A patient-derived cell model for malignant transformation in IDH-mutant glioma.

Malignant transformation (MT) is commonly seen in IDH-mutant gliomas. There has been a growing research interest in revealing its underlying mechanisms and intervening prior to MT at the early stages of the transforming process. Here we established a unique pair of matched 3D cell models: 403L, derived from a low-grade glioma (LGG), and 403H, derived from a high-grade glioma (HGG), by utilizing IDH-mutant astrocytoma samples from the same patient when the tumor was diagnosed as WHO grade 2 (tumor mutational burden (TMB) of 3.96/Mb) and later as grade 4 (TMB of 70.07/Mb), respectively. Both cell models were authenticated to a patient's sample retaining endogenous expression of IDH1 R132H. DNA methylation profiles of the parental tumors referred to LGG and HGG IDH-mutant glioma clusters. The immunopositivity of SOX2, NESTIN, GFAP, OLIG2, and beta 3-Tubulin suggested the multilineage potential of both models. 403H was more prompt to cell invasion and developed infiltrative HGG in vivo. The differentially expressed genes (DEGs) from the RNA sequencing analysis revealed the tumor invasion and aggressiveness related genes exclusively upregulated in the 403H model. Pathway analysis showcased an enrichment of genes associated with epithelial-mesenchymal transition (EMT) and Notch signaling pathways in 403H and 403L, respectively. Mass spectrometry-based targeted metabolomics and hyperpolarized (HP) 1-13C pyruvate in-cell NMR analyses demonstrated significant alterations in the TCA cycle and fatty acid metabolism. Citrate, glutamine, and 2-HG levels were significantly higher in 403H. To our knowledge, this is the first report describing the development of a matched pair of 3D patient-derived cell models representative of MT and temozolomide (TMZ)-induced hypermutator phenotype (HMP) in IDH-mutant glioma, providing insights into genetic and metabolic changes during MT/HMP. This novel in vitro model allows further investigation of the mechanisms of MT at the cellular level.

Bibliometric Patent Review of Minimally Invasive Spine Surgery.

STUDY DESIGN: This study analyzes patents associated with minimally invasive spine surgery (MISS) found on the Lens open online platform. OBJECTIVE: The goal of this research was to provide an overview of the most referenced patents in the field of MISS and to uncover patterns in the evolution and categorization of these patents. SUMMARY OF BACKGROUND DATA: MISS has rapidly progressed, with a core focus on minimizing surgical damage, preserving the natural anatomy, and enabling swift recovery, all while achieving outcomes that rival traditional open surgery. While prior studies have primarily concentrated on MISS outcomes, the analysis of MISS patents has been limited. METHODS: To conduct this study, we used the Lens platform to search for patents that included the terms "minimally invasive" and "spine" in their titles, abstracts, or claims. We then categorized these patents and identified the top 100 with the most forward citations. We further classified these patents into 4 categories: Spinal Stabilization Systems, Joint Implants or Procedures, Screw Delivery System or Method, and Access and Surgical Pathway Formation. RESULTS: Five hundred two MISS patents were identified initially, and 276 were retained following a screening process. Among the top 100 patents, the majority had active legal status. The largest category within the top 100 patents was Access and Surgical Pathway Formation, closely followed by Spinal Stabilization Systems and Joint Implants or Procedures. The smallest category was Screw Delivery System or Method. Notably, the majority of the top 100 patents had priority years falling between 2000 and 2009, indicating a moderate positive correlation between patent rank and priority year. CONCLUSIONS: Thus far, patents related to Access and Surgical Pathway Formation have laid the foundation for subsequent innovations in Spinal Stabilization Systems and Screw Technology. This study serves as a valuable resource for guiding future innovations in this rapidly evolving field.

