Enhanced Thermal Boundary Conductance across GaN/SiC Interfaces with AlN Transition Layers.

Heat dissipation plays a crucial role in the performance and reliability of high-power GaN-based electronics. While AlN transition layers are commonly employed in the heteroepitaxial growth of GaN-on-SiC substrates, concerns have been raised about their impact on thermal transport across GaN/SiC interfaces. In this study, we present experimental measurements of the thermal boundary conductance (TBC) across GaN/SiC interfaces with varying thicknesses of the AlN transition layer (ranging from 0 to 73 nm) at different temperatures. Our findings reveal that the addition of an AlN transition layer leads to a notable increase in the TBC of the GaN/SiC interface, particularly at elevated temperatures. Structural characterization techniques are employed to understand the influence of the AlN transition layer on the crystalline quality of the GaN layer and its potential effects on interfacial thermal transport. To gain further insights into the trend of TBC, we conduct molecular dynamics simulations using high-fidelity deep learning-based interatomic potentials, which reproduce the experimentally observed enhancement in TBC even for atomically perfect interfaces. These results suggest that the enhanced TBC facilitated by the AlN intermediate layer could result from a combination of improved crystalline quality at the interface and the "phonon bridge" effect provided by AlN that enhances the overlap between the vibrational spectra of GaN and SiC.

Comparing the impact of three-dimensional digital visualization technology versus traditional microscopy on microsurgeons in microsurgery: a prospective self-controlled study.

BACKGROUND: Emerging three-dimensional digital visualization technology (DVT) provides more advantages than traditional microscopy in microsurgery; however, its impact on microsurgeons' visual and nervous systems and delicate microsurgery is still unclear, which hinders the wider implementation of DVT in digital visualization for microsurgery. METHODS AND MATERIAL: Forty-two microsurgeons from the Zhongshan Ophthalmic Center were enrolled in this prospective self-controlled study. Each microsurgeon consecutively performed 30 min conjunctival sutures using a three-dimensional digital display and a microscope, respectively. Visual function, autonomic nerve activity, and subjective symptoms were evaluated before and immediately after the operation. Visual functions, including accommodative lag, accommodative amplitude, near point of convergence and contrast sensitivity function (CSF), were measured by an expert optometrist. Heart rate variability was recorded by a wearable device for monitoring autonomic nervous activity. Subjective symptoms were evaluated by questionnaires. Microsurgical performance was assessed by the video-based Objective Structured Assessment of Technical Skill (OSATS) tool. RESULTS: Accommodative lag decreased from 0.63 (0.18) diopters (D) to 0.55 (0.16) D ( P =0.014), area under the log contrast sensitivity function increased from 1.49 (0.15) to 1.52 (0.14) ( P =0.037), and heart rate variability decreased from 36.00 (13.54) milliseconds (ms) to 32.26 (12.35) ms ( P =0.004) after using the DVT, but the changes showed no differences compared to traditional microscopy ( P >0.05). No statistical significance was observed for global OSATS scores between the two rounds of operations [mean difference, 0.05 (95% CI: -1.17 to 1.08) points; P =0.95]. Subjective symptoms were quite mild after using both techniques. CONCLUSIONS: The impact of DVT-based procedures on microsurgeons includes enhanced accommodation and sympathetic activity, but the changes and surgical performance are not significantly different from those of microscopy-based microsurgery. Our findings indicate that short-term use of DVT is reliable for microsurgery and the long-term effect of using DVT deserve more consideration.

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Integrative Metabolomics and Whole Transcriptome Sequencing Reveal Role for TREM2 in Metabolism Homeostasis in Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Dysregulation of various metabolism pathways may mediate the development of AD pathology and cognitive dysfunction. Variants of triggering receptor expressed on myeloid cells-2 (TREM2) are known to increase the risk of developing AD. TREM2 plays a role in AD development by maintaining cellular energy and biosynthesis, but the precise mechanism through which it accomplishes this is unknown. Metabolomic analysis of hippocampal tissue from APP/PS1 and APP/PS1-TREM2 knockout (KO) mice found that TREM2 KO was associated with abnormalities in several metabolism pathways, and the effect was particularly pronounced in lipid metabolism and glucose metabolism pathways. Consistently, transcriptomic analysis of these mice determined that most differentially expressed genes were involved in energy metabolism pathways. We screened seven differentially expressed genes in APP/PS1-TREM2 KO mice that may influence AD development by altering energy metabolism. Integrative analysis of the metabolomic and transcriptomic profiles showed that TREM2 may regulate lipid metabolism and sphingolipid metabolism by affecting lipoprotein lipase (LPL) expression, thereby influencing AD progression. Our results prompt further studies of the interactions among TREM2, LPL, glucolipid metabolism, and sphingolipid metabolism in AD to identify new diagnostic and treatment strategies.

