sCD14-ST and Related Osteoimmunological Biomarkers: A New Diagnostic Approach to Osteomyelitis.

Osteomyelitis (OM) is a major challenge in orthopedic surgery. The diagnosis of OM is based on imaging and laboratory tests, but it still presents some limitations. Therefore, a deeper comprehension of the pathogenetic mechanisms could enhance diagnostic and treatment approaches. OM pathogenesis is based on an inflammatory response to pathogen infection, leading to bone loss. The present study aims to investigate the potential diagnostic role of a panel of osteoimmunological serum biomarkers in the clinical approach to OM. The focus is on the emerging infection biomarker sCD14-ST, along with osteoimmunological and inflammatory serum biomarkers, to define a comprehensive biomarker panel for a multifaced approach to OM. The results, to our knowledge, demonstrate for the first time the diagnostic and early prognostic role of sCD14-ST in OM patients, suggesting that this biomarker could address the limitations of current laboratory tests, such as traditional inflammatory markers, in diagnosing OM. In addition, the study highlights a relevant diagnostic role of SuPAR, the chemokine CCL2, the anti-inflammatory cytokine IL-10, the Wnt inhibitors DKK-1 and Sclerostin, and the RANKL/OPG ratio. Moreover, CCL2 and SuPAR also exhibited early prognostic value.

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning.

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.

Precise Localization for Anatomo-Physiological Hallmarks of the Cervical Spine by Using Neural Memory Ordinary Differential Equation.

In the evaluation of cervical spine disorders, precise positioning of anatomo-physiological hallmarks is fundamental for calculating diverse measurement metrics. Despite the fact that deep learning has achieved impressive results in the field of keypoint localization, there are still many limitations when facing medical image. First, these methods often encounter limitations when faced with the inherent variability in cervical spine datasets, arising from imaging factors. Second, predicting keypoints for only 4% of the entire X-ray image surface area poses a significant challenge. To tackle these issues, we propose a deep neural network architecture, NF-DEKR, specifically tailored for predicting keypoints in cervical spine physiological anatomy. Leveraging neural memory ordinary differential equation with its distinctive memory learning separation and convergence to a singular global attractor characteristic, our design effectively mitigates inherent data variability. Simultaneously, we introduce a Multi-Resolution Focus module to preprocess feature maps before entering the disentangled regression branch and the heatmap branch. Employing a differentiated strategy for feature maps of varying scales, this approach yields more accurate predictions of densely localized keypoints. We construct a medical dataset, SCUSpineXray, comprising X-ray images annotated by orthopedic specialists and conduct similar experiments on the publicly available UWSpineCT dataset. Experimental results demonstrate that compared to the baseline DEKR network, our proposed method enhances average precision by 2% to 3%, accompanied by a marginal increase in model parameters and the floating-point operations (FLOPs). The code (https://github.com/Zhxyi/NF-DEKR) is available.

Q-ADER: An Effective Q-Learning for Recommendation With Diminishing Action Space.

Deep reinforcement learning (RL) has been widely applied to personalized recommender systems (PRSs) as they can capture user preferences progressively. Among RL-based techniques, deep Q-network (DQN) stands out as the most popular choice due to its simple update strategy and superior performance. Typically, many recommendation scenarios are accompanied by the diminishing action space setting, where the available action space will gradually decrease to avoid recommending duplicate items. However, existing DQN-based recommender systems inherently grapple with a discrepancy between the fixed full action space inherent in the Q-network and the diminishing available action space during recommendation. This article elucidates how this discrepancy induces an issue termed action diminishing error in the vanilla temporal difference (TD) operator. Due to this discrepancy, standard DQN methods prove impractical for learning accurate value estimates, rendering them ineffective in the context of diminishing action space. To mitigate this issue, we propose the Q-learning-based action diminishing error reduction (Q-ADER) algorithm to modify the value estimate error at each step. In practice, Q-ADER augments the standard TD learning with an error reduction term which is straightforward to implement on top of the existing DQN algorithms. Experiments are conducted on four real-world datasets to verify the effectiveness of our proposed algorithm.

A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations.

