Craniomaxillofacial landmarks detection in CT scans with limited labeled data via semi-supervised learning.

Background: Three-dimensional cephalometric analysis is crucial in craniomaxillofacial assessment, with landmarks detection in craniomaxillofacial (CMF) CT scans being a key component. However, creating robust deep learning models for this task typically requires extensive CMF CT datasets annotated by experienced medical professionals, a process that is time-consuming and labor-intensive. Conversely, acquiring large volume of unlabeled CMF CT data is relatively straightforward. Thus, semi-supervised learning (SSL), leveraging limited labeled data supplemented by sufficient unlabeled dataset, could be a viable solution to this challenge. Method: We developed an SSL model, named CephaloMatch, based on a strong-weak perturbation consistency framework. The proposed SSL model incorporates a head position rectification technique through coarse detection to enhance consistency between labeled and unlabeled datasets and a multilayers perturbation method which is employed to expand the perturbation space. The proposed SSL model was assessed using 362 CMF CT scans, divided into a training set (60 scans), a validation set (14 scans), and an unlabeled set (288 scans). Result: The proposed SSL model attained a detection error of 1.60 ± 0.87 mm, significantly surpassing the performance of conventional fully supervised learning model (1.94 ± 1.12 mm). Notably, the proposed SSL model achieved equivalent detection accuracy (1.91 ± 1.00 mm) with only half the labeled dataset, compared to the fully supervised learning model. Conclusions: The proposed SSL model demonstrated exceptional performance in landmarks detection using a limited labeled CMF CT dataset, significantly reducing the workload of medical professionals and enhances the accuracy of 3D cephalometric analysis.

Osmosensor-mediated control of Ca2+ spiking in pollen germination.

Higher plants survive terrestrial water deficiency and fluctuation by arresting cellular activities (dehydration) and resuscitating processes (rehydration). However, how plants monitor water availability during rehydration is unknown. Although increases in hypo-osmolarity-induced cytosolic Ca2+ concentration (HOSCA) have long been postulated to be the mechanism for sensing hypo-osmolarity in rehydration1,2, the molecular basis remains unknown. Because osmolarity triggers membrane tension and the osmosensing specificity of osmosensing channels can only be determined in vivo3-5, these channels have been classified as a subtype of mechanosensors. Here we identify bona fide cell surface hypo-osmosensors in Arabidopsis and find that pollen Ca2+ spiking is controlled directly by water through these hypo-osmosensors-that is, Ca2+ spiking is the second messenger for water status. We developed a functional expression screen in Escherichia coli for hypo-osmosensitive channels and identified OSCA2.1, a member of the hyperosmolarity-gated calcium-permeable channel (OSCA) family of proteins6. We screened single and high-order OSCA mutants, and observed that the osca2.1/osca2.2 double-knockout mutant was impaired in pollen germination and HOSCA. OSCA2.1 and OSCA2.2 function as hypo-osmosensitive Ca2+-permeable channels in planta and in HEK293 cells. Decreasing osmolarity of the medium enhanced pollen Ca2+ oscillations, which were mediated by OSCA2.1 and OSCA2.2 and required for germination. OSCA2.1 and OSCA2.2 convert extracellular water status into Ca2+ spiking in pollen and may serve as essential hypo-osmosensors for tracking rehydration in plants.

Elucidating the association of obstructive sleep apnea with brain structure and cognitive performance.

BACKGROUND: Obstructive sleep apnea (OSA) is a pervasive, chronic sleep-related respiratory condition that causes brain structural alterations and cognitive impairments. However, the causal association of OSA with brain morphology and cognitive performance has not been determined. METHODS: We conducted a two-sample bidirectional Mendelian randomization (MR) analysis to investigate the causal relationship between OSA and a range of neurocognitive characteristics, including brain cortical structure, brain subcortical structure, brain structural change across the lifespan, and cognitive performance. Summary-level GWAS data for OSA from the FinnGen consortium was used to identify genetically predicted OSA. Data regarding neurocognitive characteristics were obtained from published meta-analysis studies. Linkage disequilibrium score regression analysis was employed to reveal genetic correlations between OSA and related traits. RESULTS: Our MR study provided evidence that OSA was found to significantly increase the volume of the hippocampus (IVW ß (95% CI) = 158.997 (76.768 to 241.227), P = 1.51e-04), with no heterogeneity and pleiotropy detected. Nominally causal effects of OSA on brain structures, such as the thickness of the temporal pole with or without global weighted, amygdala structure change, and cerebellum white matter change covering lifespan, were observed. Bidirectional causal links were also detected between brain cortical structure, brain subcortical, cognitive performance, and OSA risk. LDSC regression analysis showed no significant correlation between OSA and hippocampus volume. CONCLUSIONS: Overall, we observed a positive association between genetically predicted OSA and hippocampus volume. These findings may provide new insights into the bidirectional links between OSA and neurocognitive features, including brain morphology and cognitive performance.

Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model.

BACKGROUND: Accurate cephalometric analysis plays a vital role in the diagnosis and subsequent surgical planning in orthognathic and orthodontics treatment. However, manual digitization of anatomical landmarks in computed tomography (CT) is subject to limitations such as low accuracy, poor repeatability and excessive time consumption. Furthermore, the detection of landmarks has more difficulties on individuals with dentomaxillofacial deformities than normal individuals. Therefore, this study aims to develop a deep learning model to automatically detect landmarks in CT images of patients with dentomaxillofacial deformities. METHODS: Craniomaxillofacial (CMF) CT data of 80 patients with dentomaxillofacial deformities were collected for model development. 77 anatomical landmarks digitized by experienced CMF surgeons in each CT image were set as the ground truth. 3D UX-Net, the cutting-edge medical image segmentation network, was adopted as the backbone of model architecture. Moreover, a new region division pattern for CMF structures was designed as a training strategy to optimize the utilization of computational resources and image resolution. To evaluate the performance of this model, several experiments were conducted to make comparison between the model and manual digitization approach. RESULTS: The training set and the validation set included 58 and 22 samples respectively. The developed model can accurately detect 77 landmarks on bone, soft tissue and teeth with a mean error of 1.81 ± 0.89 mm. Removal of region division before training significantly increased the error of prediction (2.34 ± 1.01 mm). In terms of manual digitization, the inter-observer and intra-observer variations were 1.27 ± 0.70 mm and 1.01 ± 0.74 mm respectively. In all divided regions except Teeth Region (TR), our model demonstrated equivalent performance to experienced CMF surgeons in landmarks detection (p > 0.05). CONCLUSIONS: The developed model demonstrated excellent performance in detecting craniomaxillofacial landmarks when considering manual digitization work of expertise as benchmark. It is also verified that the region division pattern designed in this study remarkably improved the detection accuracy.

Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning.

BACKGROUND: Preoperative planning of orthognathic surgery is indispensable for achieving ideal surgical outcome regarding the occlusion and jaws' position. However, orthognathic surgery planning is sophisticated and highly experience-dependent, which requires comprehensive consideration of facial morphology and occlusal function. This study aimed to investigate a robust and automatic method based on deep learning to predict reposition vectors of jawbones in orthognathic surgery plan. METHODS: A regression neural network named VSP transformer was developed based on Transformer architecture. Firstly, 3D cephalometric analysis was employed to quantify skeletal-facial morphology as input features. Next, input features were weighted using pretrained results to minimize bias resulted from multicollinearity. Through encoder-decoder blocks, ten landmark-based reposition vectors of jawbones were predicted. Permutation importance (PI) method was used to calculate contributions of each feature to final prediction to reveal interpretability of the proposed model. RESULTS: VSP transformer model was developed with 383 samples and clinically tested with 49 prospectively collected samples. Our proposed model outperformed other four classic regression models in prediction accuracy. Mean absolute errors (MAE) of prediction were 1.41 mm in validation set and 1.34 mm in clinical test set. The interpretability results of the model were highly consistent with clinical knowledge and experience. CONCLUSIONS: The developed model can predict reposition vectors of orthognathic surgery plan with high accuracy and good clinically practical-effectiveness. Moreover, the model was proved reliable because of its good interpretability.

A virtual surgical prototype system based on gesture recognition for virtual surgical training in maxillofacial surgery.

BACKGROUND: Virtual reality (VR) technology is an ideal alternative of operation training and surgical teaching. However, virtual surgery is usually carried out using the mouse or data gloves, which affects the authenticity of virtual operation. A virtual surgery system with gesture recognition and real-time image feedback was explored to realize more authentic immersion. METHOD: Gesture recognition technology proposed with an efficient and real-time algorithm and high fidelity was explored. The recognition of hand contour, palm and fingertip was firstly realized by hand data extraction. Then, an Support Vector Machine classifier was utilized to classify and recognize common gestures after extraction of feature recognition. The algorithm of collision detection adopted Axis Aligned Bounding Box binary tree to build hand and scalpel collision models. What's more, nominal radius theorem (NRT) and separating axis theorem (SAT) were applied for speeding up collision detection. Based on the maxillofacial virtual surgical system we proposed before, the feasibility of integration of the above technologies in this prototype system was evaluated. RESULTS: Ten kinds of signal static gestures were designed to test gesture recognition algorithms. The accuracy of gestures recognition is more than 80%, some of which were over 90%. The generation speed of collision detection model met the software requirements with the method of NRT and SAT. The response time of gesture] recognition was less than 40 ms, namely the speed of hand gesture recognition system was greater than 25 Hz. On the condition of integration of hand gesture recognition, typical virtual surgical procedures including grabbing a scalpel, puncture site selection, virtual puncture operation and incision were carried out with realization of real-time image feedback. CONCLUSION: Based on the previous maxillofacial virtual surgical system that consisted of VR, triangular mesh collision detection and maxillofacial biomechanical model construction, the integration of hand gesture recognition was a feasible method to improve the interactivity and immersion of virtual surgical operation training.

