Bioengineering最新文献

{"title":"Accuracy of Intraocular Lens Power Calculation Formulas for Two Single-Piece Acrylic IOL Models in Eyes with a Shallow Anterior Chamber.","authors":"Yalan Wang, Wei Lou, Lu Liu, Xinmin Lu, Qiang Wu, Da Long, Jianyan Hu","doi":"10.3390/bioengineering13101155","DOIUrl":"https://doi.org/10.3390/bioengineering13101155","url":null,"abstract":"<p><p><b>Introduction:</b> This study evaluated whether the refractive prediction accuracy of conventional and new-generation intraocular lens (IOL) power calculation formulas differs between two single-piece acrylic IOL models in eyes with a shallow anterior chamber depth (ACD). <b>Methods:</b> In total, 100 eyes implanted with Softec HD IOLs (Lenstec) and 102 eyes implanted with SN60WF IOLs (Alcon) were included. All eyes had an ACD of ≤3.0 mm. The prediction error (PE) and mean absolute error (MAE) of 11 formulas, with and without optimized constants, were evaluated, including seven new-generation formulas (Barrett Universal II [BUII], Cooke K6 [K6], EVO 2.0, Hill-RBF 3.0, Hoffer QST, Kane, and Pearl-DGS) and four conventional formulas (Haigis, Hoffer Q, Holladay 1, and SRK/T). <b>Results:</b> The new-generation formulas (except K6) showed myopic PEs ranging from -0.108 to -0.181 D in the Softec group; most of these formulas (except Pearl-DGS and Hoffer QST) showed hyperopic PEs ranging from 0.120 to 0.310 D in the SN60WF group. Both before and after optimization, Hill-RBF 3.0 and Pearl-DGS yielded the lowest MAEs in the Softec and SN60WF groups, respectively. Before optimization, most formulas had MAEs > 0.43 D in the Softec group and <0.38 D in the SN60WF group; after optimization, the MAEs were 0.408-0.479 D and 0.334-0.377 D, respectively. <b>Conclusions:</b> The new-generation formulas with ULIB constants showed myopic and hyperopic PEs in the Softec and SN60WF groups, respectively. The SN60WF IOL model demonstrated better refractive prediction accuracy than the Softec model. Formula selection according to IOL model may help improve refractive outcomes in eyes with shallow ACDs.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D-Fiber Mapping in Biological Tissues with Computational Scattered Light Imaging Enabled Through 3D-Nanoprinting.","authors":"Mark M Vermeulen, Pieter F J van Altena, Dennis Scheidt, Angelo Accardo, Miriam Menzel","doi":"10.3390/bioengineering13101154","DOIUrl":"https://doi.org/10.3390/bioengineering13101154","url":null,"abstract":"<p><p>Computational Scattered Light Imaging (ComSLI) exploits the anisotropic scattering on aligned structures to reconstruct biological fiber networks like nerve, muscle or collagen fibers. ComSLI discerns individual fiber pathways also in densely interwoven regions with multiple crossing fibers, achieves micrometer resolution across centimeter fields of view, is compatible with various sample preparations and staining, and only requires an LED light source and a camera, making it attractive for histological tissue analysis. However, while ComSLI precisely determines the 2D (in-plane) fiber orientations, determining the 3D (out-of-plane) fiber inclinations remained challenging due to missing a-priori knowledge of the inclination angles. In this study, we measured differently inclined 3D-nanoprinted fiber bundles with ComSLI, compared the resulting scattering patterns to those obtained from nerve fibers in a tilted brain section (vervet corpus callosum), and analyzed scattering patterns and angular profiles in differently inclined brain regions. We show that the highest scattering intensity occurs along the same curved line as conical diffraction at an inclined grating. With this analytical model, we were able to estimate inclination angles of non-crossing fibers up to 60° inclination with an accuracy of 3° or better. This finally allows to quantitatively determine fiber inclinations and reconstruct 3D fiber pathways with ComSLI.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Local Gating: Toward Network-State-Dependent Neuromodulation.","authors":"Paolo Belardinelli, Elena Mongiardini, Sonia Turrini, Alessio Avenanti","doi":"10.3390/bioengineering13101151","DOIUrl":"https://doi.org/10.3390/bioengineering13101151","url":null,"abstract":"<p><p>Brain stimulation is progressively evolving from anatomically targeted interventions toward individualized, adaptive, and network-oriented approaches. Recent developments in brain-state-dependent stimulation have demonstrated that neuromodulation can be optimized by synchronizing stimulation with ongoing neural activity, while adaptive cortico-cortical paired associative stimulation (ccPAS) has extended this principle to distributed cortical networks. However, current approaches continue to define brain state primarily through local electrophysiological features, such as oscillatory phase or power, even though ccPAS is specifically designed to modify communication between interconnected cortical regions. In this opinion paper, we argue that the endogenous variable guiding ccPAS should instead describe the instantaneous physiological state of the targeted cortico-cortical pathway. We propose that online source-space functional connectivity provides a biologically meaningful and technically feasible estimate of this pathway state, thereby enabling a new framework of network-state-dependent neuromodulation. As illustrative