The aims of this study were to assess changes in tissue perfusion up to 6 weeks after endovascular therapy (EVT), in hospital and at home, and to determine differences in tissue perfusion between patients with and without clinical improvement or good angiographic result. This single-center prospective cohort study included patients undergoing EVT for Rutherford stages two to six. Hyperspectral and thermal imaging were performed at the dorsal and plantar sides of the foot. These measurements consisted of a baseline measurement pre-EVT, and six follow-up measurements obtained at 1 and 4 h and 6 weeks in hospital, and 1 day, 7 days, and 14 days at home. Clinical improvement was defined as a decrease of one or more Rutherford class or decrease in the wound surface area and a good angiographic result was accomplished when a Transatlantic Inter-Society Consensus for the Management of PAD II C or D lesion was treated and uninterrupted flow continued in at least one below-the-knee artery in continuation with the inframalleolar arteries. The study included 34 patients with 41 treated limbs. Deoxyhemoglobin values were lower 1 h post-EVT compared with baseline and increased over time up to 6 weeks post-EVT. Significant differences in deoxyhemoglobin levels at 7 and 14 days post-EVT were determined between patients with and without clinical or angiographic success. This prospective pilot study shows the feasibility of hyperspectral imaging and thermal imaging post-EVT at home, which may decrease the need for hospital visits. With an aging population, there is an increase in multimorbid, fragile, and immobile patients with peripheral arterial disease (PAD) ,2. EndovThe actual improvement of tissue perfusion post-EVT is not regularly assessed, but seems crucial for predicting wound healing, decrease in ischemic pain, and to detect whether additional interventions or early reinterventions are needed ,10,11,12The aim of this pilot study was to assess the changes in tissue perfusion, both in hospital and at home, up to 6 weeks post-EVT. The secondary aim was to determine whether there is an early detectable difference in tissue perfusion between patients with and without clinical improvement to predict outcomes at an early stage.This single-center prospective cohort study included patients undergoing EVT for Rutherford classes 2 to 5. Patients were included between November 2019 and August 2021 at the University Medical Centre Groningen (UMCG). Patient inclusion was discontinued from March 2020 until August 2020 because of the coronavirus disease 2019 (COVID-19) pandemic. The UMCG Institutional Review Board approved the study protocol (METC #2019/114). Study procedures were performed according to the Medical Research Involving Human Subjects Act and the Declaration of Helsinki. The study was registered in the Netherlands Trial Register (#NL7713). Written informed consent was obtained from all patients.Patients with PAD scheduled for EVT were eligible for inclusion when the following criteria were met: age ≥ 18 years and PAD Rutherford stages 2 to 6 with indication for elective EVT. Exclusion criteria were illiteracy or language barrier, acute limb ischemia, severe peripheral edema, cellulitis, or erysipelas of the leg or foot. Patients were not excluded if the affected limb was previously treated. Baseline characteristics, including sex, age, body mass index, smoking status, comorbidities, Rutherford classification, and ankle-brachial index (ABI), were collected from medical records.Of the 40 patients who participated in this study, 23 were previously included in a study investigating periprocedural changes with HSI and thermal imaging; however, that study focused on measurements directly before and after percutaneous transluminal angioplasty (PTA) .Indication for treatment was determined by a multidisciplinary team of vascular surgeons and interventional radiologists based on standard clinical guidelines. Target lesions were treated endovascularly with PTA. Stent placement was performed if there was residual stenosis of >30%, flow-limiting dissections, or acute recoil. Target lesions were scored according to the TransAtlantic Inter-Society Consensus for Management of Peripheral Arterial Disease (TASC-II) and the Global Limb Anatomic Staging System (GLASS) by the executing interventional radiologist . ProceduCompletion angiograms were evaluated by two vascular specialists (J.P.V. and R.B.) blinded to the results of tissue perfusion imaging. A “good” angiographic result of the treated feet was accomplished when a TASC-II C or D lesion was treated and uninterrupted flow continued in at least one below-the-knee artery in continuation with the inframalleolar arteries, resulting in expected increased perfusion post-EVT. If the completion angiogram did not meet these criteria, the angiographic result was classified as “poor,” and tissue perfusion was not expected to increase post-EVT. Angiographic results in which there was disagreement were discussed to reach consensus.Clinical outcomes were evaluated 6 weeks after revascularization in the outpatient clinic. Clinical improvement was defined as a decrease of at least one Rutherford stage for patients with Rutherford stages 2 to 5. Additionally, for patients with Rutherford stage 5, the wound surface area was clinically evaluated by a wound care nurse together with a vascular specialist and compared with the pre-EVT wound surface area. Improvement was defined when wound surface area decreased or completely healed. Duplex ultrasound imaging was performed pre-EVT and during the first visit to the outpatient clinic at 6 weeks post-EVT according to the standard of care to determine patency of treated arterial lesions. Increased risk for stenosis is defined as a peak systolic velocity of >180 cm/s or a peak velocity ratio across the stenosis of >2.0 ,21. SubsHSI is a technique that uses visible light spectroscopy to estimate the concentration of oxyhemoglobin (OxyHb) with 2 absorption peaks at approximately 542 nm and 578 nm and deoxyhemoglobin (DeoxyHb) with a single absorption peak at approximately 554 nm in the upper 1 to 2 mm of the skin . Intra- o2) was additionally determined for 15 min using the PeriFlux 6000 TcPo2  on the dorsal side of the foot with a temperature of 44 °C.Patients were in a resting position for at least 5 min before the measurements. Patients were standardly measured in supine position in hospital and at home and in semi-Fowlers position when this was not possible in the home setting. Subsequently, HSI and thermal images of the plantar and dorsal sides of the foot were taken. The image acquisition was standardized for both in-hospital and home measurements, as previously described by Kleiss et al.  Home meaThe HyperView device generates an image with the estimated concentration of OxyHb and an image of DeoxyHb. The FLIR camera records a single thermal image. Image postprocessing was performed in Matlab R2018a . After image acquisition, a region of interest (ROI) was manually drawn on the HSIs, and another ROI was manually drawn in the thermal images at the same spot. For the plantar foot, the ROI was placed at the forefoot as shown in p value of ≤0.05 was considered statistically significant.Data were recorded using the electronic data management service REDCap . Statistical analysis was performed using SPSS 23 software . According to data distribution, descriptive statistics are presented as mean ± standard deviation or median with interquartile range . For each follow-up measurement, the change in perfusion parameters was calculated by subtracting the baseline measurement from the follow-up measurement. Differences between patients with or without clinical improvement or with and without angiographic result for each follow-up moment were calculated using a Mann-Whitney U test. A o2 at different time points during follow-up due to logistical reasons such as the lockdown due to the COVID-19 pandemic and inability of patients to visit the hospital for an appointment. Other reasons for missing data were technical failure of the thermal imaging camera during 10 different follow-up assessments and withdrawal of one patient from the study during follow-up. Four measurements were excluded because artifacts were present in >5% of the ROI of the HSI measurements. An overview of the missing data per measurement moment is shown in Thirty-four patients participated in the study. There were some missing data of HSI, thermal images, and TcPThe mean age of the 34 patients was 66.9 ± 9.8 years. Patient characteristics are summarized in Postprocedural completion angiograms of the limbs were not available for two treated limbs (5%). Completion angiograms of 13 limbs (32%) showed a good angiographic result, and completion angiograms of 26 limbs (63%) showed a poor angiographic result. Clinical improvement was present in 28 limbs (68%) 6 weeks post-EVT at the outpatient’s clinic visit. From the nine limbs (22%) with Rutherford class two, three (33.3%) had improved clinically, eight (80%) improved from Rutherford class three, and seven (100%) improved from Rutherford class four. Ten (67%) limbs with Rutherford class five improved clinically or showed a decreased wound surface area. Nine (23%) limbs clinically improved and showed a good angiographic result. Nine (23%) limbs had no clinical improvement and had a poor angiographic result. Four (10%) limbs showed a good angiographic result but were not improved clinically. Seventeen (41%) limbs were clinically improved but showed poor angiographic result.o2 at the dorsal side of all treated limbs post-EVT for patients with and without clinical improvement with respect to baseline measurements. o2 at the dorsal side of all treated limbs during follow-up post-EVT for patients with and without good angiographic result with respect to baseline measurements. The perfusion values showed the same trend for all groups. OxyHb values and TcPo2 values did not change post-EVT with respect to baseline values. DeoxyHb values were higher directly post-EVT with respect to baseline but decreased over time. Temperature values were higher directly post-EVT with respect to baseline but decreased over time.p = 0.009). Similar results were seen at day 14: the limbs without clinical improvement showed a significantly higher increase in DeoxyHb  than limbs with clinical improvement . At all follow-up assessments, there were no statistically significant differences in OxyHb, temperature, or TcPo2 between the groups with and without clinical improvement.p = 0.032). Similar results were seen at day 14: the limbs with poor angiographic result showed a significantly higher increase in DeoxyHb  