choices. Additionally, diverse snake species distribution and venom variations across regions pose challenges. AI-powered image classification methods can help overcome these barriers. We propose a clinically oriented deep learning pipeline for binary classification of venomous and non-venomous snake species of India using real-world imagery data. This pipeline would serve as a baseline step towards aiding snakebite management at peripheral healthcare setups with scarce resources. MethodsThe selected dataset consisted of 20 medically important Indian species. MobileViT-S, ConvNeXt-Tiny, EfficientNet-V2-S and ResNeXt-50 (32x4d) were trained under same conditions for comparison of results. Model interpretability was evaluated using Grad-CAM ++ to ensure that classification was not performed based on background but on features like head shape and stripes present on body. For reliable implementation we connected it to a web interface with human in loop expert verification. Experts can confirm or override predictions in real time. ResultsAmong the evaluated architectures, ResNeXt-50 (32x4d) showed the most reliable and consistent performance in classifying venomous and non-venomous snakes. It achieved the highest test accuracy, sensitivity, specificity, and F1-score. The model also had strong discriminative ability, with a ROC-AUC of 0.9950 and PR-AUC of 0.9959. These results indicate dependable performance in safety-critical screening situations. Grad-CAM++ visualizations confirmed that predictions were based on anatomically relevant features, especially in the head and body contour areas. This supports model interpretability and reduces background bias. ConclusionsAlthough the dataset size and single-institution source limit how widely the results can be applied, the proposed framework shows that its possible to create a clinically oriented, ready-to-use deep learning system for snakebite triage support. This system is intended as a scalable tool to help rural healthcare workers, emergency responders, and telemedicine platforms in areas where snakebites are common. Author SummarySnakebite is a major public health concern that disproportionally affects the rural population. Delays in identifying whether a snake is venomous often lead to delayed treatment, unnecessary use of antivenom, or inappropriate referrals. In many rural settings, access to expert snake identification is limited. To address this gap, authors have developed an artificial intelligence (AI)-based image classification system that distinguishes snakes into two clinically relevant categories: venomous or non-venomous. Unlike many previous studies that focused on ideal, high-quality wildlife images, our model was trained using real-world photographs captured in emergency situations, including images taken by patients and field responders under variable lighting and background conditions. This approach improves the models relevance to practical healthcare settings. The system achieved high accuracy and was further strengthened by visual interpretability tools and expert verification to ensure reliability. By combining AI-assisted classification with human oversight, this work provides a scalable decision-support tool that may improve early triage, rational antivenom use, and surveillance in snakebite-endemic regions

BackgroundPhotobiomodulation (PBM) therapy has demonstrated therapeutic potential in promoting cellular repair, modulating inflammation, and enhancing mitochondrial function. Platelet-rich plasma (PRP) is widely used in regenerative medicine due to its concentration of growth factors and cytokines. Very small embryonic-like stem cells (VSELs), a rare population of pluripotent stem cells present in adult tissues, have emerged as a potential contributor to tissue regeneration. While PBM and PRP are used in combination, how VSELs or Multi-lineage stress enduring (MUSE) cells are at play, and the biological mechanisms underlying their synergistic effects remain incompletely characterized. ObjectiveThis exploratory pilot study aimed to evaluate whether application of the MD Biophysics laser to autologous PRP is associated with measurable changes in VSEL-related antibody marker expression, and to identify directional trends to inform future controlled studies. MethodsPRP samples were collected from participants across seven test dates (July 2024 to February 2025), yielding 18 participant-session datasets. Samples were analyzed before (Pre) and after (Post) laser application using flow cytometry conducted at a UCLA Flow Cytometry Laboratory. Four VSEL-associated antibody markers were assessed: CD45-CD34+, CXCR4+, CD133+, and SSEA-4+. Analyses were descriptive and focused on paired differences and directional trends due to the exploratory design and absence of a control group. ResultsThree of four VSEL-associated markers (CXCR4+, CD133+, and SSEA-4+) demonstrated a group-level increase in median paired differences following laser application. Directional increases were observed in 12/18 sessions for CXCR4+, 10/18 for CD133+, and 9/18 for SSEA-4+. CD45-CD34+ showed a near-equal distribution of increases and decreases. Ki-67 positivity indicated the presence of viable, proliferative cells. While no findings reached statistical significance due to limited sample size, consistent directional trends were observed across multiple markers. ConclusionApplication of PBM to autologous PRP was associated with directional increases in multiple VSEL-associated antibody markers, suggesting a potential role for stem cell activation or mobilization in the mechanism of action. Although preliminary and not statistically powered, these findings provide hypothesis-generating evidence supporting further investigation. The observed trends informed iterative protocol refinement and establish a foundation for future controlled, adequately powered studies to evaluate clinical efficacy and underlying biological mechanisms.

