Real-time synchronized monitoring for the overlap accuracy of the combining beam spot in a wavelength beam combination based on confocal planes.

Beam overlap accuracy in a wavelength beam combination system determines the beam quality and efficiency, so systematic monitoring of overlap accuracy is essential. In this work, a method of performing real-time synchronized monitoring and recording overlap accuracy for a combining beam spot is proposed. Firstly, theoretical calculations for monitoring different wavelength sub-beam positions and angular errors are established. Then, an optical design and grayscale centroid algorithm are developed to analyze and simulate the combination spots. A monitoring device was designed and constructed to meet the requirements of combining system applications, which achieved an accuracy of 8.86 µrad. Finally, the method successfully monitored the system spot fluctuation range within ±22 µrad. This study resolves the issue of distinguishing the different wavelength sub-beams and their response delays in traditional combining beams. It offers precise error data for real-time synchronized calibration of the overlap accuracy in laser beam combining technology.

The opposite effect of tapinarof between IMQ and IL-23 induced psoriasis mouse models.

Tapinarof is an aryl hydrocarbon receptor (AHR) ligand which is used to treat plaque psoriasis in adults. However, the underlying mechanism is not yet fully understood. In this study, we applied two of the most studied psoriasis mouse models: topical application of imiquimod (IMQ) and subcutaneous injection of IL-23. Although both models successfully induced psoriasis-like lesions in mice, tapinarof had a completely opposite effect on the two models. Tapinarof decreased the expression of multiple essential cytokines involved in the pathological IL-23/IL-17/IL-22 axis and ameliorated IMQ-induced psoriatic dermatitis, inhibiting keratinocyte proliferation and abnormal differentiation. However, in the IL-23-injection-model, tapinarof instead aggravated the disease. Here, tapinarof increased epidermal thickness and differentiated epidermal dysplasia in mice. Our data suggest that tapinarof may have different effects on varied types of psoriasis.

Efficient photoelectrocatalytic degradation of pollutants over hydrophobic carbon felt loaded with Fe-doped porous carbon nitride via direct activation of molecular oxygen.

Developing a photoelectric cathode capable of efficiently activating molecular oxygen to degrade pollutants is a coveted yet challenging goal. In pursuit of this, we synthesize a Fe doped porous carbon nitride catalyst (Fe-CN) using an ionothermal strategy and subsequently loaded it on the hydrophobic carbon felt (CF) to fabricate the Fe-CN/CF photoelectric cathode. This cathode benefits from the synergistic effects between the porous CN support and the highly dispersed Fe species, which enhance O2 absorption and activation. Additionally, the hydrophobic CF serves as a gas diffusion layer, accelerating O2 mass transfer. These features enable the Fe-CN/CF cathode to demonstrate notable photoelectrocatalytic (PEC) degradation efficiency. Specifically, under optimal conditions (cathodic bias of -0.3 VAg/AgCl, pH 7, and a catalyst loading of 3 mg/cm2), the system achieves a 76.4% removal rate of tetracycline (TC) within 60 min. The general application potential of this system is further underscored by its ability to remove approximately 98% of 4-chlorophenol (4-CP) and phenol under identical conditions. Subsequent investigations into the active species and degradation pathways reveal that 1O2 and h+ play dominant role during the PEC degradation process, leading to gradually breakdown of TC into less toxicity, smaller molecular intermediates. This work presents a straightforward yet effective strategy for constructing efficient PEC systems that leverage molecular oxygen activation to degrade pollutants.

The association between anion gap and length of stay in patients undergoing hip fracture surgery: data from the MIMIC-IV database.