than limbs with a good angiographic result ; p = 0.023). Additionally, similar results were determined at 6 weeks post-EVT: the limbs with a poor angiographic result showed a significantly higher increase in DeoxyHb  than limbs with good angiographic result .p = 0.018). Similar results were seen at 6 weeks post-EVT: the limbs with a good angiographic result showed a significantly higher increase in temperature  than the limbs with a poor angiographic result . At all follow-up assessments, there were no statistically significant differences in OxyHb and TcPo2 between the groups with a good and poor angiographic result. Differences in OxyHb, DeoxyHb, and temperature of the dorsal side showed the same trend as the perfusion results of the plantar side of the feet. However, no statistical differences were detected between the groups with and without clinical improvement or good angiographic result on this side of the foot.The change in temperature with respect to the baseline values at 1 h was significantly greater in the limbs with a good angiographic result than in limbs with a poor angiographic result  did not seem to change, indicating that oxygen extraction (increasing DeoxyHb) seems to increase potentially as a function of deteriorating blood flow over time. Chin et al. [This study demonstrates that DeoxyHb is the most suitable parameter for tissue perfusion measurements with HSI, which is in accordance with findings in prior studies investigating periprocedural HSI changes at the plantar side of the foot. Oxygenation results interventions for patients with PAD.This study is the first step toward noninvasive, contact-free home monitoring with HSI, although there are some limitations. One of the study limitations is the small group of 34 patients and a substantial number of missing measurements. Fewer PTA procedures were scheduled because of COVID-19, and patients were less willing to participate within the study. Home monitoring was not permitted during the COVID-19 lockdown, which resulted in missing the data of home measurements. A major downside of all the missing data was the inability to perform mixed-model statistical analyses, which was the preferred method within longitudinal data analyses and should be used within future studies.o2 showed no significant differences between the groups with and without clinical improvement or good angiographic result.When considering the homogeneity of the groups in this study, it should be noted that the classification of the groups in our study was based on subjective measures and did not include objective measures such as ankle pressure, toe pressure, ischemia, or infection status of the wounds. This study included eight patients with DM in the Rutherford two to four group and seven patients with DM in the Rutherford five and six group, which disturbs the homogeneity of the groups. This may be the reason that no differences were found in ABIs between the groups with Rutherford two to four and Rutherford five and six and that TcPSeveral other factors may affect tissue perfusion measurements, which were previously described by Kleiss et al. . These fFinally, the ROI of the HSIs was not automatically translated to the thermography images, and artifacts were manually removed of the images. This manual segmentation of HSI and thermal images could have introduced variability in the area that is segmented, which is intra-observer and inter-observer dependent. For future studies, segmentation based on intensity or machine learning should be investigated.This prospective pilot study shows the feasibility of HSI and thermal imaging post-EVT at home, which may decrease the need for hospital visits. Significant differences in DeoxyHb levels at 7 and 14 days post-EVT were determined between patients with and without clinical or angiographic success.


PDZ3, SH3, and Guanylate Kinase domains, regulate PSD-95 activity. Here we combined discrete molecular dynamics and single molecule Förster resonance energy transfer (FRET) to characterize the PSG supramodule, with time resolution spanning picoseconds to seconds. We used a FRET network to measure distances in full-length PSD-95 and model the conformational ensemble. We found that PDZ3 samples two conformational basins, which we confirmed with disulfide mapping. To understand effects on activity, we measured binding of the synaptic adhesion protein neuroligin. We found that PSD-95 bound neuroligin well at physiological pH while truncated PDZ3 bound poorly. Our hybrid structural models reveal how the supertertiary context of PDZ3 enables recognition of this critical synaptic ligand.The scaffold protein PSD-95 links postsynaptic receptors to sites of presynaptic neurotransmitter release. Flexible linkers between folded domains in PSD-95 enable a dynamic supertertiary structure. Interdomain interactions within the PSG supramodule, formed by  The fundamental structural unit of large proteins is the independently folding domain . Most prPDZ) domain (SH3) domain (GuK) domain  domain  and Guan) domain  that ser) domain . One dis) domain  region w) domain . Differe) domain . The pos) domain .In multidomain proteins, the proximity imparted by interdomain linkers results in high effective concentrations of the folded domains . This caMapping the conformational landscape of the PSG is important because these supertertiary interactions regulate protein interactions with scaffolding clients . PreviouWe have developed a workflow for dynamic structural biology that combined single molecule fluorescence measurements with simulations and disulfide mapping , which wTo generate structural models, the FRET distances from fitting the donor fluorescence decays, along with accessible volume (AV) simulations of the dyes at each labeling site, are used as restraints in rigid body docking. This can position the known structures of individual domains to generate supertertiary structural models of each limiting state. To assign dynamic timescales for exchange between states, the confocal microscopy data is further analyzed using filtered fluorescence correlation spectroscopy (fFCS). Additionally, photon distribution analysis (PDA) is used to obtain relaxation rate constants for conformational exchange on timescales close to the diffusion time. To provide an independent picture of the conformational energy landscape, we performed replica exchange discrete molecular dynamics (DMD) simulations. We compare structural models from DMD to FRET models from rigid body docking and use the DMD models to examine the unique sensitivity of each FRET pair to the underlying conformational ensemble.Here, we applied this approach to the PSG supramodule from PSD-95. Experiments and simulations agreed and revealed two distinct conformational basins for PDZ3; a fuzzy interaction with PDZ3 within a broad interface near the SH3 HOOK insertion and a second discrete binding site in GuK. Both were confirmed with disulfide mapping. Surprisingly, these supertertiary interactions in full-length PSD-95 allowed PDZ3 to interact with the synaptic adhesion protein neuroligin, a known binding partner of PDZ3 , while tOur integrative approach to dynamic structural biology resolved the structural heterogeneity of the PSG supramodule within full-length PSD-95. Combining simulation with experiments resolved global dynamics and provided residue-level details about supertertiary interactions. This approach moves beyond solving a single structure towards resolving an ensemble of conformers with differing behavior and is applicable to other dynamic multidomain proteins.To experimentally probe the location of PDZ3 within the PSG supramodule, we used 12 cysteine mutations spanning PDZ3, SH3, and GuK . These lBy using these labeling sites in different combinations, we created 12 variants . These vFluorescently labeled proteins measured with confocal microscopy with pulsed-interleaved excitation (PIE) , which aTo determine the number of conformational states present in the ensemble, we compared global fits using models that included a no-FRET state along with an increasing number of FRET states . We obseFor each variant, we plotted the intensity-based FRET efficiency against the average donor fluorescence lifetime  that maintain residue contacts, domain reorientations (R2t) that alter interdomain interaction interfaces, and domain exchange (R3t) such as large-scale translational exchange between basins  . We filtn basins . PSD-95 n basins . This reR1t suggesting fast domain motions within identified basins (4 out of 7 variants) or R3t indicating slow jumps between conformational basins (3 out of 7 variants). Three variants had their middle contribution at R2t arising from domain reorientations. Truncated PSG variants exhibited increased heterogeneity in dynamics  shows that PDZ3 did not linger in extended conformations, which were rarely sampled  with mean gR of 23.4 Å. Representative models from these three populations  reveal a diverse ensemble of conformations with PDZ3 sampling both SH3 and GuK as well as undocked conformations  for PDZ3 and SH3 against the distance between COMs for PDZ3 and GuK . This 2D3 domain . PDZ3 al3 domain , β-basin and GuK .Examination of structures within the α-basin reveals a broad ensemble of conformations with PDZ3 able to reorient around the HOOK helix and occasionally sample the SH3 RT loop Examination of the β-basin ensemble revealed a more well-defined interaction with PDZ3 binding near the interface of the nucleoside monophosphate (NMP) binding and CORE subdomains of GuK . The intTo confirm the PDZ3 interactions with SH3 and GuK, we engineered cysteine residues based on the contact frequency maps and measured the extent and rate of disulfide (DS) bond formation . DS formIncreased electrophoretic mobility indicated that DS formation was occurring for all samples . The datWe also chose two of our existing FRET variants, one with high FRET (P3-S2) and one with mid FRET P1-G2 , and scrTo describe the conformation of each limiting FRET state, we used the FRET distances from the global lifetime fits as restraints in rigid body docking. We simulated the accessible volume (AV) for both dyes at each labeling site , which wTo compare the models from rigid body docking to the DMD ensemble, we calculated the dye accessible volumes for all snapshots structures from the DMD trajectory. For each structure, we plotted the inter-AV distance  analysis . We systUsing the experimental distance bounds from our FRET network robustness analysis, we screened the 20,871 snapshot structures from DMD simulations to identify all models that are consistent with the experimental FRET data for each limiting state. This identified