The deep neural network, based on the backpropagation learning algorithm, has achieved tremendous success. However, the backpropagation algorithm is consistently considered biologically implausible. Many efforts have recently been made to address these biological implausibility issues, nevertheless, these methods are tailored to discrete neural network structures. Continuous neural networks are crucial for investigating novel neural network models with more biologically dynamic characteristics and for interpretability of large language models. The neural memory ordinary differential equation (nmODE) is a recently proposed continuous neural network model that exhibits several intriguing properties. In this study, we present a forward-learning algorithm, called nmForwardLA, for nmODE. This algorithm boasts lower computational dimensions and greater efficiency. Compared with the other learning algorithms, experimental results on MNIST, CIFAR10, and CIFAR100 demonstrate its potency.

Prototype Bayesian Meta-Learning for Few-Shot Image Classification.

Meta-learning aims to leverage prior knowledge from related tasks to enable a base learner to quickly adapt to new tasks with limited labeled samples. However, traditional meta-learning methods have limitations as they provide an optimal initialization for all new tasks, disregarding the inherent uncertainty induced by few-shot tasks and impeding task-specific self-adaptation initialization. In response to this challenge, this article proposes a novel probabilistic meta-learning approach called prototype Bayesian meta-learning (PBML). PBML focuses on meta-learning variational posteriors within a Bayesian framework, guided by prototype-conditioned prior information. Specifically, to capture model uncertainty, PBML treats both meta-and task-specific parameters as random variables and integrates their posterior estimates into hierarchical Bayesian modeling through variational inference (VI). During model inference, PBML employs Laplacian estimation to approximate the integral term over the likelihood loss, deriving a rigorous upper-bound for generalization errors. To enhance the model's expressiveness and enable task-specific adaptive initialization, PBML proposes a data-driven approach to model the task-specific variational posteriors. This is achieved by designing a generative model structure that incorporates prototype-conditioned task-dependent priors into the random generation of task-specific variational posteriors. Additionally, by performing latent embedding optimization, PBML decouples the gradient-based meta-learning from the high-dimensional variational parameter space. Experimental results on benchmark datasets for few-shot image classification illustrate that PBML attains state-of-the-art or competitive performance when compared to other related works. Versatility studies demonstrate the adaptability and applicability of PBML in addressing diverse and challenging few-shot tasks. Furthermore, ablation studies validate the performance gains attributed to the inference and model components.

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging.

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.

Mitochondrial GPX4 acetylation is involved in cadmium-induced renal cell ferroptosis.

Increasing evidences demonstrate that environmental stressors are important inducers of acute kidney injury (AKI). This study aimed to investigate the impact of exposure to Cd, an environmental stressor, on renal cell ferroptosis. Transcriptomics analyses showed that arachidonic acid (ARA) metabolic pathway was disrupted in Cd-exposed mouse kidneys. Targeted metabolomics showed that renal oxidized ARA metabolites were increased in Cd-exposed mice. Renal 4-HNE, MDA, and ACSL4, were upregulated in Cd-exposed mouse kidneys. Consistent with animal experiments, the in vitro experiments showed that mitochondrial oxidized lipids were elevated in Cd-exposed HK-2 cells. Ultrastructure showed mitochondrial membrane rupture in Cd-exposed mouse kidneys. Mitochondrial cristae were accordingly reduced in Cd-exposed mouse kidneys. Mitochondrial SIRT3, an NAD+-dependent deacetylase that regulates mitochondrial protein stability, was reduced in Cd-exposed mouse kidneys. Subsequently, mitochondrial GPX4 acetylation was elevated and mitochondrial GPX4 protein was reduced in Cd-exposed mouse kidneys. Interestingly, Cd-induced mitochondrial GPX4 acetylation and renal cell ferroptosis were exacerbated in Sirt3-/- mice. Conversely, Cd-induced mitochondrial oxidized lipids were attenuated in nicotinamide mononucleotide (NMN)-pretreated HK-2 cells. Moreover, Cd-evoked mitochondrial GPX4 acetylation and renal cell ferroptosis were alleviated in NMN-pretreated mouse kidneys. These results suggest that mitochondrial GPX4 acetylation, probably caused by SIRT3 downregulation, is involved in Cd-evoked renal cell ferroptosis.

Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network.

Aims: Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results: We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion: Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

Coexistence of Cyclic Sequential Pattern Recognition and Associative Memory in Neural Networks by Attractor Mechanisms.

