A Novel Framework for Thermoradiotherapy Treatment Planning.

PURPOSE: Thermoradiotherapy combines radiation therapy with hyperthermia to increase therapeutic effectiveness. Currently, both modalities are optimized separately and in state-of-the-art research the enhanced therapeutic effect is evaluated using equivalent radiation dose in 2-Gy fractions (EQD2). This study proposes a novel thermoradiotherapy treatment planning framework with voxelwise EQD2 radiation therapy optimizing including thermal radiosensitization and direct thermal cytotoxicity. METHODS AND MATERIALS: To demonstrate proof-of-concept of the planning framework, 3 strategies consisting of 20 radiation therapy fractions were planned for 4 prostate cancer cases with substantially different temperature distributions: (1) Conventional radiation therapy plan of 60 Gy combined with 4 hyperthermia sessions (RT60 + HT), (2) standalone uniform dose escalation to 68 Gy without hyperthermia (RT68), and (3) uniform target EQD2 that maximizes the tumor control probability (TCP) accounting for voxelwise thermal effects of 4 hyperthermia sessions without increasing normal tissue doses (RTHT + HT). Assessment included dose, EQD2, TCP, and rectal normal tissue complication probability (NTCP), alongside robustness analyses for TCP and NTCP against parameter uncertainties. RESULTS: The estimated TCP of around 76% for RT60 without hyperthermia was increased to an average of 85.9% (range, 81.3%-90.5%) for RT60 + HT, 92.5% (92.4%-92.5%) for RT68, and 94.4% (91.7%-96.6%) for RTHT + HT. The corresponding averaged rectal NTCPs were 8.7% (7.9%-10.0%), 14.9% (13.8%-17.1%), and 8.4% (7.5%-9.7%), respectively. RT68 and RTHT + HT exhibited slightly enhanced TCP robustness against parameter uncertainties compared with RT60 + HT, and RT68 presented higher and less robust rectal NTCP values compared with the other planning strategies. CONCLUSIONS: This study introduces an innovative thermoradiotherapy planning approach, integrating thermal effects into EQD2-based radiation therapy optimization. Results demonstrate an ability to achieve enhanced and uniform target EQD2 and TCP across various temperature distributions without elevating normal tissue EQD2 or NTCP compared with conventional methods. Although promising for improving clinical outcomes, realizable enhancements depend on accurate tumor- and tissue-specific data and precise quantification of hyperthermic effects, which are seamlessly integrable in the planning framework as they emerge.

Technical note: Optimization functions for re-irradiation treatment planning.

BACKGROUND: Although re-irradiation is increasingly used in clinical practice, almost no dedicated planning software exists. PURPOSE: Standard dose-based optimization functions were adjusted for re-irradiation planning using accumulated equivalent dose in 2-Gy fractions (EQD2) with rigid or deformable dose mapping, tissue-specific α/ß, treatment-specific recovery coefficients, and voxelwise adjusted EQD2 penalization levels based on the estimated previously delivered EQD2 (EQD2deliv ). METHODS: To demonstrate proof-of-concept, 35 Gy in 5 fractions was planned to a fictitious spherical relapse planning target volume (PTV) in three separate locations following previous prostate treatment on a virtual human phantom. The PTV locations represented one repeated irradiation scenario and two re-irradiation scenarios. For each scenario, three re-planning strategies with identical PTV dose-functions but various organ at risk (OAR) EQD2-functions was used: 1) reRTregular : Regular functions with fixed EQD2 penalization levels larger than EQD2deliv for all OAR voxels. 2) reRTreduce : As reRTregular , but with lower fixed EQD2 penalization levels aiming to reduce OAR EQD2. 3) reRTvoxelwise : As reRTregular and reRTreduce , but with voxelwise adjusted EQD2 penalization levels based on EQD2deliv . PTV near-minimum and near-maximum dose (D98% /D2% ), homogeneity index (HI), conformity index (CI) and accumulated OAR EQD2 (α/ß = 3 Gy) were evaluated. RESULTS: For the repeated irradiation scenario, all strategies resulted in similar dose distributions. For the re-irradiation scenarios, reRTreduce and reRTvoxelwise reduced accumulated average and near-maximum EQD2 by ˜1-10 Gy for all relevant OARs compared to reRTregular . The reduced OAR doses for reRTreduce came at the cost of distorted dose distributions with D98% = 92.3%, HI = 12.0%, CI = 73.7% and normal tissue hot spots ≥150% for the most complex scenario, while reRTregular (D98% = 98.1%, HI = 3.2%, CI = 94.2%) and reRTvoxelwise (D98%  = 96.9%, HI = 6.1%, CI = 93.7%) fulfilled PTV coverage without hot spots. CONCLUSIONS: The proposed re-irradiation-specific EQD2-based optimization functions introduce novel planning possibilities with flexible options to guide the trade-off between target coverage and OAR sparing with voxelwise adapted penalization levels based on EQD2deliv .

