Electrochemical-Fluorescent Bimodal Biosensor Based on Dual CRISPR-Cas12a Multiple Cascade Amplification for ctDNA Detection.

Circulating tumor DNA (ctDNA) is a critical biomarker for early tumor detection. However, accurately quantifying low-abundance ctDNA in human serum remains a significant challenge. To address this challenge, we introduce a bimodal biosensor tailored for detecting the epidermal growth factor receptor (EGFR) mutation L858R in specific nonsmall cell lung cancer (NSCLC) patients. This biosensor utilizes dual CRISPR-Cas12a systems to quantify the target via fluorescence and electrochemical signals. In our system, the EGFR L858R exhibits resistance to digestion by the restriction enzyme MscI, which activates the first CRISPR-Cas12a protein and inhibits the binding of magnetic beads with fluorescein (FAM)-labeled hybridization chain reaction (HCR) products, thereby reducing the fluorescence signal. This activation also inhibits the cleavage activity of the second CRISPR-Cas12a protein, allowing the electrode to sustain a higher electrochemical signal from nanomaterials. The wild-type EGFR (wt EGFR) produces the opposite effect. Consequently, the concentration of EGFR L858R can be accurately quantified and verified using both fluorescence and electrochemical signals. The biosensor offers a dynamic detection ranging from 10 fM to 1 µM, with a detection limit of 372 aM. It demonstrates excellent specificity, reproducibility, stability, and recovery rates. Moreover, the sensor's enhanced analytical sensitivity highlights its critical role in biosensing applications and early disease diagnosis.

Adolescent Idiopathic Scoliosis in Siblings Treated by Surgical Deformity Correction.

Adolescent idiopathic scoliosis (AIS) in siblings reflects genetic hypothesis; however, few studies have been published. Furthermore, to the best of our knowledge, there have been no reports in the literature of both siblings with AIS who underwent deformity corrections. A 15-year-old adolescent girl visited our clinic with back pain after recognition of the incidental findings of a scoliotic curve in the spine. Whole spine radiographs detected Lenke classification type 3CN. The patient underwent deformity correction with posterior instrumented fusion from T4 to L3 with thoracoplasty of the right 7th to 10th rib. Four years later, her 16-year-old younger brother also visited our clinic with back pain after recognition of the incidental findings of a scoliotic curve in the spine. Whole spine radiographs detected Lenke classification type 2AN. The patient underwent deformity correction with posterior instrumented fusion from T5 to L2 with thoracoplasty of the right 8th to 10th rib. In conclusion, we report on two siblings with AIS who underwent surgical treatment for different types of curves. They showed favorable outcomes after performing deformity correction with posterior instrumented fusion. Our rare case supports the underlying basis of genetic heterogeneity as a complex polygenic model.

PD-L1 expression from surgically resected lung tumors predictive of early progression in patients previously treated with targeted therapy for initially unresectable non-small cell lung cancer.

Background: Recent evidences showed that resection of lung tumor post-targeted therapy has shown progression-free survival (PFS) benefits in initially unresectable patients. The aim of this study is to evaluate pathologic findings of resected lung tumor samples in patients who have undergone prior epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) treatment, and also to assess the prognostic factors related to outcomes after resection. Methods: The deidentified data of non-small cell lung cancer (NSCLC) patients admitted to seven university hospitals affiliated with the Catholic University of Korea were obtained from the Clinical Data Warehouse (CDW) database. Among screened patients, 40 individuals who had previously undergone targeted therapies and later received surgical resection of a primary lung tumor were evaluated for the study. Results: All 40 patients were diagnosed with adenocarcinoma. Of these, 36 with EGFR mutations received prior EGFR TKI treatment. Only one postoperative complication, atrial fibrillation, was observed. At the time of resection, 19 patients showed primary lung tumor size regressing or unchanged, while 21 patients showed primary lung tumor regrowth or new lesions being developed before the resection. The group with no programmed death-ligand 1 (PD-L1) expression from resected samples showed significantly better post-resection PFS when compared to the other group (P=0.01). In the Model II multivariate analysis for post-resection PFS, PD-L1 detection from the resected sample was significantly associated with PFS [P=0.03; hazard ratio (HR) =5.465; 95% confidence interval (CI): 1.200-24.885]. Furthermore, an increase in PD-L1 expression compared to the baseline value was associated with an increasing lung tumor burden at the time of resection (P=0.03). Conclusions: Resected specimen following targeted therapy can provide valuable clinical information that can be used to predict the prognosis of patients with initially unresectable NSCLC.