Neural networks are developed to model the behavior of the brain. One crucial question in this field pertains to when and how a neural network can memorize a given set of patterns. There are two mechanisms to store information: associative memory and sequential pattern recognition. In the case of associative memory, the neural network operates with dynamical attractors that are point attractors, each corresponding to one of the patterns to be stored within the network. In contrast, sequential pattern recognition involves the network memorizing a set of patterns and subsequently retrieving them in a specific order over time. From a dynamical perspective, this corresponds to the presence of a continuous attractor or a cyclic attractor composed of the sequence of patterns stored within the network in a given order. Evidence suggests that the brain is capable of simultaneously performing both associative memory and sequential pattern recognition. Therefore, these types of attractors coexist within the neural network, signifying that some patterns are stored as point attractors, while others are stored as continuous or cyclic attractors. This article investigates the coexistence of cyclic attractors and continuous or point attractors in certain nonlinear neural networks, enabling the simultaneous emergence of various memory mechanisms. By selectively grouping neurons, conditions are established for the existence of cyclic attractors, continuous attractors, and point attractors, respectively. Furthermore, each attractor is explicitly represented, and a competitive dynamic emerges among these coexisting attractors, primarily regulated by adjustments to external inputs.

Analysis of relevant factors influencing size of breast abscess cavity during lactation: a cross-sectional study.

OBJECTIVE: The aim of this study was to investigate risk factors for the severity of breast abscess during lactation. METHODS: A cross-sectional study was conducted using data from the Questionnaire survey of breast abscess patients. According to whether the maximum abscess diameter > 5 cm, the patients were divided into two groups for univariate and multivariate regression analysis. RESULTS: 1805 valid questionnaires were included. Univariate and Binary logistic regression analysis demonstrated that low education (OR = 1.5, 95% CI 1.1-2.0, P = 0.005), non-exclusive breastfeeding (OR = 0.7, 95% CI 0.6-0.9, P = 0.004), fever > 37.5 â„ƒ (OR = 0.7, 95% CI 0.6-0.9, P = 0.003), flat or inverted nipples (OR = 0.7, 95% CI 0.6-0.9, P = 0.005), antibiotic used (OR = 0.7, 95% CI 0.6-0.9, P = 0.006), and non-medical massage (OR = 0.3, 95% CI 0.2-0.4, P < 0.001) were the effective independent influencing factors for the maximum breast abscess diameter > 5 cm. CONCLUSION: Low education, non-exclusive breastfeeding, fever > 37.5 â„ƒ, inverted or flat nipples, antibiotic used, and non-medical massage history have adverse effects on the severity of breast abscess during lactation.

A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation.

Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named [Formula: see text]-net. We provide a proof of convergence for the [Formula: see text]-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.

Efficacy of Tounongsan decoction on pyogenic liver abscess: network pharmacology and clinical trial validation.

OBJECTIVE: To elucidate the molecular mechanisms governing the effect of Tounongsan decoction (, TNS) on the pyogenic liver abscess. METHODS: Based on oral bioavailability and drug-likeness, the main active components of TNS were screened using the Traditional Chinese Medicine Systems Pharmacology platform. The GeneCard and UniProt databases were used to establish a database of pyogenic liver abscess targets. The interactive network map of drug-ingredients-target-disease was constructed using Cytoscape software (Version 3.7.2). A protein-protein interaction network was constructed using the STRING database, and the related protein interaction relationships were analyzed. biological process of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for the core targets. Finally, a clinical trial was performed to verify the reliability of the network pharmacology. RESULTS: Forty active components of TNS decoction were obtained, and 61 potential targets and 11 proteins were identified. Pathways involved in the treatment of pyogenic liver abscess include the phosphatidylinositide 3-kinases-protein kinase B (PI3K-AKT), advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE), and tumor necrosis factor (TNF) signaling pathways. The results of network pharmacology analysis combined with clinical trials validated that TNS decoction could alleviate the inflammatory response of pyogenic liver abscesses by decreasing interleukin 6 (IL-6) levels. CONCLUSIONS: TNS decoction has the characteristics of being multi-system, multi-component, and multi-target. Active ingredients in TNS, such as quercetin, kaempferol, fisetin, and ß-sitosterol, have strong potential to be candidate drugs for treating pyogenic liver abscesses. The possible mechanism of TSN decoction includes regulating immune and inflammatory responses and reducing IL-6 production to control inflammatory development.

Hsa_circ_0013561 promotes progression of nasopharyngeal carcinoma by activating JAK2/STAT3 signaling pathway.

