Carbohydrate-active enzyme (CAZyme) discovery and engineering via (Ultra)high-throughput screening.

Carbohydrate-active enzymes (CAZymes) constitute a diverse set of enzymes that catalyze the assembly, degradation, and modification of carbohydrates. These enzymes have been fashioned into potent, selective catalysts by millennia of evolution, and yet are also highly adaptable and readily evolved in the laboratory. To identify and engineer CAZymes for different purposes, (ultra)high-throughput screening campaigns have been frequently utilized with great success. This review provides an overview of the different approaches taken in screening for CAZymes and how mechanistic understandings of CAZymes can enable new approaches to screening. Within, we also cover how cutting-edge techniques such as microfluidics, advances in computational approaches and synthetic biology, as well as novel assay designs are leading the field towards more informative and effective screening approaches.

Exploring the Dose-Response Relationship Between Estimated Resistance Training Proximity to Failure, Strength Gain, and Muscle Hypertrophy: A Series of Meta-Regressions.

BACKGROUND: The proximity to failure in which sets are terminated has gained attention in the scientific literature as a potentially key resistance training variable. Multiple meta-analyses have directly (i.e., failure versus not to failure) or indirectly (e.g., velocity loss, alternative set structures) evaluated the effect of proximity to failure on strength and muscle hypertrophy outcomes categorically; however, the dose-response effects of proximity to failure have not been analyzed collectively in a continuous manner. OBJECTIVE: To meta-analyze the aforementioned areas of relevant research, proximity to failure was quantified as the number of repetitions in reserve (RIR). Importantly, the RIR associated with each effect in the analysis was estimated on the basis of the available descriptions of the training interventions in each study. Data were extracted and a series of exploratory multilevel meta-regressions were performed for outcomes related to both strength and muscle hypertrophy. A range of sensitivity analyses were also performed. All models were adjusted for the effects of load, method of volume equating, duration of intervention, and training status. RESULTS: The best fit models for both strength and muscle hypertrophy outcomes demonstrated modest quality of overall fit. In all of the best-fit models for strength, the confidence intervals of the marginal slopes for estimated RIR contained a null point estimate, indicating a negligible relationship with strength gains. However, in all of the best-fit models for muscle hypertrophy, the marginal slopes for estimated RIR were negative and their confidence intervals did not contain a null point estimate, indicating that changes in muscle size increased as sets were terminated closer to failure. CONCLUSIONS: The dose-response relationship between proximity to failure and strength gain appears to differ from the relationship with muscle hypertrophy, with only the latter being meaningfully influenced by RIR. Strength gains were similar across a wide range of RIR, while muscle hypertrophy improves as sets are terminated closer to failure. Considering the RIR estimation procedures used, however, the exact relationship between RIR and muscle hypertrophy and strength remains unclear. Researchers and practitioners should be aware that optimal proximity to failure may differ between strength and muscle hypertrophy outcomes, but caution is warranted when interpreting the present analysis due to its exploratory nature. Future studies deliberately designed to explore the continuous nature of the dose-response effects of proximity to failure in large samples should be considered.

The Relationship Between Design-Based Lateralization, Humeral bearing design, Polyethylene Angle, and Patient-Related Factors on Surgical Complications after Reverse Shoulder Arthroplasty: A Machine Learning Analysis.

INTRODUCTION: Technological advancements in implant design and surgical technique have focused on diminishing complications and optimizing performance of reverse shoulder arthroplasty (RSA). Despite this, there remains a paucity of literature correlating prosthetic features and clinical outcomes. This investigation utilized a machine learning approach to evaluate the effect of select implant design features and patient-related factors on surgical complications after RSA. METHODS: Over a 16-year period (2004 - 2020), all primary RSA performed at a single institution for elective and traumatic indications with a minimum follow-up of 2 years were identified. Parameters related to implant design evaluated in this study included inlay vs onlay humeral bearing design, glenoid lateralization (medialized or lateralized), humeral lateralization (medialized, minimally lateralized, or lateralized), global lateralization (medialized, minimally lateralized, lateralized, highly lateralized, or very highly lateralized), stem to metallic bearing neck shaft angle (NSA), and polyethylene NSA. Machine learning models predicting surgical complications were constructed for each patient and Shapley additive explanation (SHAP) values were calculated to quantify feature importance. RESULTS: A total of 3,837 RSAs were identified, of which 472 (12.3%) experienced a surgical complication. Those experiencing a surgical complication were more likely to be current smokers (Odds ratio [OR] = 1.71; P = .003), have prior surgery (OR = 1.60; P < .001), have an underlying diagnosis of sequalae of instability (OR = 4.59; P < .001) or non-union (OR = 3.09; P < .001), and required longer OR times (98 vs. 86 minutes; P < .001). Notable implant design features at an increased odds for complications included an inlay humeral component (OR = 1.67; P < .001), medialized glenoid (OR = 1.43; P = .001), medialized humerus (OR = 1.48; P = .004), a minimally lateralized global construct (OR = 1.51; P < .001), and glenohumeral constructs consisting of a medialized glenoid and minimally lateralized humerus (OR = 1.59; P < .001), and a lateralized glenoid and medialized humerus (OR = 2.68; P < .001). Based on patient- and implant-specific features, the machine learning model predicted complications after RSA with an area under the receiver operating characteristic curve (AUC ROC) of 0.61. CONCLUSIONS: This study demonstrated that patient-specific risk factors had a more substantial effect than implant design configurations on the predictive ability of a machine learning model on surgical complications after RSA. However, certain implant features appeared to be associated with a higher odd of surgical complications.

