pH-Gated Succinate Secretion Regulates Muscle Remodeling in Response to Exercise.

In response to skeletal muscle contraction during exercise, paracrine factors coordinate tissue remodeling, which underlies this healthy adaptation. Here we describe a pH-sensing metabolite signal that initiates muscle remodeling upon exercise. In mice and humans, exercising skeletal muscle releases the mitochondrial metabolite succinate into the local interstitium and circulation. Selective secretion of succinate is facilitated by its transient protonation, which occurs upon muscle cell acidification. In the protonated monocarboxylic form, succinate is rendered a transport substrate for monocarboxylate transporter 1, which facilitates pH-gated release. Upon secretion, succinate signals via its cognate receptor SUCNR1 in non-myofibrillar cells in muscle tissue to control muscle-remodeling transcriptional programs. This succinate-SUCNR1 signaling is required for paracrine regulation of muscle innervation, muscle matrix remodeling, and muscle strength in response to exercise training. In sum, we define a bioenergetic sensor in muscle that utilizes intracellular pH and succinate to coordinate tissue adaptation to exercise.

Diode effect in Josephson junctions with a single magnetic atom.

Current flow in electronic devices can be asymmetric with bias direction, a phenomenon underlying the utility of diodes1 and known as non-reciprocal charge transport2. The promise of dissipationless electronics has recently stimulated the quest for superconducting diodes, and non-reciprocal superconducting devices have been realized in various non-centrosymmetric systems3-10. Here we investigate the ultimate limits of miniaturization by creating atomic-scale Pb-Pb Josephson junctions in a scanning tunnelling microscope. Pristine junctions stabilized by a single Pb atom exhibit hysteretic behaviour, confirming the high quality of the junctions, but no asymmetry between the bias directions. Non-reciprocal supercurrents emerge when inserting a single magnetic atom into the junction, with the preferred direction depending on the atomic species. Aided by theoretical modelling, we trace the non-reciprocity to quasiparticle currents flowing by means of electron-hole asymmetric Yu-Shiba-Rusinov states inside the superconducting energy gap and identify a new mechanism for diode behaviour in Josephson junctions. Our results open new avenues for creating atomic-scale Josephson diodes and tuning their properties through single-atom manipulation.

Quantum computational advantage with a programmable photonic processor.

A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity5, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 µs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.

Retraction Note: Exploring the quantum speed limit with computer games.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The Rho guanine dissociation inhibitor α inhibits skeletal muscle Rac1 activity and insulin action.

The molecular events governing skeletal muscle glucose uptake have pharmacological potential for managing insulin resistance in conditions such as obesity, diabetes, and cancer. With no current pharmacological treatments to target skeletal muscle insulin sensitivity, there is an unmet need to identify the molecular mechanisms that control insulin sensitivity in skeletal muscle. Here, the Rho guanine dissociation inhibitor α (RhoGDIα) is identified as a point of control in the regulation of insulin sensitivity. In skeletal muscle cells, RhoGDIα interacted with, and thereby inhibited, the Rho GTPase Rac1. In response to insulin, RhoGDIα was phosphorylated at S101 and Rac1 dissociated from RhoGDIα to facilitate skeletal muscle GLUT4 translocation. Accordingly, siRNA-mediated RhoGDIα depletion increased Rac1 activity and elevated GLUT4 translocation. Consistent with RhoGDIα's inhibitory effect, rAAV-mediated RhoGDIα overexpression in mouse muscle decreased insulin-stimulated glucose uptake and was detrimental to whole-body glucose tolerance. Aligning with RhoGDIα's negative role in insulin sensitivity, RhoGDIα protein content was elevated in skeletal muscle from insulin-resistant patients with type 2 diabetes. These data identify RhoGDIα as a clinically relevant controller of skeletal muscle insulin sensitivity and whole-body glucose homeostasis, mechanistically by modulating Rac1 activity.

Characterization of erythroferrone oligomerization and its impact on BMP antagonism.