OBJECTIVE: Nasopharyngeal Carcinoma (NPC) is a malignancy of epithelium of epithelium of the nasopharynx, with the highest incidence of otolaryngeal malignancies. A growing number of studies confirm that Circular RNA (circRNA) plays an important role in tumor development, including Hsa_circ_0013561. This study aims to elucidate the process and mechanism of NPC regulation hsa_circ_0013561. METHODS: In this study, circRNA expression nodes and subcellular localization in NPC tissues were analyzed by fluorescence in situ hybridization. The expression of hsa_circ_0013561 in NPC cells was further clarified by RT-qPCR. At the same time, the lentivirus vector interfered by hsa_circ_0013561 was constructed and transfected. The cell proliferation was detected by CCK-8 method, EdU assay and plate cloning assay. The cell cycle and apoptosis were detected by flow cytometry, and the cell migration ability was detected by wound healing assay and Transwell assay. Western blotting examined the expression of apoptosis, Epithelial-Mesenchymal Transition (EMT)-associated proteins, and Janus Kinase/Signal Transductor and Activator of Transcription (JAK/STAT) signaling pathway-related proteins. RESULTS: The results showed that the expression of hsa_circ_0013561 in NPC samples was significantly upregulated and hsa_circ_0013561 localized in the cytoplasm. After down-regulating hsa_circ_0013561 expression, it significantly inhibited the proliferation and metastasis ability of NPC, inhibited EMT progression, and promoted apoptosis. Further studies showed that interference hsa_circ_0013561 significantly inhibited JAK2/STAT3 signaling pathway activation and induced the expression of apoptosis-related proteins. CONCLUSION: In summary, we found that hsa_circ_0013561 is a pro-tumor circRNA in NPC, which can reduce the activation of JAK2/STAT3 pathway by knocking down hsa_circ_0013561, thereby slowing down the malignant progression of NPC. OXFORD CENTRE FOR EVIDENCE-BASED MEDICINE 2011 LEVELS OF EVIDENCE: Level 4.

Screening of Core Prescriptions for Tremors Based on Integrated Strategy of "Empirical Prescriptions in Ancient Books-medical Cases by Prestigious Doctors-computational Analysis" / 中国实验方剂学杂志

ObjectiveBased on the integrated strategy of "empirical prescriptions in ancient books-medical cases by prestigious doctors-computational analysis"， this study aims to explore and analyze the prescriptions and medical cases for treating tremors in traditional Chinese medicine （TCM）， predict their efficacy， and obtain the core prescriptions for treating tremors in TCM， providing references for clinical application and new drug development. MethodThe Chinese Medicine Prescription Database and the China National Knowledge Infrastructure （CNKI） were searched for relevant prescriptions and medical cases for treating tremors in TCM to establish a database of prescriptions for tremors. The Traditional Chinese Medicine Inheritance Computer System （V3.0） was used to analyze and explore the medication rules including drug frequency， properties， flavor， meridian tropism， and pharmacological effects， as well as core drugs and formula associations. A multi-target drug efficacy prediction platform based on network robustness was used to evaluate the predicted efficacy of the core prescriptions obtained. Based on the integration of ancient prescriptions， prestigious doctors' medical cases， and network analysis results， the priority level of the developed prescriptions was determined through comprehensive evaluation. ResultA total of 81 ancient prescriptions were screened， involving 246 drugs， and 171 prescriptions were screened from prestigious doctors' medical cases， involving 278 drugs. The frequently used TCM drugs were mostly warm in nature and sweet in flavor， mainly acting on the liver， spleen， and kidney meridians. In terms of efficacy， they were mainly effective in tonifying deficiency， soothing liver and extinguishing wind， activating blood and resolving blood stasis， clearing heat， and resolving exterior. Through association rules and K-means clustering， the core prescriptions were composed of high-frequency drugs such as Glycyrrhizae Radix et Rhizoma， Ginseng Radix et Rhizoma， Astragali Radix， Atractylodis Macrocephalae Rhizoma， Angelicae Sinensis Radix， Poria， Gastrodiae Rhizoma， and Uncariae Ramulus Cum Uncis. Drug combinations mainly focused on tonifying Qi and nourishing blood， with the additional functions of calming wind and dredging collaterals. Clustering analysis of core prescriptions from ancient prescriptions and prestigious doctors' medical cases， as well as multi-target drug efficacy prediction， showed that Combination 1 had the highest disturbance score on the disease network. Furthermore， comparative analysis revealed consistent results with both the analysis of ancient prescriptions and prestigious doctors' medical cases， indicating its optimal development potential based on theoretical inheritance and empirical practice. In comparison， Combinations 3， 2， and 4 were less utilized in contemporary clinical practice， with lower rankings in network disturbance scores， suggesting that their development value still warranted further exploration. ConclusionTCM clinical treatment of tremors emphasizes the regulation of the liver， spleen， and kidney. In line of syndrome differentiation， drugs potent in soothing liver， extinguishing wind， activating blood， and resolving blood stasis are added based on deficiency-tonifying drugs. The core prescriptions based on Glycyrrhizae Radix et Rhizoma， Angelicae Sinensis Radix， Paeoniae Radix Alba， Astragali Radix， Poria， and Atractylodis Macrocephalae Rhizoma （combination 1） have the highest potential development value. The integrated strategy "empirical prescriptions in ancient books-medical cases by prestigious doctors-computational analysis" can be used for the screening of candidate prescriptions for new TCM drugs.