Dual-Modal Aptasensor for Sensitive Detection of Non-Small Cell Lung Cancer Exosomes Utilizing Two-Dimensional Nanopaper Co@g-C3N4@PB.

Nonsmall cell lung cancer (NSCLC), due to its lack of early symptoms, has become one of the leading causes of cancer-related deaths globally. Exosomes, small membrane vesicles secreted by cells, are widely present in human bodily fluids. In the bodily fluids of NSCLC patients, the quantity of extracellular vesicles is double that of healthy individuals, suggesting their potential as biomarkers for screening NSCLC. This study designed a dual-modal aptasensor that integrated excellent sensitivity in electrochemical detection and portability in fluorescence detection into one device. AuNPs were functionalized with exosome-capturing probes containing thiol-modified CD63 aptamers, which were immobilized on screen-printed gold electrodes. On the other hand, the carboxylated CD63 aptamer was immobilized on the surface of PB-modified g-C3N4 loaded with Co-SANs particles (Co@g-C3N4@PB). By combining these components, a sandwich structure (AuNPs/Apt1/Exo/Apt2- Co@g-C3N4@PB) was constructed, forming a probe for specific exosome recognition. First, the samples were preliminarily assessed for their positive or negative status under a fluorescence inverted microscope. Subsequently, a more in-depth quantitative analysis was conducted on suspected positive samples using electrochemical or fluorescence analysis methods. The detection limits for electrochemical analysis and fluorescence analysis were 66.68 and 33.5particles/mL, respectively. In the analysis of clinical serum exosome samples, the developed dual-modal aptasensor effectively distinguished serum specimens from those of NSCLC patients and healthy volunteers. This highlighted the inspection capability of the dual-modal adapter sensor, especially in point-of-care testing, making it a highly suitable tool for clinical applications.

MIGP: Metapath Integrated Graph Prompt Neural Network.

Graph neural networks (GNNs) leveraging metapaths have garnered extensive utilization. Nevertheless, the escalating parameters and data corpus within graph pre-training models incur mounting training costs. Consequently, GNN models encounter hurdles including diminished generalization capacity and compromised performance amidst small sample datasets. Drawing inspiration from the efficacy demonstrated by self-supervised learning methodologies in natural language processing, we embark on an exploration. We endeavor to imbue graph data with augmentable, learnable prompt vectors targeting node representation enhancement to foster superior adaptability to downstream tasks. This paper proposes a novel approach, the Metapath Integrated Graph Prompt Neural Network (MIGP), which leverages learnable prompt vectors to enhance node representations within a pretrained model framework. By leveraging learnable prompt vectors, MIGP aims to address the limitations posed by mall sample datasets and improve GNNs' model generalization. In the pretraining stage, we split symmetric metapaths in heterogeneous graphs into short metapaths and explicitly propagate information along the metapaths to update node representations. In the prompt-tuning stage, the parameters of the pretrained model are fixed, a set of independent basis vectors is introduced, and an attention mechanism is employed to generate task-specific learnable prompt vectors for each node. Another notable contribution of our work is the introduction of three patent datasets, which is a pioneering application in related fields. We will make these three patent datasets publicly available to facilitate further research on large-scale patent data analysis. Through comprehensive experiments conducted on three patent datasets and three other public datasets, i.e., ACM, IMDB, and DBLP, we demonstrate the superior performance of the MIGP model in enhancing model applicability and performance across a variety of downstream datasets. The source code and datasets are available in the website.1.

Dual electrochemical signal "signal-on-off" sensor based on CHA-Td-HCR and CRISPR-Cas12a for MUC1 detection.

