What does 'co-production' look like for food system transformation? Mapping the evidence across Transforming UK Food Systems (TUKFS) projects.

Co-production is a collaborative way of working which emphasises the exchange of diverse forms of knowledge in an equal partnership for equal benefits. Co-produced research is a key strategic aim of the UK Research and Innovation (UKRI) Transforming UK Food Systems (TUKFS) Strategic Priorities Fund; this research programme brings together researchers, policymakers, industry and communities to create positive change in the way food is produced, accessed and consumed. However, more generally, there are diverse understandings of co-production and a lack of consensus on what 'good practice' looks like. Therefore, this study aimed to identify and map examples of co-production methods employed across the TUKFS programme. Two creative workshops (n = 15 participants), conversations with TUKFS researchers and stakeholders (n = 15), and systematic analysis of project documents were used to critically explore co-production activities within six TUKFS projects. A range of co-production activities were identified. Findings highlighted areas of 'messiness' and complexity, challenges associated with applying co-production approaches and practical solutions. Four key shared principles for co-production were identified: (1) Relationships: developing and maintaining reciprocity-based partnerships; (2) Knowledge: recognising the contribution of diverse forms of expertise; (3) Power: considering power dynamics and addressing imbalances; and (4) Inclusivity: ensuring research is accessible to all who wish to participate. Opportunities for reflection and reflexivity were considered crucial across all these areas. Findings contribute important insights towards a shared conceptual understanding of co-production for food system transformation research. This paper makes recommendations for researchers, practitioners, academic institutions and funders working in this area of research and practice.

Efficient Neural Networks on the Edge with FPGAs by Optimizing an Adaptive Activation Function.

The implementation of neural networks (NNs) on edge devices enables local processing of wireless data, but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNNs) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as a real-valued time-delay neural network (RVTDNN), an augmented real-valued time-delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no block RAMs compared to the DNN with similar accuracy. We experimentally validated this computationally efficient and memory-saving FPGA implementation of the SSCNN for digital predistortion of radio-frequency (RF) power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.

FPGA implementation of a sub-400ns 6G free-space optical wireless communications transmitter.

Future wireless networks are planned to service many applications with an Ultra Low-Latency (ULL) requirement. Numerous 6G systems have been proposed including more traditional electro-magnetic (EM) antenna transmissions and optical wireless communications (OWC). For extremely wide-band operation, the traditional approaches require digital pre-distortion and other processing techniques which, in turn, require more computational resources and processing times thus increasing latency. Alternatively, OWC has the potential for extremely wide bandwidths in 6G without the need for as much digital signal processing. In order to realise ULL performance, a minimum number of digital signal processing (DSP) blocks is required, as well as an optimal design of each of these. In this letter, we propose a DSP solution for ULL and peak to average power ratio (PAPR) reduction for OWC systems. Unitary checkerboard precoding - orthogonal frequency division multiplexing (UCP-OFDM) is chosen as the modulation scheme and has been implemented within a single digital block avoiding the use of standard OFDM which would otherwise require multiple digital blocks. Experimentally validated results successfully demonstrate a 2.21184 GSps wireless link at distances of up to 2m in noisy daylight settings. Bit error rates (BER) of 0 at root mean square (RMS) error vector magnitude (EVM) of 4.09% are achieved. A complete digital line-up of an OWC transmitter chain for this work contains only three core blocks and ULL of less than 400 ns.

Fatal granulomatous amebic encephalitis initially presenting with a cutaneous lesion.

We present a case of a 66-year-old man with a cutaneous Balamuthia mandrillaris lesion that progressed to fatal granulomatous amoebic encephalitis. We provide a summary of Australian cases and describe the clinical features and approach to diagnosing this rare but devastating condition, including the importance of PCR for diagnosis.

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology.

Purpose: Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Methods and Materials: Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. Results: The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. Conclusions: This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream.

Evaluation of the clinical impact of the differences between planned and delivered dose in prostate cancer radiotherapy based on CT-on-rails IGRT and patient-reported outcome scores.