Hepcidin, a peptide hormone that negatively regulates iron metabolism, is expressed by bone morphogenetic protein (BMP) signaling. Erythroferrone (ERFE) is an extracellular protein that binds and inhibits BMP ligands, thus positively regulating iron import by indirectly suppressing hepcidin. This allows for rapid erythrocyte regeneration after blood loss. ERFE belongs to the C1Q/TNF-related protein family and is suggested to adopt multiple oligomeric forms: a trimer, a hexamer, and a high molecular weight species. The molecular basis for how ERFE binds BMP ligands and how the different oligomeric states impact BMP inhibition are poorly understood. In this study, we demonstrated that ERFE activity is dependent on the presence of stable dimeric or trimeric ERFE and that larger species are dispensable for BMP inhibition. Additionally, we used an in silico approach to identify a helix, termed the ligand-binding domain, that was predicted to bind BMPs and occlude the type I receptor pocket. We provide evidence that the ligand-binding domain is crucial for activity through luciferase assays and surface plasmon resonance analysis. Our findings provide new insight into how ERFE oligomerization impacts BMP inhibition, while identifying critical molecular features of ERFE essential for binding BMP ligands.

High-temperature Josephson diode.

Many superconducting systems with broken time-reversal and inversion symmetry show a superconducting diode effect, a non-reciprocal phenomenon analogous to semiconducting p-n-junction diodes. While the superconducting diode effect lays the foundation for realizing ultralow dissipative circuits, Josephson-phenomena-based diode effect (JDE) can enable the realization of protected qubits. The superconducting diode effect and JDE reported thus far are at low temperatures (~4 K), limiting their applications. Here we demonstrate JDE persisting up to 77 K using an artificial Josephson junction of twisted layers of Bi2Sr2CaCu2O8+δ. JDE manifests as an asymmetry in the magnitude and distributions of switching currents, attaining the maximum at 45° twist. The asymmetry is induced by and tunable with a very small magnetic field applied perpendicular to the junction and arises due to interaction between Josephson and Abrikosov vortices. We report a large asymmetry of 60% at 20 K. Our results provide a path towards realizing superconducting Josephson circuits at liquid-nitrogen temperature.

A high-throughput screening platform for enzymes active on mucin-type O-glycoproteins.

Mucin-type O-glycosylation is a post-translational modification present at the interface between cells where it has important roles in cellular communication. However, deciphering the function of O-glycoproteins and O-glycans can be challenging, especially as few enzymes are available for their assembly or selective degradation. Here, to address this deficiency, we developed a genetically encoded screening methodology for the discovery and engineering of the diverse classes of enzymes that act on O-glycoproteins. The method uses Escherichia coli that have been engineered to produce an O-glycosylated fluorescence resonance energy transfer probe that can be used to screen for O-glycopeptidase activity. Subsequent cleavage of the substrate by O-glycopeptidases provides a read-out of the glycosylation state of the probe, allowing the method to also be used to assay glycosidases and glycosyltransferases. We further show the potential of this methodology in the first ultrahigh-throughput-directed evolution of an O-glycopeptidase.

Group B Streptococcus transcriptome when interacting with brain endothelial cells.