Viewpoint-dependent highlight depiction with microdisparity for autostereoscopic displays.

The rendering of specular highlights is a critical aspect of 3D rendering on autostereoscopic displays. However, the conventional highlight rendering techniques on autostereoscopic displays result in depth conflicts between highlights and diffuse surfaces. To address this issue, we propose a viewpoint-dependent highlight depiction method with head tracking, which incorporates microdisparity of highlights in binocular parallax and preserves the motion parallax of highlights. Our method was found to outperform physical highlight depiction and highlight depiction with microdisparity in terms of depth perception and realism, as demonstrated by experimental results. The proposed approach offers a promising alternative to traditional physical highlights on autostereoscopic displays, particularly in applications that require accurate depth perception.

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.

Importance: Retinal diseases are the leading cause of irreversible blindness worldwide, and timely detection contributes to prevention of permanent vision loss, especially for patients in rural areas with limited medical resources. Deep learning systems (DLSs) based on fundus images with a 45° field of view have been extensively applied in population screening, while the feasibility of using ultra-widefield (UWF) fundus image-based DLSs to detect retinal lesions in patients in rural areas warrants exploration. Objective: To explore the performance of a DLS for multiple retinal lesion screening using UWF fundus images from patients in rural areas. Design, Setting, and Participants: In this diagnostic study, a previously developed DLS based on UWF fundus images was used to screen for 5 retinal lesions (retinal exudates or drusen, glaucomatous optic neuropathy, retinal hemorrhage, lattice degeneration or retinal breaks, and retinal detachment) in 24 villages of Yangxi County, China, between November 17, 2020, and March 30, 2021. Interventions: The captured images were analyzed by the DLS and ophthalmologists. Main Outcomes and Measures: The performance of the DLS in rural screening was compared with that of the internal validation in the previous model development stage. The image quality, lesion proportion, and complexity of lesion composition were compared between the model development stage and the rural screening stage. Results: A total of 6222 eyes in 3149 participants (1685 women [53.5%]; mean [SD] age, 70.9 [9.1] years) were screened. The DLS achieved a mean (SD) area under the receiver operating characteristic curve (AUC) of 0.918 (0.021) (95% CI, 0.892-0.944) for detecting 5 retinal lesions in the entire data set when applied for patients in rural areas, which was lower than that reported at the model development stage (AUC, 0.998 [0.002] [95% CI, 0.995-1.000]; P < .001). Compared with the fundus images in the model development stage, the fundus images in this rural screening study had an increased frequency of poor quality (13.8% [860 of 6222] vs 0%), increased variation in lesion proportions (0.1% [6 of 6222]-36.5% [2271 of 6222] vs 14.0% [2793 of 19 891]-21.3% [3433 of 16 138]), and an increased complexity of lesion composition. Conclusions and Relevance: This diagnostic study suggests that the DLS exhibited excellent performance using UWF fundus images as a screening tool for 5 retinal lesions in patients in a rural setting. However, poor image quality, diverse lesion proportions, and a complex set of lesions may have reduced the performance of the DLS; these factors in targeted screening scenarios should be taken into consideration in the model development stage to ensure good performance.

Tunable Tail Swing of Nanomillipedes.

The physical properties of graphene nanoribbons (GNRs) are closely related to their morphology; meanwhile GNRs can easily slide on surfaces (e.g., superlubricity), which may largely affect the configuration and hence the properties. However, the morphological evolution of GNRs during sliding remain elusive. We explore the intriguing tail swing behavior of GNRs under various sliding configurations on Au substrate. Two distinct modes of tail swing emerge, characterized by regular and irregular swings, depending on the GNR width and initial position relative to the substrate. The mechanism can be explained by the moiré effect, presenting both symmetric and asymmetric patterns, resembling a mesmerizing nanomillipede. We reveal a compelling correlation between the tail swing mode and the edge wrinkle patterns of GNRs induced by the moiré effect. These findings provide fundamental understanding of how edge effects influence the tribomorphological responses of GNRs, offering valuable insights for precise manipulation and operation of GNRs.