OBJECTIVE: To explore the relationship between anion gap (AG) and length of stay (LOS) in patients undergoing hip fracture surgery. METHODS: Clinical data of patients diagnosed with hip fracture and undergoing surgery were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Logistic regression analysis by adjusting different covariables and threshold effect analysis were used to analyze the relationship between AG and LOS. Subgroup analysis and interaction test were also performed to detect their relationship. Receiver Operating Characteristic (ROC) analysis was performed to identify the prediction performance and cutoff value of AG.Kaplan-Meier (KM) survival analysis was used to explore the influence of AG on overall survival. RESULTS: A total of 1508 patients were enrolled and the median LOS was 4.9 days. The correlation between AG and LOS > 7 days was observed among 3 regression models when regarding AG as continuous variables (all OR > 1, all P < 0.05). After stratifying samples with AG quartiles, their relationship was only presented in the Q4 group both in model 1 and model 2 (all P < 0.001). The risk of LOS > 7 days gradually increased with increasing AG quartiles (all P for trend < 0.05). Further, threshold effect analysis found that their association was mainly observed when AG ≥ 14 mEq/L (OR = 1.122, P < 0.001). Subgroup analysis showed that their correlation was not influenced by sex, age, BMI, ethnicity, classification of fracture, therapeutic method, CHD, hypertension, osteoporosis, diabetes and admitted to the ICU (all P for interaction > 0.05). ROC analysis identified 14.5 as the cutoff value of AG for predicted LOS > 7 days. Survival analysis found that patients in the AG < 14.5 group had better overall survival. CONCLUSION: In patients undergoing hip fracture surgery, the AG was positively correlated with LOS, and 14.5 mEq/L AG was the cutoff value for predicting LOS > 7 days. The cutoff value can favorably distinguish the survival difference of patients.

Effectiveness of Multimodal Sound Therapy with Fine Examination in the Clinical Diagnosis of Chronic Subjective Tinnitus.

Background: In chronic subjective tinnitus, existing therapeutic approaches often fall short. This study addresses this gap by exploring the efficacy of multimodal sound therapy guided by fine examination. The study focused on providing a scientific foundation for more accurate auditory evaluation, offering novel insights into managing tinnitus-related disabilities. Objective: This study aimed to assess the effectiveness of multimodal sound therapy, guided by fine examination, in the clinical diagnosis of chronic subjective tinnitus. Methods: A total of 100 patients with chronic subjective tinnitus treated in our hospital from March 2018 to March 2019 were selected as study subjects. They were divided into an experimental group and a control group based on the order of admission. The experimental group (n=50) received treatment involving various complex sounds, while the control group (n=50) received drug therapy. Fine examination was conducted in both groups, and tinnitus disability was compared. Additionally, the tinnitus disability scale score, Pittsburgh sleep quality index, and Hamilton depression and anxiety scale score were compared between the two groups. Results: After three months of treatment, the experimental group demonstrated noteworthy improvements compared to the control group. Significant reductions in tinnitus disability (P < .05), along with notable enhancements in sleep quality (P < .05), and decreased scores for depression and anxiety (P < .05) were observed in the experimental group, highlighting the efficacy of multimodal sound therapy in addressing these aspects of chronic subjective tinnitus. Conclusions: Fine examination serves as a scientific foundation for the auditory evaluation of tinnitus patients, facilitating more precise localization of the tinnitus point. Multimodal sound therapy demonstrates a notable impact on chronic subjective tinnitus, warranting further exploration and widespread application.

Unique Tubular BiOBr/g-C3 N4 Heterojunction with Efficient Separation of Charge Carriers for Photocatalytic Nitrogen Fixation.

The industrial ammonia synthesis process consumes a lot of energy and causes serious environmental pollution. As a sustainable approach for ammonia synthesis, photocatalytic nitrogen reduction employing water as the reducing agent has a lot of potential. A simple surfactant-assisted solvothermal method is used to synthesize g-C3 N4 nanotubes with flower-like spherical BiOBr grown inside and outside (BiOBr/g-C3 N4 , BC). The hollow tubular structure realizes the full use of visible light by the multi-scattering effect of light. Large surface areas and more active sites for N2 adsorption and activation are present in the distinctive spatially dispersed hierarchical structures. Particularly, the quick separation and transfer of electrons and holes are facilitated by the sandwich tubular heterojunctions and tight contact interface of BiOBr and g-C3 N4 . The maximal NH3 generation rate of the BiOBr/g-C3 N4 composite catalysts can reach 255.04 µmol⋅ g-1 ⋅ h-1 , and it is 13.9 and 5.8 times that of pure BiOBr and g-C3 N4 . This work provides a novel method for designing and constructing unique heterojunctions for efficient photocatalytic nitrogen fixation.

S-alkyl Dithiocarbamates Synthesis through Electrochemical Multicomponent Reaction of Thiols, Hydrogen Sulfide, and Isocyanides.