Early Surgery for Partial Tears of the Ulnar Collateral Ligament May Be More Cost-Effective and Result in Longer Playing Careers Than Nonoperative Management for High-Level Baseball Pitchers: A Decision-Analytic Markov Model-Based Analysis.

BACKGROUND: Nonoperative management versus early reconstruction for partial tears of the medial ulnar collateral ligament (MUCL) remains controversial, with the most common treatment options for partial tears consisting of rest, rehabilitation, platelet-rich plasma (PRP), and/or surgical intervention. However, whether the improved outcomes reported for treatments such as MUCL reconstruction (UCLR) or nonoperative management with a series of PRP injections justifies their increased upfront costs remains unknown. PURPOSE: To compare the cost-effectiveness of an initial trial of physical therapy alone, an initial trial of physical therapy plus a series of PRP injections, and early UCLR to determine the preferred cost-effective treatment strategy for young, high-level baseball pitchers with partial tears of the MUCL and with aspirations to continue play at the next level (ie, collegiate and/or professional). STUDY DESIGN: Economic and decision analysis; Level of evidence, 2. METHODS: A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 young, high-level, simulated pitchers undergoing nonoperative management with and without PRP versus early UCLR for partial MUCL tears. Utility values, return to play rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing each treatment strategy at the authors' institution. Outcome measures included costs, acquired playing years (PYs), and the incremental cost-effectiveness ratio (ICER). RESULTS: The mean total costs resulting from nonoperative management without PRP, nonoperative management with PRP, and early UCLR were $22,520, $24,800, and $43,992, respectively. On average, early UCLR produced an additional 4.0 PYs over the 10-year time horizon relative to nonoperative management, resulting in an ICER of $5395/PY, which falls well below the $50,000 willingness-to-pay threshold. Overall, early UCLR was determined to be the preferred cost-effective strategy in 77.5% of pitchers included in the microsimulation model, with nonoperative management with PRP determined to be the preferred strategy in 15% of pitchers and nonoperative management alone in 7.5% of pitchers. CONCLUSION: Despite increased upfront costs, UCLR is a more cost-effective treatment option for partial tears of the MUCL than an initial trial of nonoperative management for most high-level baseball pitchers.

A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.

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

PURPOSE: To assess the ability for ChatGPT-4, an automated Chatbot powered by artificial intelligence (AI), to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance to Google Search Engine. METHODS: Using previously validated methods, a Google search was first performed using the query "Latarjet." Subsequently, the top ten frequently asked questions (FAQs) and associated sources were extracted. ChatGPT-4 was then prompted to provide the top ten 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 based on the clinical judgement of two fellowship-trained sports medicine surgeons blinded to search platform. RESULTS: Mean (±standard deviation) accuracy to numeric-based answers were 2.9±0.9 for ChatGPT-4 versus 2.5±1.4 for Google (p=0.65). ChatGPT-4 derived information for answers only from academic sources, which was significantly different from Google Search Engine (p=0.003), 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, while Google Search Engine used 60% academic resources, 20% surgeon personal websites, and 20% medical practices (p=0.087). CONCLUSION: 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.

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.

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.

What Patients Say About Their Orthopaedic Hand and Wrist Surgeons: A Qualitative Analysis of Negative Reviews on Yelp.