Small extracellular vesicles derived from hypoxic mesenchymal stem cells promote vascularized bone regeneration through the miR-210-3p/EFNA3/PI3K pathway.

Angiogenesis is closely coupled with osteogenesis and has equal importance. Thus, promoting angiogenesis during the bone repair process is vital for ideal bone regeneration. As important mediators of cell-cell communication and biological homeostasis, mesenchymal stem cell-derived small extracellular vesicles (MSC-sEVs) have been proved to be highly involved in bone and vascular regeneration. Because hypoxia microenvironment promotes the proangiogenic activity of MSCs, in the present study, we investigate the effect and underlying molecular mechanisms of sEVs from hypoxia-preconditioned MSCs (hypo-sEVs) on angiogenesis and develop an effective strategy to promote vascularized bone regeneration. Compared to sEVs from normoxia MSCs (nor-sEVs), hypo-sEVs promoted the proliferation, migration, and angiogenesis of HUVECs and ultimately enhanced bone regeneration and new blood vessel reconstruction in a critical-size calvarial bone defect model. miRNA sequence and the verified results showed that miR-210-3p in hypo-sEVs was increased via HIF-1α under hypoxia. The upregulated miR-210-3p in hypo-sEVs promoted angiogenesis by downregulating EFNA3 expression and enhancing the phosphorylation of the PI3K/AKT pathway. Thus, this study suggests a successful strategy to enhance vascularized bone regeneration by utilizing hypo-sEVs via the miR-210-3p/EFNA3/PI3K/AKT pathway. STATEMENT OF SIGNIFICANCE: Considering the significance of vascularization in ideal bone regeneration, strategies to promote angiogenesis during bone repair are required. Hypoxia microenvironment can promote the proangiogenic potential of mesenchymal stem cells (MSCs). Nonetheless, the therapeutic effect of small extracellular vesicles (sEVs) from hypoxia-preconditioned MSCs on cranio-maxillofacial bone defect remains unknown, and the underlying mechanism is poorly understood. This study shows that hypo-sEVs significantly enhance the proliferation, migration, and angiogenesis of HUVECs as well as promote vascularized bone formation. Moreover, this work indicates that HIF-1α can induce overexpression of miR-210-3p under hypoxia, and miR-210-3p can hinder EFNA3 expression and subsequently activate the PI3K/AKT pathway. The application of hypo-sEVs provides a facile and promising strategy to promote vascularized bone regeneration in a critical-size bone defect model.

Development of a maxillofacial virtual surgical system based on biomechanical parameters of facial soft tissue.

PURPOSE: Lack of biomechanical force model of soft tissue hinders the development of virtual surgical simulation in maxillofacial surgery. In this study, a physical model of facial soft tissue based on real biomechanical parameters was constructed, and a haptics-enabled virtual surgical system was developed to simulate incision-making process on facial soft tissue and to help maxillofacial surgery training. METHODS: CT data of a 25-year-old female patient were imported into Mimics software to reconstruct 3D models of maxillofacial soft and skeletal tissues. 3dMD stereo-photo of the patient was fused on facial surface to include texture information. Insertion and cutting parameters of facial soft tissue measured on fresh cadavers were integrated, and a maxillofacial biomechanical force model was established. Rapid deformation and force feedback were realized through localized deformation algorithm and axis aligned bounding box (AABB)-based collision detection. The virtual model was validated quantitatively and qualitatively. RESULTS: A patient-specific physical model composed of skeletal and facial soft tissue was constructed and embedded in the virtual surgical system. Insertion and cutting in different regions of facial soft tissue were simulated using omega 6, and real-time feedback force was recorded. The feedback force was consistent with acquired force data of experiments conducted on tissue specimen. Real-time graphic and haptic feedback were realized. The mean score of the system performance was 3.71 given by surgeons in evaluation questionnaires. CONCLUSION: The maxillofacial physical model enabled operators to simulate insertion and cutting on facial soft tissue with realization of realistic deformation and haptic feedback. The combination of localized deformation algorithm and AABB-based collision detection improved computational efficiency. The proposed virtual surgical system demonstrated excellent performance in simulation and training of incision-making process.

