Fine-Grained Learning Behavior-Oriented Knowledge Distillation for Graph Neural Networks.

Knowledge distillation (KD), as an effective compression technology, is used to reduce the resource consumption of graph neural networks (GNNs) and facilitate their deployment on resource-constrained devices. Numerous studies exist on GNN distillation, and however, the impacts of knowledge complexity and differences in learning behavior between teachers and students on distillation efficiency remain underexplored. We propose a KD method for fine-grained learning behavior (FLB), comprising two main components: feature knowledge decoupling (FKD) and teacher learning behavior guidance (TLBG). Specifically, FKD decouples the intermediate-layer features of the student network into two types: teacher-related features (TRFs) and downstream features (DFs), enhancing knowledge comprehension and learning efficiency by guiding the student to simultaneously focus on these features. TLBG maps the teacher model's learning behaviors to provide reliable guidance for correcting deviations in student learning. Extensive experiments across eight datasets and 12 baseline frameworks demonstrate that FLB significantly enhances the performance and robustness of student GNNs within the original framework.

Visual dual-mode aptasensor for non-small cell lung cancer exosome detection via HRP self-coupling enhanced oxidized iridium nanoparticle aggregation.

The main reason for the high mortality rate of non-small cell lung cancer is that patients are usually diagnosed at an advanced stage of the disease. Exosomes, small membrane vesicles secreted by normal cells or tumor cells, play a significant role in the progression of NSCLC. This study successfully optimized the preparation of artificial nanoenzymes self-coupling with horseradish peroxidase (IrO2NPs@HRP-AptCD63), without adding any ligand, demonstrating remarkable catalytic activity suitable for detecting the EGFR protein on the surface of NSCLC exosomes. When fused with the CD63 aptamer for identifying NSCLC exosomes, IrO2NPs@HRP showed enhanced catalytic activity in the 3,3',5,5'-tetramethylbenzidine-H2O2 oxidation-reduction system, thereby enhancing the colorimetric signal. This phenomenon can be distinguished by the naked eye and quantified using a UV-Vis spectrophotometer. Meanwhile, as the redox reaction occurs, the current signal of 3,3',5,5'-tetramethylbenzidine-H2O2, acting as an electrolyte, changes. The developed aptasensor generates dual-mode signal outputs, firstly, to visually assess the samples for their positive or negative status, and subsequently employ more in-depth electrochemical or colorimetric analysis methods for a detailed quantitative analysis of suspected positive samples. The detection limits of electrochemical analysis and colorimetric analysis were 0.9 × 103 particles/mL and 0.14 × 103 particles/mL, respectively. Compared with traditional biomarkers such as CA125, this method exhibits exceptional specificity, capable of simultaneously distinguishing serum exosomes of healthy volunteers, COPD patients, and NSCLC patients, promoting exosome detection in mouse models for tumor monitoring. Additionally, it elucidates the changes in EGFR protein expression on the surface of serum exosomes throughout the developmental trajectory.

Radiological Outcomes of Re-tethering for Adolescent Idiopathic Scoliosis: A 2-to-5-year Follow-Up Case Series After Index Vertebral Body Tethering Failure.

PURPOSE: Vertebral Body Tethering (VBT) has been shown to have a less predictable outcome compared to spinal fusion in patients with adolescent idiopathic scoliosis (AIS). Tether breakage is a common mechanical event that sometimes leads to loss of correction. No data has been published that evaluates the outcome of re-tethering in patients who underwent revision surgery for failed VBT, which was the purpose of this study. METHODS: This is an analysis of a prospectively collected single center database of 290 patients who have had VBT. Patients for this study were included if they have had re-tethering after failed VBT and a minimum follow up of 24 months after index surgery as well as a minimum follow up of 12 months after revision surgery. Revision surgeries included tether exchange, tether reinforcement and/or mono- and bisegmental lateral fusion. Main outcome of interest was curve magnitude at latest follow up. RESULTS: 11 patients were identified who received VBT for 16 curves of which 13 curves have had failed index surgery. Mean follow up from index surgery was 40 months, time between index and revision surgery was 22 months and latest follow up after revision surgery 19 months. Re-tethering resulted in an additional correction of 42% for thoracic and 63% for thoracolumbar curves. These results remained clinically stable with only minor loss of correction at final follow up. No patient underwent or was indicated for spinal fusion. CONCLUSION: Re-tethering is feasible and able to achieve additional correction and a sustainable result.