Dithiocarbamates synthesis is extremely important in plenty of biomedical and agrochemical applications, especially fungicide development, but remains a great challenge. In this work, we have successfully developed a multicomponent reaction protocol to convert H2S into S-alkyl dithiocarbamates under constant current conditions. No additional oxidants nor additional catalysts are required, and due to mild conditions, the reactions display a broad substrate scope, including varieties of thiols or disulfides.

Genome-wide associated variants of subclinical atherosclerosis among young people with HIV and gene-environment interactions.

BACKGROUND: Genome-wide association studies (GWAS) have identified some variants associated with subclinical atherosclerosis (SCA) in general population but lacking sufficient validation. Besides traditional risk factors, whether and how would genetic variants associate with SCA among people with HIV (PWH) remains to be elucidated. METHOD: A large original GWAS and gene-environment interaction analysis of SCA were conducted among Chinese PWH (n = 2850) and age/sex-matched HIV-negative controls (n = 5410). Subgroup analyses by age and functional annotations of variants were also performed. RESULTS: Different from HIV-negative counterparts, host genome had a greater impact on young PWH rather than the elders: one genome-wide significant variant (rs77741796, P = 2.20 × 10-9) and eight suggestively significant variants (P < 1 × 10-6) were identified to be specifically associated with SCA among PWH younger than 45 years. Seven genomic loci and 15 genes were mapped to play a potential role on SCA among young PWH, which were enriched in the biological processes of atrial cardiac muscle cell membrane repolarization and molecular function of protein kinase A subunit binding. Furthermore, genome-wide interaction analyses revealed significant HIV-gene interactions overall as well as gene-environment interactions with alcohol consumption, tobacco use and obesity among PWH. The identified gene-environment interaction on SCA among PWH might be useful for discovering high-risk individuals for the prevention of SCA, particularly among those with tobacco use and alcohol consumption. CONCLUSION: The present study provides new clues for the genetic contribution of SCA among young PWH and is the starting point of precision intervention targeting HIV-related atherosclerosis.

Floquet Engineering Hz-Level Rabi Spectra in Shallow Optical Lattice Clock.

Quantum metrology with ultrahigh precision usually requires atoms prepared in an ultrastable environment with well-defined quantum states. Thus, in optical lattice clock systems deep lattice potentials are used to trap ultracold atoms. However, decoherence, induced by Raman scattering and higher order light shifts, can significantly be reduced if atomic clocks are realized in shallow optical lattices. On the other hand, in such lattices, tunneling among different sites can cause additional dephasing and strongly broadening of the Rabi spectrum. Here, in our experiment, we periodically drive a shallow ^{87}Sr optical lattice clock. Counterintuitively, shaking the system can deform the wide broad spectral line into a sharp peak with 5.4 Hz linewidth. With careful comparison between the theory and experiment, we demonstrate that the Rabi frequency and the Bloch bands can be tuned, simultaneously and independently. Our work not only provides a different idea for quantum metrology, such as building shallow optical lattice clock in outer space, but also paves the way for quantum simulation of new phases of matter by engineering exotic spin orbit couplings.

Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China.

Algal blooms caused by climate change and human activities have received considerable attention in recent years. Since chlorophyll a (Chl-a) can be used as an indicator of phytoplankton biomass, it has been selected as a direct indicator for monitoring and early warning of algal blooms. With the development of artificial intelligence, data-driven approaches with small sample data and high accuracy prediction have been gradually applied to water quality prediction. This study aimed at using environment factors (water quality and meteorological data) to assist the prediction of Chl-a concentration based on the optimization support vector machine (SVM) model. The most relevant environment factors were extracted from the commonly used environment factors according to the method of cosine similarity. The traditional particle swarm optimization (PSO) algorithm was adopted to optimize the ANN and SVM models, respectively. Then, the better prediction model PSO-SVM can be obtained according to the results of three scientific evaluation indicators. The latest optimization algorithm of grey wolf optimizer (GWO) was also proposed to optimize the SVM to realize high-accuracy Chl-a concentration predication. The GWO-SVM model achieved higher accuracy than the other models both in training and validation processes. Therefore, the dimension of the input vector could be reduced with using the cosine similarity method, and the prediction of Chl-a concentration in high accuracy and the early warning of algal blooms in the study area of this paper could also achieved.

Increased expression of GAB1 promotes inflammation and fibrosis in systemic sclerosis.