Mucin 1 (MUC1) is frequently overexpressed in various cancers and is essential for early cancer detection. Current methods to detect MUC1 are expensive, time-consuming, and require skilled personnel. Therefore, developing a simple, sensitive, highly selective MUC1 detection sensor is necessary. In this study, we proposed a novel "signal-on-off" strategy that, in the presence of MUC1, synergistically integrates catalytic hairpin assembly (CHA) with DNA tetrahedron (Td)-based nonlinear hybridization chain reaction (HCR) to enhance the immobilization of electrochemically active methylene blue (MB) on magnetic nanoparticles (MNP), marking the MB signal "on". Concurrently, the activation of CRISPR-Cas12a by isothermal amplification products triggers the cleavage of single-stranded DNA (ssDNA) at the electrode surface, resulting in a reduction of MgAl-LDH@Fc-AuFe-MIL-101 (containing ferrocene, Fc) on the electrode, presenting the "signal-off" state. Both MB and MgAl-LDH@Fc-AuFe-MIL-101 electrochemical signals were measured and analyzed. Assay parameters were optimized, and sensitivity, stability, and linear range were assessed. Across a concentration spectrum of MUC1 spanning from 10 fg/mL to 100 ng/mL, the MB and MgAl-LDH@Fc-AuFe-MIL-101 signals were calibrated with each other, demonstrating a "signal-on-off" dual electrochemical signaling pattern. This allows for the precise and quantitative detection of MUC1 in clinical samples, offering significant potential for medical diagnosis.

Case Report: A case suffered acute cerebral infarction after removal of temporary cardiac pacing lead which led to the perforation of interventricular septum.

We report an elderly male patient with frequent episodes of dizziness due to a complete atrioventricular block who underwent temporary pacemaker insertion in a local hospital. After the implantation of a permanent pacemaker and removal of the temporary pacemaker lead, the patient developed sudden neurological symptoms, upon which an acute cerebellar infarction was diagnosed via head CT. We will discuss the adequacy of the periprocedural administration.

Histo-pillar strip for optimal histogel block construction and biomarker analysis in 3D-lung cancer patient-derived organoids.

This study proposed an optimized histogel construction method for histological analysis by applying lung cancer patient-derived organoids (PDOs) to the developed histo-pillar strip. Previously, there is the cultured PDOs damage problem during the histogel construction due to forced detachment of the Matrigel spots from the 96-well plate bottom. To address this issue, we cultured PDO on the proposed Histo-pillar strips and then immersed them in 4% paraformaldehyde fixation solution to self-isolate PDO without damage. The 4µl patient-derived cell (PDC)/Matrigel mixtures were dispensed on the surface of a U-shaped histo-pillar strip, and the PDCs were aggregated by gravity and cultured into PDOs. Cultured PDOs were self-detached by simply immersing them in a paraformaldehyde fixing solution without physical processing, showing about two times higher cell recovery rate than conventional method. In addition, we proposed a method for embedding PDOs under conditions where the histogel temperature was maintained such that the histogel did not harden, thereby improving the problem of damaging the histogel block in the conventional sandwich histogel construction method. We performed histological and genotyping analyses using tumor tissues and PDOs from two patients with lung adenocarcinoma. Therefore, the PDO culture and improved histogel block construction method using the histo-pillar strip proposed in this study can be employed as useful tools for the histological analysis of a limited number of PDCs.

Tensorized Incomplete Multi-view Kernel Subspace Clustering.

Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are often faced with three challenging issues. First, they mostly lack the ability to recover the nonlinear subspace structures in the multiple kernel spaces. Second, they usually neglect the high-order relationship in multiple representations. Third, they often have two or even more hyper-parameters and may not be practical for some real-world applications. To tackle these issues, we present a Tensorized Incomplete Multi-view Kernel Subspace Clustering (TIMKSC) approach. Specifically, by incorporating the kernel learning technique into an incomplete subspace clustering framework, our approach can robustly explore the latent subspace structure hidden in multiple views. Furthermore, we impute the incomplete kernel matrices and learn the low-rank tensor representations in a mutual enhancement manner. Notably, our approach can discover the underlying relationship among the observed and missing samples while capturing the high-order correlation to assist subspace clustering. To solve the proposed optimization model, we design a three-step algorithm to efficiently minimize the unified objective function, which only involves one hyper-parameter that requires tuning. Experiments on various benchmark datasets demonstrate the superiority of our approach. The source code and datasets are available at: https://www.researchgate.net/publication/381828300_TIMKSC_20240629.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.