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum.

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.

U-Shape Pillar Strip for 3D Cell-Lumped Organoid Model (3D-COM) Mimicking Lung Cancer Hypoxia Conditions in High-Throughput Screening (HTS).

Hypoxia is a representative tumor characteristic associated with malignant progression in clinical patients. Engineered in vitro models have led to significant advances in cancer research, allowing for the investigation of cells in physiological environments and the study of disease mechanisms and processes with enhanced relevance. In this study, we propose a U-shape pillar strip for a 3D cell-lumped organoid model (3D-COM) to study the effects of hypoxia on lung cancer in a high-throughput manner. We developed a U-pillar strip that facilitates the aggregation of PDCs mixed with an extracellular matrix to make the 3D-COM in 384-plate array form. The response to three hypoxia-activated prodrugs was higher in the 3D-COM than in the 2D culture model. The protein expression of hypoxia-inducible factor 1 alpha (HIF-1α) and HIF-2α, which are markers of hypoxia, was also higher in the 3D-COM than in the 2D culture. The results show that 3D-COM better recapitulated the hypoxic conditions of lung cancer tumors than the 2D culture. Therefore, the U-shape pillar strip for 3D-COM is a good tool to study the effects of hypoxia on lung cancer in a high-throughput manner, which can efficiently develop new drugs targeting hypoxic tumors.

Association between blood methylmalonic acid and chronic kidney disease in the general US population: insights from multi-cycle National Health and Nutrition Examination Survey (NHANES).

Background: Chronic kidney disease (CKD) is significantly influenced by mitochondrial dysfunction (MD). Previous research suggests that methylmalonic acid (MMA) is involved in MD. Consequently, we aimed to investigate associations between blood MMA level and the prevalence of CKD as well as mortality in patients with CKD. Methods: The study included 23,587 individuals from National Health and Nutrition Examination Survey (NHANES). The NHANES datasets from 1999-2004 and 2011-2014 were utilized as separate primary and validation subsets. There were 3,554 patients with CKD. The association of blood MMA level with the prevalence of CKD was investigated using weighted logistic regression. Meanwhile, we employed weighted Cox regression models to evaluate the association between blood MMA level and all-cause mortality in patients with CKD. Results: Blood MMA levels had a significant positive association with urinary albumin-to-creatinine ratio (ß=45.29, P=0.01) and negative association with estimated glomerular filtration rate (ß=-15.27, P<0.001) in CKD patients. Blood MMA level exhibited a significant increase in participants with CKD compared with those without CKD (7.60±0.86 vs. 7.03±0.62, P<0.001). The level of blood MMA was significantly associated with the prevalence of CKD [odds ratio (OR): 1.32, 95% confidence interval (CI): 1.05-1.64, P=0.01]. In addition, blood MMA level was significantly associated with all-cause mortality in CKD participants [hazard ratio (HR): 1.26, 95% CI: 1.11-1.43, P<0.001] after adjusting for other potential predictors. Conclusions: Increased blood MMA levels were associated with more severe kidney impairment and increased risk of both the prevalence of CKD and mortality in participants with CKD.

Development of Hsp90 inhibitor to regulate cytokine storms in excessive delayed- and acute inflammation.

