Ocean warming and acidification adjust inter- and intra-specific variability in the functional trait expression of polar invertebrates.

Climate change is known to affect the distribution and composition of species, but concomitant alterations to functionally important aspects of behaviour and species-environment relations are poorly constrained. Here, we examine the ecosystem ramifications of changes in sediment-dwelling invertebrate bioturbation behaviour-a key process mediating nutrient cycling-associated with near-future environmental conditions (+ 1.5 °C, 550 ppm [pCO2]) for species from polar regions experiencing rapid rates of climate change. We find that responses to warming and acidification vary between species and lead to a reduction in intra-specific variability in behavioural trait expression that adjusts the magnitude and direction of nutrient concentrations. Our analyses also indicate that species behaviour is not predetermined, but can be dependent on local variations in environmental history that set population capacities for phenotypic plasticity. We provide evidence that certain, but subtle, aspects of inter- and intra-specific variation in behavioural trait expression, rather than the presence or proportional representation of species per se, is an important and under-appreciated determinant of benthic biogeochemical responses to climate change. Such changes in species behaviour may act as an early warning for impending ecological transitions associated with progressive climate forcing.

Assessing Laterality Errors in Radiology: Comparing Generative AI and Natural Language Processing.

PURPOSE: We compared the performance of generative AI (G-AI, ATARI) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images. METHODS: We used an NLP-based (mPower) tool to identify radiology reports flagged for laterality errors in its QA Dashboard. The NLP model detects and highlights laterality mismatches in radiology reports. From an initial pool of 1124 radiology reports flagged by the NLP for laterality errors, we selected and evaluated 898 reports that encompassed radiography, CT, MRI, and ultrasound modalities to ensure comprehensive coverage. A radiologist reviewed each radiology report to assess if the flagged laterality errors were present (reporting error - true positive) or absent (NLP error - false positive). Next, we applied ATARI to 237 radiology reports and images with consecutive NLP true positive (118 reports) and false positive (119 reports) laterality errors. We estimated accuracy of NLP and G-AI tools to identify overall and modality-wise laterality errors. RESULTS: Among the 898 NLP-flagged laterality errors, 64% (574/898) had NLP errors and 36% (324/898) were reporting errors. The text query ATARI feature correctly identified the absence of laterality mismatch (NLP false positives) with a 97.4% accuracy (115/118 reports; 95% CI = 96.5% - 98.3%). Combined Vision and text query resulted in 98.3% accuracy (116/118 reports/images; 95% CI = 97.6% - 99.0%) query alone had a 98.3% accuracy (116/118 images; 95% CI = 97.6% - 99.0%). CONCLUSION: The generative AI-empowered ATARI prototype outperformed the assessed NLP tool for determining true and false laterality errors in radiology reports while enabling an image-based laterality determination. Underlying errors in ATARI text query in complex radiology reports emphasize the need for further improvement in the technology.

Durable complete response in a patient with leptomeningeal melanoma after treatment with dabrafenib, trametinib, and nivolumab.

Leptomeningeal disease (LMD) is a devastating complication of melanoma with a dismal prognosis. We present the case of a young man with stage IV BRAF V600E mutant melanoma with lung, lymph node, and brain metastases initially treated with ipilimumab and nivolumab, who subsequently developed LMD. Upon change to BRAF/MEK targeted therapy with nivolumab, a durable complete response was achieved and remains ongoing, off treatment, 7 years from diagnosis. Management of symptomatic LMD remains a critical unmet clinical challenge, with limited clinical trial data. This exceptional case is instructive, as the first published case of the use of the triplet, and the first durable response with therapy discontinuation, in melanoma LMD. The triple-drug regimen may be considered a viable option in fit patients. This case highlights the potential for long-term disease control and the critical and urgent need to develop clinical trials inclusive of patients with LMD to define the best treatment strategies.

Long-term patient-reported outcome measures (PROMs) after primary ventral or small midline incisional hernia repair.

