Bioengineering

Ovarian cancer is one of the gynecological cancer types, which, if metastasized and not detected early, can cause deaths among women. Therefore, there is a need to accurately predict drug responses to ovarian cancer. A gynecological pathologist inspects abnormality in tissues, followed by providing a report about patients; however, such a diagnostic process is (1) hard; (2) requires experience; and (3) time consuming. Moreover, existing tools are far from perfect. Hence, we present a computational pipeline to improve predicting drug response pertaining to ovarian cancer, derived as follows. First, we download digital pathology images pertaining to ovarian bevacizumab response from the cancer imaging archive repository. We employed histogram of oriented gradients to images, constructing feature vectors, provided to Fisher linear discriminant analysis to change the representation through dimensionality reduction. Then, we provide reduced-dimensionality data for regression analysis through support vector regression coupled with various kernels and calculating the area under the ROC curve (AUC). Experimental results against transformer-based models (ViT and Swin) and other deep learning (DL) models (VGG16, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB6) demonstrate that our approach with radial kernel (named SVRD+R) yielded an AUC performance improvements of 17% against the best-performing transformer-based model (ViT) while obtaining an AUC performance improvements of 14.9% when compared against the best DL-based model (MobileNetV2). These results demonstrate the superiority and feasibility of our AI-based pipeline when tackling prediction problems pertaining to gynecologic cancer studies. MSC92B05; 68T09

BackgroundBone graft biomaterials play a critical role in bone regeneration by influencing osteoblast differentiation and mineralization. However, comparative data regarding the osteogenic potential of commonly used graft materials under standardized conditions remain limited. Method and materialIn this in vitro experimental study, osteoblast-like cells (MG-63) were cultured with four bone graft materials, including Bio-Oss, Cerasorb, Bio-Tiss Cerabone, and Pro Osteon. The relative mRNA expression of osteogenic markers (COL1 and OPN) was evaluated at 1, 7, 14, and 21 days using real-time PCR. Alkaline phosphatase (ALP) activity and mineralization capacity were also assessed using colorimetric assay and Alizarin Red staining. Data were analyzed using one-way ANOVA and Tukey post hoc test (P < 0.05). ResultsSignificant differences were observed among the tested materials across all evaluated parameters. Bio-Oss and Cerasorb demonstrated higher gene expression levels and ALP activity compared to Bio-Tiss Cerabone and Pro Osteon (P < 0.05). Mineralization analysis showed significantly greater calcium deposition in the Bio-Oss and Cerasorb groups, whereas Pro Osteon consistently exhibited the lowest osteogenic performance. ConclusionBone graft biomaterials significantly influence osteogenic activity in osteoblast-like cells. Bio-Oss and Cerasorb showed superior osteogenic potential, while Pro Osteon demonstrated weaker performance. These findings highlight the importance of material properties in optimizing bone regeneration.

Aortic stiffness is both heterogenous and anisotropic. Current non-invasive methods to estimate aortic stiffness are limited to characterizing the aortic tissue as isotropic due to the lack the techniques required to extract multi-axial strain from 3D dynamic images. Vascular deformation mapping (VDM) is a nonrigid image registration technique which has thus far been applied to map aortic growth using longitudinal imaging. In this study, we propose to use VDM to assess 3D aortic deformation by mapping diastolic and systolic images. During image registration process, penalty parameters are employed to fine-tune image alignment and penalize non-physiological deformations. These penalty parameters must be calibrated to ensure that VDM successfully reproduces multi-axial aortic motion patterns in health and disease. In this paper, we developed a calibration pipeline for these parameters using synthetic data. A rotation-free shell model was used to generate physics-based synthetic data on aortic motion incorporating patient-specific geometries, root motion, and blood pressure from a cohort of 14 subjects (healthy, Marfans syndrome and thoracic aortic aneurysm). An error metric was defined to quantify the quality of the VDM results. Furthermore, a k-means clustering technique was used to categorize the subjects into three clusters based on ascending aortic motion. Optimal penalty parameters were identified for each of the three clusters. The results indicated that patient clusters with smaller aortic root motion required larger rigidity penalty values. The calibrated parameters successively reduced errors in 3D displacement and multi-axial stretch compared to un-optimized VDM predictions, enhancing the accuracy of capturing aortic deformation from dynamic images. Among the different aortic regions, the ascending thoracic aorta exhibits the largest error reduction.