BACKGROUND: The surplus cytokines remaining after use in the early stages of the inflammatory response stimulate immune cells even after the response is over, causing a secondary inflammatory response and ultimately damaging the host, which is called a cytokine storm. Inhibiting heat shock protein 90 (Hsp90), which has recently been shown to play an important role in regulating inflammation in various cell types, may help control excessive inflammatory responses and cytokine storms. METHODS: We discovered an anti-inflammatory compound by measuring the inhibitory effect of CD86 expression on spleen DCs (sDCs) using the chemical compounds library of Hsp90 inhibitors. Subsequently, to select the hit compound, the production of cytokines and expression of surface molecules were measured on the bone marrow-derived DCs (BMDCs) and peritoneal macrophages. Then, we analyzed the response of antigen-specific Th1 cells. Finally, we confirmed the effect of the compound using acute lung injury (ALI) and delayed-type hypersensitivity (DTH) models. RESULTS: We identified Be01 as the hit compound, which reduced CD86 expression the most in sDCs. Treatment with Be01 decreased the production of pro-inflammatory cytokines (IL-6, TNF-α, and IL-1ß) in BMDC and peritoneal macrophages stimulated by LPS. Under the DTH model, Be01 treatment reduced ear swelling and pro-inflammatory cytokines in the spleen. Similarly, Be01 treatment in the ALI model decreased neutrophil infiltration and lower levels of secreted cytokines (IL-6, TNF-α). CONCLUSIONS: Reduction of CD80 and CD86 expression on DCs by Be01 indicates reduced secondary inflammatory response by Th1 cells, and reduced release of pro-inflammatory cytokines by peritoneal macrophages may initially control the cytokine storm.

Association of podocyte ultrastructural changes with proteinuria and pathological classification in type 2 diabetic nephropathy.

AIMS: Podocyte injury plays an essential role in the progression of diabetic nephropathy (DN). The associations between the ultrastructural changes of podocyte with proteinuria and the pathological classification of DN proposed by Renal Pathology Society (RPS) have not been clarified in patients with type 2 diabetic nephropathy (T2DN). METHODS: We collected 110 patients with kidney biopsy-confirmed T2DN at Peking University First Hospital from 2017 to 2022. The morphometric analysis on the podocyte foot process width (FPW) and podocyte detachment (PD) as markers of podocyte injury was performed, and the correlations between the ultrastructural changes of podocytes with severity of proteinuria and the RPS pathological classification of DN were analyzed. RESULTS: Mean FPW was significantly broader in the group of T2DN patients with nephrotic proteinuria (565.1 nm) than those with microalbuminuria (437.4 nm) or overt proteinuria (494.6 nm). The cut-off value of FPW (> 506 nm) could differentiate nephrotic proteinuria from non-nephrotic proteinuria with a sensitivity of 75.3% and a specificity of 75.8%. Percentage of PD was significantly higher in group of nephrotic proteinuria (3.2%) than that in microalbuminuria (0%) or overt proteinuria (0.2%). FPW and PD significantly correlated with proteinuria in T2DN (r = 0.473, p < 0.001 and r = 0.656, P < 0.001). FPW and PD correlated with RPS pathological classification of T2DN (r = 0.179, P = 0.014 and r = 0.250, P = 0.001). FPW value was increased significantly with more severe DN classification (P for trend =0.007). The percentage of PD tended to increase with more severe DN classification (P for trend = 0.017). CONCLUSIONS: Podocyte injury, characterized by FPW broadening and PD, was associated with the severity of proteinuria and the pathological classification of DN.

Assessing the Reproducibility of the Structured Abstracts Generated by ChatGPT and Bard Compared to Human-Written Abstracts in the Field of Spine Surgery: Comparative Analysis.