During development, the size of the neuroepithelial cell pool plays a key role in establishing brain size, determining the numbers of derived progenitors and subsequent neuronal cell types. While early histogenesis is well modelled in brain organoids, the organ-scale geometry of the telencephalon is not accurately recapitulated. Herein, we present a new approach for generating ventral and dorsal forebrain organoids which develop a large ventricular neuroepithelium, characteristic of the closed telencephalic vesicle. Using a growth medium that supports aerobic glycolysis and is typically used for endothelial cells, we modulate neuroepithelial expansion to induce a more anatomically accurate neuroepithelial layer which, upon maturation, thickens physiologically to generate the typical neurogenic layered architecture. In addition, we present a new method for embedding organoids in miniature collagen spheres which mimics native extracellular matrix, stabilizes the ventricular geometry for dynamic culture conditions, and provides a means for incorporating vascular cells for neurovascular development. Finally, we demonstrate that human organoids grown under these conditions exhibit dramatically enlarged ventricles and delayed maturation compared to mouse. Together, this approach provides a model of the forebrain neuroepithelium with morphogenetic macroscale geometry and tissue architecture, suitable for investigating neurodevelopment and disease.

In contemporary bio-imaging-based research, computer-based assessment is becoming crucial for the characterisation of biological structures, as it minimises the need for time-consuming human annotation, which is prone to human error. Furthermore, it allows for the use of optical techniques that use lower photon intensities, thereby reducing reliance on high-intensity excitation and mitigating adverse effects on their activities. This study details the development and evaluation of sophisticated deep-learning models for amoeba detection using phase-contrast imaging. Using a single-class annotated dataset comprising 88 images and 4,131 annotations, we developed nine object detection models based on Detectron 2 and six variants based on YOLO v10. The diversity of the dataset, acquired under varying setup parameters, facilitated a comprehensive evaluation of the strengths and limitations of each model. A comparative analysis of speed and accuracy was performed to identify the most efficient models for real-time detection, providing critical insights for future microscopic analyses.

At the Wisconsin National Primate Research Center, we have identified a family of rhesus carrying the microtubule-associated protein tau (MAPT) R406W mutation linked to frontotemporal dementia (FTD). Rhesus induced pluripotent stem cells (RhiPSCs) derived from these monkeys present a unique opportunity for in vitro modeling and comparison with cells derived from MAPT R406W human carriers. Here, we report the development of a reproducible method to generate RhiPSCs compliant with the standards of the International Society for Stem Cell Research (ISSCR) to support in vitro modeling of FTD-MAPT R406W. Our stepwise approach identified efficient methods for fibroblast derivation, fibroblast reprogramming to RhiPSC, and RhiPSC maintenance over continued culture. To derive fibroblasts from MAPT wild type (WT) and R406W monkeys, a combination of manual processing and overnight enzymatic digestion was required to maximize the number of low passage fibroblasts available for reprogramming. Fibroblast reprogramming to RhiPSC using Sendai viral vectors versus oriP/EBNA1 episomal plasmids revealed the latter as most efficient. Electroporation conditions for oriP/EBNA1 reprogramming were optimized to maximize plasmid uptake and cell survival. Ultimately, eight RhiPSC lines were derived from 4 donor rhesus monkeys (n=2 WT, n=2 R406W; two clonal lines per donor) and fully characterized according to ISSCR standards. RhiPSC stemness and genetic stability was best maintained on mouse embryonic fibroblast feeders in Universal Primate Pluripotency Stem Cell medium, as opposed to Essential 12 medium supplemented with IWR1, which produced cytogenetic abnormalities. Rhesus neural progenitor cells were generated using a monolayer protocol and expressed PAX6 and NESTIN after 21 days of differentiation. Our reliable method will be useful to labs seeking to derive RhiPSCs for preclinical studies. Overall, the RhiPSCs generated from MAPT R406W carriers will be a critical resource for evaluating the molecular underpinnings of tau-related neurodegeneration across primate species.