Bacterial meningitis is a life-threatening infection of the central nervous system (CNS) that occurs when bacteria are able to cross the blood-brain barrier (BBB) or the meningeal-cerebrospinal fluid barrier (mBCSFB). The BBB and mBCSFB comprise highly specialized brain endothelial cells (BECs) that typically restrict pathogen entry. Group B Streptococcus (GBS or Streptococcus agalactiae) is the leading cause of neonatal meningitis. Until recently, identification of GBS virulence factors has relied on genetic screening approaches. Instead, we here conducted RNA-seq analysis on GBS when interacting with induced pluripotent stem cell-derived BECs (iBECs) to pinpoint virulence-associated genes. Of the 2,068 annotated protein-coding genes of GBS, 430 transcripts displayed significant changes in expression after interacting with BECs. Notably, we found that the majority of differentially expressed GBS transcripts were downregulated (360 genes) during infection of iBECs. Interestingly, codY, encoding a pleiotropic transcriptional repressor in low-G + C Gram-positive bacteria, was identified as being highly downregulated. We conducted qPCR to confirm the codY downregulation observed via RNA-seq during the GBS-iBEC interaction and obtained codY mutants in three different GBS background parental strains. As anticipated from the RNA-seq results, the [Formula: see text]codY strains were more adherent and invasive in two in vitro BEC models. Together, this demonstrates the utility of RNA-seq during the BEC interaction to identify GBS virulence modulators. IMPORTANCE: Group B Streptococcus (GBS) meningitis remains the leading cause of neonatal meningitis. Research work has identified surface factors and two-component systems that contribute to GBS disruption of the blood-brain barrier (BBB). These discoveries often relied on genetic screening approaches. Here, we provide transcriptomic data describing how GBS changes its transcriptome when interacting with brain endothelial cells. Additionally, we have phenotypically validated these data by obtaining mutants of a select regulator that is highly down-regulated during infection and testing on our BBB model. This work provides the research field with a validated data set that can provide an insight into potential pathways that GBS requires to interact with the BBB and open the door to new discoveries.

Characterization of a new family of 6-sulfo-N-acetylglucosaminidases.

Sulfation is widespread in nature and plays an important role in modulating biological function. Among the strategies developed by microbes to access sulfated oligosaccharides as a nutrient source is the production of 6-sulfoGlcNAcases to selectively release 6-sulfoGlcNAc from target oligosaccharides. Thus far, all 6-sulfoGlcNAcases identified have belonged to the large GH20 family of ß-hexosaminidases. Ηere, we identify and characterize a new, highly specific non-GH20 6-sulfoGlcNAcase from Streptococcus pneumoniae TIGR4, Sp_0475 with a greater than 110,000-fold preference toward N-acetyl-ß-D-glucosamine-6-sulfate substrates over the nonsulfated version. Sp_0475 shares distant sequence homology with enzymes of GH20 and with the newly formed GH163 family. However, the sequence similarity between them is sufficiently low that Sp_0475 has been assigned as the founding member of a new glycoside hydrolase family, GH185. By combining results from site-directed mutagenesis with mechanistic studies and bioinformatics we provide insight into the substrate specificity, mechanism, and key active site residues of Sp_0475. Enzymes of the GH185 family follow a substrate-assisted mechanism, consistent with their distant homology to the GH20 family, but the catalytic residues involved are quite different. Taken together, our results highlight in more detail how microbes can degrade sulfated oligosaccharides for nutrients.

On-Chip Quantum Information Processing with Distinguishable Photons.

Multiphoton interference is at the heart of photonic quantum technologies. Arrays of integrated cavities can support bright sources of single photons with high purity and small footprint, but the inevitable spectral distinguishability between photons generated from nonidentical cavities is an obstacle to scaling. In principle, this problem can be alleviated by measuring photons with high timing resolution, which erases spectral information through the time-energy uncertainty relation. Here, we experimentally demonstrate that detection can be implemented with a temporal resolution sufficient to interfere photons detuned on the scales necessary for cavity-based integrated photon sources. By increasing the effective timing resolution of the system from 200 to 20 ps, we observe a 20% increase in the visibility of quantum interference between independent photons from integrated microring resonator sources that are detuned by 6.8 GHz. We go on to show how time-resolved detection of nonideal photons can be used to improve the fidelity of an entangling operation and to mitigate the reduction of computational complexity in boson sampling experiments. These results pave the way for photonic quantum information processing with many photon sources without the need for active alignment.

Capacity for LDL (Low-Density Lipoprotein) Retention Predicts the Course of Atherogenesis in the Murine Aortic Arch.

