MA
M Ali
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4 records found
1
Journal article
(2025)
-
T. F. Stoop, L. W. F. Seelen, F. R. van ’t Land, A. C. van der Hout, J. C. M. Scheepens, M. Ali, A. M. Stiggelbout, J.A.C. Rietjens, M. G. Besselink, More authors...
Background
The introduction of (m)FOLFIRINOX and gemcitabine-nab-paclitaxel has changed the perspective for patients with locally advanced pancreatic cancer (LAPC). Consequently, in experienced centres 23% of patients with LAPC undergo a resection with 5-year overall survival (OS) rates of up to 25%. In the Netherlands, the nationwide resection rate for LAPC remains low at 8%. The PREOPANC-4 program aims for a nationwide implementation of the international multidisciplinary best-practice to improve patient outcome.
Methods
Nationwide program implementing the international multidisciplinary best-practice for LAPC. In the training phase, multidisciplinary and surgical webinars are given by 4 international experts, leading to a clinical protocol, followed by surgical off-site and on-site proctoring sessions. In the implementation phase, the clinical protocol will be implemented in all centres, including a nationwide expert panel (2022–2024). Healthcare professionals will be trained in shared decision-making. Consecutive patients diagnosed with pathology-proven LAPC (i.e., arterial involvement > 90° and/or portomesenteric venous > 270° involvement or occlusion [DPCG criteria]) are eligible. Primary outcomes are median and 5-year OS from diagnosis, resection rate, in-hospital/30-day mortality and major morbidity (i.e., Clavien-Dindo grade ≥ IIIa), and radical resection (R0) rate. Secondary outcomes include quality of life, functioning, side effects, and patients’ healthcare satisfaction in all included patients. Outcomes will be compared with patients with borderline resectable pancreatic cancer (BRPC) treated with neoadjuvant FOLFIRINOX in the PREOPANC-2 trial (EudraCT: 2017–002036-17) and a historical cohort of patients with LAPC from the PACAP registry (NCT03513705). The existing prospective LAPC Registry and PACAP PROMs (NCT03513705) will be used for data collection. In qualitative interviews, treatment preferences, values, and experiences of LAPC patients, their relatives, and healthcare professionals will be assessed for the development of shared decision-making supportive tools. It is hypothesized that the program will double the nationwide LAPC resection rate to 16% with major morbidity < 50% and mortality ≤ 5%, and OS following resection similar to that observed in patients with BRPC.
Discussion
The PREOPANC-4 program aims to safely implement the international multidisciplinary best-practice for LAPC leading to benchmark outcomes for both short-term morbidity, mortality, and OS.
Trial registration
PREOPANC-4 program was registered at ClinicalTrials.gov (NCT05524090) on September 1, 2022. ...
The introduction of (m)FOLFIRINOX and gemcitabine-nab-paclitaxel has changed the perspective for patients with locally advanced pancreatic cancer (LAPC). Consequently, in experienced centres 23% of patients with LAPC undergo a resection with 5-year overall survival (OS) rates of up to 25%. In the Netherlands, the nationwide resection rate for LAPC remains low at 8%. The PREOPANC-4 program aims for a nationwide implementation of the international multidisciplinary best-practice to improve patient outcome.
Methods
Nationwide program implementing the international multidisciplinary best-practice for LAPC. In the training phase, multidisciplinary and surgical webinars are given by 4 international experts, leading to a clinical protocol, followed by surgical off-site and on-site proctoring sessions. In the implementation phase, the clinical protocol will be implemented in all centres, including a nationwide expert panel (2022–2024). Healthcare professionals will be trained in shared decision-making. Consecutive patients diagnosed with pathology-proven LAPC (i.e., arterial involvement > 90° and/or portomesenteric venous > 270° involvement or occlusion [DPCG criteria]) are eligible. Primary outcomes are median and 5-year OS from diagnosis, resection rate, in-hospital/30-day mortality and major morbidity (i.e., Clavien-Dindo grade ≥ IIIa), and radical resection (R0) rate. Secondary outcomes include quality of life, functioning, side effects, and patients’ healthcare satisfaction in all included patients. Outcomes will be compared with patients with borderline resectable pancreatic cancer (BRPC) treated with neoadjuvant FOLFIRINOX in the PREOPANC-2 trial (EudraCT: 2017–002036-17) and a historical cohort of patients with LAPC from the PACAP registry (NCT03513705). The existing prospective LAPC Registry and PACAP PROMs (NCT03513705) will be used for data collection. In qualitative interviews, treatment preferences, values, and experiences of LAPC patients, their relatives, and healthcare professionals will be assessed for the development of shared decision-making supportive tools. It is hypothesized that the program will double the nationwide LAPC resection rate to 16% with major morbidity < 50% and mortality ≤ 5%, and OS following resection similar to that observed in patients with BRPC.