Explainable Machine Learning Approach to Prediction of Prolonged Intesive Care Unit Stay in Adult Spinal Deformity Patients: Machine Learning Outperforms Logistic Regression.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Prolonged ICU stay is a driver of higher costs and inferior outcomes in Adult Spinal Deformity (ASD) patients. Machine learning (ML) models have recently been seen as a viable method of predicting pre-operative risk but are often 'black boxes' that do not fully explain the decision-making process. This study aims to demonstrate ML can achieve similar or greater predictive power as traditional statistical methods and follows traditional clinical decision-making processes. METHODS: Five ML models (Decision Tree, Random Forest, Support Vector Classifier, GradBoost, and a CNN) were trained on data collected from a large urban academic center to predict whether prolonged ICU stay would be required post-operatively. 535 patients who underwent posterior fusion or combined fusion for treatment of ASD were included in each model with a 70-20-10 train-test-validation split. Further analysis was performed using Shapley Additive Explanation (SHAP) values to provide insight into each model's decision-making process. RESULTS: The model's Area Under the Receiver Operating Curve (AUROC) ranged from 0.67 to 0.83. The Random Forest model achieved the highest score. The model considered length of surgery, complications, and estimated blood loss to be the greatest predictors of prolonged ICU stay based on SHAP values. CONCLUSIONS: We developed a ML model that was able to predict whether prolonged ICU stay was required in ASD patients. Further SHAP analysis demonstrated our model aligned with traditional clinical thinking. Thus, ML models have strong potential to assist with risk stratification and more effective and cost-efficient care.

Can Large Language Models (LLMs) Predict the Appropriate Treatment of Acute Hip Fractures in Older Adults? Comparing Appropriate Use Criteria With Recommendations From ChatGPT.

BACKGROUND: Acute hip fractures are a public health problem affecting primarily older adults. Chat Generative Pretrained Transformer may be useful in providing appropriate clinical recommendations for beneficial treatment. OBJECTIVE: To evaluate the accuracy of Chat Generative Pretrained Transformer (ChatGPT)-4.0 by comparing its appropriateness scores for acute hip fractures with the American Academy of Orthopaedic Surgeons (AAOS) Appropriate Use Criteria given 30 patient scenarios. "Appropriateness" indicates the unexpected health benefits of treatment exceed the expected negative consequences by a wide margin. METHODS: Using the AAOS Appropriate Use Criteria as the benchmark, numerical scores from 1 to 9 assessed appropriateness. For each patient scenario, ChatGPT-4.0 was asked to assign an appropriate score for six treatments to manage acute hip fractures. RESULTS: Thirty patient scenarios were evaluated for 180 paired scores. Comparing ChatGPT-4.0 with AAOS scores, there was a positive correlation for multiple cannulated screw fixation, total hip arthroplasty, hemiarthroplasty, and long cephalomedullary nails. Statistically significant differences were observed only between scores for long cephalomedullary nails. CONCLUSION: ChatGPT-4.0 scores were not concordant with AAOS scores, overestimating the appropriateness of total hip arthroplasty, hemiarthroplasty, and long cephalomedullary nails, and underestimating the other three. ChatGPT-4.0 was inadequate in selecting an appropriate treatment deemed acceptable, most reasonable, and most likely to improve patient outcomes.

CAR T-cells targeting FGFR4 and CD276 simultaneously show potent antitumor effect against childhood rhabdomyosarcoma.

Chimeric antigen receptor (CAR) T-cells targeting Fibroblast Growth Factor Receptor 4 (FGFR4), a highly expressed surface tyrosine receptor in rhabdomyosarcoma (RMS), are already in the clinical phase of development, but tumour heterogeneity and suboptimal activation might hamper their potency. Here we report an optimization strategy of the co-stimulatory and targeting properties of a FGFR4 CAR. We replace the CD8 hinge and transmembrane domain and the 4-1BB co-stimulatory domain with those of CD28. The resulting CARs display enhanced anti-tumor activity in several RMS xenograft models except for an aggressive tumour cell line, RMS559. By searching for a direct target of the RMS core-regulatory transcription factor MYOD1, we identify another surface protein, CD276, as a potential target. Bicistronic CARs (BiCisCAR) targeting both FGFR4 and CD276, containing two distinct co-stimulatory domains, have superior prolonged persistent and invigorated anti-tumor activities compared to the optimized FGFR4-specific CAR and the other BiCisCAR with the same 4-1BB co-stimulatory domain. Our study thus lays down the proof-of-principle for a CAR T-cell therapy targeting both FGFR4 and CD276 in RMS.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Health Care Expenditures and Use Associated with Hypertension Among U.S. Adults.