IntroductionIntraoperative glycemic dysregulation, including unrecognized hypoglycemia and stress-induced hyperglycemia, is common during elective surgery. Conventional point-of-care (POC) monitoring provides only intermittent measurements, limiting the anesthesiologists ability to detect rapid glucose fluctuations. Continuous glucose monitoring (CGM) enables real-time, trend-based assessment, potentially shifting intraoperative glycemic management from reactive to proactive. ObjectiveTo meta-analyze the analytical accuracy, intraoperative glycemic efficacy, and feasibility of subcutaneous CGM in adults undergoing elective surgery, informing anesthesiology practice. MethodsThis systematic review and meta-analysis followed the PRISMA 2020 statement. Searches were conducted in PubMed, Embase, and Cochrane Central Register of Controlled Trials from January 2010 to May 2025. Eligible studies included randomized controlled trials and prospective cohorts of adults undergoing elective surgery under general or neuraxial anesthesia using subcutaneous CGM. Primary outcomes were pooled mean absolute relative difference (MARD) and time in range (TIR, 70-180 mg/dL). Random-effects models were applied. ResultsTen studies (3 RCTs, 7 cohorts; N=557) were included. Pooled MARD was 14.1% (95% CI 11.3-16.9%; I{superscript 2}=78%), lower in non-cardiac surgery (12.7%) than cardiac procedures with hypothermia (19.2%; p=0.03). CGM improved TIR by +14.9 percentage points (95% CI 7.2-22.6; p<0.001). Clinically significant hypoglycemia was detected in 43% of patients, all missed by POC. Sensor availability exceeded 96%, with no serious device-related events. ConclusionSubcutaneous CGM provides acceptable intraoperative accuracy and improves glycemic control, supporting its integration into anesthetic management.

Cryopreservation offers an option for long-term storage and global distribution of complex in vitro models, yet protocols for multicellular microphysiolgocial systems (MPS) such as brain organoids/spheroids remain limited. Here, we systematically compared three commercially available cryopreservation (mFreSR, CryoStorCS10, and 3dGRO) and two freezing time points, and established a robust workflow for freezing and recovering brain organoids. After defrosting, we assessed morphology and metabolic activity. We also evaluated electrophysiology, calcium transients, and neurite outgrowth. In addition, we measured astrocyte migration, apoptosis, mitochondrial integrity, microglia survival, and neural marker expression. We found that organoids require a 4-week recovery period to regain structural and functional stability. Although organoids frozen at week 6 showed higher metabolic activity after recovery, organoids cryopreserved at week 2 had clearly better functional outcomes. They exhibited stronger spontaneous network firing and maintained calcium transients. Finally, incorporated microglia-like cells survived the freezing and displayed comparable morphology to unfrozen controls. Across the endpoints measured here, 3dGRO showed the most favorable overall performance; formal ranking across media awaits harmonized normalization, single-organoid electrophysiology, and prespecified QC thresholds. Together, these results define a practical and reproducible cryopreservation strategy that preserves key physiological features of brain organoids and supports the establishment of ready-to-use organoid banks. The ability to reliably store and distribute complex brain-like tissues represents an essential step toward global standardization, scalable experimentation, and wider adoption of human-relevant microphysiological systems. Together, these results demonstrate recovery of key physiological features in the subset of organoids that remain viable after thaw and support the feasibility of brain organoid banking.