Crosstalk between peripheral immunity and central nervous system in Alzheimer's disease.

The significance of peripheral immunity in the pathogenesis and progression of Alzheimer's diseases (AD) has been recognized. Brain-infiltrated peripheral immune components transporting across the blood-brain barrier (BBB) may reshape the central immune environment. However, mechanisms of how these components open the BBB for AD occurrence and development and correlations between peripheral and central immunity have not been fully explored. Herein, we formulate a hypothesis whereby peripheral immunity as a critical factor allows AD to progress. Peripheral central immune cell crosstalk is associated with early AD pathology and related risk factors. The damaged BBB permits peripheral immune cells to enter the central immune system to deprive its immune privilege promoting the progression toward developing AD. This review summarizes the influences of risk factors on peripheral immunity, alongside their functions, highlighting the concept of peripheral and central immunity as an integrated system in AD pathogenesis, which has received scant attention before.

HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using Computed Tomography Images and Text.

Prosthetic joint infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients' numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based Feature Fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4% and an area under the curve (AUC) of 95.9%, outperforming recent multimodal approaches by 2.9% in ACC and 2.2% in AUC, with a parameter count of only 68 M. Notably, the interpretability results highlighted our model's strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.

EPT-Net: Edge Perception Transformer for 3D Medical Image Segmentation.

The convolutional neural network has achieved remarkable results in most medical image seg- mentation applications. However, the intrinsic locality of convolution operation has limitations in modeling the long-range dependency. Although the Transformer designed for sequence-to-sequence global prediction was born to solve this problem, it may lead to limited positioning capability due to insufficient low-level detail features. Moreover, low-level features have rich fine-grained information, which greatly impacts edge segmentation decisions of different organs. However, a simple CNN module is difficult to capture the edge information in fine-grained features, and the computational power and memory consumed in processing high-resolution 3D features are costly. This paper proposes an encoder-decoder network that effectively combines edge perception and Transformer structure to segment medical images accurately, called EPT-Net. Under this framework, this paper proposes a Dual Position Transformer to enhance the 3D spatial positioning ability effectively. In addition, as low-level features contain detailed information, we conduct an Edge Weight Guidance module to extract edge information by minimizing the edge information function without adding network parameters. Furthermore, we verified the effectiveness of the proposed method on three datasets, including SegTHOR 2019, Multi-Atlas Labeling Beyond the Cranial Vault and the re-labeled KiTS19 dataset called KiTS19-M by us. The experimental results show that EPT-Net has significantly improved compared with the state-of-the-art medical image segmentation method.

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation.

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the tumor region, DHTrans aggregates global and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver and tumor regions. We evaluate DHT-Net on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2.5D hybrid models.

A Comparative Evaluation of Optical See-through Augmented Reality in Surgical Guidance.

During traditional surgeries, planning and instrument guidance is displayed on an external screen. Recent developments of augmented reality (AR) techniques can overcome obstacles including hand-eye discoordination and heavy mental load. Among these AR technologies, optical see-through (OST) schemes with stereoscopic displays can provide depth perception and retain the physical scene for safety considerations. However, limitations still exist in certain AR systems and the influence of these factors on surgical performance is yet to explore. To this end, experiments of multi-scale surgical tasks were carried out to compare head-mounted display (HMD) AR and autostereoscopic image overlay (AIO) AR, concerning objective performance and subjective evaluation. To solely analyze effects brought by display techniques, the tracking system in each included display system was identical and similar tracking accuracy was proved by a preliminary experiment. Focus and context rendering was utilized to enhance in-situ visualization for surgical guidance. Latency values of all display systems were assessed and a delay experiment proved the latency differences had no significant impact on user performance. Results of multi-scale surgical tasks showed that HMD outperformed in detailed operations probably due to stable resolution along the depth axis, while AIO had better performance in larger-scale operations for better depth perception. This paper helps point out the critical limitations of current OST AR techniques and potentially promotes the progress of AR applications in surgical guidance.