INTRODUCTION: This study seeks to estimate health care expenditures and use associated with hypertension, focusing on differences among racial and ethnic groups. METHODS: Data were from the 2019 Medical Expenditure Panel Survey, analyzed in 2023. The study sample included noninstitutionalized U.S. adults aged ≥18 years. Outcome variables were health care expenditures and events. Hypertension was determined by a self-reported diagnosis or diagnosis codes. Race and ethnicity were self-reported. A 2-part model was used to estimate expenditures associated with hypertension. A zero-inflated negative binomial model was used to estimate events associated with hypertension. Sampling designs were applied to generate nationally representative estimates. RESULTS: Hypertension was associated with $2,759 (95% confidence interval [CI]: $2,039, $3,479) in health care expenditures and 10.3 (95% CI: 9.3, 11.3) health care events, including prescriptions filled, in 2019 per person. Compared with non-Hispanic White adults, hypertension-associated health care expenditures were significantly lower among Hispanic adults (difference: -$1,877; 95% CI: -$3,389, -$364) and Asian adults (difference: -$2,452; 95% CI: -$4,093, -$811), and hypertension-associated health care events were significantly lower among Hispanic adults (difference: -3.8; 95% CI: -6.1, -1.6) and non-Hispanic Asian adults (difference: -4.1; 95% CI: -6.9, -1.2). Differences between non-Hispanic White adults and non-Hispanic Black adults were not statistically significant in health care expenditures (difference: -$954; 95% CI: -$2,849, $941) and events (difference: 0.3; 95% CI: -2.1, 2.8). CONCLUSIONS: This study reveals differences in health care expenditures and use associated with hypertension among racial and ethnic groups. Future studies are needed to examine potential drivers of these differences.

Diet quality from mid to late life and its association with physical frailty in late life in a cohort of Chinese adults.

BACKGROUND: It is unclear if improving diet quality after midlife could reduce the risk of physical frailty at late life. We aimed to associate changes in diet quality after midlife with physical frailty at late life. METHODS: Diet quality in 12,580 participants from the Singapore Chinese Health Study was assessed with the Dietary Approaches to Stop Hypertension (DASH) scores at baseline (1993-1998; mean age 53 years) and follow-up 3 (2014-2016; mean age 73 years). Physical frailty was assessed using the modified Cardiovascular Health Study phenotype at follow-up 3. Multivariable logistic regressions examined associations between DASH scores and physical frailty. RESULTS: Comparing participants in extreme quartiles of DASH scores, the odds ratios (OR) [95% confidence interval (CI)] for physical frailty were 0.85 (0.73,0.99) at baseline and 0.49 (0.41, 0.58) at follow-up 3. Compared to participants with consistently low DASH scores, participants with consistently high scores (OR 0.74, 95% CI: 0.59, 0.94) and those with > 10% increase in scores (OR 0.78, 95% CI: 0.64, 0.95) had lower odds of frailty. Compared to those in the lowest DASH tertiles at both time-points, significantly lower odds of physical frailty were observed in those who were in the highest DASH tertiles at both time points [0.59 (0.48, 0.73)], and in those who improved their scores from the lowest [0.68 (0.51, 0.91)] or second tertile at baseline [0.61 (0.48, 0.76)] to the highest tertile at follow-up 3. CONCLUSIONS: Maintaining a high diet quality or a substantial improvement in diet quality after midlife could lower the risk of physical frailty at late life.

An analysis of ChatGPT recommendations for the diagnosis and treatment of cervical radiculopathy.

