Objective: To assess the perceived HIV/AIDS related stigma a comprehensive and well developed stigma instrument is necessary. This study aimed to assess validity and reliability of the Persian version of HIV/AIDS related stigma scale which was developed by Kang et al for people living with HIV/AIDS in Iran.Materials and methods: Thescale was forward translatedby two bilingual academic members then both translations were discussed by expert team. Back-translation was done by two other bilingual translators then we carried out discussion with both of them. To evaluate understandability the scale was administered to 10 Persons Living with HIV/AIDS (PLWHA). Final Persian version was administered to 80 PLWHA in Qom, Iran in 2014. Test–retest reliability was assessed in a sample of 20 PLWHA after a week by intra-class correlation coefficient (ICC).Results: Cronbach’s alpha coefficient for overall scale was 0.85. Also Cronbach’s alpha coefficients for the five subscales were as follows: social rejection , negative self-worth , perceived interpersonal insecurity , financial insecurity , discretionary disclosure . Test–retest reliability was also approved with ICC = 0.78. Correlation between items and their hypothesized subscale is greater than 0.5. Correlation between an item and its own subscale was significantly higher than its correlation with other subscales.Conclusion: This study demonstrate that the Persian version of HIV/AIDS related stigma scale is valid and reliable to assess HIV/AIDS related stigma perceived by people living whit HIV/AIDS in Iran. Still after more than two decades of the pandemic of HIV/AIDS the stigma attributed to this disease remains a significant challenge for HIV/AIDS care programs . People HIV/AIDS-related stigma is not only a violation of human rights, but also contributes to the prevention and control of HIV/AIDS programs by blocking their progress and success . Stigma HIV/AIDS-related stigma and discrimination is established by many factors, including lack of awareness of HIV/AIDS spatially transmission routs, lack of access to treatment, the incurability of AIDS, and prejudices related to certain actions associated with HIV such as sexual activities, substance abuse, addiction, illness and death , 4.In Iran, the status of the HIV/AIDS epidemic has proceeded from low-prevalence into the concentrated phase, so during the past twenty years, the number of patients has grown significantly -13.In IrAlthough it is evident that this stigma is the major obstacle to an effective response to the HIV/AIDS epidemicand HIV/AIDS-related stigma elimination is targeted by international organizations, efforts to reach this goal are not effective. The complexity of HIV/AIDS-related stigma is often known as the main reason for the limited response to the phenomenon . The firIn Iran, a few studies have been conducted to validate the HIV/AIDS stigma scales. SyedAli Naghi et al., translated and used a stigma index to evaluate HIV/AIDS perceived stigma among patients living with HIV/AIDS in six cities in Iran. But that study was not a validity and reliability assessment study . AnotherFife and Wright developed a scale to measure the perceived external and internal stigma in persons living with HIV/AIDS and cancer . The scaIn our study we used the revised scale that consists of 20 items and its good size measures internal and external HIV/AIDS stigma in five subscales: social rejection (9 items), negative self-worth (4 items), perceived interpersonal insecurity (2 items), financial insecurity (3 items), and discretionary disclosure (2 items). In this scale social rejection and financial insecurity subscales represented external stigma; negative self-worth, perceived interpersonal insecurity, and discretionary disclosure represented the internal stigma .This study aimed to translate and validate the Persian version of Kang et al HIV/AIDS related stigma scale for people living with HIV/AIDS in Iran.Forward translationThe translation and validity of the HIV/AIDS stigma scale was conducted in four stages. In the first stage two bilingual nursing lecturers of the Qom University of Medical Sciences translated the scale independently, with one acting as the principle researcher. Then the two versions and original English version were discussed in an expert team to create a Persian version. The expert team members included a lecturer of community nursing, a lecturer of psychiatric nursing, an epidemiology from the Qom University of Medical Sciences, and two mental health counselors and one general practitioner from the Qom Consulting Clinics of Behavioral Disorders. In the panel each item and its two proposed translations were read aloud and experts discussed them and the suggested responses. Based on the experts’ suggestions an initial Persian version was produced.Backward translationThe initial Persian version was back-translated into English by two bilingual translators, who were faculty members of the Qom University of Medical Sciences and had not previously seen the original scale. The principle researcher compared the original version and back-translated version and then by discussions held among the principle researcher and two translators any discrepancy or variations between them were resolved. Some changes were made in the Persian version based on outcomes of those discussions.Understandability of the questionnaire The modified Persian version scale after the backward-translation process was administered to 10 persons living with HIV/AIDS (PLWHA) for evaluating understandability. The instrument was filled out by interviews by a coworker. The interviewer read each item to the participants and asked them about clarity and simplicity of the words and sentences.Psychometric testingThe final Persian version was tested for reliability and validity. A cross-sectional study was conducted on 80 PLWHA who attended the Consulting Clinics of Behavioral Disorders in Qom, Iran from September 2014 to January 2015. Qom Consulting Clinics of Behavioral Disorders is the only clinic that provides services for all PLWHA in Qom and is affiliated with the Qom University of Medical Sciences. All provided services are free. PLWHA who were diagnosed at least one month ago and were at least 18 years old were eligible to participate in our study. Patients who suffered from any major psychological disorder were excluded. Sampling was performed using the convenience sampling method considering limited access to PLWHA. In order to find study participants a member of the research team approached potential patients who attended the Clinic to receive services during the study period and all potential subjects were invited to participation. After receiving informed consent forms from all the participants the subjects themselves filled out the questionnaires. For illiterate participants (two participants) one of our coworkers read the items and possible answers and filled out the questionnaires based on the participants' responses.Psychometric testing was carried out on 80 completed HIV/AIDS-related stigma scale.Ethical considerationThis study was approved by the ethical committee of the Qom University of Medical Sciences (NO: IR.MUQ.REC.1394.91). Participation in this study was voluntary and all questionnaires were anonymous.ReliabilityScale reliability was tested by performing internal consistency analysis. Cronbach’s alpha coefficient was computed for the scale and its five subscales to measure internal consistency. Also test–retest reliability was assessed in a sample of 20 PLWHA after a week by intra-class correlation coefficient (ICC). ICCs ≤ 0. 