BACKGROUND: To cause atherosclerosis, LDLs (low-density lipoproteins) must first pass through the endothelium and then become retained in the arterial matrix. Which of these two processes is rate-limiting and predicts the topography of plaque formation remains controversial. To investigate this issue, we performed high-resolution mapping of LDL entry and retention in murine aortic arches before and during atherosclerosis development. METHODS: Maps of LDL entry and retention were created by injecting fluorescently labeled LDL followed by near-infrared scanning and whole-mount confocal microscopy after 1 hour (entry) and 18 hours (retention). By comparing arches between normal mice and mice with short-term hypercholesterolemia, we analyzed changes in LDL entry and retention during the LDL accumulation phase that precedes plaque formation. Experiments were designed to secure equal plasma clearance of labeled LDL in both conditions. RESULTS: We found that LDL retention is the overall limiting factor for LDL accumulation but that the capacity for LDL retention varied substantially over surprisingly short distances. The inner curvature region, previously considered a homogenous atherosclerosis-prone region, consisted of dorsal and ventral zones with high capacity and a central zone with low capacity for continued LDL retention. These features predicted the temporal pattern of atherosclerosis, which first appeared in the border zones and later in the central zone. The limit to LDL retention in the central zone was intrinsic to the arterial wall, possibly caused by saturation of the binding mechanism, and was lost upon conversion to atherosclerotic lesions. CONCLUSIONS: Capacity for continued LDL retention varies over short distances and predicts where and when atherosclerosis develops in the mouse aortic arch.

A localizing nanocarrier formulation enables multi-target immune responses to multivalent replicating RNA with limited systemic inflammation.

RNA vaccines possess significant clinical promise in counteracting human diseases caused by infectious or cancerous threats. Self-amplifying replicon RNA (repRNA) has been thought to offer the potential for enhanced potency and dose sparing. However, repRNA is a potent trigger of innate immune responses in vivo, which can cause reduced transgene expression and dose-limiting reactogenicity, as highlighted by recent clinical trials. Here, we report that multivalent repRNA vaccination, necessitating higher doses of total RNA, could be safely achieved in mice by delivering multiple repRNAs with a localizing cationic nanocarrier formulation (LION). Intramuscular delivery of multivalent repRNA by LION resulted in localized biodistribution accompanied by significantly upregulated local innate immune responses and the induction of antigen-specific adaptive immune responses in the absence of systemic inflammatory responses. In contrast, repRNA delivered by lipid nanoparticles (LNPs) showed generalized biodistribution, a systemic inflammatory state, an increased body weight loss, and failed to induce neutralizing antibody responses in a multivalent composition. These findings suggest that in vivo delivery of repRNA by LION is a platform technology for safe and effective multivalent vaccination through mechanisms distinct from LNP-formulated repRNA vaccines.

Costs of revision operations for distal junctional kyphosis following thoracic posterior spinal fusion for adolescent idiopathic scoliosis.

PURPOSE: To assess direct costs and risks associated with revision operations for distal junctional kyphosis/failure (DJK) following thoracic posterior spinal instrumented fusions (TPSF) for adolescent idiopathic scoliosis (AIS). METHODS: Children who underwent TPSF for AIS by a single surgeon (2014-2020) were reviewed. Inclusion criteria were minimum follow-up of 2 years, thoracolumbar posterior instrumented fusion with a lower instrumented vertebra (LIV) cranial to L2. Patients who developed DJK requiring revision operations were identified and compared with those who did not develop DJK. RESULTS: Seventy-nine children were included for analysis. Of these, 6.3% developed DJK. Average time to revision was 20.8 ± 16.2 months. Comparing index operations, children who developed DJK had significantly greater BMIs, significantly lower thoracic kyphosis postoperatively, greater post-operative lumbar Cobb angles, and significantly more LIVs cranial to the sagittal stable vertebrae (SSV), despite having statistically similar pre-operative coronal and sagittal alignment parameters and operative details compared with non-DJK patients. Revision operations for DJK, when compared with index operations, involved significantly fewer levels, longer operative times, greater blood loss, and longer hospital lengths of stay. These factors resulted in significantly greater direct costs for revision operations for DJK ($76,883 v. $46,595; p < 0.01). CONCLUSIONS: In this single-center experience, risk factors for development of DJK were greater BMI, lower post-operative thoracic kyphosis, and LIV cranial to SSV. As revision operations for DJK were significantly more costly than index operations, all efforts should be aimed at strategies to prevent DJK in the AIS population.