PURPOSE: To estimate the clinical impact of differences between delivered and planned dose using dose metrics and normal tissue complication probability (NTCP) modeling. METHODS: Forty-six consecutive patients with prostate adenocarcinoma between 2010 and 2015 treated with intensity-modulated radiation therapy (IMRT) and who had undergone computed tomography on rails imaging were included. Delivered doses to bladder and rectum were estimated using a contour-based deformable image registration method. The bladder and rectum NTCP were calculated using dose-response parameters applied to planned and delivered dose distributions. Seven urinary and gastrointestinal symptoms were prospectively collected using the validated prostate cancer symptom indices patient reported outcome (PRO) at pre-treatment, weekly treatment, and post-treatment follow-up visits. Correlations between planned and delivered doses against PRO were evaluated in this study. RESULTS: Planned mean doses to bladder and rectum were 44.9 ± 13.6 Gy and 42.8 ± 7.3 Gy, while delivered doses were 46.1 ± 13.4 Gy and 41.3 ± 8.7 Gy, respectively. D10cc for rectum was 64.1 ± 7.6 Gy for planned and 60.1 ± 9.3 Gy for delivered doses. NTCP values of treatment plan were 22.3% ± 8.4% and 12.6% ± 5.9%, while those for delivered doses were 23.2% ± 8.4% and 9.9% ± 8.3% for bladder and rectum, respectively. Seven of 25 patients with follow-up data showed urinary complications (28%) and three had rectal complications (12%). Correlations of NTCP values of planned and delivered doses with PRO follow-up data were random for bladder and moderate for rectum (0.68 and 0.67, respectively). CONCLUSION: Sensitivity of bladder to clinical variations of dose accumulation indicates that an automated solution based on a DIR that considers inter-fractional organ deformation could recommend intervention. This is intended to achieve additional rectum sparing in cases that indicate higher than expected dose accumulation early during patient treatment in order to prevent acute severity of bowel symptoms.

Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models.

The necessity of the rapid evolution of wireless communications, with continuously increasing demands for higher data rates and capacity Zheng (Big datadriven optimization for mobile networks toward 5g 30:44-51, 2016), is constantly augmenting the complexity of radio frequency (RF) transceiver architecture. A significant component in the configuration of such complex radio transceivers is the power amplifier(PA). Multiple distributed PAs are now common in proposed RF architectures. PAs exhibit non linear behaviour, causing signal distortion in transmission. Behavioural models offer a concise representation of a PAs characteristic performance which is extremely useful in simulating performance of multiple nonlinear power amplifiers. A considerable drawback with using the Recursive Least Squares (RLS) technique is that the instability of the coefficients during the training of the model. This manuscript provides a computationally efficient technique to detect the onset of instability during adaptive RLS training and subsequently to inform the decision to cease training of dynamic memory polynomial based behavioural models, to avoid the onset of instability. The proposed technique does not require modification of the RLS algorithm, merely an observation of the pre-exsisting autocorrelation function based update. This technique is experimentally validated using four different signal modulation schemes, LTE OFDM, 5G-NR, DVBS2X and WCDMA.

Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology.

Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.

Impact of Workspace Design on Radiation Therapist Technicians' Physical Stressors, Mental Workload, Situation Awareness, and Performance.

PURPOSE: Our purpose was to assess the effect of workspace configuration on radiation therapists' (RTs) physical stressors, mental workload (WL), situational awareness (SA), and performance during routine treatment delivery tasks in a simulated environment. METHODS AND MATERIALS: Fourteen RTs were randomized to 2 workspace configurations while performing 4 simulated scenarios: current (not ergonomically optimized; n = 7) and enhanced (ergonomically optimized, n = 7). Physical stressors were objectively assessed using a rapid upper limb assessment tool. Mental WL was measured at the end of each simulated scenario subjectively using the NASA Task-Load Index and objectively throughout the scenario using eye-tracking metrics (pupil diameter and blink rate). SA was measured at the end of each simulated scenario subjectively using the situation awareness and review technique. Performance was measured objectively via assessment of time-out compliance, error detection, and procedural compliance. Analysis of variance was used to test the effect of workspace configuration on physical stressors, mental WL, SA, and performance. RESULTS: The enhanced configuration significantly reduced physical stressors (rapid upper limb assessment; P < .01) and resulted in a higher rate of time-out compliance (P = .01) compared with current workspace configuration. No significant effect on other metrics was measured. CONCLUSIONS: Our results suggest that an ergonomically designed workspace may minimize physical stressors and improve the performance of RTs.

Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy.

Within artificial intelligence, machine learning (ML) efforts in radiation oncology have augmented the transition from generalized to personalized treatment delivery. Although their impact on quality and safety of radiation therapy has been limited, they are increasingly being used throughout radiation therapy workflows. Various data-driven approaches have been used for outcome prediction, CT simulation, clinical decision support, knowledge-based planning, adaptive radiation therapy, plan validation, machine quality assurance, and process quality assurance; however, there are many challenges that need to be addressed with the creation and usage of ML algorithms as well as the interpretation and dissemination of findings. In this review, the authors present current applications of ML in radiation oncology quality and safety initiatives, discuss challenges faced by the radiation oncology community, and suggest future directions.

Fitting NTCP models to bladder doses and acute urinary symptoms during post-prostatectomy radiotherapy.

BACKGROUND: To estimate the radiobiological parameters of three popular normal tissue complication probability (NTCP) models, which describe the dose-response relations of bladder regarding different acute urinary symptoms during post-prostatectomy radiotherapy (RT). To evaluate the goodness-of-fit and the correlation of those models with those symptoms. METHODS: Ninety-three consecutive patients treated from 2010 to 2015 with post-prostatectomy image-guided intensity modulated radiotherapy (IMRT) were included in this study. Patient-reported urinary symptoms were collected pre-RT and weekly during treatment using the validated Prostate Cancer Symptom Indices (PCSI). The assessed symptoms were flow, dysuria, urgency, incontinence, frequency and nocturia using a Likert scale of 1 to 4 or 5. For this analysis, an increase by ≥2 levels in a symptom at any time during treatment compared to baseline was considered clinically significant. The dose volume histograms of the bladder were calculated. The Lyman-Kutcher-Burman (LKB), Relative Seriality (RS) and Logit NTCP models were used to fit the clinical data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC), Akaike information criterion (AIC) and Odds Ratio methods. RESULTS: For the symptoms of urinary urgency, leakage, frequency and nocturia, the derived LKB model parameters were: 1) D50 = 64.2Gy, m = 0.50, n = 1.0; 2) D50 = 95.0Gy, m = 0.45, n = 0.50; 3) D50 = 83.1Gy, m = 0.56, n = 1.00; and 4) D50 = 85.4Gy, m = 0.60, n = 1.00, respectively. The AUC values for those symptoms were 0.66, 0.58, 0.64 and 0.64, respectively. The differences in AIC between the different models were less than 2 and ranged within 0.1 and 1.3. CONCLUSIONS: Different dose metrics were correlated with the symptoms of urgency, incontinence, frequency and nocturia. The symptoms of urinary flow and dysuria were poorly associated with dose. The values of the parameters of three NTCP models were determined for bladder regarding four acute urinary symptoms. All the models could fit the clinical data equally well. The NTCP predictions of urgency showed the best correlation with the patient reported outcomes.

Dosimetric effect due to the motion during deep inspiration breath hold for left-sided breast cancer radiotherapy.