BACKGROUND: Ventral hernia repair is a common elective surgical procedure lacking strong evidence for specific operative approaches. This study aimed to evaluate the outcomes of primary suture repair or polypropylene sandwich mesh repair for ventral hernias. The main outcome measures were the rate of hernia recurrence, and evaluation of long-term complications and patient-reported outcomes. METHODS: This retrospective cohort study evaluated patient perceived recurrence and pain in patients who had undergone a primary ventral hernia (epigastric, supraumbilical, or umbilical) repair or small (≤20 mm) midline incisional hernia repair 10 years after the procedure. Short-term follow-up occurred up to 6 weeks after the initial operation, while long-term follow-up included patients who were reviewed clinically or interviewed via telephone at or beyond 3 years after the procedure. RESULTS: Most (75/100, 75.0%) patients had an extra-peritoneal sandwich mesh repair. Short-term follow-up showed minimal pain and normal activities for all patients (97/97, 100%). Long-term follow-up (median 12 years [IQR 11-13]) was achieved in 95.9% (93/97) of patients with only a small number reporting a slight bulge (5/93, 5.4%) and intermittent mild discomfort (8/93, 8.6%). Nine patients (9/97, 9.3%) experienced hernia recurrence, diagnosed at a median of 26 months [interquartile range, IQR, 7-58] post-operatively. CONCLUSIONS: These findings suggest that an open sandwich mesh technique is a safe and effective method for repairing primary ventral hernias and small midline incisional hernias and is associated with favourable long-term patient-reported outcomes.

Physiological characteristics predictive of passing military physical employment standard tasks for ground close combat occupations in men and women.

Challenges for some women meeting the physical employment standards (PES) for ground close combat (GCC) roles stem from physical fitness and anthropometric characteristics. The purpose of this study was to identify the modifiable and nonmodifiable characteristics predictive of passing GCC-based PES tasks and determine the modifiable characteristics suitable to overcome nonmodifiable limitations. 107 adults (46 women) underwent multiday testing assessing regional and total lean mass (LM), percent body fat (BF%), aerobic capacity (V̇O2peak), strength, power, and PES performance. Predictors with p-value <0.200 were included in stepwise logistic regression analysis or binary logistic regression when outcomes among sexes were insufficient. Relative and absolute arm LM (OR: 4.617-8.522, p < 0.05), leg LM (OR: 2.463, p < 0.05), and upper body power (OR: 2.061, p < 0.05) predicted medicine ball chest throw success. Relative and absolute arm LM (OR: 3.734-11.694, p < 0.05), absolute trunk LM (OR: 2.576, p < 0.05), and leg LM (OR: 2.088, p < 0.05) predicted casualty drag success. Upper body power (OR: 3.910, p < 0.05), absolute trunk LM (OR: 2.387, p < 0.05), leg LM (OR: 2.290, p < 0.05), and total LM (OR: 1.830, p < 0.05) predicted maximum single lift success. Relative and absolute arm LM (OR: 3.488-7.377, p < 0.05), leg LM (OR: 1.965, p < 0.05), and upper body power (OR: 1.957, p < 0.05) predicted water can carry success. %BF (OR: 0.814, p = 0.007), V̇O2peak (OR: 1.160, p = 0.031), and lower body strength (OR: 1.059, p < 0.001) predicted repeated lift and carry success. V̇O2peak (OR: 1.540, p < 0.001) predicted 2-km ruck march success. Modifiable characteristics were the strongest predictors for GCC-based PES task success to warrant their improvement for enhancing PES performance for women.

Diagnostic accuracy of a 'sarcopenia index' based on serum biomarkers creatinine and cystatin C in 458,702 UK Biobank participants.