Systemic sclerosis (SSc) is an autoimmune disease mainly characterized by persistent inflammation and fibrosis. The receptor tyrosine kinase (RTK) signal pathway plays an important role in the process of SSc, and Grb2-associated binding protein (GAB) is crucial in activating RTK signalling. A previous study found elevated levels of GAB1 in bleomycin (BLM)-induced fibrotic lungs, but the effects of GAB1 in SSc remain unclear. Our aim was to investigate whether GAB1 was dysregulated and its potential role in SSc. Compared with healthy donors, we found GAB1 expression was 1.6-fold higher in peripheral blood mononuclear cells (PBMC), 2.5-fold higher in CD4 + T cells, and 2-fold higher in skin from of SSc patients (P < .01). At the same time, the levels of type one collagen (COLI) were also significantly increased (1.8-fold higher) in SSc skin. Additionally, BLM-induced SSc mice showed mRNA levels of Gab1 2-fold higher than saline-treated controls, and Gab1 expression correlated positively with collagen content. A further in vitro study showed silencing of GAB1 suppressed inflammatory gene expression in TNF-α induced fibroblasts. Additionally, GAB1 deficiency prominently inhibited cell proliferation and reduced COLI protein levels in TGF-ß induced fibroblasts. Taken together, these data suggest that GAB1 has a relatively high expression rate in SSc, and knockdown of GAB1 may attenuate SSc by stimulating inflammatory and fibrotic processes.

Improved vector-extrapolation-based Richardson-Lucy algorithm used for wavefront coded imaging.

The Richardson-Lucy (RL) algorithm is a well-known nonlinear restoration method and has been widely applied in the fields of astronomical image restoration, microscopic image restoration, and so on because of its capability of generating high-quality restoration results and potential in realizing super-resolution. However, when being applied to restore the wavefront coded blurry images, the classical RL algorithm converges very slowly and has to be iterated many times before obtaining a satisfactory result, which severely prohibits its real-time application. Vector-extrapolation-based RL algorithm was invented to solve this problem, but the noise amplification increases fast, and additional post-processing is needed to further improve the signal-to-noise ratio. Therefore, in this paper, an improved RL algorithm is proposed by introducing an exponential modified correction term into the framework of the original vector-extrapolation-based RL algorithm. It not only results in a bigger iteration step, which ensures a faster convergence can be obtained, but also the noise amplification is effectively prohibited. Besides that, we design a structure-similarity-index-metric-based stopping criterion, based on which the optimum number of iterations for each color channel is obtained. Experimental results reveal that the total iterations decreases approximately 78.9%, and the restored images demonstrate a superior visual quality without denoising additionally.

Genetic dissection of plexin signaling in vivo.

Mammalian plexins constitute a family of transmembrane receptors for semaphorins and represent critical regulators of various processes during development of the nervous, cardiovascular, skeletal, and renal system. In vitro studies have shown that plexins exert their effects via an intracellular R-Ras/M-Ras GTPase-activating protein (GAP) domain or by activation of RhoA through interaction with Rho guanine nucleotide exchange factor proteins. However, which of these signaling pathways are relevant for plexin functions in vivo is largely unknown. Using an allelic series of transgenic mice, we show that the GAP domain of plexins constitutes their key signaling module during development. Mice in which endogenous Plexin-B2 or Plexin-D1 is replaced by transgenic versions harboring mutations in the GAP domain recapitulate the phenotypes of the respective null mutants in the developing nervous, vascular, and skeletal system. We further provide genetic evidence that, unexpectedly, the GAP domain-mediated developmental functions of plexins are not brought about via R-Ras and M-Ras inactivation. In contrast to the GAP domain mutants, Plexin-B2 transgenic mice defective in Rho guanine nucleotide exchange factor binding are viable and fertile but exhibit abnormal development of the liver vasculature. Our genetic analyses uncover the in vivo context-dependence and functional specificity of individual plexin-mediated signaling pathways during development.

Effects of Non-Elevation-Focalized Linear Array Transducer on Ultrasound Plane-Wave Imaging.

