A precision engineered interleukin-2 (IL-2) for bolstering CD8+ T- and NK-cell activity without eosinophilia and vascular leak syndrome in non-human primates.

We have created a precisely pegylated IL-2 [SAR-444245 (SAR'245) or pegenzileukin, previously THOR- 707] designed for proliferation of target CD8+ T and NK cells for anti-cancer activity, with minimal expansion of anti-target regulatory CD4+ T cells (Tregs) that counter their action, or eosinophils that trigger vascular leak syndrome (VLS). We performed in vivo studies in non-human primate (NHP) to monitor SAR'245's safety, pharmacokinetic profile, and pharmacodynamic parameters including expansion of peripheral CD8+ T and NK cells, and effects on Tregs and eosinophils. Studies included multiple ascending dosing and repeat dosing with different regimens (QW, Q2W, Q3W and Q4W). We also conducted ex vivo studies using human primary cells to further evaluate SAR'245 stimulation of target cells alone and in combination with PD-1 checkpoint inhibitors. The pharmacokinetic profile of SAR'245 in NHP demonstrated dose-proportional exposure that was comparable with redosing. It elicited expansion of peripheral CD8+ T and NK cells that was comparable with each dose and with multiple dosing regimens. Once-weekly dosing showed no significant adverse effects, including no hallmark signs of VLS at dosing levels up to 1 mg/kg. Ex vivo, SAR'245 enhanced T-cell receptor responses alone and in combination with PD-1 inhibitors without inducing cytokines associated with cytokine release syndrome or VLS. Results support the clinical development of SAR'245 as a drug candidate for the treatment of solid tumors, alone or in combination with PD-1 inhibitory agents.

Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study.

BACKGROUND: Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown. OBJECTIVE: This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov. METHODS: Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression. RESULTS: We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95% CI 1.00-1.00; P=.02). CONCLUSIONS: Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure.

Protocol for a non-surgical model of perineural invasion for assessing neural drivers of cancer aggressiveness in mice.

Perineural invasion (PNI) is a significant risk factor for cancer recurrence and metastasis; however, its mechanisms relating to cancer aggressiveness remain poorly understood. Here, we present a protocol for a non-surgical model of PNI in mice using a neurotropic melanoma cell line that migrates from the skin to the sciatic nerve. We describe the steps for cell culture and injection, tumor burden measurements, mouse euthanasia, and tissue dissection. We then detail procedures for sample cross-section and confocal imaging.

Predicting Long-Term Clinical Outcomes of Patients With Recurrent Pericarditis.

BACKGROUND: Recurrent pericarditis (RP) is a complex condition associated with significant morbidity. Prior studies have evaluated which variables are associated with clinical remission. However, there is currently no established risk-stratification model for predicting outcomes in these patients. OBJECTIVES: We developed a risk stratification model that can predict long-term outcomes in patients with RP and enable identification of patients with characteristics that portend poor outcomes. METHODS: We retrospectively studied a total of 365 consecutive patients with RP from 2012 to 2019. The primary outcome was clinical remission (CR), defined as cessation of all anti-inflammatory therapy with complete resolution of symptoms. Five machine learning survival models were used to calculate the likelihood of CR within 5 years and stratify patients into high-risk, intermediate-risk, and low-risk groups. RESULTS: Among the cohort, the mean age was 46 ± 15 years, and 205 (56%) were women. CR was achieved in 118 (32%) patients. The final model included steroid dependency, total number of recurrences, pericardial late gadolinium enhancement, age, etiology, sex, ejection fraction, and heart rate as the most important parameters. The model predicted the outcome with a C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients into low-risk, intermediate-risk, and high-risk groups (log-rank test; P < 0.0001). CONCLUSIONS: We developed a novel risk-stratification model for predicting CR in RP. Our model can also aid in stratifying patients, with high discriminative ability. The use of an explainable machine learning model can aid physicians in making individualized treatment decision in RP patients.

Characterization of cigarette smokeomics by in situ solid-phase microextraction and confined-space direct analysis in real time mass spectrometry.

Characterization of chemical composition in cigarette smoke is essential for establishing smoke-related exposure estimates. Currently used methods require complex sample preparation with limited capability for obtaining accurate chemical information. We have developed an in situ solid-phase microextraction (SPME) method for online processing of smoke aerosols and directly coupling the SPME probes with confined-space direct analysis in real time (cDART) ion source for high-resolution mass spectrometry (MS) analysis. In a confined space, the substances from SPME probes can be efficiently desorbed and ionized using the DART ion source, and the diffusion and evaporation of volatile species into the open air can be largely avoided. Using SPME-cDART-MS, mainstream smoke (MSS) and side-stream smoke (SSS) can be investigated and the whole analytical protocol can be accomplished in a few min. More than five hundred substances and several classes of compounds were detected and identified. The relative contents of 13 tobacco alkaloids were compared between MSS and SSS. Multivariate data analysis unveiled differences between different types of cigarette smoke and also discovered the characteristic ions. The method is reliable with good reproducibility and repeatability, and has the potential to be quantitative. This study provides a simple and high-efficiency method for smokeomics profiling of complex aerosol samples with in situ online extraction of volatile samples, and direct integration of extracted probes with a modified ambient ionization technique.