BACKGROUND & AIMS: There is an emerging and urgent need to identify biomarkers of sarcopenia. A novel sarcopenia index (SI), based on serum creatinine and cystatin C, has emerged as a potential biomarker for use. The SI can predict clinical outcomes and discriminate between the presence of sarcopenia in a range of chronic and acute conditions. However, the SI has not yet been tested in a large real-world general population dataset. This study aimed to investigate the accuracy of the SI in the identification of sarcopenia in a large prospective general population cohort. METHODS: Data were taken from UK Biobank, a large prospective epidemiological study in the United Kingdom (UK). Serum creatinine and cystatin C values were used to calculate the SI [creatinine (mg/dl)/cystatin C (mg/dl) × 100]. Probable sarcopenia was defined by maximum handgrip strength (HGS). Muscle mass was assessed using bioelectrical impedance analysis. Low muscle mass was defined as an appendicular lean mass (ALM) index below prespecified thresholds. Confirmed sarcopenia was defined as both low HGS and low muscle mass. Pearson correlation coefficients and logistic regression were used to explore the association between various sarcopenia traits (probable sarcopenia, low ALM index, and confirmed sarcopenia) and the SI. The diagnostic value of the SI was investigated using the area under the receiver operating characteristic curve (area under the curve, AUC). RESULTS: 458,702 participants were included in the analysis (46.4% males, mean age, males: 68.7 (±8.2) years; females: 68.2 (±8.0) years)). Probable sarcopenia was observed in 4.5% of males and 6.1% of females; low ALM index in 2.8% of males and 0.7% of females; confirmed sarcopenia in 0.3% of males and 0.1% of females. SI was significantly lower in individuals with confirmed sarcopenia (males: 86.3 ± 16.6 vs. 99.5 ± 15.3, p < .01; females: 73.6 ± 13.7 vs. 84.6 ± 14.0, p < .01). For every 1-unit increase in the SI, the odds of confirmed sarcopenia were reduced by 5% in males (odds ratio (OR): 0.95, p < 0.001) and 7% in females (OR: 0.923, p < 0.001). The AUC showed acceptable discriminative ability of confirmed sarcopenia (males: AUC = 0.731; females: AUC = 0.711). CONCLUSIONS: Using a large real-world dataset of almost half a million people, our study indicated the SI has acceptable diagnostic accuracy when identifying those with sarcopenia and may be a useful biomarker to aid the stratification of those at risk and in need of intervention.

Role of atmospheric rivers in shaping long term Arctic moisture variability.

Atmospheric rivers (ARs) reaching high-latitudes in summer contribute to the majority of climatological poleward water vapor transport into the Arctic. This transport has exhibited long term changes over the past decades, which cannot be entirely explained by anthropogenic forcing according to ensemble model responses. Here, through observational analyses and model experiments in which winds are adjusted to match observations, we demonstrate that low-frequency, large-scale circulation changes in the Arctic play a decisive role in regulating AR activity and thus inducing the recent upsurge of this activity in the region. It is estimated that the trend in summertime AR activity may contribute to 36% of the increasing trend of atmospheric summer moisture over the entire Arctic since 1979 and account for over half of the humidity trends in certain areas experiencing significant recent warming, such as western Greenland, northern Europe, and eastern Siberia. This indicates that AR activity, mostly driven by strong synoptic weather systems often regarded as stochastic, may serve as a vital mechanism in regulating long term moisture variability in the Arctic.

Protocol Variation in Functional Coronary Angiography Among Patients With Suspected Angina With Non-Obstructive Coronary Arteries: A Nationwide Snapshot of Current Practice Within Australia and New Zealand.