applications, we consider pathways spanning different levels of experimental characterization, from established sensorimotor interactions to the less-characterized ventral premotor cortex (PMv)-posterior superior temporal sulcus (pSTS) pathway within the action-observation network (AON). This distinction highlights that the proposed framework is pathway-general, while its experimental implementation requires pathway-specific characterization of stimulation parameters and appropriate physiological readouts. In this framework, resting-state EEG is rapidly reconstructed in source space to estimate pathway connectivity, which is subsequently used to personalize stimulation targets and guide state-dependent ccPAS of a functionally defined cortico-cortical circuit. Finally, we discuss the technological prerequisites, methodological challenges, biological rationale, and open scientific questions that must be addressed before pathway-state-guided neuromodulation can be experimentally validated. We argue that redefining brain state as the instantaneous physiological state of the communication pathway targeted by stimulation may represent the next conceptual step in brain stimulation.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization.","authors":"Antony Garcia, Adrián Noriega de la Colina, Gabrielle Britton, Xinming Huang","doi":"10.3390/bioengineering13101152","DOIUrl":"https://doi.org/10.3390/bioengineering13101152","url":null,"abstract":"<p><p>Black-box models limit the adoption of artificial intelligence in medicine because their predictions are difficult to interpret and reproduce. We present a statistically grounded framework for interpretable, rule-based clinical classification using the Bernoulli Naïve Bayes (BNB) model. Supervised chi-square-guided binarization converts continuous variables into binary indicators by selecting thresholds that maximize association with the clinical outcome within the training folds, which allows BNB to operate on continuous medical data without sacrificing transparency. On three benchmark datasets, Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction, the framework reached areas under the receiver operating characteristic curve of 0.794, 0.984, and 0.918, respectively. Probabilistic reliability was assessed with a leakage-safe cross-validated calibration analysis reporting Brier score and calibration intercept and slope, and post hoc beta calibration improved probability calibration across datasets. These results indicate that an interpretable, statistically motivated framework can perform comparably to more complex models while providing explicit decision rules expressed in clinical units and risk estimates that are reliable after calibration. A complete worked example further shows that model inference can be reproduced from a printed reference table using only basic arithmetic, without software or proprietary tools, a property that may support auditable use of artificial intelligence in clinical settings.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable Inertial Phenotyping of Freezing of Gait in Parkinson's Disease: Biomechanical Signatures, Subject-Independent Classification, and Clinical Associations.","authors":"Augusto Ielo, Lilla Bonanno, Angelo Quartarone, Serena Dattola","doi":"10.3390/bioengineering13101150","DOIUrl":"https://doi.org/10.3390/bioengineering13101150","url":null,"abstract":"<p><p>Freezing of gait (FoG) in Parkinson's disease is clinically heterogeneous, yet wearable-sensor studies predominantly focus on binary FoG detection. We investigated whether right-ankle inertial signals can characterize clinically meaningful FoG manifestations within a three-stage conditional framework. Accelerometer and gyroscope data from 22 participants in the FoG-STAR dataset were segmented into 1 s windows. Three subject-independent classification stages were evaluated separately: locomotor non-FoG versus FoG, kinetic versus akinetic FoG, and shuffling versus trembling. Nine interpretable biomechanical features were compared with Logistic Regression, RBF-SVM, Random Forest, and a raw temporal 1D-CNN using frozen participant-independent partitions. The CNN yielded a mean Stage-1 balanced accuracy (0.877 ± 0.034), whereas for Stage-2 kinetic-akinetic discrimination, Logistic Regression and the CNN yielded mean balanced accuracies of 0.730 ± 0.044 and 0.727 ± 0.065, respectively. Stage-3 performance was lower and more variable (Logistic Regression, 0.666 ± 0.133). Nested leave-one-subject-out analysis restricted to mixed-phenotype participants further highlighted subject-level heterogeneity. Kinetic FoG showed greater residual movement amplitude and movement-rate characteristics than akinesia. Protocol-observed locomotor FoG burden was positively associated with FoG-Q (ρ=0.536) and MDS-UPDRS III (ρ=0.785). Wearable inertial signals therefore showed clinically interpretable differences among FoG manifestations, while finer phenotype discrimination remained heterogeneous and requires external validation in independent cohorts.