BACKGROUND: Due to recent advances in artificial intelligence (AI), language model applications can generate logical text output that is difficult to distinguish from human writing. ChatGPT (OpenAI) and Bard (subsequently rebranded as "Gemini"; Google AI) were developed using distinct approaches, but little has been studied about the difference in their capability to generate the abstract. The use of AI to write scientific abstracts in the field of spine surgery is the center of much debate and controversy. OBJECTIVE: The objective of this study is to assess the reproducibility of the structured abstracts generated by ChatGPT and Bard compared to human-written abstracts in the field of spine surgery. METHODS: In total, 60 abstracts dealing with spine sections were randomly selected from 7 reputable journals and used as ChatGPT and Bard input statements to generate abstracts based on supplied paper titles. A total of 174 abstracts, divided into human-written abstracts, ChatGPT-generated abstracts, and Bard-generated abstracts, were evaluated for compliance with the structured format of journal guidelines and consistency of content. The likelihood of plagiarism and AI output was assessed using the iThenticate and ZeroGPT programs, respectively. A total of 8 reviewers in the spinal field evaluated 30 randomly extracted abstracts to determine whether they were produced by AI or human authors. RESULTS: The proportion of abstracts that met journal formatting guidelines was greater among ChatGPT abstracts (34/60, 56.6%) compared with those generated by Bard (6/54, 11.1%; P<.001). However, a higher proportion of Bard abstracts (49/54, 90.7%) had word counts that met journal guidelines compared with ChatGPT abstracts (30/60, 50%; P<.001). The similarity index was significantly lower among ChatGPT-generated abstracts (20.7%) compared with Bard-generated abstracts (32.1%; P<.001). The AI-detection program predicted that 21.7% (13/60) of the human group, 63.3% (38/60) of the ChatGPT group, and 87% (47/54) of the Bard group were possibly generated by AI, with an area under the curve value of 0.863 (P<.001). The mean detection rate by human reviewers was 53.8% (SD 11.2%), achieving a sensitivity of 56.3% and a specificity of 48.4%. A total of 56.3% (63/112) of the actual human-written abstracts and 55.9% (62/128) of AI-generated abstracts were recognized as human-written and AI-generated by human reviewers, respectively. CONCLUSIONS: Both ChatGPT and Bard can be used to help write abstracts, but most AI-generated abstracts are currently considered unethical due to high plagiarism and AI-detection rates. ChatGPT-generated abstracts appear to be superior to Bard-generated abstracts in meeting journal formatting guidelines. Because humans are unable to accurately distinguish abstracts written by humans from those produced by AI programs, it is crucial to exercise special caution and examine the ethical boundaries of using AI programs, including ChatGPT and Bard.

Corrigendum: Criteria for differentiating left bundle branch pacing and left ventricular septal pacing: a systematic review.

[This corrects the article DOI: 10.3389/fcvm.2022.1006966.].

ER-GET: Emotion Recognition Based on Global ECG Trajectory.

In recent years, the recognition of human emotions based on electrocardiogram (ECG) signals has been considered a novel area of study among researchers. Despite the challenge of extracting latent emotion information from ECG signals, existing methods are able to recognize emotions by calculating the heart rate variability (HRV) features. However, such local features have drawbacks, as they do not provide a comprehensive description of ECG signals, leading to suboptimal recognition performance. For the first time, we propose a new strategy to extract hidden emotional information from the global ECG trajectory for emotion recognition. Specifically, a period of ECG signals is decomposed into sub-signals of different frequency bands through ensemble empirical mode decomposition (EEMD), and a series of multi-sequence trajectory graphs is constructed by orthogonally combining these sub-signals to extract latent emotional information. Additionally, to better utilize these graph features, a network has been designed that includes self-supervised graph representation learning and ensemble learning for classification. This approach surpasses recent notable works, achieving outstanding results, with an accuracy of 95.08% in arousal and 95.90% in valence detection. Additionally, this global feature is compared and discussed in relation to HRV features, with the intention of providing inspiration for subsequent research.

Dual Information Enhanced Multiview Attributed Graph Clustering.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

Electrochemical detection of myoglobin using an ultrasensitive label-free sensor derived from cubic-ZIF67@Au-rGOF-NH2 composite of MOF and GOF.

Markers of myocardial injury, such as myoglobin (Mb), are substances swiftly released into the peripheral bloodstream upon myocardial cell injury or altered cardiac activity. During the onset of acute myocardial infarction, patients experience a significant surge in serum Mb levels. Given this, precise detection of Mb is essential, necessitating the development of innovative assays to optimize detection capabilities. This study introduces the synthesis of a three-dimensional hierarchical nanocomposite, Cubic-ZIF67@Au-rGOF-NH2, utilizing aminated reduced graphene oxide and zeolite imidazolium ester framework-67 (ZIF67) as foundational structures. Notably, this novel material, applied in a label-free electrochemical immunosensor, presents a groundbreaking approach for detecting myocardial injury markers. Experimental outcomes revealed ZIF67 and AuNPs exhibit enhanced affinity and growth on the 3D-rGOF-NH2 matrix, thus amplifying electrical conductivity while preserving the inherent electrochemical attributes of ZIF67. As a result, the Cubic-ZIF67@Au-rGOF-NH2 label-free electrochemical immunosensor exhibited a broad detection range and high sensitivity for Mb. The derived standard curve was ΔIp = 16.67552lgC+275.245 (R = 0.993) with a detection threshold of 3.47 fg/ml. Moreover, recoveries of standards spiked into samples ranged between 96.3% and 108.7%. Importantly, the devised immunosensor retained notable selectivity against non-target proteins, proving its potential clinical utility based on exemplary sample analysis performance.