BACKGROUND: Functional coronary angiography (FCA) for endotype characterisation (vasospastic angina [VSA], coronary microvascular disease [CMD], or mixed) is recommended among patients with angina with non-obstructive coronary arteries. Whilst clear diagnostic criteria for VSA and CMD exist, there is no standardised FCA protocol. Variations in testing protocol may limit the widespread uptake of testing, generalisability of results, and expansion of collaborative research. At present, there are no data describing protocol variation across an entire geographic region. Therefore, we aimed to capture current practice variations in the approach to FCA to improve access and standardisation for diagnosis of coronary vasomotor disorders in Australia and New Zealand. METHOD: Between July 2022 and July 2023, we conducted a national survey across all centres in Australia and New Zealand with an active FCA program. The survey captured attitudes towards FCA and protocols used for diagnosis of coronary vasomotor disorders at 33 hospitals across Australia and New Zealand. RESULTS: Survey responses were received from 39 clinicians from 33 centres, with representation from centres within all Australian states and territories and both North and South Islands of New Zealand. A total of 21 centres were identified as having an active FCA program. In general, respondents agreed that comprehensive physiology testing helped inform clinical management. Barriers to program expansion included cost, additional catheter laboratory time, and the absence of an agreed-upon national protocol. Across the clinical sites, there were significant variations in testing protocol, including the technique used (Doppler vs thermodilution), order of testing (hyperaemia resistance indices first vs vasomotor function testing first), rate and dose of acetylcholine administration, routine use of temporary pacing wire, and routine single vs multivessel testing. Overall, testing was performed relatively infrequently, with very little follow-on FCA performed, despite nearly all respondents believing this would be clinically useful. CONCLUSIONS: This survey demonstrates, for the first time, variations in FCA protocol among testing centres across two entire countries. Furthermore, whilst FCA was deemed clinically important, testing was performed relatively infrequently with little or no follow-on testing. Development and adoption of a standardised national FCA protocol may help improve patient access to testing and facilitate further collaborative research within Australia and New Zealand.

Characterization of early non-responders within behavioral weight loss treatment.

Objective: Given that low early (4 weeks) weight loss (WL) predicts longer-term WL, the purpose of this study was to identify factors associated with poor early WL. Methods: 438 adults with overweight/obesity participating in an Internet-delivered behavioral WL program provided weights at baseline and 4 weeks. Participants were stratified by percent WL at 4 weeks: LOW: <2% WL, MEDIUM: 2 to <4% WL, HIGH: ≥4% WL and groups were compared on baseline variables (demographics, physical activity, and psychosocial measures) and 4-week intervention adherence. Results: 37.4%, 40.9%, and 21.7% of participants had LOW, MEDIUM, and HIGH early WL respectively. LOW was more likely to be female compared to HIGH and less likely to be non-Hispanic White compared to MEDIUM and HIGH (p's<0.05). After controlling for demographic differences, LOW had lower baseline physical activity compared to HIGH and watched fewer video lessons, self-monitored calorie intake and weight on fewer days, and were less likely to achieve the exercise goal compared to MEDIUM and HIGH (p's<0.05). Conclusion: Findings can inform future adaptive interventions which tailor treatment based upon early WL to improve WL outcomes for more individuals.

Proximity-based activation of AURORA A by MPS1 potentiates error correction.

Faithfull cell division relies on mitotic chromosomes becoming bioriented with each pair of sister kinetochores bound to microtubules oriented toward opposing spindle poles. Erroneous kinetochore-microtubule attachments often form during early mitosis, but are destabilized through the phosphorylation of outer kinetochore proteins by centromeric AURORA B kinase (ABK) and centrosomal AURORA A kinase (AAK), thus allowing for re-establishment of attachments until biorientation is achieved. MPS1-mediated phosphorylation of NDC80 has also been shown to directly weaken the kinetochore-microtubule interface in yeast. In human cells, MPS1 has been proposed to transiently accumulate at end-on attached kinetochores and phosphorylate SKA3 to promote microtubule release. Whether MPS1 directly targets NDC80 and/or promotes the activity of AURORA kinases in metazoans remains unclear. Here, we report a novel mechanism involving communication between kinetochores and centrosomes, wherein MPS1 acts upstream of AAK to promote error correction. MPS1 on pole-proximal kinetochores phosphorylates the C-lobe of AAK thereby increasing its activation at centrosomes. This proximity-based activation ensures the establishment of a robust AAK activity gradient that locally destabilizes mal-oriented kinetochores near spindle poles. Accordingly, MPS1 depletion from Drosophila cells causes severe chromosome misalignment and erroneous kinetochore-microtubule attachments, which can be rescued by tethering either MPS1 or constitutively active AAK mutants to centrosomes. Proximity-based activation of AAK by MPS1 also occurs in human cells to promote AAK-mediated phosphorylation of the NDC80 N-terminal tail. These findings uncover an MPS1-AAK cross-talk that is required for efficient error correction, showcasing the ability of kinetochores to modulate centrosome outputs to ensure proper chromosome segregation.