4 was considered poor to fair, 0.41–0.60 moderate, 0.61–0.80 good, and > 0.80 excellent .ValidityScale validity was tested by assessing convergent and discriminant validity. Pearson’s correlation coefficients were calculated for each item with its own subscale and with other subscales. Convergent validity was passed when correlation coefficients were greater than 0.4. Correlation between an item and its own subscale greater than the correlation between that item and another subscale was considered satisfactory in the test of discriminant validity .For statistical analysis SPSS software version 21 was used.Forward translationEleven items in both forward translations were identical, although there were some differences in the wording of the other items.Expert panelExperts accepted 14 translated items without any changes. Other items were modified based on the panel’s suggestions. Also the team suggested including the neutral option in the response option. Then participants rated their extended agreement with each item by a 5-point Likert type scale. Points were assigned as follows: 1= strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5= strongly agree. Total possible scores ranged from 20 to 100. The higher score showed a stronger sense of feeling stigmatized.Backward translationSixteen items were back-translated almost identically by both translators. After comparing the back-translated items and original version some discrepancies were detected in 10 items. After discussion with the two back-translators, 12 items were approved without any changes and for the other items some revisions were done based on the translators’ comments.Understandability of the questionnaire None of the participants mentioned unclear or difficult words or sentences. Hence, all the items passed the understandability evaluation.Psychometric testingData from 80 completed questionnaires were analyzed. The mean age of the participants was 33.01 ± 7.99 and most participants (57.7%) were male. The majority of participants were never married (50.0%), resided in cities (93.4%), and jobless (51.9%). All most half of the subjects had a high school diploma (46.3%). The prevalent rout of infection (46.3%) was sharing needles. Time post HIV diagnosis in most of the patients (62.5%) was less than one year . Mean score of perceived stigma was 72.04 ± 12.08. Means score of perceived stigma in social rejection, negative self-worth, perceived interpersonal insecurity, financial insecurity, and discretionary disclosure subscales were 33.51 ± 6.95, 12.60 ± 3.87, 8.08 ± 1.81, 9.49 ± 2.89, and 8.36 ± 1.92, respectively. Means score of external and internal perceived stigma were 43.00 ± 8.53 and 29.04 ± 5.34, respectively.Reliability and Validity Cronbach’s alpha coefficient for the overall scale was 0.85. Also Cronbach’s alpha coefficients for the five subscales were as follows: social rejection , negative self-worth , perceived interpersonal insecurity , financial insecurity , and discretionary disclosure . Test–retest reliability was also approved due to a good ICC values (ICC = 0.78).The correlation between items and their hypothesized subscale were higher than 0.5 for all items. The minimum coefficient was 0.53 for item 1 and the maximum was 0.93 for item 19. Correlation between an item and its own subscale was significantly higher than its correlation with other subscales. Also, correlation between an item and scale other than its hypothesized scale were lower than the correlation between that item and its hypothesized scale, so all items passed the test for convergent and discriminant validity .The final Persian version of HIV/AIDS-related stigmascale consists of 20 items and measures internal and external HIV/AIDS stigma in five subscales: social rejection (9 items), negative self-worth (4 items), perceived interpersonal insecurity (2 items), financial insecurity (3 items), and discretionary disclosure (2 items). In this scale social rejection and financial insecurity subscales represented external stigma; negative self-worth, perceived interpersonal insecurity, and discretionary disclosure represented the internal stigma. Participants can rate their extended agreement with each item by a 5-point Likert type scale. Points were assigned as follows: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree. Total possible scores ranged from 20 to 100. The higher score showed a stronger sense of feeling stigmatized.Stigma is the major obstacle to an effective response to the HIV/AIDS epidemic. The complexity of HIV/AIDS-related stigma is often known as the main reason for the limited response to the phenomenon . To asseHence, using both qualitative (process of translation) and quantitative methods (psychometric testing) strengthens the power of findings. In this study we used a multi-method approach in translating the HIV/AIDS related stigma scale and that is one of the main strengths of our study. This approach can decrease sources of bias in the process of translation and adaptation of the questionnaire .Based on translation validity assessment, we translated the original questioner into Persian by two bilinguals. It is important that at least two qualified translators translate independently. This can provide conditions leading to the best translation .Our research team conducted a focus group discussion to examine in detail the Persian version of the questionnaire to ensure the correct meaning of words such as stigma and discrimination and that such words are clearly stated in order to avoid misunderstandings. Our team consisted of two mental health counselors and one general practitioner from the Qom Consulting Clinics of Behavioral Disorders who spend a lot of time with HIV/AIDS patients and so we carried out focus group discussions with these experts in order to obtain valuable and relevant information. It was evident that conducting focus groups with experts helped to remove some words or sentences that were not clear or understandable in the viewpoint of the target group , 25.After back-translation, we carried out discussions with both of the back-translators to reach a version similar to the original