OBJECTIVE: The objective of this study was to assess the safety and accuracy of ChatGPT recommendations in comparison to the evidence-based guidelines from the North American Spine Society (NASS) for the diagnosis and treatment of cervical radiculopathy. METHODS: ChatGPT was prompted with questions from the 2011 NASS clinical guidelines for cervical radiculopathy and evaluated for concordance. Selected key phrases within the NASS guidelines were identified. Completeness was measured as the number of overlapping key phrases between ChatGPT responses and NASS guidelines divided by the total number of key phrases. A senior spine surgeon evaluated the ChatGPT responses for safety and accuracy. ChatGPT responses were further evaluated on their readability, similarity, and consistency. Flesch Reading Ease scores and Flesch-Kincaid reading levels were measured to assess readability. The Jaccard Similarity Index was used to assess agreement between ChatGPT responses and NASS clinical guidelines. RESULTS: A total of 100 key phrases were identified across 14 NASS clinical guidelines. The mean completeness of ChatGPT-4 was 46%. ChatGPT-3.5 yielded a completeness of 34%. ChatGPT-4 outperformed ChatGPT-3.5 by a margin of 12%. ChatGPT-4.0 outputs had a mean Flesch reading score of 15.24, which is very difficult to read, requiring a college graduate education to understand. ChatGPT-3.5 outputs had a lower mean Flesch reading score of 8.73, indicating that they are even more difficult to read and require a professional education level to do so. However, both versions of ChatGPT were more accessible than NASS guidelines, which had a mean Flesch reading score of 4.58. Furthermore, with NASS guidelines as a reference, ChatGPT-3.5 registered a mean ± SD Jaccard Similarity Index score of 0.20 ± 0.078 while ChatGPT-4 had a mean of 0.18 ± 0.068. Based on physician evaluation, outputs from ChatGPT-3.5 and ChatGPT-4.0 were safe 100% of the time. Thirteen of 14 (92.8%) ChatGPT-3.5 responses and 14 of 14 (100%) ChatGPT-4.0 responses were in agreement with current best clinical practices for cervical radiculopathy according to a senior spine surgeon. CONCLUSIONS: ChatGPT models were able to provide safe and accurate but incomplete responses to NASS clinical guideline questions about cervical radiculopathy. Although the authors' results suggest that improvements are required before ChatGPT can be reliably deployed in a clinical setting, future versions of the LLM hold promise as an updated reference for guidelines on cervical radiculopathy. Future versions must prioritize accessibility and comprehensibility for a diverse audience.

The Effect of Intraoperative Overdistraction on Subsidence Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective cohort. OBJECTIVE: The purpose of this study was to evaluate the effect of overdistraction on interbody cage subsidence. BACKGROUND: Vertebral overdistraction due to the use of large intervertebral cage sizes may increase the risk of postoperative subsidence. METHODS: Patients who underwent anterior cervical discectomy and fusion between 2016 and 2021 were included. All measurements were performed using lateral cervical radiographs at 3 time points - preoperative, immediate postoperative, and final follow-up >6 months postoperatively. Anterior and posterior distraction were calculated by subtracting the preoperative disc height from the immediate postoperative disc height. Cage subsidence was calculated by subtracting the final follow-up postoperative disc height from the immediate postoperative disc height. Associations between anterior and posterior subsidence and distraction were determined using multivariable linear regression models. The analyses controlled for cage type, cervical level, sex, age, smoking status, and osteopenia. RESULTS: Sixty-eight patients and 125 fused levels were included in the study. Of the 68 fusions, 22 were single-level fusions, 35 were 2-level, and 11 were 3-level. The median final follow-up interval was 368 days (range: 181-1257 d). Anterior disc space subsidence was positively associated with anterior distraction (beta = 0.23; 95% CI: 0.08, 0.38; P = 0.004), and posterior disc space subsidence was positively associated with posterior distraction (beta = 0.29; 95% CI: 0.13, 0.45; P < 0.001). No significant associations between anterior distraction and posterior subsidence (beta = 0.07; 95% CI: -0.06, 0.20; P = 0.270) or posterior distraction and anterior subsidence (beta = 0.06; 95% CI: -0.14, 0.27; P = 0.541) were observed. CONCLUSIONS: We found that overdistraction of the disc space was associated with increased postoperative subsidence after anterior cervical discectomy and fusion. Surgeons should consider choosing a smaller cage size to avoid overdistraction and minimize postoperative subsidence.

Practical guidance and workflows for identifying fast evolving non-coding genomic elements using PhyloAcc.