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances and Challenges in mRNA Cancer Vaccines: Individualized Design and Delivery.","authors":"Liyun Huang, Yuting Tang, Xiaobing Chen, Zhen Wu, Junlan Xiao, Qi Xiang, Huajian Tian","doi":"10.3390/bioengineering13101153","DOIUrl":"https://doi.org/10.3390/bioengineering13101153","url":null,"abstract":"<p><p>Since the widespread use of mRNA-based COVID-19 vaccines, significant progress has also been made in mRNA cancer vaccines. Currently, mRNA cancer vaccines represent one of the most promising treatments for advanced tumors, with several candidates in clinical trials. Compared with traditional treatment methods, mRNA cancer vaccines have several advantages. However, several challenges of mRNA cancer vaccines remain, including the accurate and rapid identification of neoantigens and hotspot mutations that match different HLA types. To address these challenges, we review four key aspects of mRNA cancer vaccine development. These include neoantigen identification technologies, machine learning and AI prediction, mRNA sequence and structure design, and the development of LNP delivery systems. Additionally, we analyze the pipelines of leading companies in this field, providing insights into personalized mRNA cancer vaccines. This review also discusses future challenges and prospects, aiming to help guide this promising field toward the full realization of its potential.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-Specific Implant Design: Current Evidence for Artificial Intelligence, Imaging and Computational Planning-A Systematic Review of the Biomedical-Indexed Literature.","authors":"Adrian Barbilian, Marius Moga, Mark Edward Pogarasteanu, Vlad Tau, Alex Lucaci, Constantin-Andreas Gorgan, Alexandru Diaconu, Robert Daniel Dobrotă, Razvan Adam, Cristian Stoica","doi":"10.3390/bioengineering13101149","DOIUrl":"https://doi.org/10.3390/bioengineering13101149","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and objectives: &lt;/strong&gt;Patient-specific skeletal implants are designed through a pipeline of imaging, computational planning and, increasingly, artificial intelligence. Each pillar has its own validation literature, but no review has asked what evidence supports each one in implant design itself. We mapped that evidence from the biomedical-indexed literature in this paper.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A systematic review was conducted under a protocol committed before retrieval (PRISMA 2020, PRISMA-S, SWiM). MEDLINE, Europe PMC, Crossref and OpenAlex were searched on 2 and 3 September 2026 for primary studies from 2015 onwards applying artificial intelligence, an evaluated imaging pipeline or computational planning to an implant whose geometry derives from the individual patient's anatomy. Engineering indexes were unavailable, so this review maps biomedical-indexed literature, and a bounded probe was used to estimate what that omits. Two artificial intelligence screeners from different vendors screened every record; every clinical study was appraised twice, independently, by assessors from different vendors. Claims about validation quality are reported on the studies read in full. All screening, charting and appraisals were machine-performed and no human-verified subsample exists.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 5059 records, 639 studies were included: 373 technical studies, 179 clinical series, 75 comparative studies and 12 randomised trials. Computational planning was applied in 601, an evaluated imaging step in 219 and artificial intelligence in 36. Randomised evidence came from 12 small trials, 10 of them craniomaxillofacial, which evaluated the delivered implant rather than the planning method; virtual surgical planning was used in 11 of them. When restricting to the four studies that measure deviation from the virtual plan, with smaller meaning better, patient-specific implants deviated less from the plan (standardised mean difference -1.21, 95% CI -1.69 to -0.74). Revision or removal was less frequent (3 of 135 against 25 of 149; risk ratio 0.28, 95% CI 0.11 to 0.72); operative time, complications and infection gave intervals compatible with a difference either way. No randomised trial was at low risk of bias, and 69 of 75 comparative studies were at serious risk, mostly of unadjusted confounding, so all estimates are of very low certainty. Among technical studies read in full, independent validation material was used in fewer than half, and files were shared in about one in fourteen. Of 36 artificial intelligence studies, 4 were evaluated on independent data, 2 were used for a real patient, none were clinically comparative, and 8 of 17 cranial studies relied on AutoImplant family benchmarks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The design pipeline is unequally validated: planning has clinical evidence concentrated in one region, computational methods are widely applied but rarely validated independently or sha","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Prediction of Bioprinting Quality Using Integrated Experimental and Computational Data.","authors":"Khulood Abu Maria, Sara Abu Tarboosh, Adi El-Dalahmeh, Ola Tarawneh","doi":"10.3390/bioengineering13101148","DOIUrl":"https://doi.org/10.3390/bioengineering13101148","url":null,"abstract":"<p><p>Three-dimensional (3D) bioprinting of hydrogel inks is an enabling technology for tissue engineering and the fabrication of personalized drug-delivery systems. However, in most laboratories the extrusion parameters are still optimized by slow, resource-intensive trial-and-error, because printability