Whole-body SPECT bone scintigraphy reflects skeletal metabolic activity throughout the body and plays an indispensable role in the screening, treatment evaluation, and prognostic assessment of bone metastases in tumors. However, the automatic detection and segmentation of hypermetabolic bone lesions remain challenging due to low contrast, limited spatial resolution, and complex lesion distributions. In this study, we proposed Bone-Segnet, a dual-view guided automatic segmentation network for hypermetabolic bone lesions that integrated multi-scale feature modeling, global context modeling, and view-conditioned modulation. Pixel-level annotated anterior and posterior whole-body bone scintigraphy images were used for model training and prediction. The proposed network enhanced the recognition of low-contrast and small-scale lesions through small-lesion enhancement and multi-scale contextual modeling. A Transformer module was further introduced to strengthen global feature representation, while cross-view collaborative modeling was achieved by incorporating the complementary characteristics of anterior and posterior imaging. Experimental results demonstrated that the proposed method outperformed existing approaches across multiple evaluation metrics, with the Dice score improving from 0.7440 to 0.8750, indicating a substantial improvement in segmentation performance. Further quantitative analysis based on the segmentation results revealed significant differences among disease types in lesion count, pixel burden, and spatial distribution patterns, reflecting the heterogeneity of disease-related skeletal metabolic activity. Overall, the proposed method improved automatic lesion segmentation performance and enabled quantitative analysis of lesion burden and spatial distribution patterns, providing objective data support for the assessment of related diseases. Index Terms--Whole-body SPECT, bone lesion segmentation, dual-view modeling, quantitative analysis.

Major depressive disorder (MDD) is a common psychiatric disorder that requires reliable and objective assessment for early clinical intervention. Electroencephalography (EEG) is widely used for this purpose because it provides a non-invasive and low-cost measure of brain activity with high temporal resolution. However, EEG-based depression detection remains challenging due to the nonlinear nature of EEG signals, inter-subject variability, and the limited availability of subject-independent evaluation. To address these issues, this paper proposes a hybrid quantum-classical multiscale long short-term memory with parameterized quantum circuit branches (MS-LSTM-PQC) framework for subject-level EEG-based depression detection. The proposed model extracts temporal representations at multiple scales using parallel LSTM branches and incorporates eyes-closed (EC) and eyes-open (EO) condition information through condition-aware feature fusion. To further enhance the learned representations, scale-specific LSTM features are processed using PQC-based quantum branches implemented with TensorFlow Quantum (TFQ), providing an additional nonlinear feature transformation before classification. Experiments were conducted on the Mumtaz EEG depression dataset using EC-only, EO-only, and merged EC+EO conditions with 1-s, 2-s, and 3-s EEG windows. To reduce subject-level data leakage, all experiments were evaluated using 5-fold and 10-fold GroupKFold validation. The best overall accuracies across the evaluated settings were 92.05% and 95.08% under 5-fold and 10-fold GroupKFold validation, respectively. The 2-s merged EC+EO setting provided the most stable performance across validation protocols. In addition, Integrated Gradients (IG)-based explainability analysis showed that frontal and fronto-central channels, especially Fz, showed higher contributions to the model decision. These results suggest that multiscale temporal learning with quantum-enhanced feature transformation can support subject-level EEG-based depression detection under leakage-controlled evaluation.

High bit-depth medical images preserve subtle intensity variations that are important for quantitative analysis and clinical interpretation, but their large dynamic range poses challenges for efficient compression. We propose a bit-plane-aware dual-stream compression framework for 16-bit medical images by separately modeling the most significant bit (MSB) and least significant bit (LSB) components. The MSB structural stream is encoded using JPEG coding with a Duplicate Segment Skipping (DSS) strategy to exploit spatial and segment-level redundancy, while the LSB detail stream is compressed using learned image compression to represent residual variations and fine-grained details. Experiments on four MRI and CT datasets show that the proposed method consistently outperforms representative traditional and learning-based codecs, achieving the lowest bit rate across all datasets. Meanwhile, it preserves high reconstruction fidelity. As a downstream application, we further demonstrate that the compressed bitstreams can be effectively integrated with DNA encoding and converted into sequences with favorable biochemical properties.