Effects of different polymers on biomimetic mineralization of small intestine submu-cosal scaffolds / 北京大学学报(医学版)

Objective:To explore the effects of different polymers on in vitro biomimetic mineralization of small intestinal submucosa(SIS)scaffolds,and to evaluate the physicochemical properties and bio-compatibility of the SIS scaffolds.Methods:The SIS scaffolds prepared by freeze-drying method were im-mersed in simulated body fluid(SBF),mineralized liquid containing polyacrylic acid(PAA)and mine-ralized liquid containing PAA and polyaspartic acid(PASP).After two weeks in the mineralized solu-tion,the liquid was changed every other day.SBF@SIS,PAA@SIS,PAA/PASP@SIS scaffolds were ob-tained.The SIS scaffolds were used as control group to evaluate their physicochemical properties and bio-compatibility.We observed the bulk morphology of the scaffolds in each group,analyzed the microscopic morphology by environment scanning electron microscopy and determined the porosity and pore size.We also analyzed the surface elements by energy dispersive X-ray spectroscopy(EDX),analyzed the struc-ture of functional groups by Flourier transformed infrared spectroscopy(FTIR),detected the water ab-sorption rate by using specific gravity method,and evaluated the compression strength by universal me-chanical testing machine.The pro-cell proliferation effect of each group of scaffolds were evaluated by CCK-8 cell proliferation method.Results:Under scanning electron microscopy,the scaffolds of each group showed a three-dimensional porous structure with suitable pore size and porosity,and crystal was observed in all the mineralized scaffolds of each group,in which the crystal deposition of PAA/PASP@SIS scaffolds was more regular.At the same time,the collagen fibers could be seen to thicken.EDX analysis showed that the characteristic peaks of Ca and P were found in the three groups of mineralized scaffolds,and the highest peaks were found in the PAA/PASP@SIS scaffolds.FTIR analysis proved that all the three groups of mineralized scaffolds were able to combine hydroxyapatite with SIS.All the scaf-folds had good hydrophilicity.The compressive strength of the mineralized scaffold in the three groups was higher than that in the control group,and the best compressive strength was found in PAA/PASP@SIS scaffold.The scaffolds of all the groups could effectively adsorb proteins,and PAA/PASP@SIS group had the best adsorption capacity.In the CCK-8 cell proliferation experiment,the PAA/PASP@SIS scaffold showed the best ability to promote cell proliferation with the largest number of living cells observed.Con-clusion:Compared with other mineralized scaffolds,PAA/PASP@SIS scaffolds prepared by mineralized solution containing both PAA and PASP have better physicochemical properties and biocompatibility and have potential applications in bone tissue engineering.

Mandibular condyle localization in orthognathic surgery based on mandibular move-ment trajectory and its preliminary accuracy verification / 北京大学学报(医学版)