Discussion
The PREOPANC-4 program aims to safely implement the international multidisciplinary best-practice for LAPC leading to benchmark outcomes for both short-term morbidity, mortality, and OS.
Trial registration
PREOPANC-4 program was registered at ClinicalTrials.gov (NCT05524090) on September 1, 2022. ...
Background
The introduction of (m)FOLFIRINOX and gemcitabine-nab-paclitaxel has changed the perspective for patients with locally advanced pancreatic cancer (LAPC). Consequently, in experienced centres 23% of patients with LAPC undergo a resection with 5-year overall survival (OS) rates of up to 25%. In the Netherlands, the nationwide resection rate for LAPC remains low at 8%. The PREOPANC-4 program aims for a nationwide implementation of the international multidisciplinary best-practice to improve patient outcome.
Methods
Nationwide program implementing the international multidisciplinary best-practice for LAPC. In the training phase, multidisciplinary and surgical webinars are given by 4 international experts, leading to a clinical protocol, followed by surgical off-site and on-site proctoring sessions. In the implementation phase, the clinical protocol will be implemented in all centres, including a nationwide expert panel (2022–2024). Healthcare professionals will be trained in shared decision-making. Consecutive patients diagnosed with pathology-proven LAPC (i.e., arterial involvement > 90° and/or portomesenteric venous > 270° involvement or occlusion [DPCG criteria]) are eligible. Primary outcomes are median and 5-year OS from diagnosis, resection rate, in-hospital/30-day mortality and major morbidity (i.e., Clavien-Dindo grade ≥ IIIa), and radical resection (R0) rate. Secondary outcomes include quality of life, functioning, side effects, and patients’ healthcare satisfaction in all included patients. Outcomes will be compared with patients with borderline resectable pancreatic cancer (BRPC) treated with neoadjuvant FOLFIRINOX in the PREOPANC-2 trial (EudraCT: 2017–002036-17) and a historical cohort of patients with LAPC from the PACAP registry (NCT03513705). The existing prospective LAPC Registry and PACAP PROMs (NCT03513705) will be used for data collection. In qualitative interviews, treatment preferences, values, and experiences of LAPC patients, their relatives, and healthcare professionals will be assessed for the development of shared decision-making supportive tools. It is hypothesized that the program will double the nationwide LAPC resection rate to 16% with major morbidity < 50% and mortality ≤ 5%, and OS following resection similar to that observed in patients with BRPC.
Discussion
The PREOPANC-4 program aims to safely implement the international multidisciplinary best-practice for LAPC leading to benchmark outcomes for both short-term morbidity, mortality, and OS.
Trial registration
PREOPANC-4 program was registered at ClinicalTrials.gov (NCT05524090) on September 1, 2022.
The introduction of (m)FOLFIRINOX and gemcitabine-nab-paclitaxel has changed the perspective for patients with locally advanced pancreatic cancer (LAPC). Consequently, in experienced centres 23% of patients with LAPC undergo a resection with 5-year overall survival (OS) rates of up to 25%. In the Netherlands, the nationwide resection rate for LAPC remains low at 8%. The PREOPANC-4 program aims for a nationwide implementation of the international multidisciplinary best-practice to improve patient outcome.
Methods
Nationwide program implementing the international multidisciplinary best-practice for LAPC. In the training phase, multidisciplinary and surgical webinars are given by 4 international experts, leading to a clinical protocol, followed by surgical off-site and on-site proctoring sessions. In the implementation phase, the clinical protocol will be implemented in all centres, including a nationwide expert panel (2022–2024). Healthcare professionals will be trained in shared decision-making. Consecutive patients diagnosed with pathology-proven LAPC (i.e., arterial involvement > 90° and/or portomesenteric venous > 270° involvement or occlusion [DPCG criteria]) are eligible. Primary outcomes are median and 5-year OS from diagnosis, resection rate, in-hospital/30-day mortality and major morbidity (i.e., Clavien-Dindo grade ≥ IIIa), and radical resection (R0) rate. Secondary outcomes include quality of life, functioning, side effects, and patients’ healthcare satisfaction in all included patients. Outcomes will be compared with patients with borderline resectable pancreatic cancer (BRPC) treated with neoadjuvant FOLFIRINOX in the PREOPANC-2 trial (EudraCT: 2017–002036-17) and a historical cohort of patients with LAPC from the PACAP registry (NCT03513705). The existing prospective LAPC Registry and PACAP PROMs (NCT03513705) will be used for data collection. In qualitative interviews, treatment preferences, values, and experiences of LAPC patients, their relatives, and healthcare professionals will be assessed for the development of shared decision-making supportive tools. It is hypothesized that the program will double the nationwide LAPC resection rate to 16% with major morbidity < 50% and mortality ≤ 5%, and OS following resection similar to that observed in patients with BRPC.