Participatory qualitative methods such as Photovoice are increasingly used to link research with social action. Recent advances in artificial intelligence (AI) may enhance data analysis, inference, and action planning within such participatory approaches. This study explored medical students' perceptions of social justice using conventional Photovoice analysis and assessed the potential contribution of generative AI (genAI). Nine students joined a six-week seminar, "Exploring the Concept of Social Justice Using Photovoice." An initial two-hour session covered ethics, the Photovoice framework, and photography techniques. Participants then captured images reflecting their views on social justice, wrote narratives, and engaged in guided group discussions. Human researchers and students conducted a three-stage Photovoice analysis: 1) selecting photographs, 2) contextualizing them with participant narratives, and 3) inductively coding themes. To explore how AI might support data analysis, the research team analyzed the same data with five generative tools including Sonix, ChatGPT, and Copilot. AI-generated themes and visual representations were compared with human-derived results for congruence, depth, and suggested action steps. Conventional analysis identified five major themes: (1) Social Justice and Inequality, (2) Contradictions and the Costs of Justice, (3) Community and Collective Action, (4) Environment and Environmental Justice, and (5) Perception, Subjectivity, and Perspective. AI-assisted analysis yielded six unified themes that closely aligned with human findings. Traditional Photovoice images conveyed authentic, lived experiences and strong emotional meaning, providing a powerful foundation for advocacy. AI-generated images and thematic summaries offered efficiency, creativity, and reduced researcher bias, improving generalizability. However, they lacked the emotional depth and contextual nuance present in participant-created visuals.

Patellar osteochondral allograft (OCA) transplantation is widely used to treat large full-thickness cartilage defects, yet long-term failure and reoperation rates remain high. Although surface congruity and osseous integration are emphasized clinically, cartilage thickness and mechanical compatibility between donor and recipient are not considered. Our previous work suggests that cartilage thickness mismatch can amplify local deformation at the graft boundary, potentially compromising graft longevity. This study investigates how combined mismatches in cartilage thickness and mechanical properties influence the local strain environment at the patellar OCA interface. Simplified two-dimensional axisymmetric finite element models of patellar OCA repair were developed in ABAQUS. Donor-to-recipient cartilage thickness ratios ranging from 0.33 to 3.25 were evaluated together with donor-recipient Youngs modulus mismatches (2.5-7.0 MPa). Cartilage was modeled using homogeneous linear elastic and functionally graded material formulations to account for depth-dependent stiffness. A compressive pressure of 1.0 MPa was applied to represent patellofemoral joint loading, and peak compressive and shear strains were quantified at the graft boundary. Cartilage thickness mismatch produced localized high-strain regions (HSR) of compressive and shear strain at the donor-recipient interface that were absent in thickness-matched constructs. Strain amplification increased with both thickness and mechanical property mismatch. Compressive strain exhibited directional asymmetry, with donor-side-thicker configurations producing greater amplification than recipient-side-thicker configurations. Incorporating depth-dependent cartilage stiffness reduced peak strain magnitudes but did not eliminate mismatch-driven strain amplification. These findings demonstrate that cartilage thickness and mechanical disparity can create HSR at the patellar OCA graft boundary that may predispose grafts to impaired integration and long-term failure.

Chronic conditions such as Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN) remain underdiagnosed in community settings, particularly in resource-limited populations. Conventional diagnostic approaches rely on episodic measurements and laboratory-based assessments, limiting scalability for large-scale screening. Non-invasive physiological monitoring systems offer a potential pathway for accessible and rapid wellness assessment in real-world environments. This study aimed to evaluate the feasibility, signal acquisition performance, and exploratory physiological signal characteristics of a non-invasive multimodal monitoring system (Hayl) in community-based screening settings. Methods: A prospective, cross-sectional, multicenter observational pilot study was conducted across rural and urban screening camps in south India. A total of 281 adult participants were enrolled, including individuals with known T2DM, HTN, and those without known comorbidities, encompassing both symptomatic and asymptomatic subjects. Physiological data were acquired using the Hayl system, which integrates photoplethysmography (PPG) and temperature sensing. Signal acquisition feasibility, waveform quality, and derived signal characteristics were evaluated. Comparative and exploratory analyses were performed across predefined clinical subgroups. The study was conducted under Institutional Ethics Committee approval in accordance with guidelines from the Indian Council of Medical Research. Conclusion: The Hayl system demonstrated high feasibility for physiological signal acquisition, with successful PPG recordings in 274 participants (97.5%) and temperature signals in 279 participants (99.3%). Most recordings exhibited high waveform quality (74.0%), with observable variations in signal characteristics across clinically relevant subgroups. Reduced pulse variability and increased waveform irregularity were more frequently observed in participants with T2DM and HTN, while symptomatic individuals demonstrated greater signal variability compared to asymptomatic participants. Temperature measurements were stable, with a mean peripheral temperature of 33.4 with a variation of 1.2C degrees. These findings support the potential of Hayl as a non-invasive multimodal platform for community-based wellness screening and exploratory signal-based physiological assessment. Further large-scale and longitudinal studies are required to establish clinical utility.