Brain Re-Irradiation Robustly Accounting for Previously Delivered Dose.

(1) Background: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) planning pathway aims to facilitate anatomically appropriate and radiobiologically meaningful re-irradiation (reRT). This work evaluated the STRIDeR pathway for robustness compared to a more conservative manual pathway. (2) Methods: For ten high-grade glioma reRT patient cases, uncertainties were applied and cumulative doses re-summed. Geometric uncertainties of 3, 6 and 9 mm were applied to the background dose, and LQ model robustness was tested using α/ß variations (values 1, 2 and 5 Gy) and the linear quadratic linear (LQL) model δ variations (values 0.1 and 0.2). STRIDeR robust optimised plans, incorporating the geometric and α/ß uncertainties during optimisation, were also generated. (3) Results: The STRIDeR and manual pathways both achieved clinically acceptable plans in 8/10 cases but with statistically significant improvements in the PTV D98% (p < 0.01) for STRIDeR. Geometric and LQ robustness tests showed comparable robustness within both pathways. STRIDeR plans generated to incorporate uncertainties during optimisation resulted in a superior plan robustness with a minimal impact on PTV dose benefits. (4) Conclusions: Our results indicate that STRIDeR pathway plans achieved a similar robustness to manual pathways with improved PTV doses. Geometric and LQ model uncertainties can be incorporated into the STRIDeR pathway to facilitate robust optimisation.

Treatment plan optimisation for reirradiation.

BACKGROUND: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) project aims to create a clinically viable re-irradiation planning pathway within a commercial treatment planning system (TPS). Such a pathway should account for previously delivered dose, voxel-by-voxel, taking fractionation effects, tissue recovery and anatomical changes into account. This work presents the workflow and technical solutions in the STRIDeR pathway. METHODS: The pathway was implemented in RayStation (version 9B DTK) to allow an original dose distribution to be used as background dose to guide optimisation of re-irradiation plans. Organ at risk (OAR) planning objectives in equivalent dose in 2 Gy fractions (EQD2) were applied cumulatively across the original and re-irradiation treatments, with optimisation of the re-irradiation plan performed voxel-by-voxel in EQD2. Different approaches to image registration were employed to account for anatomical change. Data from 21 patients who received pelvic Stereotactic Ablative Radiotherapy (SABR) re-irradiation were used to illustrate the use of the STRIDeR workflow. STRIDeR plans were compared to those produced using a standard manual method. RESULTS: The STRIDeR pathway resulted in clinically acceptable plans in 20/21 cases. Compared to plans produced using the laborious manual method, less constraint relaxation was required or higher re-irradiation doses could be prescribed in 3/21. CONCLUSION: The STRIDeR pathway used background dose to guide radiobiologically meaningful, anatomically-appropriate re-irradiation treatment planning within a commercial TPS. This provides a standardised and transparent approach, offering more informed re-irradiation and improved cumulative OAR dose evaluation.

Technical Note: Collimator angle optimization for multiple brain metastases in dynamic conformal arc treatment planning.