one. Also, psychometric properties of our study confirmed usefulness of the translated questionnaire.To assess the internal reliability and construct validity of the HIV/AIDS-related stigma we analyzed data from 80 PLWH. Cronbach’s alpha coefficient for the overall scale was 0.85 and that showed a good internal consistency of our translated version. Also, for all the subscales Cronbach’s alpha coefficients were greater than or equal to 0.7, except for the "perceived interpersonal insecurity" subscale which was 0.57. In Li et al.'s previous studies that used this scale, similar reliability was reached. They reported Cronbach’s alpha coefficient at 0.4 for the "perceived interpersonal insecurity" subscale. This might be due to the fact that the scale contained only two items of this subscale .Our analyses showed the correlation between items and their hypothesized subscale was greater than 0.5. Correlation between an item and its own subscale was significantly higher than its correlation with other subscales. Also, correlation between an item and scale other than its hypothesized scale was lower than the correlation between that item and its hypothesized scale. Hence, all items passed the test for convergent and discriminant validity.The stigma scale with 20-items was used in this study and had good size and so could determine internal and external stigma in the five components. This provided comprehensive information on rejection, negative self-worth, perceived interpersonal insecurity, financial insecurity, and discretionary experienced in patients living with HIV/AIDS.Whereas the stigma tool used by Ebrahimi Kalan et al., was short and in the review of stigma did not have sufficient integrity. Ebrahimi Kalan et al.'sinstrument measured three subscales in HIV-related stigma, but it does not assess internal and external stigma. They suggested future research in Iran that may explore longer versions of the stigma scale that may have meaningful subscales . AnotherIn this study a major limitation was the co-layering of stigma  which was not addressed in our scale.The findings of this study show that the Persian version of the HIV/AIDS-related stigma scale is valid and reliable to assess HIV/AIDS-related stigma perceived by people living with HIV/AIDS in Iran.


We present a proof-of-principle study about the use of a sensor for the nondestructive monitoring of strength development in hydrating concrete. The nondestructive evaluation technique is based on the propagation of highly nonlinear solitary waves (HNSWs), which are non-dispersive mechanical waves that can form and travel in highly nonlinear systems, such as one-dimensional particle chains. A built-in transducer is adopted to excite and detect the HNSWs. The waves are partially reflected at the transducer/concrete interface and partially transmitted into the concrete. The time-of-flight and the amplitude of the waves reflected at the interface are measured and analyzed with respect to the hydration time, and correlated to the initial and final set times established by the penetration test (ASTM C 403). The results show that certain features of the HNSWs change as the concrete curing progresses indicating that it has the potential of being an efficient, cost-effective tool for monitoring strengths/stiffness development. The mixture progressively develops as the hydration process continues and eventually initial set is achieved. The initial set is the time between the first contact of water with the cement grains and the time at which workability is lost. Final set is the time required for the fresh concrete transform from plastic into a rigid state. The initial and final set times are established in accordance with ASTM C403  and corrAt final set, measurable mechanical properties start to develop in concrete and continue to grow progressively. Knowing the rate of strength development at early ages is critical in establishing the timeframe for construction-related activities, such as when to saw joints or to open the roadway to traffic for a newly-placed concrete pavement. Concrete samples are cast so that strength testing can be performed to determine when sufficient strength has been achieved to open the roadway to traffic . The useSince the hydration is an exothermic reaction, the concrete mixture has a specific heat signature. One approach commonly employed for monitoring strength gain in the concrete is to monitor the temperature of the concrete as a function of time. The maturity of the concrete can then be estimated by integrating the area between the temperature-time curve and a datum. Maturity-strength relationships can then be established for a specific mixture so that the strength of the mix can be estimated by monitoring the heat of the mixture as is hydrated. Unfortunately, the maturity-strength relationship is very specific to the concrete mixture proportions for which it was developed. It is no longer representative if the concrete mixture proportions or the source of the constituents within the mixture are varied. The disadvantage of this method is that preliminary testing must be performed to establish this maturity-strength relationship. Also, the relationship must be re-established if at any time the source or quantity of a particular constituent in the concrete mixture is changed. For these reasons, direct but simple nondestructive measurements are needed to monitor the development of the mechanical properties in newly-placed concrete pavements. However, nondestructive measurements can be also utilized to: determine the ultimate strength of concrete or its elastic modulus; establish the time when forms can be removed; decide when prestress can be added to bridges; advance a segmental bridge construction.vice versa. Thus, in order to use both shear and longitudinal waves; at least four transducers are required. If the access to the back wall of the sample is impractical, the wave reflection method can be adopted. In this approach, the amplitude of the shear waves [i.e., as both transmitter and receiver. As the wave speed in the buffer material is constant irrespective of the concrete age; only one wave parameter (the amplitude) can be exploited unless two transducers (one S- and one L-) are used.Nondestructive evaluation (NDE) techniques have been widely adopted over the past two decades for monitoring the development of strength in fresh concrete. Most of these techniques are based on the measurement of the velocity of linear bulk ultrasonic waves propagating through a concrete sample. Traditionally, commercial transducers are used to generate longitudinal –11, or bar waves –19 or thar waves ,21, or bar waves  at an inar waves . The useet al. [Besides the use of bulk waves, other NDE technologies proposed to observe the growth of mechanical properties in concrete are based on guided ultrasonic waves , surfaceet al.  comparedIn