Sex differences in patients with and without high-risk factors associated with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Controversy remains regarding the appropriate screening for intracranial aneurysms or for the treatment of aneurysmal subarachnoid hemorrhage (aSAH) for patients without known high-risk factors for rupture. This study aimed to assess how sex affects both aSAH presentation and outcomes for aSAH treatment. METHOD: A retrospective cohort study was conducted of all patients treated at a single institution for an aSAH during a 12-year period (August 1, 2007-July 31, 2019). An analysis of women with and without high-risk factors was performed, including a propensity adjustment for a poor neurologic outcome (modified Rankin Scale [mRS] score > 2) at follow-up. RESULTS: Data from 1014 patients were analyzed (69% [n = 703] women). Women were significantly older than men (mean ± SD, 56.6 ± 14.1 years vs 53.4 ± 14.2 years, p < 0.001). A significantly lower percentage of women than men had a history of tobacco use (36.6% [n = 257] vs 46% [n = 143], p = 0.005). A significantly higher percentage of women than men had no high-risk factors for aSAH (10% [n = 70] vs 5% [n = 16], p = 0.01). The percentage of women with an mRS score > 2 at the last follow-up was significantly lower among those without high-risk factors (34%, 24/70) versus those with high-risk factors (53%, 334/633) (p = 0.004). Subsequent propensity-adjusted analysis (adjusted for age, Hunt and Hess grade, and Fisher grade) found no statistically significant difference in the odds of a poor outcome for women with or without high-risk factors for aSAH (OR = 0.7, 95% CI = 0.4-1.2, p = 0.18). CONCLUSIONS: A higher percentage of women versus men with aSAH had no known high-risk factors for rupture, supporting more aggressive screening and management of women with unruptured aneurysms.

The Number of Patients Lost to Follow-Up May Exceed the Fragility Index of a Randomized Controlled Trial Without Reversing Statistical Significance: A Systematic Review and Statistical Model.

PURPOSE: To (1) analyze trends in the publishing of statistical fragility index (FI)-based systematic reviews in the orthopaedic literature, including the prevalence of misleading or inaccurate statements related to the statistical fragility of randomized controlled trials (RCTs) and patients lost to follow-up (LTF), and (2) determine whether RCTs with relatively "low" FIs are truly as sensitive to patients LTF as previously portrayed in the literature. METHODS: All FI-based studies published in the orthopaedic literature were identified using the Cochrane Database of Systematic Reviews, Web of Science Core Collection, PubMed, and MEDLINE databases. All articles involving application of the FI or reverse FI to study the statistical fragility of studies in orthopaedics were eligible for inclusion in the study. Study characteristics, median FIs and sample sizes, and misleading or inaccurate statements related to the FI and patients LTF were recorded. Misleading or inaccurate statements-defined as those basing conclusions of trial fragility on the false assumption that adding patients LTF back to a trial has the same statistical effect as existing patients in a trial experiencing the opposite outcome-were determined by 2 authors. A theoretical RCT with a sample size of 100, P = .006, and FI of 4 was used to evaluate the difference in effect on statistical significance between flipping outcome events of patients already included in the trial (FI) and adding patients LTF back to the trial to show the true sensitivity of RCTs to patients LTF. RESULTS: Of the 39 FI-based studies, 37 (95%) directly compared the FI with the number of patients LTF. Of these 37 studies, 22 (59%) included a statement regarding the FI and patients LTF that was determined to be inaccurate or misleading. In the theoretical RCT, a reversal of significance was not observed until 7 patients LTF (nearly twice the FI) were added to the trial in the distribution of maximal significance reversal. CONCLUSIONS: The claim that any RCT in which the number of patients LTF exceeds the FI could potentially have its significance reversed simply by maintaining study follow-ups is commonly inaccurate and prevalent in orthopaedic studies applying the FI. Patients LTF and the FI are not equivalent. The minimum number of patients LTF required to flip the significance of a typical RCT was shown to be greater than the FI, suggesting that RCTs with relatively low FIs may not be as sensitive to patients LTF as previously portrayed in the literature; however, only a holistic approach that considers the context in which the trial was conducted, potential biases, and study results can determine the merits of any particular RCT. CLINICAL RELEVANCE: Surgeons may benefit from re-examining their interpretation of prior FI reviews that have made claims of substantial RCT fragility based on comparisons between the FI and patients LTF; it is possible the results are more robust than previously believed.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation.