PURPOSE: To reduce the exposed area by the multileaf collimator between lesions for single-isocenter dynamic conformal arc (DCA) therapy for stereotactic radiosurgery treatment of multiple brain metastases by optimizing the collimator angle orientation. In particular, this is achieved by the avoidance of collimator angles where multiple lesions are exposed by the same leaf pairs. METHODS: An algorithm that estimates the quality of an arc by considering the target projections onto the plane perpendicular to the central axis of the arc beam. A penalty proportional to the exposure of healthy tissue between metastases is assigned to each control point and each feasible collimator angle from a discretized set of angles. The algorithm can generate two outputs: the fixed optimal collimator angle over all the control points, or the optimal collimator angle trajectory through all the control points considering the rotation speed of the collimator. The first output is based on explicit enumeration of all collimator angles, and the second one generates the optimal trajectory using dynamic programming to find the globally optimal solution with respect to the objective function cost. The algorithm was validated on eight clinical cases having a different number of cranial metastases: two metastases (n = 1), three metastases (n = 5), four metastases (n = 1), and five metastases (n = 1). Plans with optimized fixed collimator angles and plans with optimized dynamic collimator trajectories were compared between each other. RESULTS: When comparing optimal dynamic trajectories to fixed optimal collimator trajectories, the resulting plans demonstrated a total reduction of the exposed area between lesions over the entire beam configuration from 21.7% up to 71.3%; similarly, beam-wise reductions ranging from 5.83% to over 90% have been registered. CONCLUSION: Collimator angle optimization has the potential to reduce the magnitude of the exposed area between lesions in an efficient way for non-isocentric treatments where multiple lesions are treated simultaneously. Dynamic trajectories are capable of limiting the island blocking problem more than optimal fixed trajectories by exploiting the extra degree of freedom of rotating the multileaf collimator. The algorithm can also lead to time saving during the treatment planning process.

Increased accuracy of planning tools for optimization of dynamic multileaf collimator delivery of radiotherapy through reformulated objective functions.

The purpose of this study is to examine in a clinical setting a novel formulation of objective functions for intensity-modulated radiotherapy treatment plan multicriteria optimization (MCO) that we suggested in a recent study. The proposed objective functions are extended with dynamic multileaf collimator (DMLC) delivery constraints from the literature, and a tailored interior point method is described to efficiently solve the resulting optimization formulation. In a numerical planning study involving three patient cases, DMLC plans Pareto optimal to the MCO formulation with the proposed objective functions are generated. Evaluated based on pre-defined plan quality indices, these DMLC plans are compared to conventionally generated DMLC plans. Comparable or superior plan quality is observed. Supported by these results, the proposed objective functions are argued to have a potential to streamline the planning process, since they are designed to overcome the methodological shortcomings associated with the conventional penalty-based objective functions assumed to cause the current need for time-consuming trial-and-error parameter tuning. In particular, the increased accuracy of the planning tools imposed by the proposed objective functions has the potential to make the planning process less complicated. These conclusions position the proposed formulation as an alternative to existing methods for automated planning.

The influence of breathing motion and a variable relative biological effectiveness in proton therapy of left-sided breast cancer.

BACKGROUND: Proton breast radiotherapy has been suggested to improve target coverage as well as reduce cardiopulmonary and integral dose compared with photon therapy. This study aims to assess this potential when accounting for breathing motion and a variable relative biological effectiveness (RBE). METHODS: Photon and robustly optimized proton plans were generated to deliver 50 Gy (RBE) in 25 fractions (RBE = 1.1) to the CTV (whole left breast) for 12 patients. The plan evaluation was performed using the constant RBE and a variable RBE model. Robustness against breathing motion, setup, range and RBE uncertainties was analyzed using CT data obtained at free-breathing, breath-hold-at-inhalation and breath-hold-at-exhalation. RESULTS: All photon and proton plans (RBE = 1.1) met the clinical goals. The variable RBE model predicted an average RBE of 1.18 for the CTVs (range 1.14-1.21) and even higher RBEs in organs at risk (OARs). However, the dosimetric impact of this latter aspect was minor due to low OAR doses. The normal tissue complication probability (NTCP) for the lungs was low for all patients (<1%), and similar for photons and protons. The proton plans were generally considered robust for all patients. However, in the most extreme scenarios, the lowest dose received by 98% of the CTV dropped from 96 to 99% of the prescribed dose to around 92-94% for both protons and photons. Including RBE uncertainties in the robustness analysis resulted in substantially higher worst-case OAR doses. CONCLUSIONS: Breathing motion seems to have a minor effect on the plan quality for breast cancer. The variable RBE might impact the potential benefit of protons, but could probably be neglected in most cases where the physical OAR doses are low. However, to be able to identify outlier cases at risk for high OAR doses, the biological evaluation of proton plans taking into account the variable RBE is recommended.