depends on the combined and often nonlinear influence of ink composition, extrusion pressure, and nozzle geometry rather than on any single variable. Alginate-methylcellulose (Alg-MC) is a widely used pharmaceutical bioink whose shear-thinning behavior makes this optimization particularly demanding. Here we developed a physics-informed machine-learning framework that predicts extrusion print quality before printing. A hybrid dataset of 3280 labeled samples was assembled by combining laboratory extrusion experiments, computational fluid dynamics (CFD) simulations of nozzle flow, and controlled augmentation with a deep belief network (DBN). Five physics-informed features encoding pressure-to-diameter and shear-rate interactions were engineered to embed rheological domain knowledge. Five base classifiers alongside two ensemble strategies, seven models in all, were benchmarked under five-fold stratified cross-validation. The voting ensemble performed best (accuracy, 93.3%; F1 = 0.929), narrowly ahead of the stacking ensemble (93.3% accuracy; F1 = 0.928). Alg:MC ratio, extrusion pressure, and the pressure-diameter interaction term stood out as the dominant predictors, consistent with established alginate rheology. On external validation-withholding one composition ratio and one needle gauge entirely from training and testing on those genuinely unseen conditions-accuracy was 75.9%, a stricter test than a random held-out split. Overall, the framework offers a practical, interpretable way to cut down experimental burden in pharmaceutical bioink development.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Microfluidic Preparation for Calcium Alginate Microspheres Based on Response Surface Methodology.","authors":"Na Li, Yuanbo Gao, Liang Lu, Yu Liu, Zhenbin Zhang, Xing Zhang","doi":"10.3390/bioengineering13101144","DOIUrl":"https://doi.org/10.3390/bioengineering13101144","url":null,"abstract":"<p><p>Although calcium alginate microspheres prepared by microfluidic methods have been widely investigated, the combined effects of dispersed-phase composition, continuous-phase properties, and surfactant combinations on microsphere formation remain insufficiently understood. In this study, a microfluidic system was established using sodium alginate as the aqueous phase, mineral oil as the continuous phase, and calcium chloride as the receiving phase. Response surface methodology was applied to model and optimize the key parameters involved in microsphere formation. The regression model showed good fit and reliable predictive ability. Under the optimized conditions, validation experiments yielded an average target-size fraction of 81.25%, which agreed well with the predicted value, demonstrating the feasibility and reproducibility of the optimized process. Biological evaluation showed that the optimized alginate microspheres exhibited no significant cytotoxicity toward BxPC-3 cells and maintained high cell viability, as confirmed by Live/Dead staining and CCK-8 assay. Confocal imaging further showed that the encapsulated BxPC-3 cells remained highly viable and formed three-dimensional clusters within the alginate matrix. These findings provide a reproducible microfluidic approach for generating calcium alginate microspheres with potential utility in cell encapsulation, three-dimensional culture, and related biomedical applications.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Combined Iron Oxide Magnetic Nanoparticles and Magnetic Field Exposure on Osteogenic Differentiation of Mesenchymal Stem Cells: A Systematic Review and Meta-Analysis.","authors":"Maksim Solopov, Roman Ishchenko, Viktor Turchin, Viktoria Bushe, Anna Kavelina, Gulnara Akopian, Elizaveta Lebed, Andrey Popandopulo","doi":"10.3390/bioengineering13101143","DOIUrl":"https://doi.org/10.3390/bioengineering13101143","url":null,"abstract":"<p><p>Combined iron oxide magnetic nanoparticles (MNPs) and external magnetic fields (MFs) are explored to direct osteogenic differentiation of mesenchymal stem cells (MSCs) in bone tissue engineering, yet exposure parameters vary widely. This systematic review and meta-analysis searched PubMed and Dimensions, with Consensus and SciSpace as exploratory scoping aids (final search 22 November 2025). Twenty-five studies entered qualitative synthesis and twenty-two quantitative synthesis ('MNP + MF' vs. 'MNP without MF'; standardized mean difference [SMD] as Hedges' g; restricted maximum likelihood random-effects model with Hartung-Knapp-Sidik-Jonkman confidence intervals [CIs]). Across nine outcomes, effect directions were predominantly positive; uncorrected significance held only for alkaline phosphatase (ALP) activity (SMD = +4.42; 95% CI 0.97-7.86; k = 14) and mineralization (SMD = +2.41; 0.03-4.80; k = 7). After Holm correction, no outcome remained significant. Heterogeneity was critical (I² > 75% for five outcomes). Funnel-plot asymmetry for ALP suggested possible small-study effects; trim-and-fill reduced the pooled SMD to +1.34 (-1.60-4.27). Findings constitute a low-certainty, hypothesis-generating signal rather than confirmed efficacy; standardized protocols and blinded designs are needed before translational claims.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"13 10","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149261648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}