HIF-1α Regulates Osteogenesis of Periosteum-Derived Stem Cells Under Hypoxia Conditions via Modulating POSTN Expression.

Periosteum is indispensable in bone repair and is an important source of skeletal stem cells (SSCs) for endogenous bone regeneration. However, there are only a few studies about SSCs in periosteum. The craniomaxillofacial bone regeneration is done under the hypoxia microenvironment, in which HIF-1α plays an important role. The effect of HIF-1α on periosteum-derived stem cells (PDSCs) and the mechanisms of PDSCs activation under hypoxia conditions are unknown. In this study, the calvarial bone defect was established, with the periosteum removed or retained. Results show that the bone regeneration was severely impaired in the periosteum removed group. Moreover, pluripotent PDSCs isolated from the periosteum were positive for mesenchymal stem cell (MSC) markers. To determine the role of HIF-1α, the expression of HIF-1α was knocked down in vivo and in vitro, impairing the bone regeneration or osteogenesis of PDSCs. Furthermore, the knockdown of HIF-1α expression also reduced periostin (POSTN) expression, and recombinant POSTN addition partly rescued the osteogenic inhibition. Finally, to explore the mechanism under POSTN activation, the phosphorylation level of the PI3K/AKT pathway was assessed in transfected PDSCs. The phosphorylation level of PI3K and AKT was enhanced with HIF-1α overexpression and inhibited with HIF-1α knockdown, and the addition of PI3K activator or AKT activator could partly rescue POSTN expression. In conclusion, as a potential target to promote bone repair under the hypoxia microenvironment, HIF-1α can regulate the osteogenic differentiation of PDSCs via the PI3K/AKT/POSTN pathway, which lay a solid foundation for periosteum-based craniomaxillofacial bone regeneration.

Challenges and strategies for in situ endothelialization and long-term lumen patency of vascular grafts.

Vascular diseases are the most prevalent cause of ischemic necrosis of tissue and organ, which even result in dysfunction and death. Vascular regeneration or artificial vascular graft, as the conventional treatment modality, has received keen attentions. However, small-diameter (diameter < 4 mm) vascular grafts have a high risk of thrombosis and intimal hyperplasia (IH), which makes long-term lumen patency challengeable. Endothelial cells (ECs) form the inner endothelium layer, and are crucial for anti-coagulation and thrombogenesis. Thus, promoting in situ endothelialization in vascular graft remodeling takes top priority, which requires recruitment of endothelia progenitor cells (EPCs), migration, adhesion, proliferation and activation of EPCs and ECs. Chemotaxis aimed at ligands on EPC surface can be utilized for EPC homing, while nanofibrous structure, biocompatible surface and cell-capturing molecules on graft surface can be applied for cell adhesion. Moreover, cell orientation can be regulated by topography of scaffold, and cell bioactivity can be modulated by growth factors and therapeutic genes. Additionally, surface modification can also reduce thrombogenesis, and some drug release can inhibit IH. Considering the influence of macrophages on ECs and smooth muscle cells (SMCs), scaffolds loaded with drugs that can promote M2 polarization are alternative strategies. In conclusion, the advanced strategies for enhanced long-term lumen patency of vascular grafts are summarized in this review. Strategies for recruitment of EPCs, adhesion, proliferation and activation of EPCs and ECs, anti-thrombogenesis, anti-IH, and immunomodulation are discussed. Ideal vascular grafts with appropriate surface modification, loading and fabrication strategies are required in further studies.

NAA at a high concentration promotes efficient plant regeneration via direct somatic embryogenesis and SE-mediated transformation system in Ranunculus sceleratus.

The novel methods for efficient plant regeneration via direct somatic embryogenesis (SE) and SE-mediated transformation system under high concentration of NAA in Ranunculus sceleratus were established. On MS media containing a high concentration of NAA (10.0 mg/L) in the dark, all inoculated explants (root, stem and leaf) formed somatic embryos at high frequencies, respectively, 66.03, 126.47 and 213.63 embryoids per explant, and 100% of the embryoids developed into plantlets on 1/2 MS rooting media. Morphological and histological analyses revealed that SE in R. sceleratus followed a classical pattern. All inoculated explants can be used as receptors for genetic transformation in R. sceleratus, through direct SE-mediated method after Agrobacterium infection. RcLEC1-B, as a marker gene, changed the number and morphology of flower organs and the development of cuticle in R. sceleratus, which indicated that the efficient transgenic system of R. sceleratus was established. To our knowledge, this is the first observation that both direct SE and transgenic transformation system, via induction of a single plant growth regulator, have been successfully constructed in R. sceleratus.