Incorporation of relative biological effectiveness uncertainties into proton plan robustness evaluation.

BACKGROUND: The constant relative biological effectiveness (RBE) of 1.1 is typically assumed in proton therapy. This study presents a method of incorporating the variable RBE and its uncertainties into the proton plan robustness evaluation. MATERIAL AND METHODS: The robustness evaluation was split into two parts. In part one, the worst-case physical dose was estimated using setup and range errors, including the fractionation dependence. The results were fed into part two, in which the worst-case RBE-weighted doses were estimated using a Monte Carlo method for sampling the input parameters of the chosen RBE model. The method was applied to three prostate, breast and head and neck (H&N) plans for several fractionation schedules using two RBE models. The uncertainties in the model parameters, linear energy transfer and α/ß were included. The resulting DVH error bands were compared with the use of a constant RBE without uncertainties. RESULTS: All plans were evaluated as robust using the constant RBE. Applying the proposed methodology using the variable RBE models broadens the DVH error bands for all structures studied. The uncertainty in α/ß was the dominant factor. The variable RBE also shifted the nominal DVHs towards higher doses for most OARs, whereas the direction of this shift for the clinical target volumes (CTVs) depended on the treatment site, RBE model and fractionation schedule. The average RBE within the CTV, using one of the RBE models and 2 Gy(RBE) per fraction, varied between 1.11-1.26, 1.06-1.16 and 1.14-1.25 for the breast, H&N and prostate patients, respectively. CONCLUSIONS: A method of incorporating RBE uncertainties into the robustness evaluation has been proposed. By disregarding the variable RBE and its uncertainties, the variation in the RBE-weighted CTV and OAR doses may be underestimated. This could be an essential factor to take into account, especially in normal tissue complication probabilities based comparisons between proton and photon plans.

Toward robust adaptive radiation therapy strategies.

PURPOSE: To set up a framework combining robust treatment planning with adaptive re-optimization in order to maintain high treatment quality, to respond to interfractional geometric variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. METHODS: The authors propose robust adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan applied during the first fractions should be able to handle anticipated systematic and random errors. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient having received a dose that does not satisfy specified plan quality criteria, the plan is re-optimized based on these individually measured errors. The re-optimized plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In this study, three different adaptive strategies are introduced and investigated. (a) In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust re-optimization. (b) In the second strategy, the degree of conservativeness is adapted in response to the measured dose delivery errors. (c) In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated treatments are subjected to systematic and random errors that are either similar to the anticipated errors or unpredictably larger in order to critically evaluate the performance of these three adaptive strategies. RESULTS: According to the simulations, robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Moreover, combining robust planning with adaptation leads to improved organ-at-risk protection. In case of unpredictably larger treatment errors, the first strategy in combination with at most weekly adaptation performs best at notably improving treatment quality in terms of target coverage and organ-at-risk protection in comparison with a non-adaptive approach and the other adaptive strategies. CONCLUSION: The authors present a framework that provides robust plan re-optimization or margin adaptation of a treatment plan in response to interfractional geometric errors throughout the fractionated treatment. According to the simulations, these robust adaptive treatment strategies are able to identify candidates for an adaptive treatment, thus giving the opportunity to provide individualized plans, and improve their treatment quality through adaptation. The simulated robust adaptive framework is a guide for further development of optimally controlled robust adaptive therapy models.

Very high-energy electron (VHEE) beams in radiation therapy; Treatment plan comparison between VHEE, VMAT, and PPBS.