Comparative genomics provides ample ways to study genome evolution and its relationship to phenotypic traits. By developing and testing alternate models of evolution throughout a phylogeny, one can estimate rates of molecular evolution along different lineages in a phylogeny and link these rates with observations in extant species, such as convergent phenotypes. Pipelines for such work can help identify when and where genomic changes may be associated with, or possibly influence, phenotypic traits. We recently developed a set of models called PhyloAcc, using a Bayesian framework to estimate rates of nucleotide substitution on different branches a phylogenetic tree and evaluate their association with pre-defined or estimated phenotypic traits PhyloAcc-ST and PhyloAcc-GT both allow users to define a priori a set of target lineages and then compare different models to identify loci accelerating in one or more target lineages. Whereas ST considers only one species tree across all input loci, GT considers alternate topologies for every locus. PhyloAcc-C simultaneously models molecular rates and rates of continuous trait evolution,allowing the user to ask whether the two are associated. Here we describe these models and provide tips and workflows on how to prepare the input data and run PhyloAcc.

Relation of preconception eating behaviours to dietary pattern trajectories and gestational weight gain from preconception to late pregnancy.

Studies examining preconception eating behaviours with longitudinal dietary patterns from preconception to late pregnancy as well as gestational weight gain (GWG) are limited. We derived dietary pattern trajectories from preconception to late-pregnancy, and related preconception eating behaviours to these trajectories and GWG. Preconception eating behaviours were assessed using the Three-Factor Eating Questionnaire measuring cognitive restraint (CR) - conscious restriction of food intake, emotional eating (EE) - overeating in response to negative emotions, and uncontrolled eating (UE) - overeating with a feeling of lack of control. Dietary intakes were measured at preconception, 20-21 and 34-36 weeks' gestation with food frequency questionnaires. Dietary patterns were determined using factor analysis, and trajectories derived using group-based trajectory modelling. Inadequate and excessive GWG were defined according to Institute of Medicine guidelines based on weights at preconception and the last antenatal visit (median: 38 weeks' gestation). Two dietary patterns were derived: 'Fast Food, Fried Snacks and Desserts (FFD)' and 'Soup, Fish and Vegetables (SFV)'. Adherence trajectories from preconception to late-pregnancy were characterised as consistently high ("stable-high") and low ("stable-low"). Women with higher UE scores had higher odds of being in the "stable-high" trajectory (n = 34) of the FFD pattern [Odds Ratio (OR): 1.25, 95% Confidence Interval (CI): 1.03, 1.51], compared to "stable-low" (n = 260). Percentages of women with inadequate, adequate or excessive GWG were 21.7% (n = 70), 25.8% (n = 83), and 52.5% (n = 169), respectively; women with higher EE scores had a higher likelihood of excessive GWG [Relative Risk Ratio (RRR): 1.35, 95% CI: 1.02, 1.80], but this association was attenuated after adjusting for preconception body mass index. Eating behaviour interventions to improve dietary patterns among pregnant women may need to start as early as preconception, incorporating strategies to manage UE.

A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution.

Genomes contain conserved non-coding sequences that perform important biological functions, such as gene regulation. We present a phylogenetic method, PhyloAcc-C, that associates nucleotide substitution rates with changes in a continuous trait of interest. The method takes as input a multiple sequence alignment of conserved elements, continuous trait data observed in extant species, and a background phylogeny and substitution process. Gibbs sampling is used to assign rate categories (background, conserved, accelerated) to lineages and explore whether the assigned rate categories are associated with increases or decreases in the rate of trait evolution. We test our method using simulations and then illustrate its application using mammalian body size and lifespan data previously analyzed with respect to protein coding genes. Like other studies, we find processes such as tumor suppression, telomere maintenance, and p53 regulation to be related to changes in longevity and body size. In addition, we also find that skeletal genes, and developmental processes, such as sprouting angiogenesis, are relevant.

Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery.

OBJECTIVE: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT's 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. METHODS: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. RESULTS: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT's GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. CONCLUSION: ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model's performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model's responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

Structural Equation Modelling to Identify Psychometric Determinants of Medication Adherence in a Survey of Kidney Dialysis Patients.