Tissue phantoms that mimic microvasculature and perfusion are essential for modelling vascular function, guiding interventions, and calibrating imaging systems, which require faithful replication of vascular geometry and flow. Conventional fabrication strategies, including wire-based molding, lithographic micromachining, and additive manufacturing, offer useful capabilities but remain constrained by predefined designs, rectangular channel cross-sections, limited scalability, and high production costs. Reliance on predefined digital vascular models restricts design flexibility and limits the ability to capture the natural variability and complexity of real vascular systems. Here, we present a lithography-free, fractal-generating approach based on a modified Lifted Hele-Shaw Cell (LHSC) technique, in which vascular networks emerge spontaneously via interfacial fluid instabilities. Unlike pre-designed methods, these structures are governed by fluid properties and flow conditions, enabling adaptive, physiologically relevant geometries with smooth Gaussian cross-sections and natural diameter tapering. We demonstrate four phantom designs: a planar vascular tree, an anatomically guided cerebral network, a retinal vascular model, and a conformable curved substrate phantom. Validation using Laser Speckle Contrast Imaging confirms structural fidelity and physiologically relevant flow consistent with Murrays law. This platform uniquely integrates realistic vascular architecture with emergent, fractal driven formation, highlighting its potential as a reproducible and biologically relevant alternative to conventional vascular phantom fabrication. Furthermore, the availability of such realistic in vitro vascular models can reduce reliance on animal experiments and contribute towards more ethical and sustainable preclinical research.

PurposeTo characterize the frequency-dependent bioimpedance properties of major ocular tissues in intact ex vivo porcine eyes under simulated surgical conditions and evaluate tissue separability at discrete frequencies. MethodsBioimpedance spectra were acquired from sclera, corneal epithelium, iris, lens, vitreous, and retina in intact ex vivo porcine eyes using a two-electrode probe and a precision LCR meter over 5 kHz to 1 MHz. Measurements were obtained under balanced salt solution and ophthalmic viscosurgical device conditions. Probe-tissue contact was verified by microscope visualization and optical coherence tomography. Tissue separability at 5, 50, 100, and 900 kHz was evaluated using global and pairwise statistical comparisons, effect sizes, and ROC-based separability metrics. Robotic-stabilized and handheld measurements were also compared. ResultsOcular tissues demonstrated distinct, frequency-dependent impedance magnitude distributions. Across sampled frequencies, 60% to 80% of tissue pairs showed significant differences after multiplicity correction. Median pairwise effect sizes ranged from Cohens d = 0.48 at 5 kHz to 1.04 to 1.06 at 50 to 100 kHz. Median ROC-based separability was 0.91 at 5 kHz and 0.76 to 0.77 at 50 to 900 kHz. Robotic-stabilized measurements showed lower variance than handheld measurements, although tissue-specific impedance ranges and frequency-dependent trends were preserved across acquisition modes. ConclusionsMajor ocular tissues exhibit reproducible, frequency-dependent bioimpedance signatures in intact ex vivo eyes under simulated surgical preparation. These findings establish a physiologically relevant ocular impedance reference dataset and support bioimpedance as a complementary modality for tissue differentiation in ophthalmic microsurgery.