Real-world study of lazertinib as second-line or greater treatment in advanced non-small cell lung cancer.

BACKGROUND: Lazertinib is an oral, third-generation EGFR-TKI, which specifically targets the EGFR T790M mutation along with activating mutations Ex19del and L858R. More real-world data are needed to evaluate its efficacy and safety in treating locally advanced and metastatic non-small cell lung cancer (NSCLC) following prior EGFR TKI treatment. METHODS: This multicenter retrospective study was conducted at seven university hospitals affiliated to the Catholic Medical Center (CMC) in Korea. A clinical data warehouse (CDW) platform was used to access and extract information. RESULTS: A total of 48 patients were assessed. The majority were female (75%) and diagnosed with adenocarcinoma (95.8%). All patients had the EGFR mutation at diagnosis, 27 (56.3%) had the exon 19 deletion, 20 (41.7%) had the L858R mutation, and one (2.0%) had the exon 18 mutation. The median progression-free survival (PFS) was 15.4 months. At 6, 12, and 18 months, PFS rates were 79.1%, 53.6%, and 27.3%, respectively. When PFS was analyzed by prior TKI duration (<18 months vs. >18 months), significant differences were noted at the 6 and 9-month mark (p = 0.013 and p = 0.010, respectively). In multivariate analysis for PFS, only prior TKI duration and ECOG score showed statistical significance (p = 0.026 and p = 0.049, respectively). In the multivariate analysis for OS, ECOG score showed statistical significance (p = 0.006). Among 48 patients, 34 (70.8%) experienced adverse events (AEs) related to lazertinib. The most frequent AEs were skin reaction (29.8%), diarrhea (21.3%), and peripheral neuropathy (20.8%). CONCLUSIONS: The results suggest that lazertinib is effective in second or more line settings, with tolerable safety profile. More patient data are necessary to find possible prognostic markers associated with patient outcome.

PRDX1 Interfering Peptide Disrupts Amino Acids 70-90 of PRDX1 to Inhibit the TLR4/NF-κB Signaling Pathway and Attenuate Neuroinflammation and Ischemic Brain Injury.

Ischemic stroke ranks among the leading causes of death and disability in humans and is accompanied by motor and cognitive impairment. However, the precise mechanisms underlying injury after stroke and effective treatment strategies require further investigation. Peroxiredoxin-1 (PRDX1) triggers an extensive inflammatory cascade that plays a pivotal role in the pathology of ischemic stroke, resulting in severe brain damage from activated microglia. In the present study, we used molecular dynamics simulation and nuclear magnetic resonance to detect the interaction between PRDX1 and a specific interfering peptide. We used behavioral, morphological, and molecular experimental methods to demonstrate the effect of PRDX1-peptide on cerebral ischemia-reperfusion (I/R) in mice and to investigate the related mechanism. We found that PRDX1-peptide bound specifically to PRDX1 and improved motor and cognitive functions in I/R mice. In addition, pretreatment with PRDX1-peptide reduced the infarct area and decreased the number of apoptotic cells in the penumbra. Furthermore, PRDX1-peptide inhibited microglial activation and downregulated proinflammatory cytokines including IL-1ß, IL-6, and TNF-α through inhibition of the TLR4/NF-κB signaling pathway, thereby attenuating ischemic brain injury. Our findings clarify the precise mechanism underlying PRDX1-induced inflammation after ischemic stroke and suggest that the PRDX1-peptide can significantly alleviate the postischemic inflammatory response by interfering with PRDX1 amino acids 70-90 and thereby inhibiting the TLR4/NF-κB signaling pathway. Our study provides a theoretical basis for a new therapeutic strategy to treat ischemic stroke.

Comparative Analysis of Monoaxial and Polyaxial Pedicle Screws in the Surgical Correction of Adolescent Idiopathic Scoliosis.