Background Patients often turn to online reviews as a source of information to inform their decisions regarding care. Existing literature has analyzed factors associated with positive online patient ratings among hand and wrist surgeons. However, there is limited in-depth analysis of factors associated with low patient satisfaction for hand and wrist surgeons. The focus of this study is to examine and characterize extremely negative reviews of hand and wrist surgeons on Yelp.com. Methods A search was performed using the keywords "hand surgery" on Yelp.com for eight major metropolitan areas including Washington DC, Dallas, New York, Phoenix, Los Angeles, San Francisco, Boston, and Seattle. Only single-star reviews (out of a possible 5 stars) of hand and wrist surgeons were included. The complaints in the 1-star reviews were then categorized into clinical and nonclinical categories. Result A total of 233 single-star reviews were included for analysis, which resulted in 468 total complaints. Of these complaints, 81 (18.8%) were clinically related and 351 (81.3%) were nonclinical in nature. The most common clinical complaints were for complication (24 complaints, 6%), misdiagnosis (16 complaints, 4%), unclear treatment plan (16 complaints, 4%), and uncontrolled pain (15 complaints, 3%). The most common nonclinical complaints were for physician bedside manner (93 complaints, 22%), financially related (80 complaints, 19%), unprofessional nonclinical staff (61 complaints, 14%), and wait time (46 complaints, 11%). The difference in the number of complaints for surgical and nonsurgical patients was statistically significant ( p < 0.05) for complication and uncontrolled pain. Clinical Relevance Patient satisfaction is dependent on a multitude of clinical and nonclinical factors. An awareness of online physician ratings is essential for hand and wrist surgeons to maintain and improve patient care and patient satisfaction. We believe the results of our study could be used to further improve the quality of care provided by hand and wrist surgeons.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

PURPOSE: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. METHODS: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. RESULTS: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. CONCLUSIONS: We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

Advances in the understanding and exploitation of carbohydrate-active enzymes.

Carbohydrate-active enzymes (CAZymes) are responsible for the biosynthesis, modification and degradation of all glycans in Nature. Advances in genomic and metagenomic methodologies, in conjunction with lower cost gene synthesis, have provided access to a steady stream of new CAZymes with both well-established and novel mechanisms. At the same time, increasing access to cryo-EM has resulted in exciting new structures, particularly of transmembrane glycosyltransferases of various sorts. This improved understanding has resulted in widespread progress in applications of CAZymes across diverse fields, including therapeutics, organ transplantation, foods, and biofuels. Herein, we highlight a few of the many important advances that have recently been made in the understanding and applications of CAZymes.

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.

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.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review.

PURPOSE: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS: A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS: AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE: AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy.

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.

Microsurgical Resection of Brainstem Cavernous Malformations in Older Adults: A Multicenter, 30-Year Experience.

BACKGROUND AND OBJECTIVES: Microsurgical resection is the only curative intervention for symptomatic brainstem cavernous malformations (BSCMs), but the management of these lesions in older adults (≥65 years) is not well described. This study sought to address this gap by examining the safety and efficacy of BSCM resection in a cohort of older adults. METHODS: Records of patients who underwent BSCM resection over a 30-year period were reviewed retrospectively. Baseline characteristics and outcomes were compared between older (≥65 years) and younger (<65 years) patients. RESULTS: Of 550 patients with BSCM who met inclusion criteria, 41 (7.5%) were older than 65 years. Midbrain (43.9% vs 26.1%) and medullary lesions (19.5% vs 13.6%) were more common in the older cohort than in the younger cohort (P = .01). Components of the Lawton BSCM grading system (ie, lesion size, crossing axial midpoint, developmental venous anomaly, and timing of hemorrhage) were not significantly different between cohorts (P ≥ .11). Mean (SD) Elixhauser comorbidity score was significantly higher in older patients (1.86 [1.06]) than in younger patients (0.66 [0.95]; P < .001). Older patients were significantly more likely than younger patients to have poor outcomes at final follow-up (28.9% vs 13.8%, P = .01; mean follow-up duration, 28.7 [39.1] months). However, regarding relative neurological outcome (preoperative modified Rankin Scale to final modified Rankin Scale), rate of worsening was not significantly different between older and younger patients (23.7% vs 14.9%, P = .15). CONCLUSION: BSCMs can be safely resected in older patients, and when each patient's unique health status and life expectancy are taken into account, these patients can have outcomes similar to younger patients.

Sphingomyelinase-responsive nanomicelles for targeting atherosclerosis.

Atherosclerosis, a leading cause of cardiovascular diseases requires approaches to enhance disease monitoring and treatment. Nanoparticles offer promising potential in this area by being customisable to target components or molecular processes within plaques, while carrying diagnostic and therapeutic agents. However, the number of biomarkers available to target this disease is limited. This study investigated the use of sphingomyelin-based nanomicelles triggered by sphingomyelinase (SMase) in atherosclerotic plaques. Accumulation of iron oxide-based nanomicelles in the plaque was demonstrated by fluorescence, MR imaging and electron microscopy. These findings demonstrate the possibility of utilising SMase as a mechanism to retain nanoprobes within plaques, thus opening up possibilities for future therapeutic interventions.