this paper we present the proof-of-principle of a novel NDE paradigm to monitor the strength development of hydrating concrete. The method is based on the use of a transducer described for the first time in  able to et al. [et al. [et al. [et al. [et al. [The interaction of the HNSWs with linear systems was studied earlier –38. Mancet al.  studied  [et al.  evaluate [et al.  studied  [et al.  analyzedWith respect to previous works on stress waves for the NDE of concrete, the differences of the proposed approach are: (1) it exploits the propagation of HNSWs in granular systems; (2) it employs a cost-effective actuator/sensor in a combined form; (3) it measures several waves' parameters  that can be eventually used to correlate few concrete variables; (4) it does not require, unlike UPV method, the exact knowledge of the distance between a transmitter and a receiver and does not require the access to the sample back-wall. The proposed HNSW-based method may resemble the Schmidt hammer, which can be used to estimate the hardness and strength of concrete  and rockThe paper is organized as follows. Next section describes the underlying basis of HNSWs following the analytical formulation adopted in ,42. The 2.F of granules and the closest approach δ of particle centers. In A is given by:a is the diameter of the beads, and ν and E are the Poisson's ratio and Young's modulus of the material constituting the particles, respectively.In general, in a chain of spherical particle the interaction between two adjacent beads is governed by the Hertz's law ,44:(1)FSV depends on the maximum dynamic strain mξ [mF between the particles in the discrete chain [0F, the initial strain of the system is referred to as 0ξ. It should be noted that in configurations like the one shown in sV has a nonlinear dependence on the normalized maximum strain rξ = mξ/0ξ, or on the normalized force rf = mF/0F, expressed by the following equation [0c is the wave speed in the chain initially compressed with a force 0F in the limit fr =1, and ρ is the density of the material. When rf (or rξ ) is very large, sV in a “sonic vacuum” can be closely approximated by [x is the coordinate along the wave propagation direction.The combination of the nonlinear interaction  and a zetrain ξm  which, ite chain . When thequation :(3)VS=cmated by :(5)ξ=, and speed are correlated to the progression of the hydration process. When a single pulse reaches the interface with the material to be tested, the pulse is partially reflected. When the pulse interacts with a “soft” medium, secondary reflected solitary waves (SSW) form in the granular crystal, in addition to the primary reflected solitary waves (PSW) ,36,37,40In order to generate and detect the HNSWs a cost-effective transducer recently developed  was usedThe low-carbon steel was chosen in order to ease the movement of the striker by means of an electromagnet which, in turn, was controlled by a National Instrument–PXI unit running in LabView.The position of the sensor beads in the chain takes into account a few factors. First, the consolidation of the HNSWs is complete approximately few beads away from the location of the impact. Second, the sensor bead cannot be located too close to the end of the chain otherwise we would measure the interference of the reflected and incident solitary waves. Third, the distance between two sensor beads should be as large as possible to minimize the relative error associated with the measurement of the spatial distance between the sensors. However, this distance must be kept reasonable to avoid using an unpractical long transducer.3.To prove the feasibility of the proposed NDE method, an experiment was performed in the laboratory on a 15.24 cm by 30.5 cm cylindrical concrete specimen. The mixture for the concrete is summarized in The concrete was mixed at a batch plant approximately seven miles away from the laboratory. The time water contacted the cement was estimated to be around 8:00 a.m. Therefore, one half-hour was added to the duration of the test to account for the travel time from the batch plant to the laboratory. A 40 × 40 × 0.254 mm aluminum sheet and the actuator were placed on top of the specimen five minutes after casting the specimen. A photo of the setup is shown in The experiment began at 10:05 AM, immediately after placing the transducer above the sample. Ten measurements were taken every 15 min for a duration of ten hours. The initial and final set times were established by performing the ASTM C 403 on mortar wet sieved from the concrete sample.i.e., the sensor bead closer to the chain/concrete interface. In both figures, the small value of the standard deviation demonstrates the capability of the transducer to generate repeatable pulses. The penetration resistance as established by the ASTM C 403 is superimposed. The slope of the TOF curves indicates the presence of a two-stage behavior. In the first stage, lasting about 300 min, the TOF values of both the PSW and SSW show a rapid drop. Although the transition between the two stages is close to the time of initial set established with the penetration test, there is not a perfect agreement between the novel and the conventional methodology. It is believed that the sieving process used to extract the mortar sample from the concrete for the penetration test, has different local characteristics with respect to the concrete sample monitored with the solitary waves and therefore some degree of discrepancy should be expected.In order to quantify the effect of aging on some characteristics of the solitary waves, It should be also mentioned that the penetration test measures the penetration resistance and defines the initial and final set using two arbitrarily-chosen values, but the HNSW-based method measures the “effective” stiffness of the concrete sample at least in the local region underneath the transducer.After 300 min, the decrease in the TOF continues with a considerably different rate  until the end of the experiment. Around 485 min (8 h 5 min) there is a slight decrease in the gradient which could be associated with the time of final set, 460 min (7 h 40 min), determined by the penetration test. However this second slope change is not as visible as the first one and additional experiments are necessary to demonstrate if the TOF can be used to identify the final set.In order to minimize any effect associated with the variation of the incident wave speed, the speed of the primary reflected wave was normalized with respect to the speed of the incident wave. The results are presented in By comparing As the concrete gains strength, the contact time decreases. Based upon the values shown in In order to quantify the effect of concrete age on the pulse amplitude, 4.This article shows the working principles of a transducer able to generate and detect highly nonlinear solitary waves (HNSWs) and applied to