Objective:To establish and assess the precision of pre-surgical condyle position planning using mandibular movement trajectory data for orthognathic surgery.Methods:Skull data from large-field cone beam computed tomography(CBCT)and dental oral scan data were imported into IVSPlan 1.0.25 software for 3D reconstruction and fusion,creating 3D models of the maxilla and mandible.Trajectory da-ta of mandibular movement were collected using a mandibular motion recorder,and the data were inte-grated with the jaw models within the software.Subsequently,three-dimensional trajectories of the con-dyle were obtained through matrix transformations,rendering them visually accessible.A senior oral and maxillofacial surgeon with experience in both diagnosis and treatment of temporomandibular joint disease and orthognathic surgery selected the appropriate condyle position using the condyle movement trajectory interface.During surgical design,the mobile mandibular proximal segment was positioned accordingly.Routine orthognathic surgical planning was completed by determining the location of the mandibular distal segment,which was based on occlusal relationships with maxilla and facial aesthetics.A virtual mandible model was created by integrating data from the proximal and distal segment bone.Subsequently,a solid model was generated through rapid prototyping.The titanium plate was pre-shaped on the mandibular model,and the screw hole positions were determined to design a condylar positioning guide device.In accordance with the surgical plan,orthognathic surgery was performed,involving mandibular bilateral sagittal split ramus osteotomy(SSRO).The distal segment of the mandible was correctly aligned inter-maxillary,while the proximal bone segment was positioned using the condylar positioning guide device and the pre-shaped titanium plate.The accuracy of this procedure was assessed in a study involving 10 patients with skeletal class Ⅱ malocclusion.Preoperative condyle location planning and intraoperative po-sitioning were executed using the aforementioned techniques.CBCT data were collected both before the surgery and 2 weeks after the procedure,and the root mean square(RMS)distance between the preope-rative design position and the actual postoperative condyle position was analyzed.Results:The RMS of the condyle surface distance measured was(1.59±0.36)mm(95％CI:1.35-1.70 mm).This value was found to be significantly less than 2 mm threshold recommended by the expert consensus(P＜0.05).Conclusion:The mandibular trajectory may play a guiding role in determining the position of the mandibular proximal segment including the condyle in the orthognathic surgery.Through the use of a con-dylar positioning guide device and pre-shaped titanium plates,the condyle positioning can be personalized and customized with clinically acceptable accuracy.

Correlation between gene polymorphisms of coagulation factor Ⅻ rs1801020 and resistin rs1862513 and unexplained recurrent spontaneous abortion / 国际检验医学杂志

Objective To investigate the correlation between gene polymorphisms of coagulation factor Ⅻ(FⅫ)rs1801020 and resistin rs1862513 and unexplained recurrent spontaneous abortion(URSA).Methods A total of 189 patients diagnosed with URSA and 191 healthy postpartum women during the same period were selected from the obstetric clinic of Changning Maternity and Infant Health Hospital from January 2020 to December 2022.The probe PCR was used to detect gene polymorphisms of rs1801020 and rs1862513 in peripheral blood,and the differences in genotype distribution between the groups were observed.Results The frequencies of geno-types and alleles for F Ⅻ rs1801020 in the URSA-A group were 4.9％(CC),35.7％(CT),59.5％(TT),22.7％(C),and 77.3％(T),respectively.In the control A group,the frequencies were 8.0％(CC),47.1％(CT),44.9％(TT),31.5％(C)and 68.5％(T).The frequencies of genotypes and alleles for resistin rs1862513 in the URSA-B group were 11.3％(CC),47.3％(CG),41.4％(GG),34.9％(C)and 65.1％(G).In the control B group,the frequencies were 10.2％(CC),34.1％(CG),55.7％(GG),27.3％(C)and 72.7％(G).There was no significant difference in genotype frequency of the two loci(P＞0.05),but there was a sig-nificant difference in allele frequency(P＜0.05).The distribution frequency of F Ⅻ rs1801020 T allele in the URSA group was higher than that in the control group(X2=6.32,OR=1.567,95％CI:1.100-2.238,P=0.012).The distribution frequency of resistin rs1862513 G allele in URSA group was lower than that in con-trol group(X2=4.96,OR=1.433,95％CI:1.050-1.969,P=0.026).The mutation of F Ⅻ rs1801020 C to T was a risk factor for the occurrence of URSA,while the mutation of rs1862513 C to G was a protective factor for the occurrence of URSA(P＜0.05).The combined genotype analysis showed that compared to the popu-lation carrying the rs1801020 CC+rs1862513 CC genotype combination,the population carrying the rs1801020 TT+rs1862513 CG genotype had a significantly higher risk of URSA(OR=5.684,95％CI:1.210-30.920,P=0.035).Conclusion FⅫ rs1801020 T allele may increase the risk of URSA and resistin rs1862513 G al-lele may the risk of URSA.People with rs1801020 TT+rs1862513 CG genotype combination is more likely to develop URSA than those with rs1801020 CC+rs1862513 CC genotype combination.