The integration of artificial intelligence (AI) in cardiology requires healthcare worker acceptance for successful implementation. Understanding attitudes and educational needs is crucial for developing effective training programs. A cross-sectional survey was conducted among 408 healthcare workers treating cardiac diseases in Riyadh, Saudi Arabia. We assessed AI acceptance, knowledge levels, and training preferences using validated scales. Statistical analyses included descriptive statistics, chi-square tests, correlation analysis, reliability testing, and multiple logistic regression. Of 408 participants, 407 provided complete responses. The sample comprised predominantly young (87.0% aged [&le;]30), female (75.7%) medical residents (89.9%) with limited AI experience (86.7% never used AI clinically). Internal consistency was excellent (Cronbachs = 0.892). Moderate acceptance was observed: 49.9% were aware of AI applications in cardiology, 46.7% were willing to learn, and 42.8% were willing to use AI clinically. However, 49.1% acknowledged lacking sufficient AI knowledge. Logistic regression identified willingness to learn (OR = 3.24, 95% CI: 2.15-4.89) and training interest (OR = 2.87, 95% CI: 1.94-4.25) as the strongest predictors of AI acceptance. The model explained 68.4% of variance (Nagelkerke R{superscript 2} = 0.684) with an AUC of 0.847. Medical residents demonstrate moderate AI acceptance but significant knowledge gaps. Educational interventions--particularly hands-on learning and institutional training programs--are the strongest drivers of AI readiness, surpassing demographic predictors. Integrating AI literacy systematically into medical curricula is essential for successful AI adoption in cardiovascular care. Author summaryHealthcare workers worldwide are increasingly encountering artificial intelligence (AI) tools in clinical settings, yet their readiness to adopt these technologies--particularly in specialized fields like cardiology--remains poorly understood, especially in rapidly developing healthcare systems. In this study, we surveyed 407 healthcare workers in Riyadh, Saudi Arabia, to understand their current attitudes, knowledge gaps, and learning preferences regarding AI in cardiac diagnosis. Our findings reveal that while most participants hold cautious optimism about AI, nearly half acknowledge lacking the knowledge needed to use it confidently. Crucially, we found that educational factors--specifically willingness to learn and interest in institutional training--were far stronger predictors of AI acceptance than demographic characteristics such as age or gender. This means that AI readiness is not a fixed trait determined by who someone is, but a teachable and trainable capacity. These results carry direct implications for medical educators and policymakers: structured, hands-on AI training integrated throughout medical curricula can meaningfully accelerate adoption of beneficial technologies in cardiovascular care and beyond.

In this study, we evaluated the impact of different in vitro 3D culture modelling methods on the activity of doxorubicin (DOX) and 5-fluorouracil (5-FU) in human melanoma spheroids. Human melanoma A375 and IGR39 spheroids were generated using the hanging drop and non-adhesive surface methods. Spheroid growth dynamics were assessed by measuring changes in spheroid diameter. To compare the effects of anticancer drugs in spheroids of different sizes, spheroids of approximately 200 and 400 {micro}m were formed. Drug activity was evaluated based on spheroid growth and cell viability using the MTT assay. A375 spheroids formed using the non-adhesive surface method were more sensitive to DOX than spheroids formed using the hanging drop method. In smaller A375 spheroids, 10 {micro}M 5-FU reduced cell viability more effectively in spheroids formed using the hanging drop method. In contrast, IGR39 spheroids formed by the hanging drop method were more resistant than those formed on a non-adhesive surface. However, in IGR39 spheroids, the effects of DOX and 5-FU on growth and viability did not significantly differ between formation methods. In conclusion, A375 spheroid growth was not significantly influenced by the formation method, whereas IGR39 spheroid growth depended on the method used. A375 spheroids formed on non-adhesive surfaces were more sensitive to DOX, whereas 5-FU activity depended on drug concentration and spheroid size. In IGR39 spheroids, the effects of DOX and 5-FU on growth and viability were largely independent of the spheroid formation method. Based on these results, it can be concluded that the researchers should carefully select the spheroid formation method for their studies, as this may influence the results of the tested compounds effect on their size and viability.