Accurate quantification of transplanted cardiac spheroids requires three-dimensional localisation within intact myocardium, yet this remains technically challenging. Optical clearing and light-sheet microscopy enable volumetric imaging of injection sites, but automated segmentation is difficult when transplanted spheroids and host tissue are labelled with the same fluorescent markers and cannot be separated by simple thresholding. We developed a random forest based pixel classification workflow for 3D detection of injected hiPSC derived cardiomyocyte and H9c2 spheroids in optically cleared rabbit myocardium. A supervised classifier trained on intensity, edge, and texture features generated a segmentation then grouped pixels via connected component analysis to reconstruct individual spheroids. The method showed good agreement with manual annotation and enabled automated extraction of spheroid size and spatial metrics. This accessible workflow enables reproducible three-dimensional quantification of transplanted spheroids in large light-sheet microscopy datasets and provides a practical route from volumetric imaging to spatial metrics in cardiac regeneration studies.

Plantar tissue adaptation during activity is thought to contribute to diabetic foot ulceration (DFU), yet most existing studies only measure compressive quasi-static properties. This pilot study developed an ultrasound-loadcell measurement tool, PlantarSense, and used an infrared thermometer to measure dynamic compressive and shear energy dissipation ratio (EDR) and temperature of plantar-tissue at the first metatarsal head (1stMTH) and calcaneus in people living with and without diabetes at baseline, post-walk, and post-recovery. People living with diabetes showed significantly greater post-walk temperature increases (11.0 % vs 6.9% in controls at calcaneus, p=0.03) and less complete thermal recovery than controls. Baseline compressive EDR at the 1stMTH was significantly higher in people living with diabetes (67.8% vs 56.0% in controls, p=0.04). EDR modulation was greater from shear loading (21.5%) than compression (5.4%) and post-walk induced reductions in EDR were present in all participants, but people living with diabetes showed a 20% lower recovery than controls. Impaired thermoregulation and tissue adaptation in people living with diabetes was demonstrated by plantar temperature and EDR differences in post-walk and post-recovery. Future work is needed to test more participants with a greater range of diabetes progression to quantify statistically significant plantar tissue differences to inform DFU risk management.

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.

Purpose: Suicide and self-harm are major public health concerns characterized by substantial clinical and psychosocial heterogeneity. While latent class analysis has been used to identify subgroups of people with suicidal behavior, the extent to which such population-level phenotyping complements explainable artificial intelligence-based classification models remain unclear. Methods: We applied latent class analysis to a cross-sectional, publicly available dataset of 1000 individuals presenting with self-harm and suicide-related behaviors at Colombo South Teaching Hospital, Kalubowila, Sri Lanka. Sociodemographic, psychosocial, and clinical variables were used to identify latent subgroups. Class characteristics and suicide prevalence were examined and compared with variable importance patterns reported in a previously published explainable artificial intelligence (XAI)-based suicide classification study using the same dataset. Results: Four latent classes were identified. Two classes exhibited very high suicide prevalence (91.2% [95% CI: 87.7-93.8] and 99.0% [95% CI: 96.4-99.7]), whereas two classes showed low prevalence (<1%). The two high-prevalence classes differed markedly in lifetime psychiatric hospitalization history, with one class showing a 100% prevalence of prior hospitalization and the other substantially lower hospitalization rates. These patterns partially aligned with, and extended beyond, variable importance findings from the XAI-based model. Conclusion: Latent class analysis identified distinct subgroups with substantially different suicide prevalence and clinical profiles, underscoring the heterogeneity of individuals presenting with self-harm. Comparison with XAI-based suicide classification model findings suggest that unsupervised phenotyping and supervised classification provide complementary perspectives, offering population-level context that may enhance the interpretability of suicide assessment frameworks. Keywords: suicide; self-harm; latent class analysis; explainable artificial intelligence; machine learning