PURPOSE: To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues. METHODS: A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment. RESULTS: Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability. CONCLUSIONS: DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework. LEVEL OF EVIDENCE: Level IV, scoping review of Level I to IV studies.

A Machine Learning Model Demonstrates Excellent Performance in Predicting Subscapularis Tears Based on Pre-Operative Imaging Parameters Alone.

PURPOSE: To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. METHODS: Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push-off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. RESULTS: Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging-related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c-statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. CONCLUSIONS: In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. LEVEL OF EVIDENCE: Level III, diagnostic case-control study.

ChatGPT-4 Performs Clinical Information Retrieval Tasks Using Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure.

PURPOSE: To assess the ability of ChatGPT-4, an automated Chatbot powered by artificial intelligence, to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance with Google Search Engine. METHODS: Using previously validated methods, a Google search was first performed using the query "Latarjet." Subsequently, the top 10 frequently asked questions (FAQs) and associated sources were extracted. ChatGPT-4 was then prompted to provide the top 10 FAQs and answers concerning the procedure. This process was repeated to identify additional FAQs requiring discrete-numeric answers to allow for a comparison between ChatGPT-4 and Google. Discrete, numeric answers were subsequently assessed for accuracy on the basis of the clinical judgment of 2 fellowship-trained sports medicine surgeons who were blinded to search platform. RESULTS: Mean (± standard deviation) accuracy to numeric-based answers was 2.9 ± 0.9 for ChatGPT-4 versus 2.5 ± 1.4 for Google (P = .65). ChatGPT-4 derived information for answers only from academic sources, which was significantly different from Google Search Engine (P = .003), which used only 30% academic sources and websites from individual surgeons (50%) and larger medical practices (20%). For general FAQs, 40% of FAQs were found to be identical when comparing ChatGPT-4 and Google Search Engine. In terms of sources used to answer these questions, ChatGPT-4 again used 100% academic resources, whereas Google Search Engine used 60% academic resources, 20% surgeon personal websites, and 20% medical practices (P = .087). CONCLUSIONS: ChatGPT-4 demonstrated the ability to provide accurate and reliable information about the Latarjet procedure in response to patient queries, using multiple academic sources in all cases. This was in contrast to Google Search Engine, which more frequently used single-surgeon and large medical practice websites. Despite differences in the resources accessed to perform information retrieval tasks, the clinical relevance and accuracy of information provided did not significantly differ between ChatGPT-4 and Google Search Engine. CLINICAL RELEVANCE: Commercially available large language models (LLMs), such as ChatGPT-4, can perform diverse information retrieval tasks on-demand. An important medical information retrieval application for LLMs consists of the ability to provide comprehensive, relevant, and accurate information for various use cases such as investigation about a recently diagnosed medical condition or procedure. Understanding the performance and abilities of LLMs for use cases has important implications for deployment within health care settings.

The artificial intelligence advantage: Supercharging exploratory data analysis.

Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.