Plane-wave ultrasound imaging (PWUS) has become an important method of ultrasound imaging in recent years as its frame rate has exceeded 10,000 frames per second, allowing ultrasound to be used for two-dimensional shear wave detection and functional brain imaging. However, compared to the traditional focusing and scanning method, PWUS images always suffer from a degradation of lateral resolution and contrast. To improve the image quality of PWUS, many different beamforming algorithms have been proposed and verified. Yet the influence of transducer structure is rarely studied. For this paper, the influence of using an acoustic lens for PWUS was evaluated. Two linear array transducers were fabricated. One was not self-focalized in the elevation direction (non-elevation-focalized transducer, NEFT); the other one was a traditional elevation-focalized transducer (EFT). An initial simulation was conducted to show the influence of elevation focusing. Then the images obtained with NEFT on a standard ultrasound imaging phantom were compared with those obtained with EFT. It was demonstrated that, in a relatively deep region, the contrast of an NEFT image is better than that of an EFT image. These results indicate that a more sophisticated design of ultrasound transducer would further improve the image quality of PWUS.

[N,N'-Bis(2,6-diethyl-4-phenyl-phen-yl)butane-2,3-di-imine-κ(2) N,N']di-bromido-nickel(II).

The complex molecule in the title compound, [NiBr2(C36H40N2)], has mirror symmetry. The Ni(II) atom and two Br atoms are located on the mirror plane. The Ni(II) atom is four-coordinated by the two Br atoms and two N atoms from an N,N'-bis(2,6-diethyl-4-phenyl-phen-yl)butane-2,3-di-imine ligand in a distorted tetra-hedral geometry. The dihedral angle formed between the two adjacent benzene rings is 47.1 (1)°.

Light Scattering Method for Aerosol Sizing Based on Machine Learning.

Optical scattering has been widely used for aerosol sizing due to its noninvasive and real-time measurement. However, it is crucial to retrieve the particle size distribution (PSD) of aerosols without prior knowledge of the refractive index. Now, it has been a great challenge to measure the refractive index in situ. In this study, a novel PSD sensing method utilizing the light scattering angular spectrum (LSAS) and machine learning techniques is proposed to address this challenge. The complex nonlinear relationship between LSAS and PSD can be constructed while accounting for the refractive index of aerosols. A miniaturized prototype sensor is designed and tested on different sizes of aerosol samples. The experiment results showed that the maximum Kullback-Leibler divergence (DKL) of PSD is 0.07, which indicates that the sensing method can provide the ability for highly accurate aerosol PSD measurement without requiring prior knowledge of the refractive index. The compacted prototype sensor shows great potential for aerosol analysis in conventional field measurements outside the laboratory.

Association between sleep quality and uncertainty stress among healthcare professionals in hospitals in China: a nationwide cross-sectional survey.

OBJECTIVES: Sleep quality is a critical concern among healthcare professionals, yet the role of uncertainty stress has been inadequately explored. This study aims to explore the associations between sleep quality and uncertainty stress among healthcare professionals working in Chinese hospitals. DESIGN: Cross-sectional survey. SETTING: Data were collected via a cross-sectional survey administered to healthcare professionals across three Chinese provinces from 29 September 2022 to 18 January 2023. PARTICIPANTS: A total of 1902 participants contributed valid responses for analysis. PRIMARY OUTCOME MEASURE: Sleep quality. RESULTS: Out of 1902 respondents, 26.4% reported uncertainty stress and 50.5% experienced insomnia. Binary logistic regression analysis revealed that higher uncertainty stress significantly predicted poor sleep quality (OR=3.89; 95% CI 3.06, 4.95; p<0.001) while controlling for sociodemographic characteristics. Furthermore, linear regression analysis confirmed the similar relationship between uncertainty stress and sleep quality (ß=3.10; 95% CI 2.67, 3.52; p<0.001). CONCLUSIONS: The study highlights a significant association between uncertainty stress and impaired sleep quality among Chinese healthcare professionals. These findings suggest the necessity for targeted strategies to mitigate uncertainty stress, which may potentially promote better sleep and overall well-being in healthcare settings.

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks.

Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is paramount. However, traditional identification methods, which often involve staining and culturing procedures, are not only time-consuming but also laborious. This underscores the need for a novel approach that enables rapid and automated cell line identification, thereby enhancing research efficiency and accuracy. Raman spectroscopy, renowned for its label-free, rapid, and noninvasive nature, offers invaluable molecular insights into samples, making it a widely utilized technique in the biological field. In this study, the identification of one normal and five cancer cell lines was achieved using a sparrow search algorithm-convolutional neural networks (SSA-CNN), considering both the full spectra and fingerprint region perspectives. The SSA-CNN model demonstrated exceptional performance, not just in binary classification, but also in accurately distinguishing among six cell lines. It achieved the highest accuracy (around 95 %), and the lowest standard error (≤3%), for both the full spectra and fingerprint region. Based on the highly accurate SSA-CNN model, proposed the application of gradient-weighted class activation mapping (Grad-CAM) to visualize the Raman feature peaks. Upon comparing the visualized Raman features with reported biomarkers, found that not only were common biomolecules such as glucose, proteins, and liquids visualized, but specific feature peaks also aligned with reported biomarkers. The aforementioned results clearly demonstrated that the proposed strategy not only classifies cancer cell lines with remarkable accuracy but also served as a valuable tool for the discovery of novel biomarkers.

Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization.

PROBLEMS: Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the current Raman prediction models fail to cover all the molecular sub-types of breast cancer, and lack the visualization of the model. AIMS: Using Raman spectroscopy combined with convolutional neural network (CNN) to construct a prediction model for the existing known molecular sub-types of breast cancer, and selected critical peaks through visualization strategies, so as to achieve the purpose of mining specific biomarker information. METHODS: Optimizing network parameters with the help of sparrow search algorithm (SSA) for the multiple parameters in the CNN to improve the prediction performance of the model. To avoid the contingency of the results, multiple sets of data were generated through Monte Carlo sampling and used to train the model, thereby improving the credibility of the results. Based on the accurate prediction of the model, the spectral regions that contributed to the classification were visualized using Gradient-weighted Class Activation Mapping (Grad-CAM), achieving the goal of visualizing characteristic peaks. RESULTS: Compared with other algorithms, optimized CNN could obtain the highest accuracy and lowest standard error. And there was no significant difference between using full spectra and fingerprint regions (within 2 %), indicating that the fingerprint region provided the most contribution in classifying sub-types. Based on the classification results from the fingerprint region, the model performances about various sub-types were as follows: CNN (95.34 %±2.18 %)>SVM(94.90 %±1.88 %)>PLS-DA(94.52 %±2.22 %)> KNN (80.00 %±5.27 %). The critical features visualized by Grad-CAM could match well with IHC information, allowing for a more distinct differentiation of sub-types in their spatial positions. CONCLUSION: Raman spectroscopy combined with CNN could achieve accurate and rapid identification of breast cancer molecular sub-types. Proposed visualization strategy could be proved from biochemistry information and spatial location, demonstrated that the strategy might be used for the mining of biomarkers in future.

Towards the Clinical Translation of 3D PLGA/ß-TCP/Mg Composite Scaffold for Cranial Bone Regeneration.

Recent years have witnessed the rapid development of 3D porous scaffolds with excellent biocompatibility, tunable porosity, and pore interconnectivity, sufficient mechanical strength, controlled biodegradability, and favorable osteogenesis for improved results in cranioplasty. However, clinical translation of these scaffolds has lagged far behind, mainly because of the absence of a series of biological evaluations. Herein, we designed and fabricated a composite 3D porous scaffold composed of poly (lactic-co-glycolic) acid (PLGA), ß-tricalcium phosphate (ß-TCP), and Mg using the low-temperature deposition manufacturing (LDM) technique. The LDM-engineered scaffolds possessed highly porous and interconnected microstructures with a porosity of 63%. Meanwhile, the scaffolds exhibited mechanical properties close to that of cancellous bone, as confirmed by the compression tests. It was also found that the original composition of scaffolds could be maintained throughout the fabrication process. Particularly, two important biologic evaluations designed for non-active medical devices, i.e., local effects after implantation and subchronic systemic toxicity tests, were conducted to evaluate the local and systemic toxicity of the scaffolds. Additionally, the scaffolds exhibited significant higher mRNA levels of osteogenic genes compared to control scaffolds, as confirmed by an in vitro osteogenic differentiation test of MC3T3-E1 cells. Finally, we demonstrated the improved cranial bone regeneration performance of the scaffolds in a rabbit model. We envision that our investigation could pave the way for translating the LDM-engineered composite scaffolds into clinical products for cranial bone regeneration.