BackgroundDirect conversion of human somatic cells into functional neurons could offer a faster way to generate patient-specific neurons for use in regenerative medicine, disease modelling, and drug development. Although it has been reported that neuronal direct reprogramming bypasses the intermediate pluripotent state, no reports have included time-lapse experiments, potentially overlooking transient intermediate states. Recent studies have shown that the conversion of human mesenchymal stromal cells (hMSCs) into neuron-like cells involves a transition through a transient intermediate state. Therefore, further research is needed to fully understand the process by which human somatic cells can become neurons without cell division. In this study we investigates whether direct neuronal reprogramming of human bone marrow-derived MSC (hBM-MSCs), dental pulp-derived MSC (hDP-MSCs), and adult human dermal fibroblasts (HDFa), involves a transient intermediate state, and sought to further validate the neuronal identity of hMSC-derived induced neurons. MethodsIn this study, we conducted time-lapse experiments to observe the transformation of hBM-MSCs, hDP-MSCs and HDFa, into neurons using a small-molecule-based direct reprogramming protocol. Cellular and ultrastructural changes were further characterized by confocal and electron microscopy. ResultsDirect conversion of hBM-MSCs, hDP-MSCs and HDFa into neuron-like cells occurred rapidly and in absence of cell division. Time-lapse analyses revealed that reprogramming proceeds through a transient intermediate state characterized by distinct morphological changes and dynamic nuclear remodelling. Furthermore, we found that neuron-like cells derived from hBM-MSCs and hDP-MSCs exhibit neuronal polarization, expressed specific neuronal and synaptic markers, formed interconnected cellular networks, and exhibited functional plasticity, providing further evidence that hMSCs can become functional neurons. ConclusionsThis study provides clear evidence that the direct neuronal reprogramming process involves a transition through an intermediate, transient state. Our findings also provide further evidence that hMSCs can become functional neurons. In summary, our work provides new insights into the direct neuronal reprogramming process, which is essential for advancing both developmental biology and regenerative medicine.

This work aimed to establish a translationally viable, xeno-free, serum-free platform and protocol for the isolation and expansion of human salivary stem/progenitor cells (hS/PCs) suitable for regulatory qualification and future FDA-approved first-in-human autologous regenerative therapy trials for the treatment of hyposalivation disorders. Parotid gland specimens from non-cancerous regions/tissues were collected from consented surgical patients. Primary hS/PCs were isolated from tissue specimens, cultured in animal-component-free conditions, expanded to produce millions of cells, then enriched for CD44+ stem/progenitor cells by magnetic cell sorting. Normal epithelial purity was assessed using cytokeratins 5/14. Anti-CD133/PROM1 (cancer marker) and anti- fibroblast (clone TE-7) antibodies were used to demonstrate a lack of contaminating cells. Phenotype validation was performed by flow cytometry and immunocytochemistry on both CD44+ sorted and unsorted populations. Senescence-associated beta-galactosidase (SA-{beta}-gal) assays were performed across serial passages (P1-P6). Pluripotency was demonstrated by culture under conditions supporting lineage-specific differentiation. Primary hS/PCs demonstrated consistent expansion and epithelial morphology under serum-free conditions. CD44 expression remained high (>95%) throughout expansion, with negligible detection of CD133 or fibroblast markers, confirming epithelial purity and absence of tumorigenic or stromal contamination. Immunocytochemistry corroborated these expression profiles. SA-{beta}-gal staining revealed only a minor, passage-dependent increase (5-16%) in senescent cells from multiple donors, indicating retention of proliferative potential. Our defined, animal-free culture system supports stable expansion of pure low passage hS/PCs under conditions compatible with good manufacturing practice (GMP).