Microscopy-based analysis of red blood cell (RBC) morphology is widely used to study phenotypes in sickle cell disease (SCD). Although AI models have been developed to automate classification, most are trained on pre-cropped single-cell images and thus struggle with full-scope microscopic images containing densely packed cells and diverse morphologies, which require both accurate detection and fine-grained classification. We propose an end-to-end computational framework to identify individual RBCs in full-scope microscopy images and classify them into five morphological categories: discocytes (DO), echinocytes (E), elongated and sickle-shaped cells (ES), granular cells (G), and reticulocytes (R). We first evaluate advanced detection-classification models, including You Only Look Once (YOLO) and Detection Transformers (DETR), and demonstrate that while these models effectively detect cells, their classification performance falls short of specialized classifiers trained on single-cell images, particularly for minority phenotypes. To address this limitation, we introduce a two-step framework in which a YOLO-based detector localizes and crops individual cells from full-scope images, followed by a fine-tuned DenseNet121 ensemble classifier that assigns each cell to one of the five morphological categories. The proposed framework achieves a detection-level F1-score of 0.9661 and a weighted-average classification F1-score of 0.9708, with an overall classification accuracy of 97.06%. Compared with the single-step YOLO26n baseline, the two-step pipeline yields a macro-average F1-score improvement of +0.1675, with particularly substantial gains for minority classes (E: +0.1623; G: +0.2774; R: +0.2603). Overall, this hybrid framework demonstrates a practical strategy for adapting fast, general-purpose detection models to domain-specific biomedical tasks by combining them with specialized classifiers, delivering both efficiency and high accuracy for scientific and clinical image analysis.

Objectives: Accurate assessment of orthognathic surgical accuracy is essential in the evaluation of operative techniques. Surgical accuracy is often reported as rotational and translational deviations from planned positions. This results in 6 separate values, translation in three planes, anterior-posterior (AP), superior-inferior (SI) and medial-lateral (ML) and rotations about three axes, pitch, roll and yaw. However, rotations will influence 3-dimensional positions and translational discrepancies. Methods: We have derived a mathematical formula using Euclidean geometry and quadratic functions that quantifies the impact of rotations on translational discrepancies. This allows for the calculation of a total discrepancy value that incorporates the three translations and rotations. Furthermore, we developed an interactive web-based application using the open-source shiny R package. Results: We have successfully reduced equations from Euclidean geometry into a quadratic form. The equation is as follows, [4(sin{theta}/2)2-2]x2 + [8d(sin{theta}/2)2-2d]x + 4d2(sin{theta}/2)2 = 0, where {theta} represents the rotational discrepancy in radians and d represents the translation discrepancy. This allows us to solve for the correction needed to be made to translational discrepancies to account for the influence of rotational discrepancies. We successfully developed a web application with a user-friendly graphical user interface. Clinicians upload their own data in the excel (.xlsx) file format and the application automatically performs the necessary calculations over many patients, returning a downloadable table of results. Conclusion: We present a mathematical formula incorporated into a web-application to combine translational and rotational discrepancies for deeper insight when evaluating orthognathic surgical accuracy. Clinical Relevance: This allows surgeons to account for rotational influence on 3-dimensional translational discrepancies.

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