monitor the hydration of fresh concrete. The transducer consists of a chain of spherical particles with instrumented beads, while an electromagnet is used for the generation and detection of the HNSWs. This transducer is used in a preliminary study aimed at developing a novel nondestructive testing method to estimate the initial and final set of fresh concrete and the temporal strength/stiffness development of the concrete. To prove the feasibility of the methodology, the transducer was used to monitor one concrete sample during hydration. It was found that a single pulse perpendicularly incident to the actuator/cement interface induces two reflected pulses, namely the primary solitary wave (PSW) and the secondary solitary wave (SSW). The amplitudes and the time-of-flight (TOF) of these two reflected waves are found to be strongly dependent on the stiffness of the sample. The trend of the HNSWs characteristics are compared with the penetration resistance obtained from the ASTM C 403 performed on mortar samples sieved from the concrete mixture. As the concrete became stiffer, the TOF of both the PSW and SSW decreased continuously. Two transition points were observed in the trend for the TOF with time. These points approximately corresponded to the times of initial and final set established using the ASTM C403 test.Although the results are encouraging, more experiments are necessary to generalize the proposed methodology and to determine the relationship between HNSW parameters and the mechanical properties of material, which can then be related to penetration resistance from ASTM C403. First of all, the repeatability must be investigated by testing several samples of the same concrete batch and concrete samples with different water/cement ratios. Then, the effect of the spherical particles' size and material on the determination of the concrete properties should be studied to evaluate any effect on the prediction of the initial and final set. For instance, by enlarging the particles size the spatial wavelength increases, and the wave speed and amplitude decrease. By augmenting either the mass of the striker or the precompression force, both the amplitude and the wave speed increase. Future studies may also focus on finding the optimal design of the non-linear medium to maximize the sensitivity of the proposed technology to the changes of the concrete mechanical characteristics. This study would determine, for example, if these transducer parameters affect the set time determined using the HNSW device. Finally, a comparative study between the HNSW-based technology and current methodologies, such as those based on the use of bulk waves and the Schmidt hammer, should be carried out to quantify advantages and limitations of the proposed technique.i.e., close to the actuator, are significantly different than in the far field, the HNSWs-based features may not be representative of the whole structure. Compared to the Schmidt hammer, which performs under a similar principle, the HNSW approach is nondestructive and robust in terms of pulse repeatability. Furthermore, the HNSW approach has more features to exploit and can be used to estimate “soft” materials such as fresh concrete. Overall, the preliminary results discussed show that the proposed technique has promising potential in characterizing the time-dependent strength-development of concrete. In the experiment conducted in this study a thin aluminum sheet was located between the chain of particles and the concrete, to prevent the free falling of the granules into the fresh concrete. Future studies should consider the effect of the sheet thickness on the repeatability and effectiveness of the proposed methodology. Alternatively the tube containing the particles can be tapered at the bottom in order to guarantee the contact of the last particle of the chain and the concrete without the free fall of the granules inside the fresh concrete. Future studies may consider particles of larger diameter or magnetostrictive sensors or piezo cylinders [If the results found in this study are confirmed by the comprehensive studies summarized above, the novel nondestructive approach and the transducer described could provide some advantages over other conventional nondestructive testing methods based on linear ultrasonic bulk waves. In fact, the present approach uses only one transducer (instead of at least two) and does not require accessibility to the back-wall. With respect to the wave reflection method, where only the reflection coefficient is affected by the cement age, the present approach can virtually exploit three parameters: (1) the TOF of the primary reflected waves, (2) the TOF of the secondary reflected waves, and (3) the amplitude of the reflected waves. Moreover the HNSW-based method does not require the use of electronics for the generation of high-voltage input signals, contrary to piezoelectric transducers. It is acknowledged that the method presented in this paper implies that hydration is uniform in the whole material, by providing “effective” materials properties near the surface. If the hydration conditions are such that the mechanical properties of the material in the near field, ylinders  for the


There is an error in the funding statement. Please refer to the correct funding statement below.This work was made possible by support from the President's Emergency Plan for AIDS Relief (PEPFAR) through Cooperative Agreement 5U2GPS001930-05 from the Department of Health and Human Services (DHHS)/Centers for Disease Control and Prevention (CDC), Global AIDS Program. The findings and conclusions included in its content are solely the responsibility of the author (s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry. Publication of this article was funded in part by the Open Access Promotion Fund of the Johns Hopkins University Libraries. This work was supported through technical assistance from the Johns Hopkins Center for AIDS Research (1P30AI094189). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. β-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum β-lactamase (ESBL) producing bacteria has limited the use of β-lactams. Due to the concern of complex drug responses, many β-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some β-lactams. Using quantitative modeling, we show that β-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics. Antibiotic resistance is a growing problem that the World Health Organization describes as “one of the top three threats to global health.” To date, bacteria have developed resistance to all antibiotics used in clinical settings. Unfortunately, the evolution of antibiotic resistant bacteria is accelerating, as antibiotics continue to be misused and overused. As the antibiotic pipeline is drying up, it becomes increasingly critical to utilize the antibiotics already on the market more effectively. The key to designing better regimens lies in the ability to predict how bacteria will respond to a particular antibiotic treatment. Because of this, we need a simple metric that characterizes this pathogen-antibiotic interaction that can be easily measured and used to design dosing protocols that will effectively clear an infection. To help guide the design of effective protocols, we use quantitative modeling to develop a metric that is easy to measure and quantifies the pathogen-antibiotic interaction. Through optimized antibiotic regimens, our strategy could extend the use of first-line antibiotics, improve treatment outcome, and preserve last-resort antibiotics. Bacteria eventually develop resistance to all antibiotics they encounter –3. Unforin vitro, such as the inoculum effect, a phenomenon in which the minimum inhibitory concentration (MIC) of an antibiotic increases as the bacterial density increases . We. We40]. Using physiologically relevant parameters, our model generates PK/PD profiles that are characteristic of Bla-mediated CAT. Starting from a sufficiently high initial density, the population exhibits an initial decline upon antibiotic treatment, followed by eventual recovery due to intrinsic and Bla-mediated degradation of the antibiotic . SufficiFor a fixed initial antibiotic concentration, the model predicts a switch-like dependence of population survival over the initial population density: the population can only survive if starting at a sufficiently high density . If too Our results illustrate the defining features of a CAT bacterial response involving antibiotic-triggered death. In particular, the population will appear resistant when its initial density is sufficiently high and it is given enough time to recover. These features form the basis for the preemptive practice of disregarding β-lactams when an ESBL-pathogen is identified. However, our model also indicates that the population is sensitive when its initial density is sufficiently low or when it is examined in a short time window. Given these properties, we reason that optimal antibiotic dosing may remain effective in eliminating bacteria. If so, an immediate next question is how to best design the treatment protocol.lumped metric based on a bacterial population’s response to a single dose of antibiotic that will allow us to reliably predict its response to periodic antibiotic treatment without needing to know the underlying molecular-level parameters.This task would be straightforward if we could determine the specific molecular mechanisms and defining parameters for each pathogen-antibiotic pair: under such a scenario, we could in theory use a model specific to the pair to examine efficacy of different dosing protocols. This is impractical, however, as many ESBL pathogens are poorly characterized at the molecular level and there are many different ESBL enzymes . A more A typical metric to quantify efficacy of an antibiotic is the minimum inhibitory concentration (MIC), which can be measured by disk diffusion and microbroth dilution methods after a certain duration of antibiotic treatment . Howevera0 > 0.5), the recovery time increases exponentially with the initial antibiotic concentration until the antibiotic concentration is too high (a0 > 10) and the recovery time becomes infinite. This dependence is an intrinsic property of antibiotic-mediated lysis. Under low concentrations of antibiotic (0.5 < a0 < 10), the recovery time is primarily determined by how fast the antibiotic is degraded by Bla. Under increasing concentrations of antibiotic (a0 > 10), the rate of antibiotic degradation is essentially saturated (limited by the population size and the constant production rate of Bla) and the recovery time is primarily determined by the lysis rate. β-lactams’ killing rate is time-, not dose-, dependent and is reflected in the model’s lysis rate’s non-linear dependence on the antibiotic concentration (Hill coefficient = 3) [We first tested the predictive power of the recovery time in injection-based dosing protocols. With the base-parameter set, our model predicts a monotonic dependence of the recovery time on the antibiotic concentrations for single-dose treatment . Once thent = 3) . Once thAs noted above, the recovery time could represent a simple, yet reliable, metric in predicting outcomes from periodic treatment. To test this notion, we examined the consequence of periodic dosing of varying antibiotic concentrations. For each concentration, we varied the dosing periods from 0.1 to 2 times the corresponding recovery time, and obtained the final population density after 100 doses. Our modeling results confirmed the predictive power of the recovery time: as long as the initial antibiotic concentration is sufficiently high to cause significant initial lysis, the population will reach a high final density if the period is greater than the recovery time; the population goes extinct otherwise .-10 , different combinations of antibiotic concentrations and dosing periods eliminate a population with varying efficacy. To quantify this efficacy, we calculated the minimum number of doses necessary to reduce the population density to below 10-10 . The resa0 < 10) to be most effective, the model suggests they should be delivered at low-to-intermediate period lengths (period = 20–50% recovery time) at which the population is most vulnerable. At the end of each period, the bacteria are still lysing, have almost reached minimum density, but have not yet experienced the benefits of Bla. At this point, the antibiotic has not been completely removed; thus the population will be subjected to a slightly higher concentration of antibiotic at each additional dose. If the antibiotic is delivered too frequently, the accumulated antibiotic increases the rate of lysis, thus causing higher amounts of Bla to be released, ultimately leading to the faster removal of the antibiotic. However, Bla cannot fully degrade the antibiotic before the next dose is added and the population quickly dies off. Although the population is cleared, a higher number of doses is necessary because the degree of lysis per dose is not maximized. In other words, subsequent doses are applied before the full extent of lysis from the previous dose is observed. However, if the antibiotic is delivered too infrequently, then the population will have the chance to recover between doses. Once again, these conditions do not maximize the degree of lysis per dose and more doses are necessary to achieve the same amount of population decrease associated with doses applied more frequently.For intermediate antibiotic concentrations (1 < a0 > 10) are associated with fewer doses, they have the highest net antibiotic concentration. These concentrations may not be optimal, due to potential adverse effects associated with using excessive amounts of antibiotic, such as the destruction of the