Deep inspiration breath-hold (DIBH) radiotherapy for left-sided breast cancer can reduce cardiac exposure and internal motion. We modified our in-house treatment planning system (TPS) to retrospectively analyze breath-hold motion log files to calculate the dosimetric effect of the motion during breath hold. Thirty left-sided supine DIBH breast patients treated using AlignRT were studied. Breath-hold motion was recorded ­ three translational and three rotational displacements of the treatment surface ­ the Real Time Deltas (RTD). The corresponding delivered dose was estimated using the beam-on portions of the RTDs. Each motion was used to calculate dose, and the final estimated dose was the equally weighted average of the multiple resultant doses. Ten of thirty patients had internal mammary nodes (IMN) purposefully included in the tangential fields, and we evaluated the percentage of IMN covered by 40 Gy. The planned and delivered heart mean dose, lungs V20 (volume of the lungs receiving > 20 Gy), percentage of IMN covered by 40 Gy, and IMN mean dose were compared. The averaged mean and standard deviation of the beam-on portions of the absolute RTDs were 0.81 ± 1.29 mm, 0.68 ± 0.85mm, 0.76 ± 0.85 mm, 0.96° ± 0.49°, 0.93° ± 0.43°, and 1.03° ± 0.50°, for vertical, longitudinal, lateral, yaw, roll, and pitch, respectively. The averaged planned and delivered mean heart dose were 99 and 101 cGy. Lungs V20 were 6.59% and 6.74%. IMN 40 Gy coverage was 83% and 77%, and mean IMN dose was 4642 and 4518 cGy. The averaged mean motion during DIBH was smaller than 1 mm and 1°, which reflects the relative reproducibility of the patient breath hold. On average, the mean heart dose and lungs V20 were reasonably close to what have been planned. IMN 40 Gy coverage might be modestly reduced for certain cases.

Direct aperture optimization using an inverse form of back-projection.

Direct aperture optimization (DAO) has been used to produce high dosimetric quality intensity-modulated radiotherapy (IMRT) treatment plans with fast treatment delivery by directly modeling the multileaf collimator segment shapes and weights. To improve plan quality and reduce treatment time for our in-house treatment planning system, we implemented a new DAO approach without using a global objective function (GFO). An index concept is introduced as an inverse form of back-projection used in the CT multiplicative algebraic reconstruction technique (MART). The index, introduced for IMRT optimization in this work, is analogous to the multiplicand in MART. The index is defined as the ratio of the optima over the current. It is assigned to each voxel and beamlet to optimize the fluence map. The indices for beamlets and segments are used to optimize multileaf collimator (MLC) segment shapes and segment weights, respectively. Preliminary data show that without sacrificing dosimetric quality, the implementation of the DAO reduced average IMRT treatment time from 13 min to 8 min for the prostate, and from 15 min to 9 min for the head and neck using our in-house treatment planning system PlanUNC. The DAO approach has also shown promise in optimizing rotational IMRT with burst mode in a head and neck test case.

Gadigaleyrodes froggatti, a new genus and species of whitefly (Hemiptera: Aleyrodidae) from Australia.

A new monotypic genus of whiteflies (Aleyrodidae), Gadigaleyrodes gen.n., is described and illustrated for G. froggatti sp.n. from New South Wales, Australia. Specimens were collected by W. W. Froggatt in 1899 on Syncarpia glomulifera, and subsequently by P. S. Gillespie on an unknown climbing plant. The genus has unusual morphological features with traits common to both Aleurodicinae and Aleyrodinae. The subfamily placement is discussed, and a key provided to discriminate this taxon from similar whitefly genera in Australia.

Thrombus causes fluctuations in arterial drug delivery from intravascular stents.

Arterial drug concentrations determine local toxicity. As such the emergent safety concerns surrounding drug-eluting stents mandate an investigation of the factors contributing to fluctuations in arterial drug uptake. Drug-eluting stents were implanted into porcine coronary arteries, arterial drug uptake was followed and modeled using 2-dimensional computational drug transport. Arterial drug uptake in vivo occurred faster than predicted by free drug diffusion, thus an alternate, mechanism for rapid transport has been proposed involving carrier-mediated transport. Though there was minimal variation in vivo in release kinetics from stent to stent, arterial drug deposition varied by up to 114% two weeks after stent implantation. The extent of adherent mural thrombus also fluctuated by 113% within 3 days after implantation. The computational drug transport model predicted that focal and diffuse thrombi elevate arterial drug deposition in proportion to the thrombus size by reducing drug washout subsequently increasing local drug availability. Fluctuations in arterial drug uptake are commonly reported. We now explain that variable peristrut thrombus can explain such observations even in the face of a narrow range of drug release from the stent. The mural thrombus effects on arterial drug deposition may be circumvented by forcing slow, rate limiting arterial transport that cannot be further hindered by mural thrombus.