many more models than is typical for rigid body docking, from which only a handful of top scoring structures emerge. To condense this large number of models, we represented the PDZ3 center of mass for each compatible model as a sphere and then just display the surface formed by the overlapping spheres. This emphasizes the fuzziness of the experimental states, which encompass a broad conformational ensemble like the basins from DMD. The boundaries of α-basin ensemble are enumerated by the variance of the FNR distributions  but arisWe used this structural information, along with the dynamic timescales from correlation analysis, to construct a conformational landscape of the PSG supramodule within PSD-95 consistent with DMD and FRET measurements . We perfDK) of 10 µM  contain lysines and arginines near the canonical PDZ motif while neuroligin contains histidines, which explains why NL10 has pH sensitivity. In the fuzzy α-basin, interactions with positive charges in the HOOK satisfy negatively charged residues in PDZ3, which facilitates uncharged peptide binding. Additionally, fuzziness increases the favorability of the α-basin by allowing PDZ3 to reorient to satisfy multiple interactions. These supertertiary interactions also affect other ligands. Measurements of CRIPT binding to PDZ3 suggested that PDZ3 adopted two interconverting conformational states in the PSG with different kinetics but the same equilibrium affinity . Thus, tRattus norvegicus, the PSG truncation (residues 303–274) and truncated PDZ3 (303–415) were expressed in the Rosetta 2 strain of E. coli (EMD Millipore) and purified by a combination of Ni-affinity, ion exchange and size exclusion chromatography as previously described for these exact protein constructs  followed by dialysis.The full-length PSD-95 from nstructs . Each vanstructs . For smTFluorescently labeled PSD-95 was encapsulated in 100 nm liposomes supplemented with 0.1% biotinylated phosphoethanolamine . Unencapsulated protein was removed by desalting on Sepharose CL-4B . Liposomes were attached via streptavidin to a quartz slide passivated with biotinylated-BSA. FRET data was collected at 10 frames/second using an Andor iXon EMCCD camera . All smFRET measurements were performed in 50 mm Tris 150 mM NaCl pH 7.5 supplemented with 1 mM cycooctatetraene, 0.5% w/v glucose, 7.5 units/mL glucose oxidase and 100 units/mL catalase. Alternating illumination using 640 and 532 nm lasers allowed for the identification of molecules containing one donor and one acceptor. Microscopy data was analyzed using MATLAB to perform the mathematics needed to correlate donor and acceptor images, extract single-molecule intensity time traces and calculate FRET efficiency. Per molecule gamma normalization based upon acceptor photobleaching events was used to correct for distortions in the measured intensities between the donor and acceptor channels .GG-TRR|<1ms) and (2) the observed FRET stoichiometry (0.3<SPIE < 0.7)  .The PDZ3 and SH3-GuK domains of the PSG core were reconstructed using homology modeling of I-TASSER (Iterative Threading ASSEmbly Refinement) hierarchical approach using multiple threading alignments and iterative structural assembly simulations. The I-TASSER method involves four consecutive stages: (a) template identification or threading by LOMETS, (b) structure assembly by replica-exchange Monte Carlo simulations, (c) model selection and structure refinement using REMO and FG-MD, and (d) structure-based function annotation using COFACTOR . The indReplica exchange DMD (rxDMD) simulations were performed with 18 neighboring replicas in the temperature range of 275–360 K to efficiently sample the conformational free energy landscape. In the rxDMD scheme, temperature was exchanged between replicas at periodic time intervals, according to the Metropolis criterion, to overcome any local energy barriers that may limit efficient sampling of the conformational states. Production runs followed 3000 timestep energy minimization runs, where a DMD timestep corresponded to ~50 fs. The conditions for replica exchange were checked every 1000-time units and the frames were saved every 100-time units. Anderson’s thermostat was used to maintain temperature at 300 K in all simulations and the heat exchange factor was set to 0.1. Each replica simulation lasted for 660 ns that resulted in a total simulation time of ~11.9 μs.gR for the 18 simulated replicas is provided in The thermodynamics of the PSG core were computed using the Weighted Histogram Analysis Method (WHAM) . The timPMFR is the multiple dimensional parameter such inter-domain distances, p(PMFR) is the probability density derived from WHAM, T is the temperature of interest and kB is the Boltzmann constant. Our 2D free energy landscape was computed as a function of interdomain distances between PDZ3, SH3Hook, SH3barrel, and GuK. The free energy contours are scaled in units of kcal/mol.where https://www.compbio.dundee.ac.uk/downloads/oc) was utilized to group similar conformations of the PSG core. Depending on the pair-wise RMSD matrix, the clustering algorithm iteratively combines nearby clusters. The ‘cluster distance’ was determined based on all pairwise distances between elements of corresponding clusters. We used the mean of all values to compute the distance between two clusters, and the centroid structure of each cluster was chosen with the smallest average distance to other elements in the cluster.The hierarchical clustering program (The disulfide bond modeling of the interdomain contacts in PSG was performed for the two basins (α and β) in the free energy landscape. From the structural ensembles corresponding to the reaction coordinates in the residual contact frequency map, we ranked contact residue pairs and selected the top three pairs near the interface that did not involve the hydrophobic and salt-bridge interactions. The selected pairs were situated at the peripheral sites of the interdomain interface, and the corresponding residues were either polar or weakly hydrophobic in nature .4 and 1.75 mM 1, 10-phenanthroline  into 20 mM Tris pH 7.4, 150 mM NaCl. Disulfide oxidation reactions were performed at 25°C with 2 μM protein concentration for the PSG fragment or 0.5 μM for full-length PSD-95. Disulfide formation was initiated by the addition of 0.5 mM CuSOthroline . Time poBinding experiments used protein constructs described above with a 10-residue synthetic peptide corresponding to the C-terminal 10 residues from rat neuroligin 1 a (HPHSHSTTRV), which was synthesized with an N-terminal fluorescein label . Fluorescence polarization measurements were carried out in black 96-well plates measured on a Wallac Victor 2 Plate Reader (Perkin Elmer). For all measurements peptide was held at concentrations of 50 nm (or 100 nM) while the protein concentrations ranged from 1 to 100 μM. Measurements at pH 7.4 were made in 20 mM Tris, 150 mM NaCl, 1 mM DTT, and 1 mM EDTA. Measurements at pH 6.0, were made in 20 mM MES, 150 mM NaCl, 1 mM DTT, and 1 mM EDTA. Four readings were taken for each well, then averaged. All experiments were carried out in triplicate and fitted with the Hill Equation for a single site binding site model.http://www.mpc.hhu.de/en/software. DMD simulation engine is available at http://www.moleculesinaction.com. MATLAB scripts used to analyze smTIRF are included as MFD is made available at  This paper presents data of fundamental importance and is methodologically exceptional. It is of broad interest to investigators studying the function and regulation of protein scaffolds, dynamic protein structure, and the regulation of the postsynaptic density at excitatory synapses. The authors develop an integrated approach using fluorescence-based biochemical methods, disulfide mapping, and discrete molecular dynamic simulations to study the dynamic supertertiary conformation of the synaptic scaffold protein PSD-95. The overall research strategy serves as a textbook example to the field. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Mary B Kennedy as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Volker Dötsch as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Jelle Hendrix (Reviewer #2); Andrew Lee (Reviewer #3).Thank you for submitting your article "Fuzzy Supertertiary Interactions within PSD-95 Enable Ligand Binding" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:eLife.1. Although this study is important and even ground-breaking, it is very difficult to read as presently written. The authors need to take several steps to improve the clarity of the presentation, in particular for the broad audience of The authors need to reduce the use of jargon throughout the Results section. In the Introduction, it would be useful to include a summary of the experimental trajectory that lists the major methods used and their acronym abbreviations so that readers can refer back to a single place to decode the acronyms as they read the results. For example, after your sentence, "Here we applied this approach to the PSG supramodule", add the following sentences: "We made multiparameter fluorescence measurements (MFM) in which single-molecular Fluorescent Resonance Energy Transfer (FRET) data were acquired by sub-ensemble Time-Correlated Single Photon Counting (seTCSPC). We used Total Internal Reflectance Fluorescence microscopy (smTIRF) to compare fluorescence measurements from truncated PSG supramodules to those from full-length PSD-95. Measurements were analyzed by Photon Distribution Analysis (PDA) to confirm the existence of two limiting energy states. To resolve fast dynamics, we performed filtered Fluorescence Correlation Spectroscopy (fFCS). To fully map and refine the conformational energy landscape of the supramodule, we performed a replica exchange Discrete Molecular Dynamics (DMD). Predicted conformations were verified by disulfide (DS) mapping after the introduction of pairs of cysteine mutations. Finally, we used the FRET restraints to simulate the accessible volume (AV) for dye pairs at each labeling site."2. The authors need to carefully explain the utility of each of the experimental, statistical, and simulation methods that they use at each point. For example, they should include in the introduction a summary of the flow of the analysis, perhaps a shortened version of the summary in the first part of the public review.a. The discussion of almost every figure should start with a process-like overview of the different steps that are performed to arrive at the final result; when appropriate, including a diagram of the analysis as part A of the figure.eLife does not have limits on the number of main figures needed to justify the conclusions of a study.b. Many