Discussion
The PREOPANC-4 program aims to safely implement the international multidisciplinary best-practice for LAPC leading to benchmark outcomes for both short-term morbidity, mortality, and OS.
Trial registration
PREOPANC-4 program was registered at ClinicalTrials.gov (NCT05524090) on September 1, 2022.
Ingested turbulence affects propeller noise at frequencies above the 2nd Blade Passing Frequency. The extension of the Amiet model to rotating structures is a useful tool to predict this phenomenon. However, comparison with the experimental results reveal discrepancies between predicted and measured acoustic spectra. A likely explanation for this mismatch lies in turbulence distortion.This paper investigates the effects of the propeller-induced flow field on incoming turbulence to obtain a comprehensive description of the flow physics and enhance, in future studies, Amiet’s prediction model.Lattice Boltzmann Very Large Eddy Simulations of a reference propeller operating at low-Reynolds number and subjected to turbulent inflow are performed. The spatial and temporal evolution of isotropic grid-generated turbulence approaching the propeller plane is characterized . The analysis shows that the leading edge interacts with anisotropic turbulence. This is due to the rotational flow induced by the propeller, streamtube contraction, and leading edge distortion.In addition, the effect of turbulence on the laminar separation bubble, conventionally present in flow at low Reynolds number, whose dynamics affects the acoustics at high frequencies, is analyzed. Acoustic spectra, obtained through the Ffowcs-Williams and Hawkings analogy applied to the propeller surface, are then linked to the aerodynamic sources.
...
Ingested turbulence affects propeller noise at frequencies above the 2nd Blade Passing Frequency. The extension of the Amiet model to rotating structures is a useful tool to predict this phenomenon. However, comparison with the experimental results reveal discrepancies between predicted and measured acoustic spectra. A likely explanation for this mismatch lies in turbulence distortion.This paper investigates the effects of the propeller-induced flow field on incoming turbulence to obtain a comprehensive description of the flow physics and enhance, in future studies, Amiet’s prediction model.Lattice Boltzmann Very Large Eddy Simulations of a reference propeller operating at low-Reynolds number and subjected to turbulent inflow are performed. The spatial and temporal evolution of isotropic grid-generated turbulence approaching the propeller plane is characterized . The analysis shows that the leading edge interacts with anisotropic turbulence. This is due to the rotational flow induced by the propeller, streamtube contraction, and leading edge distortion.In addition, the effect of turbulence on the laminar separation bubble, conventionally present in flow at low Reynolds number, whose dynamics affects the acoustics at high frequencies, is analyzed. Acoustic spectra, obtained through the Ffowcs-Williams and Hawkings analogy applied to the propeller surface, are then linked to the aerodynamic sources.
Virtual coaches have the potential to address the low adherence common to eHealth applications for behavior change by, for example, providing motivational support. However, given the multitude of factors affecting users’ attitudes toward virtual coaches, more insights are needed on how such virtual coaches can be designed to affect these attitudes in a specific use context positively. Especially valuable are insights that are based on users interacting with such a virtual coach for longer. We thus conducted a study in which more than 500 smokers interacted with the text-based virtual coach Sam in five sessions. In each session, Sam assigned smokers a new preparatory activity for quitting smoking and provided motivational support for doing the activity. Based on a mixed-methods analysis of users’ willingness to continue working and their relationship with Sam, we obtained eight themes for users’ attitudes toward Sam. These themes relate to whether Sam is seen as human or artificial, specific characteristics of Sam (e.g., caring character), the interaction with Sam, and the relationship with Sam. We used these themes to formulate literature-based recommendations to guide designers of virtual coaches for behavior change. For example, letting the virtual coach get to know users and disclose more information about itself may improve its relationship with users.
...
Virtual coaches have the potential to address the low adherence common to eHealth applications for behavior change by, for example, providing motivational support. However, given the multitude of factors affecting users’ attitudes toward virtual coaches, more insights are needed on how such virtual coaches can be designed to affect these attitudes in a specific use context positively. Especially valuable are insights that are based on users interacting with such a virtual coach for longer. We thus conducted a study in which more than 500 smokers interacted with the text-based virtual coach Sam in five sessions. In each session, Sam assigned smokers a new preparatory activity for quitting smoking and provided motivational support for doing the activity. Based on a mixed-methods analysis of users’ willingness to continue working and their relationship with Sam, we obtained eight themes for users’ attitudes toward Sam. These themes relate to whether Sam is seen as human or artificial, specific characteristics of Sam (e.g., caring character), the interaction with Sam, and the relationship with Sam. We used these themes to formulate literature-based recommendations to guide designers of virtual coaches for behavior change. For example, letting the virtual coach get to know users and disclose more information about itself may improve its relationship with users.
We introduce a generalized concept of blending and deblending, and establish the generalized-blending and - deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. ...
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. ...
We introduce a generalized concept of blending and deblending, and establish the generalized-blending and - deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data.
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data.