Directionally non-rotating electric field therapy delivered through implanted electrodes as a glioblastoma treatment platform: A proof-of-principle study.

Background: While directionally rotating tumor-treating fields (TTF) therapy has garnered considerable clinical interest in recent years, there has been comparatively less focus on directionally non-rotating electric field therapy (dnEFT). Methods: We explored dnEFT generated through customized electrodes as a glioblastoma therapy in in vitro and in vivo preclinical models. The effects of dnEFT on tumor apoptosis and microglia/macrophages in the tumor microenvironment were tested using flow-cytometric and qPCR assays. Results: In vitro, dnEFT generated using a clinical-grade spinal cord stimulator showed antineoplastic activity against independent glioblastoma cell lines. In support of the results obtained using the clinical-grade electrode, dnEFT delivered through a customized, 2-electrode array induced glioblastoma apoptosis. To characterize this effect in vivo, a custom-designed 4-electrode array was fabricated such that tumor cells can be implanted into murine cerebrum through a center channel equidistant from the electrodes. After implantation with this array and luciferase-expressing murine GL261 glioblastoma cells, mice were randomized to dnEFT or placebo. Relative to placebo-treated mice, dnEFT reduced tumor growth (measured by bioluminescence) and prolonged survival (median survival gain of 6.5 days). Analysis of brain sections following dnEFT showed a notable increase in the accumulation of peritumoral macrophage/microglia with increased expression of M1 genes (IFNγ, TNFα, and IL-6) and decreased expression of M2 genes (CD206, Arg, and IL-10) relative to placebo-treated tumors. Conclusions: Our results suggest therapeutic potential in glioblastoma for dnEFT delivered through implanted electrodes, supporting the development of a proof-of-principle clinical trial using commercially available deep brain stimulator electrodes.

A survey exploring ophthalmologists' attitudes and beliefs in performing immediate sequential bilateral cataract surgery in Singapore.

BACKGROUND: To evaluate current practice patterns of Immediate Sequential Bilateral Cataract Surgery (ISBCS) by ophthalmologists in Singapore and assess their attitudes towards performing ISBCS in future cataract care. METHODS: An anonymised electronic survey, modified to local context from a similar study conducted in the United Kingdom, was distributed to members of the College of Ophthalmologists, Academy of Medicine, Singapore, from 20 June to 1 September 2023. An initial screening question on prior experience with ISBCS directed the rest of the survey. Questions explored ophthalmologists' current ISBCS practice patterns and the importance of factors affecting their willingness to perform ISBCS. Results were descriptively analysed. RESULTS: Results collated 2 months upon survey dissemination saw a total of 58 respondents from 235 eligible members (24.7% response rate). Of these, 16 (27.6%) were currently performing ISBCS, 37 (63.8%) had never performed, and 5 (8.6%) had stopped performing. In considering ISBCS, patient convenience (n = 11, 68.8%) and reduced hospital visits (n = 8, 50.0%) were the most important factors nominated. The most important barriers to performing ISBCS were medico-legal issues (n = 31, 83.8%) and risk of endophthalmitis (n = 27, 73.0%), followed by perceived lack of evidence for its effectiveness (n = 19, 51.4%). CONCLUSION: This is one of the first studies evaluating ophthalmologists' sentiments towards performing ISBCS in an Asian country. It highlights some of the most pertinent barriers and concerns that ophthalmologists face in performing and offering ISBCS. This study provides a better understanding of the potential role and prospects of ISBCS in future cataract care in Singapore.

Ocular image-based deep learning for predicting refractive error: A systematic review.

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors. Main text: We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n â€‹= â€‹5), OCT-based (n â€‹= â€‹1), and external ocular photo-based (n â€‹= â€‹3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models. Conclusions: The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells.

Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. In this study, we investigated functional drivers of post-treatment recurrent GBM through integrative genomic analyses, genome-wide genetic perturbation screens in patient-derived GBM models and independent lines of validation. Specific genetic dependencies were found consistent across recurrent tumor models, accompanied by increased mutational burden and differential transcript and protein expression compared to its primary GBM predecessor. Our observations suggest a multi-layered genetic response to drive tumor recurrence and implicate PTP4A2 (protein tyrosine phosphatase 4A2) as a modulator of self-renewal, proliferation and tumorigenicity in recurrent GBM. Genetic perturbation or small-molecule inhibition of PTP4A2 acts through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1) and its downstream molecular players, exploiting a functional dependency on ROBO signaling. Because a pan-PTP4A inhibitor was limited by poor penetrance across the blood-brain barrier in vivo, we engineered a second-generation chimeric antigen receptor (CAR) T cell therapy against ROBO1, a cell surface receptor enriched across recurrent GBM specimens. A single dose of ROBO1-targeted CAR T cells doubled median survival in cell-line-derived xenograft (CDX) models of recurrent GBM. Moreover, in CDX models of adult lung-to-brain metastases and pediatric relapsed medulloblastoma, ROBO1 CAR T cells eradicated tumors in 50-100% of mice. Our study identifies a promising multi-targetable PTP4A-ROBO1 signaling axis that drives tumorigenicity in recurrent GBM, with potential in other malignant brain tumors.