normal microbial flora, interference with the immune response, increased nephrotoxicity, and selection for antibiotic resistant mutants [A final aspect to consider when designing a regimen is the total amount of antibiotic delivered . Althoug mutants ,60–63. A mutants ,35. UsinWe note that the predictive power of the recovery time is maintained for low or moderate inoculum sizes. In particular, our modeling demonstrates that a multi-dose regimen will clear a population if the time between doses is less than one recovery time, regardless of effective antibiotic concentration and inoculum size . SimilarAdditionally, the predictive power of the recovery time is maintained for an antibiotic with dose-dependent killing (Hill coefficient = 1) or an antibiotic with time-dependent killing (Hill coefficient = 10): a multi-dose regimen will clear a population if the time between doses is less than one recovery time, regardless of effective antibiotic concentration and degree of antibiotic-mediated killing .The predictive power of the recovery time can be applied to bacteria with varying rates of Bla synthesis and accumulation as long as the antibiotic concentration applied has an effective recovery time . When thThe predictive power is upheld as long as the recovery times associated with subsequent doses are sufficiently similar to the original recovery time measured from a single dose. Recovery times of subsequent doses depend on two main factors: the activity of Bla in the environment and the concentration of antibiotic. On one hand, if there is insufficient time for Bla to degrade between doses, then it will compound with each dose until the population is being protected by higher concentrations of Bla, relative to when the first dose was administered. As a result, the increasing pool of Bla will degrade the antibiotic faster, the recovery time of subsequent doses will decrease, and the population can recover when dosed at period lengths less than the original recovery time . This woa0 < 1) are ineffective for IV drip treatment because the dose is too weak when delivered over a longer period of time. However, when the dose concentration is sufficiently high, the IV-drip recovery time is longer than the injection recovery time because the IV-drip is exposing the bacteria to a higher concentration for a longer period of time  to 2 times the corresponding recovery time and calculated the resulting final bacterial density. The model shows that the predictive power of the recovery time is maintained when the antibiotic dosing concentration is sufficiently large with a long enough recovery time (a0 > 1.5): a multi-IV-dose regimen will eventually eliminate the population if the dosing period is less than one recovery time, regardless of effective antibiotic concentration , we applied 100 doses of the antibiotic at periods ranging from the infusion duration  require the least doses when delivered at low-to-intermediate period lengths (period = 20–60% recovery time) because that is when the population is most vulnerable. Again, the initial antibiotic concentrations too low to have a recovery time (a0 < 1) do not clear the infection, regardless of the dosing interval or number of doses applied. The concentrations with an infinite recovery time (a0 > 5) require only a single dose and thus the dosing frequency does not matter.We next evaluated the efficacy of each effective concentration-period combination by calculating the minimum number of doses necessary to reduce the population density to below 10-10 . RelativAlthough the number of doses necessary to clear an infection might be the same for a range of antibiotic concentrations and periods, the least amount of total antibiotic is needed for intermediate antibiotic concentrations applied at 20–60% of the associated recovery time .A bacterial population often consists of phenotypically or genetically heterogeneous subpopulations,67. For a0 < 0.3), the recovery time is 0, regardless of the presence of persisters. As antibiotic concentration starts to increase (0.3 < a0 < 26), the population of normal cells starts to undergo the population crash and recovery while the persisters grow and die very slowly (a0 > 26) that would normally kill a population without persisters (a0 > 26) at period lengths > 0.3 recovery time. The persisters growing and lysing 1000fold more slowly are still present at a low density at the end of most of the different regimens tested. Here, the successful regimens are those delivering high concentrations of antibiotic (a0 > 10) with long period lengths (period > 0.65 recovery time)  . Similarry time) . These ra0 < 2.3, both subpopulations recover at similar rates, thus the population as a whole recovers quickly. For 2.3 < a0 < 26, the more resistant subpopulation recovers faster and determines the population level recovery time. For a0 > 26 neither subpopulation can recover. The predictive power is upheld for this mixed population: a multi-dose regimen will clear a population if the time between doses is less than one recovery time, regardless of effective antibiotic concentration and degree of antibiotic-mediated killing , one can readily identify the scenarios in which first- and second-line antibiotics may still be applied and chose the most effective treatment protocol. Whenever a new pathogen arises, it can be evaluated and added to the library.The recovery time of a pathogen under a single dose of antibiotic is a metric that is easy to measure and could guide the choice of an appropriate multi-dose antibiotic regimen for a wide range of infections. Measurements of the recovery time can be carried out in high resolution using commercially available microplate readers . A critiThe ability to predict the outcome of a multi-dose treatment without knowing the underlying resistant mechanism would remove the uncertainty that prevents clinicians from using first-line β-lactams when an ESBL-producing pathogen is detected. Given ESBL-producing bacteria’s prevalence ,83–85, on), antibiotic concentration (a), and Bla concentration (b). Our base model consists of the following ordinary differential equations:g and l represent bacteria growth and lysis, respectively. Initial conditions of n(0) = 0.1, b(0) = 0, and a(0) = 0.01–100 were used for all simulation results, except for n(0) = 0.01 or 0.001. The rest of the parameters are defined in a table in The interaction between a β-lactam and a bacterial population expressing Bla can be simplified to the interactions between three main components: population density Click here for additional data file.S1 Fig Time courses for populations with initial densities that are 10x and 100x smaller than the base model.  Recovery time curves for populations with initial densities that are 10x and 100x smaller than the base model.  