The lack of therapeutic options to fight Covid-19 has contributed to the current global pandemic. Despite the emergence of effective vaccines, development of broad-spectrum antiviral treatment remains a significant challenge, in which antimicrobial photodynamic therapy (aPDT) may play a role, especially at early stages of infection. aPDT of the nares with methylene blue (MB) and non-thermal light has been successfully utilized to inactivate both bacterial and viral pathogens in the perioperative setting. Here, we investigated the effect of MB-aPDT to inactivate human betacoronavirus OC43 and SARS-CoV-2 in vitro and in a proof-of-principle COVID-19 clinical trial to test, in a variety of settings, the practicality, technical feasibility, and short-term efficacy of the method. aPDT yielded inactivation of up to 6-Logs in vitro, as measured by RT-qPCR and infectivity assay. From a photo-physics perspective, the in vitro results suggest that the response is not dependent on the virus itself, motivating potential use of aPDT for local destruction of SARS-CoV-2 and its variants. In the clinical trial we observed variable effects on viral RNA in nasal-swab samples as assessed by RT-qPCR attributed to aPDT-induced RNA fragmentation causing falsely-elevated counts. However, the viral infectivity in clinical nares swabs was reduced in 90% of samples and undetectable in 70% of samples. This is the first demonstration based on quantitative clinical viral infectivity measurements that MB-aPDT is a safe, easily delivered and effective front-line technique that can reduce local SARS-CoV-2 viral load.
Percutaneous vertebroplasty produces higher intravertebral pressures in vertebrae containing a simulated lytic metastasis than in intact vertebrae. Pressures generated in the tumor specimens are sufficiently elevated to cause embolic phenomena.
ImportanceArtificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.ObjectiveTo evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.Data SourcesA systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.Study SelectionStudies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.Data Extraction and SynthesisThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.Main Outcomes and MeasuresDiagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.ResultsOf 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).Conclusions and RelevanceThe findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.
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