Orphan Applicants in Orthopedic Surgery: Where Do Allopathic Applicants Without an Affiliated Residency Program Match?

Background  Orthopedic surgery is one of the most competitive specialties to match into a residency. With a plethora of qualified applicants and the subjective nature of matching into any residency program, it can be difficult to accurately assess the chances of successfully matching into orthopedic surgery and the types of programs an applicant will match into. The purpose of this study is to compare the types of programs that students from medical schools with and without home programs match. Methods This was a five-year retrospective study (2019 to 2023) analyzing 155 United States Doctor of Medicine (M.D.) programs and their orthopedic residency-matched students. Of the 155 programs, 40 were excluded from the study due to the lack of obtainable data. For each medical school, we analyzed several variables: the presence of a home program, the total number of orthopedic residency matches, residency program matches, and residency program affiliation (academic, community, university-affiliated community-based, military). Results Of the 2066 total matched applicants from institutions with home programs, 1508 (73%) matched into academic centers, 315 (15.3%) into university-affiliated community programs, 172 (8.3%) into community programs, and 71 (3.4%) into military programs. In contrast, of the 219 total matched applicants from institutions without home programs (orphan applicants), 144 (67.8%) matched into academic programs, 36 (16.4%) into university-affiliated community programs, 28 (12.8%) into community programs, and 11 (5%) into military programs. Conclusion A greater proportion of students from institutions with home programs matched into academic centers compared to orphan applicants (73% vs. 65.8%). A greater proportion of orphan applicants matched into community programs (12.8% vs. 8.3%).

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

A Randomized, Controlled Trial Evaluating Perioperative Risk-Stratification and Risk-Based, Protocol-Driven Management After Elective Major Cancer Surgery.

OBJECTIVE: We evaluated the efficacy of risk-based, protocol-driven management versus (vs) usual management after elective major cancer surgery to reduce 30-day rates of postoperative death or serious complications (DSC) . SUMMARY BACKGROUND DATA: Major cancer surgery is associated with significant perioperative risks which result in worse long-term outcomes. METHODS: Adults scheduled for elective major cancer surgery were stratified/randomized to risk-based escalating levels of care, monitoring, and co-management vs usual management. The primary study outcome was 30-day rate of DSC. Additional outcomes included complications, adverse events, health care utilization, health-related quality of life (HRQOL), and disease-free and overall survival (DFS and OS). RESULTS: Between August 2014 and June 2020, 1529 patients were enrolled and randomly allocated to the study arms; 738 patients in the Intervention Arm and 732 patients in the Control Arm were eligible for analysis. 30-day rate of DSC with the intervention was 15.0% (95% CI, 12.5-17.6%) vs 14.1%, (95% CI, 11.6-16.6%) with usual management (P=0.65). There were no differences in 30-day rates of complications or adverse events (including return to the operating room); postoperative length of stay; rate of discharge to home; or 30, 60, or 90-day HRQOL or rates of hospital readmission or receipt of anti-neoplastic therapy between the study arms. At median follow-up of 48 months, OS (P=0.57) and DFS (P=0.91) were similar. CONCLUSIONS: Risk-based, protocol-driven management did not reduce 30-day rate of DSC after elective major cancer surgery compared to usual management, nor improve postoperative health care utilization, HRQOL, or cancer outcomes. Trials are needed to identify cost-effective, tailored perioperative strategies to optimize outcomes after major cancer surgery.

Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models.

Objective: Surgical registries play a crucial role in clinical knowledge discovery, hospital quality assurance, and quality improvement. However, maintaining a surgical registry requires significant monetary and human resources given the wide gamut of information abstracted from medical records ranging from patient co-morbidities to procedural details to post-operative outcomes. Although natural language processing (NLP) methods such as pretrained language models (PLMs) have promised automation of this process, there are yet substantial barriers to implementation. In particular, constant shifts in both underlying data and required registry content are hurdles to the application of NLP technologies. Materials and Methods: In our work, we evaluate the application of PLMs for automating the population of the Society of Thoracic Surgeons (STSs) adult cardiac surgery registry (ACS) procedural elements, for which we term Cardiovascular Surgery Bidirectional Encoder Representations from Transformers (CS-BERT). CS-BERT was validated across multiple satellite sites and versions of the STS-ACS registry. Results: CS-BERT performed well (F1 score of 0.8417 ± 0.1838) in common cardiac surgery procedures compared to models based on diagnosis codes (F1 score of 0.6130 ± 0.0010). The model also generalized well to satellite sites and across different versions of the STS-ACS registry. Discussion and Conclusions: This study provides evidence that PLMs can be used to extract the more common cardiac surgery procedure variables in the STS-ACS registry, potentially reducing need for expensive human annotation and wide scale dissemination. Further research is needed for rare procedural variables which suffer from both lack of data and variable documentation quality.