Purpose: Medication non-adherence in dialysis patients is associated with increased mortality and higher healthcare costs. We assessed whether medication adherence is influenced by specific psychometric constructs measuring beliefs about the necessity for medication and concerns about them. We also tested whether medication knowledge, health literacy, and illness perceptions influenced this relationship. Patients and Methods: This study is based on data from a cross-sectional in-person questionnaire, administered to a random sample of all adult dialysis patients at a teaching hospital. The main outcome was self-assessed medication adherence (8-Item Morisky Medication Adherence Scale). The predictors were: concerns about medications and necessity for medication (Beliefs About Medication Questionnaire); health literacy; medication knowledge (Medication Knowledge Evaluation Tool); cognitive, emotional, and comprehensibility Illness perceptions (Brief Illness Perception Questionnaire). Path analysis was performed using structural equations in both covariance and variance-based models. Results: Necessity for medication increased (standardized path coefficient [ß] 0.30 [95% CI 0.05, 0.54]) and concerns about medication decreased (standardized ß -0.33 [-0.57, -0.09]) medication adherence, explaining most of the variance in outcome (r2=0.95). Medication knowledge and cognitive illness perceptions had no effects on medication adherence, either directly or indirectly. Higher health literacy, greater illness comprehension, and a more positive emotional view of their illness had medium-to-large sized effects in increasing medication adherence. These were indirect rather and direct effects mediated by decreases in concerns about medications (standardized ß respectively -0.40 [-0.63,-0.16], -0.60 [-0.85, -0.34], -0.33 [-0.52, -0.13]). Conclusion: Interventions that reduce patients' concerns about their medications are likely to improve adherence, rather than interventions that increase patients' perceived necessity for medication. Improving patients' general health literacy and facilitating a better understanding and more positive perception of the illness can probably achieve this. Our study is potentially limited by a lack of generalizability outside of the population and setting in which it was conducted.

ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.

BACKGROUND CONTEXT: Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care. The current literature has yet to investigate the ability of ChatGPT to assist clinical decision making with regard to degenerative spondylolisthesis. PURPOSE: The study aimed to compare ChatGPT's concordance with the recommendations set forth by The North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and assess ChatGPT's accuracy within the context of the most recent literature. METHODS: ChatGPT-3.5 and 4.0 was prompted with questions from the NASS Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and graded its recommendations as "concordant" or "nonconcordant" relative to those put forth by NASS. A response was considered "concordant" when ChatGPT generated a recommendation that accurately reproduced all major points made in the NASS recommendation. Any responses with a grading of "nonconcordant" were further stratified into two subcategories: "Insufficient" or "Over-conclusive," to provide further insight into grading rationale. Responses between GPT-3.5 and 4.0 were compared using Chi-squared tests. RESULTS: ChatGPT-3.5 answered 13 of NASS's 28 total clinical questions in concordance with NASS's guidelines (46.4%). Categorical breakdown is as follows: Definitions and Natural History (1/1, 100%), Diagnosis and Imaging (1/4, 25%), Outcome Measures for Medical Intervention and Surgical Treatment (0/1, 0%), Medical and Interventional Treatment (4/6, 66.7%), Surgical Treatment (7/14, 50%), and Value of Spine Care (0/2, 0%). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-3.5 generated a concordant response 66.7% of the time (6/9). However, ChatGPT-3.5's concordance dropped to 36.8% when asked clinical questions that NASS did not provide a clear recommendation on (7/19). A further breakdown of ChatGPT-3.5's nonconcordance with the guidelines revealed that a vast majority of its inaccurate recommendations were due to them being "over-conclusive" (12/15, 80%), rather than "insufficient" (3/15, 20%). ChatGPT-4.0 answered 19 (67.9%) of the 28 total questions in concordance with NASS guidelines (P = 0.177). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-4.0 generated a concordant response 66.7% of the time (6/9). ChatGPT-4.0's concordance held up at 68.4% when asked clinical questions that NASS did not provide a clear recommendation on (13/19, P = 0.104). CONCLUSIONS: This study sheds light on the duality of LLM applications within clinical settings: one of accuracy and utility in some contexts versus inaccuracy and risk in others. ChatGPT was concordant for most clinical questions NASS offered recommendations for. However, for questions NASS did not offer best practices, ChatGPT generated answers that were either too general or inconsistent with the literature, and even fabricated data/citations. Thus, clinicians should exercise extreme caution when attempting to consult ChatGPT for clinical recommendations, taking care to ensure its reliability within the context of recent literature.