The Critical Role of Fillers in Composite Polymer Electrolytes for Lithium Battery.

With excellent energy densities and highly safe performance, solid-state lithium batteries (SSLBs) have been hailed as promising energy storage devices. Solid-state electrolyte is the core component of SSLBs and plays an essential role in the safety and electrochemical performance of the cells. Composite polymer electrolytes (CPEs) are considered as one of the most promising candidates among all solid-state electrolytes due to their excellent comprehensive performance. In this review, we briefly introduce the components of CPEs, such as the polymer matrix and the species of fillers, as well as the integration of fillers in the polymers. In particular, we focus on the two major obstacles that affect the development of CPEs: the low ionic conductivity of the electrolyte and high interfacial impedance. We provide insight into the factors influencing ionic conductivity, in terms of macroscopic and microscopic aspects, including the aggregated structure of the polymer, ion migration rate and carrier concentration. In addition, we also discuss the electrode-electrolyte interface and summarize methods for improving this interface. It is expected that this review will provide feasible solutions for modifying CPEs through further understanding of the ion conduction mechanism in CPEs and for improving the compatibility of the electrode-electrolyte interface.

Fractured morphology of femoral head associated with subsequent femoral neck fracture: Injury analyses of 2D and 3D models of femoral head fractures with computed tomography.

Background: The injury of femoral head varies among femoral head fractures (FHFs). In addition, the injury degree of the femoral head is a significant predictor of femoral neck fracture (FNF) incidence in patients with FHFs. However, the exact measurement methods have yet been clearly defined based on injury models of FHFs. This study aimed to design a new measurement for the injury degree of the femoral head on 2D and 3D models with computed tomography (CT) images and investigate its association with FHFs with FNF. Methods: A consecutive series of 209 patients with FHFs was assessed regarding patient characteristics, CT images, and rate of FNF. New parameters for injury degree of femoral head, including percentage of maximum defect length (PMDL) in the 2D CT model and percentage of fracture area (PFA) in the 3D CT-reconstruction model, were respectively measured. Four 2D parameters included PMDLs in the coronal, cross-sectional and sagittal plane and average PMDL across all three planes. Reliability tests for all parameters were evaluated in 100 randomly selected patients. The PMDL with better reliability and areas under curves (AUCs) was finally defined as the 2D parameter. Factors associated with FNF were determined by binary logistic regression analysis. The sensitivity, specificity, likelihood ratios, and positive and negative predictive values for different cut-off values of the 2D and 3D parameters were employed to test the diagnostic accuracy for FNF prediction. Results: Intra- and inter-class coefficients for all parameters were ≥0.887. AUCs of all parameters ranged from 0.719 to 0.929 (p < 0.05). The average PMDL across all three planes was defined as the 2D parameter. The results of logistic regression analysis showed that average PMDL across all three planes and PFA were the significant predictors of FNF (p < 0.05). The cutoff values of the average PMDL across all three planes and PFA were 91.65% and 29.68%. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, predictive positive value and negative predictive value of 2D (3D) parameters were 91.7% (83.3%), 93.4% (58.4%), 13.8 (2.0), 0.09 (0.29), 45.83% (10.87%), and 99.46% (98.29%). Conclusion: The new measurement on 2D and 3D injury models with CT has been established to assess the fracture risk of femoral neck in patients with FHFs in the clinic practice. 2D and 3D parameters in FHFs were a feasible adjunctive diagnostic tool in identifying FNFs. In addition, this finding might also provide a theoretic basis for the investigation of the convenient digital-model in complex injury analysis.

Effects of CO2 atmosphere on low-rank coal pyrolysis based on ReaxFF molecular dynamics.