Relaxation training and written emotional disclosure for tension or migraine headaches: a randomized, controlled trial.

BACKGROUND AND PURPOSE: Behavioral medicine interventions that directly reduce arousal and negative emotions, such as relaxation training (RT), are conceptually different from interventions that temporarily increase negative emotions, such as written emotional disclosure (WED), but no studies have directly compared their efficacy. We compared the effects of RT and WED on people with tension or migraine headaches. METHODS: College students with either tension (n = 51) or migraine (n = 90) headaches were randomized to one of three groups: RT, WED, or a neutral writing control condition; four sessions were held over 2 weeks. Mood was measured before and after each session, and outcomes (headache frequency, severity, disability, and general physical symptoms) were assessed at baseline and at 1-month and 3-month follow-ups. RESULTS: As expected, RT led to an immediate increase in calmness, whereas WED led to an immediate increase in negative mood, for both headache samples. Intent-to-treat analyses showed that, for the tension headache sample, RT led to improved headache frequency and disability compared to both WED and the control group, but WED had no effect. For migraine headaches, RT improved pain severity relative to the control group, but WED again had no effect. CONCLUSIONS: A brief RT protocol was effective for tension headaches, but WED had no effect on health status for either tension or migraine headaches. Modifications to WED, such as targeting people with unresolved stress, providing guidance to enhance the potency of the writing, or including additional at-home writing and exposure exercises, may improve its efficacy for people with headaches and other health problems.

Emotional approach coping and self-efficacy moderate the effects of written emotional disclosure and relaxation training for people with migraine headaches.

OBJECTIVES: We tested whether emotional skills and headache management self-efficacy (HMSE) moderated effects of written emotional disclosure (WED) compared with control writing and a different intervention, relaxation training (RT). DESIGN/METHODS: Undergraduates with migraine headaches reported emotional approach coping (EAC) and HMSE; were randomized to WED, RT, or control; and assessed on health measures at baseline and 3-month follow-up. RESULTS: Greater EAC predicted improvement following WED compared with RT and control, whereas low HMSE predicted improvement following both WED and RT, compared with control. CONCLUSIONS: Emotional skill may specifically - and low health management self-efficacy may generally - predict positive responses to WED.

Intravascular drug release kinetics dictate arterial drug deposition, retention, and distribution.

Millions of patients worldwide have received drug-eluting stents to reduce their risk for in-stent restenosis. The efficacy and toxicity of these local therapeutics depend upon arterial drug deposition, distribution, and retention. To examine how administered dose and drug release kinetics control arterial drug uptake, a model was created using principles of computational fluid dynamics and transient drug diffusion-convection. The modeling predictions for drug elution were validated using empiric data from stented porcine coronary arteries. Inefficient, minimal arterial drug deposition was predicted when a bolus of drug was released and depleted within seconds. Month-long stent-based drug release efficiently delivered nearly continuous drug levels, but the slow rate of drug presentation limited arterial drug uptake. Uptake was only maximized when the rates of drug release and absorption matched, which occurred for hour-long drug release. Of the two possible means for increasing the amount of drug on the stent, modulation of drug concentration potently impacts the magnitude of arterial drug deposition, while changes in coating drug mass affect duration of release. We demonstrate the importance of drug release kinetics and administered drug dose in governing arterial drug uptake and suggest novel drug delivery strategies for controlling spatio-temporal arterial drug distribution.