Can generative artificial intelligence pass the orthopaedic board examination?

Background: Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education. ChatGPT, an LLM launched in late 2022 has demonstrated the ability to produce accurate, detailed answers, potentially enabling it to aid in medical education and clinical decision-making. The purpose of this study is to evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations using Self-Assessment Exams from the AAOS database and approved literature as a proxy for the Orthopaedic Board Examination. Methods: 301 SAE questions from the AAOS database and associated AAOS literature were input into ChatGPT's interface in a question and multiple-choice format and the answers were then analyzed to determine which answer choice was selected. A new chat was used for every question. All answers were recorded, categorized, and compared to the answer given by the OITE and SAE exams, noting whether the answer was right or wrong. Results: Of the 301 questions asked, ChatGPT was able to correctly answer 183 (60.8%) of them. The subjects with the highest percentage of correct questions were basic science (81%), oncology (72.7%, shoulder and elbow (71.9%), and sports (71.4%). The questions were further subdivided into 3 groups: those about management, diagnosis, or knowledge recall. There were 86 management questions and 47 were correct (54.7%), 45 diagnosis questions with 32 correct (71.7%), and 168 knowledge recall questions with 102 correct (60.7%). Conclusions: ChatGPT has the potential to provide orthopedic educators and trainees with accurate clinical conclusions for the majority of board-style questions, although its reasoning should be carefully analyzed for accuracy and clinical validity. As such, its usefulness in a clinical educational context is currently limited but rapidly evolving. Clinical relevance: ChatGPT can access a multitude of medical data and may help provide accurate answers to clinical questions.

Corrigendum: Chromatin structure and context-dependent sequence features control prime editing efficiency.

[This corrects the article DOI: 10.3389/fgene.2023.1222112.].

In Situ Photo-Crosslinkable Protein Bioadhesive for Bone Graft Fixation.

Bone grafting is a fundamental dental surgery procedure widely used for implant placement and periodontal disease management treatments. Despite its broad applications, vertical bone augmentation presents unique challenges, including the risk of graft displacement due to gravitational and masticatory forces. Traditional physical stabilization methods introduce additional complexities and risks, underscoring the need for innovative fixation technologies. This study aimed to develop an in situ photo-crosslinkable bioadhesive hydrogel (iPBAH) as a multifunctional bone graft binder to enhance the process of bone reconstruction. The bioadhesive is composed of mussel-derived adhesive protein (MAP) fused with the cell-adhesive peptide RGD. The numerous tyrosine residues in MAP facilitate rapid photo-crosslinking, enabling efficient hydrogel formation using visible blue light. Subsequently, iPBAH underwent comprehensive characterization to evaluate its suitability as a multifunctional bone graft binder. iPBAH efficiently underwent in situ crosslinking through harmless exposure to visible light within minutes and displayed several exceptional properties, including a microporous structure, underwater adhesion, extended durability, high compressive strength, and biocompatibility. In vivo assessments, using male Sprague-Dawley rats, demonstrated that iPBAH binder significantly enhanced bone regeneration in a rat calvarial bone defect model. The in situ crosslinking of the iPBAH binder during bone graft transplantation can effectively fill irregular and complex defect shapes while simultaneously preventing graft material leakage. The improved physical attributes of the bound graft material can enhance its resistance to external forces, thereby ensuring sustained retention over time. Moreover, the interaction between iPBAH and surrounding tissues promotes adhesion and integration of the graft material with host tissues in the defect area. In addition, the included RGD peptide in iPBAH can augment inherent cell recruitment, adhesion, and growth, consequently expediting osteogenesis.