PURPOSE: The aim of this study was to evaluate the performance of very high-energy electron beams (VHEE) in comparison to clinically derived treatment plans generated with volumetric modulated arc therapy (VMAT) and proton pencil beam scanning (PPBS) technology. We developed a custom optimization script that could be applied automatically across modalities to eliminate operator bias during IMRT optimization. METHODS: Four clinical cases were selected (prostate cancer, lung cancer, pediatric brain tumor, and head and neck cancer (HNC)). The VHEE beams were calculated in the EGSnrc/DOSXYZnrc Monte Carlo code for 100 and 200 MeV beams. Treatment plans with VHEE, VMAT, and PPBS were optimized in a research version of RayStation using an in-house developed script to minimize operator bias between the different techniques. RESULTS: The in-house developed script generated similar or superior plans to the clinically used plans. In the comparisons between the modalities, the integral dose was lowest for the PPBS-generated plans in all cases. For the prostate case, the 200 MeV VHEE plan showed reduced integral dose and reduced organ at risk (OAR) dose compared to the VMAT plan. For all other cases, both the 100 and the 200 MeV VHEE plans were superior to the VMAT plans, and the VHEE plans showed better conformity and lower spinal cord dose in the pediatric brain case and lower brain stem dose in the HNC case when compared to the PPBS plan. CONCLUSIONS: The automated optimization developed in this study generated similar or superior plans as compared to the clinically used plan and represents an unbiased approach to compare treatment plans generated for different modalities. In the present study, we also show that VHEE plans are similar or superior to VMAT plans with reduced mean OAR dose and increased target conformity for a variety of clinical cases, and VHEE plans can even achieve reductions in OAR doses compared to PPBS plans for shallow targets. With increased VHEE energy, better conformity and even higher reductions in mean OAR doses are achieved. On the whole, VHEE was intermediate between photon VMAT and PPBS for OAR sparing.

Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning.

PURPOSE: To formulate convex planning objectives of treatment plan multicriteria optimization with explicit relationships to the dose-volume histogram (DVH) statistics used in plan quality evaluation. METHODS: Conventional planning objectives are designed to minimize the violation of DVH statistics thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more closely relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. RESULTS: We investigated the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives were generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume was observed in plans optimized within the proposed framework. CONCLUSION: The initial computational study indicates that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives than using the conventional approach.

Inclusion of a variable RBE into proton and photon plan comparison for various fractionation schedules in prostate radiation therapy.

PURPOSE: A constant relative biological effectiveness (RBE) of 1.1 is currently used in proton radiation therapy to account for the increased biological effectiveness compared to photon therapy. However, there is increasing evidence that proton RBE vary with the linear energy transfer (LET), the dose per fraction, and the type of the tissue. Therefore, this study aims to evaluate the impact of disregarding variations in RBE when comparing proton and photon dose plans for prostate treatments for various fractionation schedules using published RBE models and several α/ß assumptions. METHODS: Photon and proton dose plans were created for three generic prostate cancer cases. Three BED3Gy equivalent schedules were studied, 78, 57.2, and 42.8 Gy in 39, 15, and 7 fractions, respectively. The proton plans were optimized assuming a constant RBE of 1.1. By using the Monte Carlo calculated dose-averaged LET (LETd ) distribution and assuming α/ß values on voxel level, three variable RBE models were applied to the proton dose plans. The impact of the variable RBE was studied in the plan comparison, which was based on the dose distribution, DVHs, and normal tissue complication probabilities (NTCP) for the rectum. Subsequently, the physical proton dose was reoptimized for each proton plan based on the LETd distribution, to achieve a homogeneous RBE-weighted target dose when applying a specific RBE model and still fulfill the clinical goals for the rectum and bladder. RESULTS: All the photon and proton plans assuming RBE = 1.1 met the clinical goals with similar target coverage. The proton plans fulfilled the robustness criteria in terms of range and setup uncertainty. Applying the variable RBE models generally resulted in higher target doses and rectum NTCP compared to the photon plans. The increase was most pronounced for the fractionation dose of 2 Gy(RBE), whereas it was of less magnitude and more dependent on model and α/ß assumption for the hypofractionated schedules. The reoptimized proton plans proved to be robust and showed similar target coverage and doses to the organs at risk as the proton plans optimized with a constant RBE. CONCLUSIONS: Model predicted RBE values may differ substantially from 1.1. This is most pronounced for fractionation doses of around 2 Gy(RBE) with higher doses to the target and the OARs, whereas the effect seems to be of less importance for the hypofractionated schedules. This could result in misleading conclusions when comparing proton plans to photon plans. By accounting for a variable RBE in the optimization process, robust and clinically acceptable dose plans, with the potential of lowering rectal NTCP, may be generated by reoptimizing the physical dose. However, the direction and magnitude of the changes in the physical proton dose to the prostate are dependent on RBE model and α/ß assumptions and should therefore be used conservatively.