Pyrolysis of low-rank coal in CO2 atmosphere can reduce carbon emissions while comprehensively utilizing coal resources. Based on ReaxFF molecular dynamics (ReaxFF-MD), the pyrolysis processes of low-rank coal in inert and CO2 atmosphere are simulated. By comparing the evolution of pyrolysis products, the influences of CO2 on the pyrolysis characteristic and product distribution are analyzed. It is found that CO2 slightly inhibits the conversion of char to tar in the early stage of pyrolysis. In the later stage, CO2 significantly promotes the decomposition of char and increases the yield of tar and pyrolysis gas. According to the different bond breaking behaviors of coal molecules, the pyrolysis process can be divided into pyrolysis activation stage, initial pyrolysis stage, accelerated pyrolysis stage and secondary pyrolysis stage. The reforming reaction of CO2 with alkanes generates free hydrogen radicals, which promotes the cleavage of ether bond, Car-Car bridge bond and aliphatic C-C bond. Compared with in inert atmosphere, final yield of light tar in CO2 atmosphere increases from 17.98% to 20.68%. In general, the CO2 atmosphere helps to improve the tar yield and tar quality of low-rank coal pyrolysis.

Early detection of visual impairment in young children using a smartphone-based deep learning system.

Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Although certain features of visually impaired children, such as facial appearance and ocular movements, can assist ophthalmic practice, applying these features to real-world screening remains challenging. Here, we present a mobile health (mHealth) system, the smartphone-based Apollo Infant Sight (AIS), which identifies visually impaired children with any of 16 ophthalmic disorders by recording and analyzing their gazing behaviors and facial features under visual stimuli. Videos from 3,652 children (≤48 months in age; 54.5% boys) were prospectively collected to develop and validate this system. For detecting visual impairment, AIS achieved an area under the receiver operating curve (AUC) of 0.940 in an internal validation set and an AUC of 0.843 in an external validation set collected in multiple ophthalmology clinics across China. In a further test of AIS for at-home implementation by untrained parents or caregivers using their smartphones, the system was able to adapt to different testing conditions and achieved an AUC of 0.859. This mHealth system has the potential to be used by healthcare professionals, parents and caregivers for identifying young children with visual impairment across a wide range of ophthalmic disorders.

TREM2 and Microglia Contribute to the Synaptic Plasticity: from Physiology to Pathology.

Synapses are bridges for information transmission in the central nervous system (CNS), and synaptic plasticity is fundamental for the normal function of synapses, contributing substantially to learning and memory. Numerous studies have proven that microglia can participate in the occurrence and progression of neurodegenerative diseases (NDDs), such as Alzheimer's disease (AD), by regulating synaptic plasticity. In this review, we summarize the main characteristics of synapses and synaptic plasticity under physiological and pathological conditions. We elaborate the origin and development of microglia and the two well-known microglial signaling pathways that regulate synaptic plasticity. We also highlight the unique role of triggering receptor expressed on myeloid cells 2 (TREM2) in microglia-mediated regulation of synaptic plasticity and its relationship with AD. Finally, we propose four possible ways in which TREM2 is involved in regulating synaptic plasticity. This review will help researchers understand how NDDs develop from the perspective of synaptic plasticity.

Combined Proximal Tibial Osteotomy for Adult Kashin-Beck Disease with Severe Varus Knee Osteoarthritis: Case Report and Literature Review.

BACKGROUND: Kashin-Beck disease (KBD) is an endemic, chronic osteoarthropathy that seriously affects joint function and can lead to severe knee deformity. Osteotomy is considered to be one of the effective methods for the treatment of this disease. Therefore, we designed a novel type of osteotomy named combined proximal tibial osteotomy (CPTO), which combines the characteristics of opening-wedge high tibial osteotomy and tibial condylar valgus osteotomy. CASE PRESENTATION: We report the case of a 48-year-old male with knee pain and varus deformity who was diagnosed with KBD and varus knee osteoarthritis (Kellgren-Lawrence stage IV). Considering the patient's relatively young age, a varus deformity of the right knee of 16.79°, and an intra-articular instability, we performed a CPTO treatment. In this procedure, we performed an L-shaped osteotomy from the medial edge of the proximal tibia to the intercondylar eminence and an osteotomy from the medial side of the proximal tibia to the lateral side through the same incision, to adjust the leg alignment and the congruity of the joint by valgus correction. At 29 months follow-up, this patient achieved satisfactory results, with a varus right knee of 2.87°. There was significant improvement in his right knee function, pain, and joint stability. CONCLUSIONS: CPTO may be an acceptable treatment for KBD patients with severe knee varus deformity and intra-articular instability. It can be considered as an alternative treatment, especially for patients with advanced osteoarthritis needing knee preservation.