of the supplemental figures (even if only exemplary subpanels thereof), should be moved to the main text. Supplemental material should not contain essential elements that are needed to understand the main text. In view of this, at a minimum, Figure 1 Suppl 1, parts of Figure 3 Supplements 1 and 2, and parts of Figure 6 Supplements 1 and 2 should be moved to the main text. 3. The authors should explain clearly why they used two different methods to integrate fluorescence data with modeling data . What is meant exactly by the statement, "For state A, the best-fit models showed some divergence with PDZ3 near the HOOK insertion but also extended without interdomain contacts ." What divergence are they referring to?4. In order to interpret the disulfide mapping experiment correctly, the authors need to show data from negative controls testing cysteine pairs that are predicted NOT to interact.5. The authors need to explain how the approach taken in this paper compares to their previous simulated annealing approach of mapping PDZ3 using FRET data in their McCann et al., 2012 paper. That study resulted in a model in which PDZ3 binds to a completely different interface, which is not mentioned in this manuscript.6. The authors need to discuss whether some of the FRET variants may be undergoing increased domain-swap dimerization compared to the wild type. Might this occurence have influenced their data?7. p. 17, line 32: A proper reference should be given here, probably after "10-phenanthroline".8. p. 13, line 4: "residues 303-274" is a typo and should be fixed.9. Small typo in Figure 5F legend: (%/min) => (/min)?10. Supp file 3A: caption described 'average' and log-average, yet the table only describes average. Please also change 'Average' to the T_R to be consistent with PDA, and describe clearly how it is calculated. Essential revisions:1. Although this study is important and even ground-breaking, it is very difficult to read as presently written. The authors need to take several steps to improve the clarity of the presentation, in particular for the broad audience of eLife.We thank the reviewers for their suggestions as to how we can best improve the clarity of the manuscript given the broad array of methods and analyses, which each carry their own acronyms and jargon. We have added a couple paragraphs to the Introduction that explains each method and defines the acronyms, which can serve as a reference point. We have also included a new workflow diagram as Figure 2, which clarifies how the different methods are connected to the analyses in use. We have also made changes to the manuscript to use more common language in place of technical jargon and reduce the number acronyms used in the text.2. The authors need to carefully explain the utility of each of the experimental, statistical, and simulation methods that they use at each point. For example, they should include in the introduction a summary of the flow of the analysis, perhaps a shortened version of the summary in the first part of the public review.a. The discussion of almost every figure should start with a process-like overview of the different steps that are performed to arrive at the final result; when appropriate, including a diagram of the analysis as part A of the figure.b. Many of the supplemental figures (even if only exemplary subpanels thereof), should be moved to the main text. Supplemental material should not contain essential elements that are needed to understand the main text. In view of this, at a minimum, Figure 1 Suppl 1, parts of Figure 3 Supplements 1 and 2, and parts of Figure 6 Supplements 1 and 2 should be moved to the main text. eLife does not have limits on the number of main figures needed to justify the conclusions of a study.We appreciate the opportunity to move more data and analysis to the main text. Specifically, we have increase the number of main text figures from 7 to 10 and moved one of the supplemental tables to the main text. We thank the reviewers for their suggestions as to which panels were key to understanding the manuscript and have moved all the suggested panels to the main text. This includes the former Figure 1—figure supplements 1, 2 and 3; Figure 3—figure supplement 2; along with Figure 6—figure supplement 2.3. The authors should explain clearly why they used two different methods to integrate fluorescence data with modeling data . What is meant exactly by the statement, "For state A, the best-fit models showed some divergence with PDZ3 near the HOOK insertion but also extended without interdomain contacts ." What divergence are they referring to?We thank the reviewers for this suggestion to improve the clarity regarding our use of two different methods to generate models based on the FRET data. We see the two methods as complementary with rigid body docking representing a standard front line method for structural modeling based on FRET data while the DMD snapshot screening allowed us to provide more details about the structural ensemble for each of the predominant states and also to test the robustness and thereby resolution of the FRET model. Rigid body docking relied only on distances obtained from fitting experimental data, which is often the case in FRET studies. The scoring functions in rigid body docking identify a single best structure (or family of structures) for a given set of distance restraints. In this work, all 12 variants were globally fit to assign the experimental distances to their corresponding states. This allowed us to identify a small number of top scoring models for each set of distance restraints.In this study, we had an independent dataset of structural models from DMD simulations, which is not always the case in FRET studies. The DMD ensemble, along with FRET Network Robustness Analysis, allowed us to revisit the fluorescence data to validate the experimental models from rigid body docking and also to establish the responsiveness of individual variants to the underlying conformational distribution. Additionally, because the DMD generates such a well-sampled energy landscape compared to rigid body docking, selecting models from the DMD ensemble provided a more complete picture of the basin fuzziness than is possible from handful of structures.By using these two methods along with disulfide mapping, we provide validation that rigid body docking can accurately resolve dynamic structures, given sufficient time resolution relative to the conformational exchange. We also show that well equilibrated DMD simulations can be used for the purposes of FRET modeling and resolve finer details of the basin fuzziness under experimental conditions in addition to providing estimations of the model uncertainty.Regarding the quoted text, we erroneously referred to the differences between modeling approaches as “divergence”. We meant that in addition to the presence of similar models from rigid body docking and DMD screening, which position PDZ3 near HOOK, the DMD screening identified models with PDZ3 farther away from the HOOK, which did not occur amongst the top ranked structures from rigid body docking. We have corrected this in the manuscript.4. In order to interpret the disulfide mapping experiment correctly, the authors need to show data from negative controls testing cysteine pairs that are predicted NOT to interact.We agree that controls are a critical part of the disulfide mapping experiments and thank the reviewers for this suggestion. As a negative control, we selected a cysteine pair that showed low FRET in our 2012 PNAS paper (Q374C-K591C), which was not included in this work. This cysteine pair should not be involved in contact interfaces identified from simulations or modeling. This cysteine pair showed no evidence of intramolecular disulfide formation. In the manuscript, we have added discussion of this negative control and provide an additional figure  to document that this variant does not form disulfides.5. The authors need to explain how the approach taken in this paper compares to their previous simulated annealing approach of mapping PDZ3 using FRET data in their McCann et al., 2012 paper. That study resulted in a model in which PDZ3 binds to a completely different interface, which is not mentioned in this manuscript.We apologize for this oversight and thank the reviewers for bringing it to our attention. The original modeling used rigid body docking, which was very similar to the approach used herein. The main problem with our earlier smTIRF model was that the experimental data was collected with insufficient time resolution to resolve the two conformational basins. Each FRET restraint reported the time averaged distance for that pair, but as our current work shows, these variants have independent dynamic signatures that depend on the specific structural interconversion pathways of the domains and the experimental sensitivity of each FRET pair. We have included this commentary on our previous model in the revised discussion on page 9.6. The authors need to discuss whether some of the FRET variants may be undergoing increased domain-swap dimerization compared to the wild type. Might this occurence have influenced their data?-5 molar concentrations. The PSG fragment runs very true to its calculated formula weight while the full-length protein does migrate faster than expected based on formula weight but not high enough to be a multimer.We are very interested in the prospect of domain swapping as has been suggested previously. However, we have not seen evidence for this at the concentrations used here. As reported in our 2012 PNAS paper, both full-length PSD-95 and the PSG fragment are monodisperse as judged by size exclusion chromatography, which suggests that lack of stably populated oligomeric states under these conditions at 10The DS mapping experiments did reveal some higher molecular weight species. However, these higher order species never accounted for more than 5% of the total input. Thus, any homomeric, intramolecular interaction is transient and not well occupied under the buffer conditions and concentrations used in these studies. Our size exclusion and disulfide mapping experiments are carried out at protein concentrations that are orders of magnitude higher than used for single molecule imaging. Thus, dimerization is unlikely at the single-molecule concentrations used for the present FRET experiments. If dimerization were to occur, we would expect the appearance of additional static subpopulations in the MFD histograms. If dimerization were significant, we would also expect the appearance of an additional diffusion term in fluorescence correlation curves, which was not the case in these experiments.


Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours. Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians. Recent advances in treatment  has significantly improved survival times for subgroups of patients. However, much work is still to be done in the field of lung cancer, especially in screening and early detection. Automated detection and segmentation would immediately impact the clinical workflow in radiotherapy, one of the most common treatment modalities for lung cancer2. Radiotherapy uses medical imaging, especially computed tomography (CT), to obtain accurate tumor localization and electron densities for the purpose of treatment planning dose calculations3. Accurate segmentation of the tumor and organs at risk are also essential as errors might lead to over- or under-irradiation of both the tumor and/or healthy tissue. It has been estimated that a 1 mm shift of the tumor segmentation could affect the radiotherapeutic dose calculations by up to 15%5. Therefore, automated accurate segmentation can significantly reduce the time needed by clinicians to carryout treatment planning, and adaptive re-planning of treatment depending on the changes in the tumor.Lung cancer is the deadliest of all cancers afflicting both sexes, accounting for 18.4% of the total cancer deaths worldwide in 2018, almost equal to breast and colon cancers combined6. Currently, such segmentations and appropriate RECIST measurements are performed manually or semi-automatically, consuming valuable time and resources, as well as being prone to inter- and intra-observer variability7.Equally important are the lesion and organ at risk segmentation process for radiation oncologists for radiotherapy planning, and the measurement of lesions within the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 framework for radiologists, both laborious manual routines which impose an avoidable workload9. Radiomics has the potential to facilitate personalized medicine via diagnostic and predictive models based on phenotypic properties of the region of interest (ROI) being analyzed10. ROI segmentation is currently considered to be one of the most time-intensive and laborious steps within the entire radiomics workflow11.Another field to profit directly from automated detection and delineation of lesions is radiomics, the high-throughput mining of quantitative features from medical images and their subsequent correlation with clinical and/or biological endpoints13. Deep learning, a branch of AI-based artificial neural networks, has been successfully applied on images to solve problems such as classification or segmentation15. Several attempts have been made to adapt these methods for medical imaging problems, including tumor detection and segmentation on CT images19. A major hurdle in developing fully automated software that can be applied to any CT is the heterogeneity of the datasets, especially when acquired from multiple centers20. CT scans with different acquisition- or reconstruction parameters present lung structures differently. The methods described in the current literature usually lack a CT preprocessing module in the pipeline, and the problem of data harmonization is left to be solved by a data-driven approach, requiring large datasets representing all aspects of this inhomogeneity.The recent advancement of machine learning techniques, combined with improvements in the quality and archiving of medical images, have fueled intensive research in the field of artificial intelligence (AI) for medical imaging analysisTaking into consideration these clinical and research needs for lung tumor segmentation, the implementation of automated detection software that is capable of fast and accurate delineation of NSCLC on thoracic CT scans is desirable, bordering on necessity. The applications and benefits include, but are not limited to: (1) CT-based automated screening of lung cancer; (2) Retrospective analysis of entire databases of patients who underwent thoracic CT in daily care for research purposes; (3) Consistent and reproducible segmentations, which are important in planning and monitoring (radio)therapy, and in research; (4) Follow-up of treated primary lung cancer; (5) Automation and acceleration of certain aspects of the clinical radiotherapy workflow, making adaptive re-planning more feasible.Automated segmentation of NSCLC tumors requires prior identification of the lesion as NSCLC. Invasive tissue biopsy is currently the clinical gold standard in identifying NSCLC. However, an accurate automated segmentation tool requires high detection accuracy. Therefore, software that can automatically segment NSCLC tumors could also be used as a detection method, decreasing the need for invasive biopsies.In this work, we present a fully automated lung tumor detection and 3D volumetric segmentation pipeline that is capable of handling a large variety of CT acquisition and reconstruction parameters. Furthermore, we externally validate our method on three datasets, compare the volumetric prognostic factor to an existing clinical standard, compare the quantitative performance to a similar published method, and compare the preference score, speed, and reproducibility of our method to those of experts in a prospective clinical trial setting.Overall, 1328 thoracic volumetric CT scans with corresponding 3-dimensional tumor segmentations were used in order to train, test, and externally validate a fully automated method for detection and segmentation of NSCLC in standard-of-care images. Datasets 1–7 were combined and randomly divided into training and testing datasets with 999 patients and 93 patients, respectively  imaThe ability of the system to detect tumors was assessed lung-wise and yielded a sensitivity of 0.97 and specificity of 0.99 in the external validation dataset and an area under the receiver operating characteristic curve (AUC) of 0.98. Confusion matrices for the detection performance can be found in supplementary materials Fig. . The medModel performance was also separately assessed in regard to groupings of image slice-thickness, tumor size, expert-reported tumor complexity, and tumor location. The sub-cohorts were analyzed for significant differences in model performance, with the results reported in Table Box plots showing DSC distributions in the sub cohort's tumor size and tumor complexity for both test and validation datasets are shown in Fig. Examples of the automatically generated segmentations  in comparison to contours segmented by experts are shown in Fig. 19 was evaluated on dataset 8 and compared to our model. The performance of the published model was evaluated using two different inputs: (i) as described in the original article (using patches of 256 × 256 pixels centered on the tumor); (ii) using the whole slice. For that dataset, our method achieved a DSC of 0.87 (IQR = 0.12), whereas the published method achieved a DSC of 0.83 (IQR = 0.16) when the cropped tumor regions were used and a DSC of 0.09 (IQR = 0.19) in the fully automated configuration (no pre-cropping). Figures for DSC, Ji, and H95th are provided in the supplementary materials  the time needed for the processes of manual and automated segmentation; (2) inter and intra-observer variability; (3) the preference of experts for manual or automatically generated segmentations.p < 0.0001). The mean time for the automated method was 2.78 s/patient (SD = 0.44), whereas the mean time for manual segmentation was 172.19 s/patient (SD = 158.99)  Fig. .Fig. 5CoThe median DSC for intra-observer variability among all experts was 0.88 (IQR = 0.12) whereas automated segmentations were 100% reproducible. Individual intra-observer variability scores are reported in Fig. The results for assessment of the variability between expert clinicians and the proposed automatic segmentation method achieved on the validation dataset 8 are presented in Fig. For the third endpoint, we had 40 participants from four different backgrounds: four health/medicine master students, 17 computer scientists, 12 medical doctors working in the field of medical imaging, and seven medical specialists (radiologists or radiation oncologists). In order to quantitatively evaluate the qualitative preferences of experts regarding automated vs manual contours, we developed a software tool which allowed experts to visually compare the segmentation and choose their preferences.On average, the participants preferred the automatic segmentation above the expert’s contour in 55% (IQR = 12%) of the cases Fig. . Among tWe presented a deep learning-based approach that is able to achieve state-of-the-art detection and 3D volumetric segmentation of NSCLC on CT scans. Although several attempts to develop lung cancer CT detection and segmentation methods have been previously made, we believe our work is standing out, especially in its external validation and ability to work on full thoracic CT scans without further input needed by a human operator. To improve detection and segmentation performance, we introduced several complementary steps to the automatic segmentation pipeline: (1) a harmonization routine for the preprocessing of CT scans in order to more comprehensively unify patterns on the images for the models to learn from; (2) a robust computer vision-based method to isolate the lung area, allowing the subsequent deep learning step to focus on the region of interest; (3) a dynamically changing loss function for the training procedure, allowing us to control and modify the quality of produced segmentation; (4) CTs of lung abnormalities other than NSCLC were included in the training dataset as negative examples, allowing our method to exclude them from the detection and segmentation process; (5) lung CT slices without contours were also used in the training process as negative samples, thereby increasing the number of unique training samples and decreasing the false-positive rate of the model; (6) although a 2D DL architecture was employed, a 3D post-processing routine produced volumetric segmentation. A prospective, registered in silico clinical trial showed that the performance of the automatic segmentation model is acceptable by modern clinical standards and that participants preferred automatic segmentations more often than the manual contours. Furthermore, RECIST and tumor volume based on the automatic contours were able to generate a more significant split of survival groups than manual contours.17 used a Recurrent 3D-DenseUNet architecture to segment lung cancers which allowed them to obtain a DSC of 0.74 on a validation dataset of 40 patients. Jue et al. (2019)19 evaluated several 2D convolutional neural network (CNN) architectures such as U-net, Segnet, full-resolution residual neural network (FRRN), and incremental multiple resolution residual network (MRRN) to segment patches of 160 × 160 pixels centered around the tumor, achieving DSC of 0.68 on the external validation dataset. Zhang et al. (2020)21 used a modified version of ResNet to automatically segment GTV and achieved an averaged dice similarity coefficient (DSC) of 0.73 on the test set, lacking however external validation of the model. Ardila et al. (2019)16 developed a deep learning-based software, which can detect lung cancer on low dose CTs with an AUC of 94.4%. In our study we were not able to evaluate a patient based AUC for lung cancer detection since all patients had cancer, instead, we have demonstrated that our model was able to detect lungs containing cancer on low dose CTs with a robust AUC of 0.96 in the test and 0.98 in the external validation datasets. Additionally, we evaluated the