Development of an intermediate chromatography step in an insulin purification process. The use of a High Throughput Process Development approach based on selectivity parameters.

Recent innovations in designing purification processes for biopharmaceutical production have enabled initial screening (optimization) of chromatographic conditions for binding to be performed in miniaturized batch format. The present report demonstrates the possibility of using this format to screen for selectivity and illustrates the need for careful adjustment of protocols when highly abundant, tightly-binding impurities are present in the sample. This batch format approach was used to choose a chromatography medium (resin) from a selection of available resins for the purification of recombinant insulin expressed in E. coli and to screen binding and elution conditions. Subsequent optimization was performed in small packed columns using a Design of Experiments (DoE) approach with statistical modeling before scaling up to a small pilot scale experiment. In this study insulin was effectively purified from the more tightly-binding C-peptide, and a reduction in insulin variants was also noted using the optimized conditions.

Development and evaluation of an efficient approach to volumetric arc therapy planning.

An efficient method for volumetric intensity modulated arc therapy (VMAT) planning was developed, where a single arc (360 degrees or less) is delivered under continuous variation of multileaf collimator (MLC) segments, dose rate, and gantry speed. Plans can be generated for any current linear accelerator that supports these degrees of freedom. MLC segments are derived from fluence maps at relatively coarsely sampled angular positions. The beam segments, dose rate, and gantry speed are then optimized using direct machine parameter optimization based on dose volume objectives and leaf motion constraints to minimize arc delivery time. The method can vary both dose rate and gantry speed or alternatively determine the optimal plan at constant dose rate and gantry speed. The method was used to retrospectively generate variable dose rate VMAT plans to ten patients (head and neck, prostate, brain, lung, and tonsil). In comparison to step-and-shoot intensity modulated radiation therapy, dosimetric plan quality was comparable or improved, estimated delivery times ranged from 70 to 160 s, and monitor units were consistently reduced in nine out of the ten cases by an average of approximately 6%. Optimization and final dose calculation took between 5 and 35 min depending on plan complexity.

Nucleosynthetic signatures of the first stars.

The chemically most primitive stars provide constraints on the nature of the first stellar objects that formed in the Universe; elements other than hydrogen, helium and traces of lithium present within these objects were generated by nucleosynthesis in the very first stars. The relative abundances of elements in the surviving primitive stars reflect the masses of the first stars, because the pathways of nucleosynthesis are quite sensitive to stellar masses. Several models have been suggested to explain the origin of the abundance pattern of the giant star HE0107-5240, which hitherto exhibited the highest deficiency of heavy elements known. Here we report the discovery of HE1327-2326, a subgiant or main-sequence star with an iron abundance about a factor of two lower than that of HE0107-5240. Both stars show extreme overabundances of carbon and nitrogen with respect to iron, suggesting a similar origin of the abundance patterns. The unexpectedly low Li and high Sr abundances of HE1327-2326, however, challenge existing theoretical understanding: no model predicts the high Sr abundance or provides a Li depletion mechanism consistent with data available for the most metal-poor stars.

Purification of native dehydrin from Glycine Max cv., Pisum sativum, and Rosmarinum officinalis by affinity chromatography.

An improved method for the purification of dehydrin from soy (glycine max) is described. Acidic extraction of soy whey was followed by a three step chromatographic process: capture on copper charged Chelating Sepharose Big Beads, intermediate hydrophobic interaction chromatography on Source 15 PHE, and a polishing step on blue Sepharose. The 32-kDa native soy dehydrin was purified to a purity of greater than 98.5% with an overall recovery of 63%. When compared to a previously published purification procedure, recovery, time requirements, and sample preparation steps were improved. The developed method is readily scaleable. Preliminary results show that the process can be used for dehydrins from rosemary (Rosmarinum officinalis) and pea (Pisum sativum).