Safety and pharmacokinetics of sirolimus-eluting stents in the canine cerebral vasculature: 180 day assessment.

OBJECTIVE: We evaluated local and systemic pharmacokinetics and pharmacodynamics of sirolimus-eluting stents (SES) in canine cerebral vessels. METHODS: SES (1.5 x 8 mm, 79 microg/479 microg sirolimus) and control stents (1.5 x 8 mm stainless steel with or without polymer) were implanted in canine basilar and ventral spinal arteries. Animals were sacrificed for local pharmacokinetic (36 animals at 1, 3, 8, 30, 90, 180 days) and pharmacodynamic (60 animals at 3, 30, 90, 180 days) assessment. RESULTS: Postrecovery adverse clinical events were not serious, requiring no unscheduled treatment. Histologically, brain and spinal cord sections revealed scattered microinfarcts and minimal gliosis consistent with postprocedure changes in all four stent-treatment groups. All stented vessels at all time points demonstrated good luminal patency with low injury and inflammation scores and no thrombosis of either stented or branch arteries. Endothelialization was complete in all stent groups by 30 days. Intimal smooth muscle cell scores were reduced in both SES groups at 30, 90, and 180 days. Systemic sirolimus levels peaked between 1 and 7 hours postimplant (maximum concentration, 1.2 +/- 1.47, 79 microg; 4.5 +/- 1.23 ng/ml, 479 microg), then declined rapidly to 1 ng/ml or less by 96 hours. Peak local tissue sirolimus levels were 41.5 ng/mg (79 microg) and 65 ng/mg (479 microg). CONCLUSION: SES in canine cerebral vessels were associated with good luminal patency to 180 days, with complete endothelialization and no evidence of acute thrombosis. This model has shown that SES deployed within the brain do not cause neurotoxicity during a 180-day time course, even when exaggerated doses are used. The findings support the contention that SES are safe to use and maintain patency in cerebral vessels.

Segmental vessel wall shear stress and neointimal formation after sirolimus-eluting stent implantation: physiological insights in a porcine coronary model.

BACKGROUND: Low vessel-wall shear stress promotes atherosclerosis and restenosis. We conducted serial analysis of vessel-wall shear stress following placement of metal and sirolimus (SRL) stents to determine the relationship between shear stress and neointima. METHODS: Serial quantitative coronary angiography, intracoronary ultrasound (IVUS), and Doppler flow analysis were performed at baseline, immediately poststent, and at 30 and 90 days on 16 stents (metal, n = 8; SRL, n = 8) implanted in the coronary arteries of eight miniswine. Segmental vessel-wall shear stress (dyn/cm2) was calculated at 10 sections within the stent and normalized to the average proximal and distal reference vessel shear stress using IVUS and hyperemic average peak flow velocity. At 90 days, histological analysis was completed to determine vessel-wall morphometry on corresponding sections from each stent. RESULTS: Stent placement resulted in a similar degree of in-stent stenosis (-5% to 25%) and immediate post-in-stent shear stress. At 30 days, the IVUS neointimal cross-sectional area and percentage of area stenosis were significantly less in SRL (1.2+/-0.8 mm2; 12.7+/-8.5%) versus metal stents (2.3+/-0.4 mm2; 28.2+/-3.4%, P < .003). In-stent normalized shear stress was less for SRL (0.93+/-0.07) versus metal (1.07+/-0.08, P = .002) stents. At 90 days, the mean neointimal area was similar for the SRL (2.50+/-0.47 mm2) and metal stents (2.72+/-1.15 mm2). Linear regression documented a negative correlation between poststent shear stress and neointima for metal stents (r = .61, P < .0001). In the SRL stents, however, the post-in-stent shear stress had a positive correlation with neointima (r = .40, P = .0002). CONCLUSIONS: The placement of oversized stents causes alteration of segmental vessel-wall shear stress, which appears to be an important physiological stimulus for neointimal formation, and may influence the pharmacodynamics of SRL-eluting stent in the porcine coronary model.