performance of a published 3D U-net-based approach on our validation dataset, where our model outperformed the published method.To set our model in the context of similar published work, Kamal et al. (2018)22.The state-of-the-art detection accuracy and the fact that it accepts any CT containing the lungs as input means the software can be used as a method for screening and detection of lung cancer. This is further corroborated by the fact that CT scans acquired using different parameters can be directly put in, making our method multi-vendor and multi-reconstruction compliant to a certain degree. The inclusion of cases that were hard to segment without a co-registered PET scan allows the deep learning networks to learn how to differentiate tumors from other lung abnormalities such as atelectasis and tumors with mediastinal involvement, which in conjunction with the accurate segmentation of the 3D tumor volume means it can be used clinically in radiotherapy settings or for big data radiomics  research. The robust automatic volumetric and RECIST measurements will subsequently have a positive impact on sample size calculations for clinical trials23, the deep learning network was also learning inaccuracies, such as contoured air (that certainly is not cancerous). However, this effect can be alleviated by increasing the training dataset size.Although we attempted to address the flaws and limitations of previous research while developing our software, there were limitations to our work. The ground truth segmentations were originally made on primary NSCLC. Therefore, although the software has a high detection accuracy, it is hypothetically limited to the detection and segmentation of primary NSCLC tumors. Moreover, by considering medical expert contours as the ground truth and taking into account the high interobserver variability of the contouring processIn future work we will utilize the evaluated image factors  in order to give a confidence score to each segmentation produced, providing added information to the user about which segmentations might need more attention. Additionally, we think it would be interesting to evaluate our method in a prospective clinical trial setting for tumor response to treatment evaluation utilizing the automatic volumetric RECIST measurement. Since our method was trained only on the planning/pretreatment CT scans, post-treatment changes in the tumor and lung structures may impose extra challenges on our automated segmentation approach.Further tuning of the model on NSCLC CT scans, and other independent NSCLC datasets can improve the performance of the software, and advance it towards clinical implementation.The ability of the software developed in this study to handle full thoracic CT scans with different acquisition and reconstruction parameters and without further human intervention represents the pillar for its clinical transition. Clinical application of this software following prospective validation can have a positive impact on the management of lung cancer patients, as it will improve the detection accuracy, and provide a fast, consistent, and reliable volumetric segmentation for treatment  purposes. Furthermore, the use of the software in large radiomics studies will allow automation and will reduce the time needed to complete the studies in a robust manner, as it will significantly decrease the time needed for the rate-limiting part of the workflow—tumor segmentation.The pretreatment CT scans of 1414 NSCLC patients were retrospectively collected and anonymized by each center and approved by the respective institutional review boards. A description of the data were provided in Table 24, the requirement for written informed consent was waived. The institutional review board of Maastricht University Medical Center has waived the need for informed consent since the data were anonymized and retrospectively collected with no intervention planned for participants based on the study, and no compensations were provided. The images in dataset 8 were segmented by five radiation oncologists, which allowed us to compare the performance of the deep learning segmentation model to multiple manual delineations. All other segmentations were performed by a radiologist or radiation-oncologist at the center where the diagnosis was made and checked by at least one segmentation expert at our site. The expert segmentations were considered the ground truth for training and further evaluations. Eighty-six patients from various datasets were excluded due to missing tumor contours and the lack of a PET scan to perform the segmentations according to a clinical protocol. Survival data and CT scans for datasets 1 and 6 were collected from the open sources.In this study, which followed the Standards for Reporting of Diagnostic Accuracy Studies statement2. After spatial normalization, an image with standard bone window settings  is saved, as it is used as an input in the lung isolation step of the workflow. In order to smooth the effect of different reconstruction methods on the image and to reduce the computational burden, intensity values are aggregated into bins of equal width. This also allows optimization of storage and image processing by packing the images into a much shorter 8-bit integer range and by filtering high-frequency noise. Hereafter, the image is cropped or padded with air intensity values to arrive at a resolution of 512 × 512 pixels, which is chosen as input for the selected deep learning architecture. All image processing and deep learning modeling steps were performed in Python 3.7 with the libraries and respective versions detailed in supplementary materials Table suppl. 1.Data inhomogeneity necessitated the harmonization of CT data in order to achieve comparable representations of the tumor region. Furthermore, several steps were introduced to reduce computational power requirements and image noise and to optimize the contrast. The first step is the extraction of a 3D array with voxel intensity values represented as Hounsfield Units (HU) from Digital Imaging and Communications in Medicine (DICOM) data. Next, the image contrast is enhanced using a lung window setting (window width (WW) of 1500 HU and window level (WL) of −600 HU) to highlight lung structures. All voxel intensities outside of the upper and lower limits are assigned the value of the closest limit. Following this, nearest-neighbor interpolation is applied to obtain isotropic spatial resolution in the axial plane so that each pixel has a size of 1 × 1 mmn is the number of slices (=5).A robust algorithm for the isolation of the lung region was developed in order to focus on the ROI and allow for the use of whole-body CT scans as input. First, the CT couch is detected and removed from the image volume. Air-filled connected volumes are detected and region growing and morphological operations are applied in order to remove small vessels and to connect adjacent regions, resulting in a 3D binary lung mask. The spine axis is identified and the lung mask is halved and symmetrically flipped about the sagittal plane, keeping the union of the flipped and the original lung masks. By doing so, the algorithm is optimized for handling lung abnormalities such as atelectasis, pulmonary infiltration, consolidation, and fibrosis. To accurately identify the spine axis, a further algorithm was developed which identifies the center of the spine using the stored preprocessed image with bone window settings as described in the previous section Fig. . A “boneint Fig. .1\documet is recalculated slice-wise by using exponential smoothing:Due to irregularities in patient positioning and anatomy, the central spine position SThis method of flipping the lung mask allows for the inclusion of regions that contain large-sized abnormalities, such as lung collapse, which obscure parts of the lung, whereas commonly used methods exclude those regions Fig. .r =−5) is applied to the resulting lung mask in order to have a margin around the lung area. The final binary lung mask is used to isolate the lung region within the original image by setting all the voxel values outside the mask to the normalized air value.A morphological dilation with the circle kernel  activations were replaced with Exponential Linear Unit (ELU) in order to alleviate the gradient vanishing problem and kick-start the training process29. Second, dropout layers with the dropout rate (p = 0.5) were introduced prior to the two last layers of the U-net encoder to prevent overfitting30.The widely used 2D U-net convolutional neural network (CNN) was employed for slice-wise tumor segmentation32, or they require a dimensionality reduction using interpolation or other image processing methods. 2D architectures do not have this problem and can process CT scans in the original resolution; (3) our main goal was to develop a pipeline that can be used in a clinical setting, and a 2D architecture allows for significantly lower requirements for executing PC. Our software does not require GPUs and can run on a regular laptop .A 2D CNN architecture was chosen for several reasons: (1) by using a 2D input the training dataset can be increased by more than a factor of 60, as overall more than 60,000 unique slices were available in the training set; (2) due to calculation costs, most present deep 3D architectures could analyze only a subvolume of the medical imageIn order to increase the robustness of the system to a wide range of imaging parameters, the training dataset was expanded using augmentation techniques with the following parameters: random rotation around the image center pixel in a range of 0–25 degrees with a probability of 60%, random horizontal and vertical shifts of the image in the range of 12% of image shape with a probability of 25%, random zooming of the image with a maximum of 3% of the image shape with a probability of 10%.The loss function was calculated by combining the Dice similarity coefficient (DSC) loss and the binary cross-entropy, and privilege was given to the DSC loss during the first 50 epochs. The privilege was defined by the coefficients before the DSC and cross-entropy terms in the loss function. By adding the binary cross-entropy component to the loss function, negative samples (slices without contour) could also contribute to the training.33. The cosine annealing scheduler was used to adjust the learning rate during the training process. A checkpoint function tracking the DSC on the test dataset was used to keep the best weights.The model was trained for 300 epochs using eight NVIDIA GTX 1080 Ti GPUs. The Adam algorithm was used for the stochastic optimization of the loss function34. The connected region containing the most voxels is defined as the primary gross tumor target volume (GTV-1) for quantitative assessment. The final mask is resampled to the original image shape using cv2.INTER_BITS interpolation.Predicted 2D binary masks are stacked into a 3D volume and connected component extraction is applied as a post-processing step, whereby only spatially connected mask regions are extractedIn order to evaluate tumor detection performance, we generated lung-based labels, where lungs containing a tumor segmentation were assigned a positive label and lungs without were labeled negative. For cases where a tumor was present in both lungs of a patient, both were labeled positive. The ability of the system to detect tumors was assessed by calculating the area under the receiver operating characteristic curve (ROC AUC) and generating a confusion matrix.Automatically generated binary masks were resampled to the original image