PurposeGlasdegib is a Sonic Hedgehog (SHH) pathway inhibitor used for treating newly diagnosed acute myeloid leukemia in elders or patients unfit for intensive chemotherapy. This study sought to demonstrate growth inhibition and increased apoptosis of B-cell acute lymphoblastic leukemia (B-ALL) in vitro under glasdegib, alone and combined with inotuzumab, using a novel co-culture system and validated chemosensitivity testing model to determine whether glasdegib with and without inotuzumab may represent a promising treatment strategy in B-ALL. MethodsSeven blood and marrow samples from B-ALL patients were co-cultured with HS-5 stromal cells in a co-culturing system designed to mimic the tumor microenvironment to maintain B-ALL cell viability for chemosensitivity testing under glasdegib and inotuzumab. ResultsCo-culturing improved B-ALL viability from four to nine days. Dosage-dependent responses to glasdegib were consistent among B-ALL samples on day four based on culture viability, and varied based on expressions of SSH genes GLI1, GLI3, SMO, and PTCH1. Combination with inotuzumab had varied effects on treatment response. ConclusionCo-culturing B-ALL cells with HS-5 stromal cells improves B-ALL growth and viability. Glasdegib with and without inotuzumab treatments impact the viability of co-cultured B-ALL cells by day four. SHH gene expressions suggest different B-ALL patients may be sensitive or resistant to glasdegib and inotuzumab.

Traditional agricultural safety assessments often rely on raw incident counts that emphasize exposure but underrepresent outcome severity. This study presents a multi-criteria impact framework to distinguish frequency-driven activity patterns from severity-driven risk across the U.S. Midwest. Agricultural incident records from 2012 to 2023 across seven states were analyzed using descriptive statistics, county-level mapping, and quartic kernel density estimation. Comparative impact indices were constructed using Analytic Hierarchy Process (AHP) and Geometric-Fuzzy AHP weighting schemes to integrate incident frequency, outcome severity, and post-incident survivability. Results indicate that while overall incident frequency is strongly concentrated in northwestern Iowa, reflecting intensive agricultural activity, fatal outcomes exhibit a broader spatial footprint extending across central and northern Iowa and into central-southern Minnesota. Severity-weighted mapping further consolidates northwestern Iowa and the Minnesota-Iowa corridor as dominant high-impact zones. At the regional scale, Geometric-Fuzzy AHP produced consistently lower mean scores and reduced dispersion than AHP, yielding smoother spatial gradients while preserving the primary hotspot structure. These findings demonstrate that frequency-based mapping alone fails to capture the multi-dimensional nature of agricultural risk. By explicitly linking incident locations with survival infrastructure, this research provides an evidence-based framework for targeting safety interventions and improving rural emergency medical service planning.

PurposeTo evaluate the retinal safety of repeated low-level red-light (RLRL) therapy using the Eyerising Myopia Management Device (EMMD) by analysing exposure parameters relative to established thermal and photochemical retinal injury thresholds and empirical human exposure data. MethodsEmission characteristics of the EMMD were measured in an accredited laboratory under worst-case conditions. Parameters assessed included wavelength, intraocular power, corneal irradiance, and retinal image characteristics across accommodative states. These measurements were compared with international safety standards, maximum permissible exposure limits, and experimentally derived retinal injury thresholds from animal studies and validated computational models. The effects of repeated exposures from RLRL therapy using the EMMD were evaluated using photochemical additivity principles and repair kinetics, and further contextualised using human volunteer exposure data. ResultsThe EMMD emitted red laser radiation at 654-655 nm with a maximum intraocular power of approximately 1 mW through a 7 mm pupil, placing it within Class 3R and marginally above the Class 2 limit. Corneal irradiance was approximately 26 W m- 2, well below conservative photochemical exposure limits. Thermal injury modelling indicated retinal damage thresholds above device exposure, including under worst-case assumptions of minimal retinal image size and absence of eye movements. Accounting for repeated daily exposures and photochemical additivity, safety margins remained approximately 3-fold for a 7 mm pupil and approximately 8-fold for a more realistic 4 mm pupil. Human volunteer studies demonstrated no detectable structural or functional retinal injury at exposure levels approximately five times higher than those produced by the EMMD. ConclusionExposure parameters of RLRL therapy using the EMMD remain well below conservative retinal injury thresholds under prescribed use conditions. Integration of experimental, modelling, and human data indicates substantial safety margins, supporting its safe clinical use.