Background: Although several biomechanical studies have been reported, few clinical studies have compared the efficacy of monoaxial and polyaxial pedicle screws in the surgical treatment of adolescent idiopathic scoliosis (AIS). This study aims to compare the radiological and clinical outcomes of mono- and polyaxial pedicle screws in the surgical treatment of AIS. Methods: A total of 46 AIS patients who underwent surgery to treat scoliosis using pedicle screw instrumentation (PSI) and rod derotation (RD) were divided into two groups according to the use of pedicle screws: the monoaxial group (n = 23) and polyaxial group (n = 23). Results: The correction rate of the main Cobb's angle was higher in the monoaxial group (70.2%) than in the polyaxial group (65.3%) (p = 0.040). No differences in the rotational correction of the apical vertebra were evident between the two groups. SRS-22 scores showed no significant differences according to the type of pedicle screws used. Conclusions: The use of polyaxial pedicle screws resulted in coronal, sagittal, and rotational correction outcomes comparable to those associated with the use of monoaxial pedicle screws for surgical treatment using PSI and RD to treat moderate cases of AIS.

Prediction of TKI response in EGFR-mutant lung cancer patients-derived organoids using malignant pleural effusion.

Patient-derived organoids (PDOs) are valuable in predicting response to cancer therapy. PDOs are ideal models for precision oncologists. However, their practical application in guiding timely clinical decisions remains challenging. This study focused on patients with advanced EGFR-mutated non-small cell lung cancer and employed a cancer organoid-based diagnosis reactivity prediction (CODRP)-based precision oncology platform to assess the efficacy of EGFR inhibitor treatments. CODRP was employed to evaluate EGFR-tyrosine kinase inhibitors (TKI) drug sensitivity. The results were compared to those obtained using area under the curve index. This study validated this index by testing lung cancer-derived organoids in 14 patients with lung cancer. The CODRP index-based drug sensitivity test reliably classified patient responses to EGFR-TKI treatment within a clinically suitable 10-day timeline, which aligned with clinical drug treatment responses. This approach is promising for predicting and analyzing the efficacy of anticancer, ultimately contributing to the development of a precision medicine platform.

The efficacy of anterior vertebral body tethering in lenke type 6 curves for adolescent idiopathic scoliosis.

PURPOSE: Spinal fusion is the standard treatment for severe forms of adolescent idiopathic scoliosis (AIS). However, with the lowest instrumented vertebra that is usually located at L3 or L4, patients are prone to develop adjacent segment degeneration in the long term. Vertebral body tethering (VBT) as motion preserving technique has become an alternative for select patients with AIS. Several studies have presented the outcome after thoracic VBT but no study has analyzed the outcome after VBT for Lenke type 6 curves. METHODS: This is a retrospective single center data analysis of patients who have had bilateral VBT for Lenke type 6 curves and a minimum follow up of 24 months. Radiographic analysis was performed on several time points. Suspected tether breakages were additionally analyzed with respect to location and time at occurrence. RESULTS: 25 patients were included. Immediate thoracic curve correction was 55.4% and 71.7% for TL/L curves. Loss of correction was higher for TL/L curves and resulted in a correction rate of 48.3% for thoracic curves and 48.9% for TL/L curves at 24 months post-operatively. 22 patients were suspected to have at least one segment with a tether breakage. Three patients required a re-VBT but no patient received posterior spinal fusion. CONCLUSION: Bilateral VBT for Lenke type 6 curves is feasible and shows a significant curve correction for thoracic and TL/L curves at a minimum of 24 months post-operatively. Tether breakage rate and loss of correction remain an unfavorable observation that needs to be improved in the future.

Screening for metastasis-related genes in mouse melanoma cells through sequential tail vein injection.

Tumor metastasis, responsible for approximately 90% of cancer-associated mortality, remains poorly understood. Here in this study, we employed a melanoma lung metastasis model to screen for metastasis-related genes. By sequential tail vein injection of mouse melanoma B16F10 cells and the subsequently derived cells from lung metastasis into BALB/c mice, we successfully obtained highly metastatic B16F15 cells after five rounds of in vivo screening. RNA-sequencing analysis of B16F15 and B16F10 cells revealed a number of differentially expressed genes, some of these genes have previously been associated with tumor metastasis while others are novel discoveries. The identification of these metastasis-related genes not only improves our understanding of the metastasis mechanisms, but also provides potential diagnostic biomarkers and therapeutic targets for metastatic melanoma.