resolution using cv2.INTER_BITS interpolation before comparing with manual segmentations. The contouring performance of the proposed pipeline, as well as the doctor's variability, were assessed by using the volumetric Dice similarity coefficient (DSC), Jaccard index (Ji), and 95th percentile Hausdorff distance (H95th). Additionally, we have evaluated quantitative contouring performance using Surface DSC and Added Path Length (APL).The DSC is a measure of overlap between two volumes and was computed as:Jaccard index, used for gauging the similarity between two volumes, was computed as:To evaluate the maximum deviation between the automatically segmented surface boundary and the ground truth surface boundary, the 95th percentile of Hausdorff distance (H95th) was used. Hausdorff distance (H) is defined as:τa and βτb are the border regions of A and B at a given tolerance τ, where τ is a maximum deviation from the ground truth contour which would not be penalized35. Tolerance τ for the NSCLC segmentation task have been evaluated on dataset 8 using segmentations of five experts.Surface DSC at tolerance τ was computed as:xy is the pixel spacing in the axial plane in mm36.APL was defined as follows:In addition to the model performance evaluation on the test and validation datasets, the variability between expert clinicians was assessed and displayed against the performance of our method by comparing the volumetric DSC among all possible comparison pairs, i.e., experts were compared with each other as well as with the proposed method.37.To better gauge the performance of our model under varying circumstances, it was evaluated with regard to slice-thickness, tumor complexity, tumor size, and tumor location. Tumor size subgroups were chosen based on the overall tumor size distribution in the training set. Furthermore, expert subjective tumor complexity labels were defined. To describe the complexity of the tumor, two medical doctors were asked to label the test and validation dataset as follows: for tumors where segmentation cannot be performed without a corresponding PET scan the labels were set to “1”, and “0” otherwise. In case of disagreement, the label “1” was chosen. Additionally, one medical doctor have also labeled the tumor locations on the test and validation datasets, where tumor locations were defined as follows: lung parenchyma, mediastinum, and chest-wall involvement. Tumor locations were selected based on the discussion with clinical experts and existing published research38. Statistical significance was assessed using a two-sided Mann–Whitney–Wilcoxon test with Bonferroni correction. Survival evaluation was done in R (version 4.0.2) using survival (version 3.1–12) and survminer (version 0.4.7) packages. To estimate the difference between survival groups a log-rank test was applied. High and low survival groups were separated by the median tumor volume or median RECIST measurement respectively. Random sampling with a replacement bootstrapping strategy was used to compute confidence intervals for AUC values.For all non-normally distributed scores the median and interquartile range (IQR) were reported, as well as the frequency histogramsThis trial was registered at clinicaltrials.gov (NCT04164186). For the first and second endpoints , participants used a state-of the-art commercial software (MIM version 7.0.4) to produce the segmentations. In order to make the conditions of the trial close to the real clinical practice, experts had CT and PET scans available for each patient and they were able to use a semi-automated segmentation solution provided by MIM, while the proposed method generated the segmentation using only CT scans.For the third endpoint , a software tool was developed in-house. The tool has two interactive screens with the first screen showing the description of the experiment and a small questionnaire. In order to analyze preferences at different levels of expertize, the participants were asked to specify their training . The second screen displays comparisons between pairs of segmented axial CT slices (automatic vs. expert) with randomized screen positions, blinded to the participant. For each comparison pair, the participants were asked to select the more accurate contour. Finally, a table was generated containing the choices made. Screenshots of this tool are provided in supplementary materials Figs. .The software tool presents scans and contours from the external validation datasets 8. It randomly selects 100 pairs of contoured CT slices, where the DSC between the contours was higher than 0.7. During the assessment, participants were able to adjust the image contrast by changing window settings (WW and WL) and leaving comments.nm is the number of times where preference was given to the proposed method,The preference of the experts was evaluated using the qualitative preference score, defined as:no is a number of cases in total.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


High incidence of cases and deaths due to coronavirus disease 2019 (COVID-19) have been reported in prisons worldwide. This study aimed to evaluate the impact of different COVID-19 vaccination strategies in epidemiologically semi-enclosed settings such as prisons, where staff interact regularly with those incarcerated and the wider community.We used a metapopulation transmission-dynamic model of a local prison in England and Wales. Two-dose vaccination strategies included no vaccination, vaccination of all individuals who are incarcerated and/or staff, and an age-based approach. Outcomes were quantified in terms of COVID-19-related symptomatic cases, losses in quality-adjusted life-years (QALYs), and deaths.Compared to no vaccination, vaccinating all people living and working in prison reduced cases, QALY loss and deaths over a one-year period by 41%, 32% and 36% respectively. However, if vaccine introduction was delayed until the start of an outbreak, the impact was negligible.Vaccinating individuals who are incarcerated and staff over 50 years old averted one death for every 104 vaccination courses administered. All-staff-only strategies reduced cases by up to 5%. Increasing coverage from 30 to 90% among those who are incarcerated reduced cases by around 30 percentage points.The impact of vaccination in prison settings was highly dependent on early and rapid vaccine delivery. If administered to both those living and working in prison prior to an outbreak occurring, vaccines could substantially reduce COVID-19-related morbidity and mortality in prison settings.The online version contains supplementary material available at 10.1186/s12889-022-13219-4. Due to concerns about outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many countries introduced non-pharmaceutical interventions (NPIs) in prisons at an early stage of the coronavirus disease 2019 (COVID-19) pandemic. High population turnover, including rooms with multiple occupancy, poor ventilation and poor sanitation can lead to rapid transmission of respiratory pathogens in prison settings , 4: it iIn the UK, a range of measures were introduced in prisons from March 2020: all social visits, education, training and employment activities were halted; a “compartmentalisation” strategy was introduced, which involved isolation of symptomatic individuals, shielding of vulnerable individuals and quarantining of people newly transferred to the prison; physical distancing of 2 m amongst both staff and individuals who are incarcerated was also implemented where possible . These mDespite these interventions, 19,066 people in custody tested positive for COVID-19 across 128 Prison or Youth Custody Service establishments in England and Wales between 16 March 2020 and 30 September 2021 . A studyThere have also been concerns raised about the impact of the restrictions on the mental health of those living in prison .Due to the elevated risk of transmission and the difficulties of implementing physical distancing measures effectively, the World Health Organization’s Strategic Advisory Group of Experts on Immunisation (WHO-SAGE-I) listed those living and working in detention facilities among the groups that should be prioritised for COVID-19 vaccination . Vaccina. We also looked at how this impact varies with timing of vaccination, to provide insights for future booster vaccination campaigns and outbreak response.There have been a number of modelling studies investigating SARS-CoV-2 transmission in prisons and detention facilities internationally –18, one We used CovidM, a transmission-dynamic mathematical model for SARS-CoV-2 transmission  which haTo account for the semi-enclosed nature of prison settings , the prison population was considered as a metapopulation with three interacting sub-populations: (A) Staff Group 1 – staff who have no contact with individuals who are incarcerated; (B) Staff Group 2 – staff who have contact with both (A) Staff Group 1 and those who are incarcerated; and (C) individuals who are incarcerated. Mixing within these three sub-populations was assumed to be homogenous. Contact patterns between the three sub-populations were informed by knowledge of the author working within HMPPS  and vaccine efficacy were also varied in order to capture the rapidly changing variant and vaccine landscape.We allowed introduction of new infections into the prison population via daily contact between staff and the community. The rate of new introductions was based on estimated incidence rate per 10,000 people per day as reported in the Office for National Statistics COVID-19 Infection Survey : mean inThe basic reproduction number was assumed to have a value of 5 based on a mean estimate for R0 of the delta variant , which wIn the base case, the prison population was fully susceptible at t = 0. A scenario analysis was undertaken in which 10%, 30% and 50% of the population were assumed to have prior immunity.Seven COVID-19 vaccination scenarios were considered: no vaccination (1); vaccination of Staff Group 1 only (2); Staff Group 2 only (3); all staff (4); all people who are incarcerated (5); all people living or working in prison who are over 50 years old—reflecting the prioritisation strategy used in initial COVID-19 vaccination rollout in the UK (6) ; and allIn the base case, vaccination was administered prior to an outbreak. A secondary analysis explored the impact of using vaccination as part of outbreak response. Increasing the number of individuals vaccinated per day to 50 was also explored under this scenario.Coverage among new staff and people who are newly incarcerated was assumed to be the same as coverage achieved inside the prison. Uptake was varied between 36%  and 87%The base case values used for vaccine effectiveness against infection and disease were based on efficacy against Delta, to ensure consistency with assumptions made about transmissibility and severity . EfficacThe impact of each vaccination strategy on cases and deaths was tracked over a one-year period. A longer time horizon of five years was explored in the sensitivity analysis. The health burden was also quantified in terms of quality-adjusted life years (QALYs) lost, taking into account the impact of symptomatic cases, non-fatal hospitalisations, non-fatal admissions to intensive care and adverse events following immunisation (AEFI) . A spreaTo account for the higher health burden experienced by those who are incarcerated in comparison with the general population, a secondary analysis was run in which the age-specific SARS-CoV-2 infection-fatality ratios were shifted downwards by 10 years , 6, 7. TA probabilistic sensitivity