Computational image analysis of distortion, sharpness, and depth of field in a next-generation hybrid exoscopic and microsurgical operative platform.

Objective: The development of surgical microscope-associated cameras has given rise to a new operating style embodied by hybrid microsurgical and exoscopic operative systems. These platforms utilize specialized camera systems to visualize cranial neuroanatomy at various depths. Our study aims to understand how different camera settings in a novel hybrid exoscope system influence image quality in the context of neurosurgical procedures. Methods: We built an image database using captured cadaveric dissection images obtained with a prototype version of a hybrid (microsurgical/exoscopic) operative platform. We performed comprehensive 4K-resolution image capture using 76 camera settings across three magnification levels and two working distances. Computer algorithms such as structural similarity (SSIM) and mean squared error (MSE) were used to measure image distortion across different camera settings. We utilized a Laplacian filter to compute the overall sharpness of the acquired images. Additionally, a monocular depth estimation deep learning model was used to examine the image's capability to visualize the depth of deeper structures accurately. Results: A total of 1,368 high-resolution pictures were captured. The SSIM index ranged from 0.63 to 0.85. The MSE was nearly zero for all image batches. It was determined that the exoscope could accurately detect both the sharpness and depth based on the Laplacian filter and depth maps, respectively. Our findings demonstrate that users can utilize the full range of camera settings available on the exoscope, including adjustments to aperture, color saturation, contrast, sharpness, and brilliance, without introducing significant image distortions relative to the standard mode. Conclusion: The evolution of the camera incorporated into a surgical microscope enables exoscopic visualization during cranial base surgery. Our result should encourage surgeons to take full advantage of the exoscope's extensive range of camera settings to match their personal preferences or specific clinical requirements of the surgical scenario. This places the exoscope as an invaluable asset in contemporary surgical practice, merging high-definition imaging with ergonomic design and adaptable operability.

Multi-tissue transcriptome-wide association study identifies novel candidate susceptibility genes for cataract.

Introduction: Cataract is the leading cause of blindness among the elderly worldwide. Twin and family studies support an important role for genetic factors in cataract susceptibility with heritability estimates up to 58%. To date, 55 loci for cataract have been identified by genome-wide association studies (GWAS), however, much work remains to identify the causal genes. Here, we conducted a transcriptome-wide association study (TWAS) of cataract to prioritize causal genes and identify novel ones, and examine the impact of their expression. Methods: We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 54 tissues (including 49 from the Genotype Tissue Expression (GTEx) Project v8) with cataract using FUSION software. Meta-analyzed GWAS summary statistics from 59,944 cataract cases and 478,571 controls, all of European ancestry and from two cohorts (GERA and UK Biobank) were used. We then examined the expression of the novel genes in the lens tissue using the iSyTE database. Results: Across tissue-specific and multi-tissue analyses, we identified 99 genes for which genetically predicted gene expression was associated with cataract after correcting for multiple testing. Of these 99 genes, 20 (AC007773.1, ANKH, ASIP, ATP13A2, CAPZB, CEP95, COQ6, CREB1, CROCC, DDX5, EFEMP1, EIF2S2, ESRRB, GOSR2, HERC4, INSRR, NIPSNAP2, PICALM, SENP3, and SH3YL1) did not overlap with previously reported cataract-associated loci. Tissue-specific analysis identified 202 significant gene-tissue associations for cataract, of which 166 (82.2%), representing 9 unique genes, were attributed to the previously reported 11q13.3 locus. Tissue-enrichment analysis revealed that gastrointestinal tissues represented one of the highest proportions of the Bonferroni-significant gene-tissue associations (21.3%). Moreover, this gastrointestinal tissue type was the only anatomical category significantly enriched in our results, after correcting for the number of tissue donors and imputable genes for each reference panel. Finally, most of the novel cataract genes (e.g., Capzb) were robustly expressed in iSyTE lens data. Discussion: Our results provide evidence of the utility of imputation-based TWAS approaches to characterize known GWAS risk loci and identify novel candidate genes that may increase our understanding of cataract etiology. Our findings also highlight the fact that expression of genes associated with cataract susceptibility is not necessarily restricted to lens tissue.

Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI.

BACKGROUND: Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). METHODS: Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. RESULTS: 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). CONCLUSION: Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

Dilute concentrations of maritime fuel can modify sediment reworking activity of high-latitude marine invertebrates.

Multiple expressions of climate change, in particular warming-induced reductions in the type, extent and thickness of sea ice, are opening access and providing new viable development opportunities in high-latitude regions. Coastal margins are facing these challenges, but the vulnerability of species and ecosystems to the effects of fuel contamination associated with increased maritime traffic is largely unknown. Here, we show that low concentrations of the water-accommodated fraction of marine fuel oil, representative of a dilute fuel oil spill, can alter functionally important aspects of the behaviour of sediment-dwelling invertebrates. We find that the response to contamination is species specific, but that the range in response among individuals is modified by increasing fuel concentrations. Our study provides evidence that species responses to novel and/or unprecedented levels of anthropogenic activity associated with the opening up of high-latitude regions can have substantive ecological effects, even when human impacts are at, or below, commonly accepted safe thresholds. These secondary responses are often overlooked in broad-scale environmental assessments and marine planning yet, critically, they may act as an early warning signal for impending and more pronounced ecological transitions.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

North American Cancer Center Clinical Research Capacity and Benchmarking in the Postpandemic Era.

PURPOSE: Cancer center clinical trial offices (CCTOs) support trial development, activation, conduct, regulatory adherence, data integrity, and compliance. In 2018, the Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) Steering Committee conducted and published survey results to benchmark North American CCTOs, including trial volume, accrual, full time equivalents (FTEs), and budget. The survey was readministered in 2023 to assess contemporary CCTO performance and capacity with results presented here. METHODS: The 28 question 2023 survey was sent to directors of AACI's clinical member cancer centers. Survey participation was voluntary, no compensation was provided, and data requested covered operations during 2022. Definitions were consistent with National Cancer Institute (NCI) CCTO reporting requirements and AACI staff anonymously compiled results for descriptive statistical reporting. RESULTS: The survey response rate was 61% (60/99). The median annual CCTO budget was $11.5 million (M) US dollars (USD) versus $8.2M USD in 2018. These budgets support a median of 150 FTEs versus 104 previously, and a median total of 384 versus 280 interventional treatment trials and a median of 479 versus 531 interventional treatment accruals. Sources of support for CCTO annual budgets were primarily from industry revenue (45.3%) or institutional support (31.7%). Nearly 60% of centers reported activating NCI-sponsored studies within 90 days but only 9% reported meeting a 90-day activation timeline for industry sponsored studies. CONCLUSION: Contemporary benchmarks for CCTO operations through this survey demonstrate larger staff sizes, larger budgets, more trials supported, but fewer patients enrolled to interventional treatment trials in comparison with 2018. These data shine a critical light on the increasing complexity of cancer clinical trials, the importance of external funding sources, and necessary operational efficiency upgrades to provide cutting-edge cancer research and care.