analysis was carried out using Latin hypercube sampling with 500 iterations, with parameter values taken from the distributions described in Table Partial rank correlation coefficients (PRCC) were calculated to assess the influence of different parameters on the total cases averted and total QALY loss averted over one year. Univariate sensitivity analyses were then performed for the parameters identified as the most important drivers of uncertainty, to investigate whether variation in these parameters impacted the conclusions made about which vaccination strategies were the most effective.Vaccinating all people living and working in prison settings reduced cases, QALY loss and deaths by 41.1%, 31.7% and 35.9% respectively, and was the strategy with the largest impact on all three outcomes Fig. . Only vaIn the base case analysis, staff-only vaccination strategies had a lower impact on cases, QALY loss and deaths than the strategies involving vaccination of people who are incarcerated. Vaccinating all staff reduced overall cases, QALY loss and deaths by 3.8%, 5.9% and 8.1%  and scenario 2 (homogenous mixing among Staff Group 2 and those who are incarcerated with no contact with Staff Group 1) Fig. .Fig. 3CaGreater infection-induced immunity prior to the outbreak also reduced the impact of vaccination strategies, but the relative impact of scenario 7 compared to all other strategies was increased. Assuming 50% prior immunity, scenario 5 reduced peak incidence to five cases per day, whereas scenario 7 reduced peak incidence to 2.5 cases per day Fig. .Fig. 4InAt lower values of R0  the difference in peak incidence between scenarios was reduced Fig. . HoweverWhen vaccination was introduced in response to an outbreak rather than prior to introduction of infection, peak incidence remained at around 26 new clinical cases per day under all scenarios  advised that those living or working in prison should be vaccinated along with their age and risk group, in line with the general population. The results of this study suggest that vaccinating both those living or working in prison according to their age could have had more impact on morbidity and mortality than any strategy targeting staff only, but suggests that greater impact could have been achieved by rapidly increasing coverage among individuals who are incarcerated.As illustrated by the high COVID-19 case and death rates in UK prisons despite physical distancing measures, effectively reducing R0 through non-pharmaceutical interventions can be challenging in prison settings . MoreoveThe model includes staff and allows for importation of cases via contact between staff and the community. Observational studies in the US have identified staff as an important source of introductions of SARS-CoV-2 infections into prisons and detention facilities . The modThe approach taken to account for the higher prevalence of underlying conditions among those who are incarcerated uses a widely-cited finding that the general health of a 50-year-old resident in prison is similar to that of someone then years their senior in the community . Whilst Increased risk of cardiovascular or thromboembolic events following SARS-CoV-2 infection, as well as other longer term symptoms such as fatigue, were not included in the QALY calculations . TherefoIn terms of the health burden associated with AEFIs, only minor systemic and fatal events were considered. However, an estimate of one Quality-Adjusted Life Day lost on average per minor AEFI is likely to be conservative and therefore will go some way to capturing the impact of other AEFIs. The inclusion of more severe AEFIs such as myocarditis or thromboembolism were considered, but given these where not included in the QALY loss calculations associated with SARS-CoV-2 infection, their inclusion as AEFIs would introduce bias.The scarcity of data on contact patterns between sub-populations in prison was also a key limitation: the mixing patterns used had to be informed by expert opinion rather than empirical estimates. However, varying these contact patterns did not substantially alter the conclusions in terms of the relative effectiveness of each vaccination strategy.Also due to limited data on contact patterns, the model structure did not account for room occupancy structure. This could be an important limitation, given that previous studies have found a significant difference between infection rates in residents of dormitories (with three or more occupants) versus cells (with up to two) . For theThere also remains substantial uncertainty around the value of the vaccine-related parameters. The large influence of vaccine efficacy against disease (2nd dose) on uncertainty in outcomes is important to note, given that this is a parameter that has previously decreased with the emergence of new variants . HoweverThe strong influence of resident turnover on total cases averted could be an important consideration when looking to generalise these findings across different prison types. This analysis only considered local prisons, which serve the courts and have a high turnover. The results therefore may differ for other prison types, such as high security or open prisons.Vaccine uptake was assumed to be 68% based on uptake in prisons in California . Uptake The impact of specific NPIs, such as isolation of those who are infected, was not explored explicitly within the model structure. Instead it was assumed introduction of NPIs will lower R0, informed by the reduction in R0 due to NPIs reported in a previous study in UK prisons . The R0 An important additional benefit of vaccinating prison populations that was not accounted for in this model was the impact of outbreaks in prisons on transmission in the community. Previous mathematical modelling studies investigating effectiveness and cost-effectiveness of prison-based interventions against a range of diseases have taken into account contact between prison populations and the general population . The impFinally, the model did not consider the economic costs: from a healthcare perspective, for HMPPS, or indeed for those who are incarcerated if illness or NPIs cause delays in court proceedings. This would be a logical next step for future analyses, whilst considering vaccination in combination with more specific sets of NPIs.Whilst we did not benefit from the granularity of data available to Ryckman and colleagues, a number of our key findings concurred with their conclusions. They found that increasing vaccination in staff did not have a substantial impact on infections and hospitalisations among residents, just as we found that staff-only strategies were not found to have a large impact on cases, QALYs and deaths among those who are incarcerated. They also found that vaccination timing was important: outbreaks were estimated to be twice as large if vaccination was carried out during rather than prior to introduction of infection; just as we found that vaccination did not appreciably alter the outbreak trajectory if only implemented once infection was introduced.This is the first model-based analysis of COVID-19 vaccination strategies in prisons in England and Wales. A previous modelling study has investigated the impact of specific NPIs , in whicVarying vaccine efficacy, duration of vaccine immunity, prior infection-induced immunity and transmissibility of large ranges was intended to increase the generalisability of the findings in the context of new variants or new vaccine formulations.Other countries and regions have taken differing approaches to vaccination in prisons: some are prioritising the vaccination of people who are incarcerated, others are taking a staff-only approach and others have not specifically included prisons in their vaccination strategy . The sizHowever, the general findings may still be valuable in other settings, such as the substantially greater benefit of vaccinating prison populations prior to introduction of infection. Globally equitable vaccine access is needed to ensure that vaccines reach people most in need, but the finding that an age-based approach is the most efficient strategy  may be particularly valuable in countries where supply remains more constrained.The approach taken in which prisons are considered a metapopulation, with people who are incarcerated and staff groups who have different contact rates with each other and the community, could also be extended to investigate the impact of COVID-19 outbreaks in prisons on community transmission.Our findings suggest that any vaccination strategy including both staff and people who are incarcerated is the most effective at reducing cases, health loss (in terms of QALYs), and deaths in prison settings. However, the impact of all strategies was dependent on timely vaccine delivery.Staff-only strategies had a negligible indirect benefit in terms of reduction in cases amongst those who are incarcerated. Therefore, going forward, increasing vaccination coverage among those who are incarcerated, through increasing uptake in those yet to receive vaccine doses or with future booster campaigns, could have a substantial impact on the COVID-19 burden in prison settings.Introducing vaccination at the start of an outbreak did not have an appreciable impact on cases regardless of the vaccination strategy taken. Therefore delivery of vaccines in prison settings must take place prior to introduction of infection to prevent large outbreaks; if vaccination is introduced too late, strict NPIs would likely also be required.For settings where vaccine supply remains constrained, the most efficient strategy was found to be vaccination of all those living or working in prison aged 50 years or older. Additional file 1:Supplementary Table 1. Age distribution of people living and working in an average local male prison.Supplementary Table 2. Input data on age-specific susceptibility to infection and severity of disease. Supplementary Table 3. Additional epidemiological parameters. Supplementary Figure 1. Incidence of new clinical cases over time under each vaccination scenario, if vaccination is introduced at the start of an outbreak. Supplementary Figure 2. Incidence of new clinical cases over time under each vaccination scenario, if vaccination is introduced at the start of an outbreak and administered at a rate of 50 doses per day. Supplementary Figure 3. Independently varying vaccine uptake between 30% and 90% among staff and individuals who are incarcerated. Supplementary Figure 4. Sensitivity of estimated total QALY loss averted under each vaccination scenario to variation in parameters. Supplementary Figure 5. Change in cases averted over one year with change in resident turnover, efficacy against disease (2nd dose), duration of vaccine immunity and duration of natural immunity. Supplementary Figure 6. Sum of clinical cases over five years when vaccine and natural immunity are assumed not to wane vs. waning of natural immunity of 16% over one year and waning of vaccine immunity of 19% over six months. Supplementary Figure 7. Incidence of new clinical cases over five years under each vaccination scenario, including uncertainty captured using probabilistic sensitivity analysis. Supplementary Figure 8. Cases over one year in an average local male prison, by sub-population, under each of seven vaccination scenarios. Supplementary Figure 9. QALY loss over one year in an average local male prison, by sub-population, under each of the seven vaccination scenarios. Supplementary Figure 10. Deaths over one year in an average male prison, by sub-population, under each of seven vaccination scenarios.