Final density depends on dosing frequency. Despite the lower initial densities, both models followed the trend where periods less than one recovery time eliminate the population as long as the initial antibiotic concentration is sufficiently high to cause significant initial decline. This indicates that the recovery time is a viable tool for predicting treatment outcomes for a range of population sizes.  Dose number necessary to reduce a population below critical threshold depends on antibiotic concentration and period length. The fewest number of doses corresponds to the antibiotic concentrations with the longest recovery times; however, intermediate concentrations can be effective when applied at low to intermediate period lengths.  Total antibiotic concentration delivered depends on single dose concentration. The regimens applying doses of lower concentrations of effective antibiotic will eliminate the population just as effectively as the regimens using high concentrations, but with less total antibiotic.(PDF)Click here for additional data file.S2 Fig Recovery time depends less on antibiotic concentration if the Hill coefficient (H) is high enough. When H = 1, the recovery time is dose dependent, increasing as the antibiotic concentration increases. When H = 10, the recovery time quickly transitions from being 0 to infinite (the population has been wiped out). Once past the threshold, increasing the antibiotic concentration will not continue to increase the recovery time.  Final density depends on dosing frequency. Despite the different Hill coefficients, both models followed the trend where periods less than one recovery time eliminate the population as long as the initial antibiotic concentration is sufficiently high to cause significant initial decline. This indicates that the recovery time is a viable tool for predicting treatment outcomes for a range of antibiotics with different modes of killing (time vs. dose dependent).(PDF)Click here for additional data file.S3 Fig(A-E) No Bla production. (A) Time course. The recovery of bacteria that do not produce Bla depends on the intrinsic removal of the antibiotic caused by natural degradation and turnover by the body’s fluid. (B) Recovery time curve. Because the bacteria still undergo the process of lysing before recovering, the dosing protocol based on recovery time is still applicable. Without the production of Bla to aid in the recovery of the population, the recovery time is longer and monotonically dependent on the antibiotic concentration. (C) Final density. Protocols using periods of less than one recovery time are effective at eliminating the population, regardless of antibiotic concentration. (D) Dose number. For each antibiotic concentration and period combination, the corresponding number of doses necessary to eliminate the population was calculated. The regimens using antibiotics associated with longer recovery times require the fewest doses; however, low concentrations of antibiotic (a0 ≈ 1) can be effective if applied at period lengths of 0.10–0.50 period/RT. (E) Total antibiotic delivered. Despite many different regimens requiring the same number of doses to clear an infection, the regimens could be differentiated by the total amount of antibiotic delivered. When comparing regimens with the same number of doses, the amount of antibiotics delivered decreases as the dose concentration decreases. (F-J) Low inducible Bla production. (F) Time course. The population recovers faster than a population that does not produce Bla because it generates sufficient Bla to degrade the antibiotic; however, the recovery time is slower than a population that constitutively produces Bla because more bacteria lyse before sufficient Bla accumulates to effectively remove the antibiotic. (G) Recovery time. The recovery time increases as the initial antibiotic concentration increases. (H) Final density. Protocols using periods of less than one recovery time are effective at clearing an infection, regardless of antibiotic concentration. (I) Dose number. A larger range of antibiotic concentrations require the lowest number of doses because the population is more sensitive to lower antibiotic concentrations, relative to constitutive producers. (J) Total antibiotic delivered. Despite the higher number of doses, the least amount of antibiotic is delivered for regimens using lower antibiotic concentrations. (K-P) High Inducible Bla Production. (K) Time course. Increased Bla production results in less lysis, faster antibiotic degradation, and, consequently, a faster population recovery. (L) Recovery time curve. The non-monotonic dependence reflects the generation of the Eagle effect: a higher antibiotic concentration can generate faster population response. (M) Final density. Protocols using periods of less than one recovery time are effective at clearing an infection, regardless of antibiotic concentration. (N) Dose number. The dose number is highly complex in terms of the dependence on the initial antibiotic concentration and period length. The dependence on the recovery time, however, appears simpler: the fewest number of doses is needed for antibiotic concentrations with the longest recovery times that are delivered at intermediate-to-long dosing frequencies. Concentrations producing the longer recovery times require fewer doses because they can reduce the bacterial density more severely than concentrations with shorter recovery times. (O) Total antibiotic delivered. Despite the higher dose numbers, the least antibiotic is delivered for regimens applying low concentrations of antibiotics at intermediate-to-long periods. (P-T) High constitutive Bla production. (P) Time course. Here, bacteria constitutively produce Bla at a rate an order of magnitude greater than the bacteria that constitutively produce low amounts of Bla. As a result, the bacteria produce sufficient Bla such that the private Bla present in the periplasm can provide sufficient protection against the antibiotic. As such, the overall population growth rate is always greater than the lysis rate; thus the population will always increase in density. (Q) Recovery time. Because the bacteria produce such high amounts of Bla, the population experiences no or little initial decrease in density. Thus, the recovery time is close to 0 until stronger antibiotic concentrations are applied. (R) Final density. Protocols using periods of less than one recovery time are effective at clearing an infection, as long as the antibiotic concentration induces a sufficiently long recovery time (see (S) Dose number. The shorter periods are associated with increased number of doses because there would be less time between when cells lyse and the Bla will not have the chance to naturally degrade before the next round of lysis adds more Bla to the environment. Higher concentrations and longer period lengths result in lower dose numbers. (T) Total antibiotic delivered. The least amount of antibiotic delivered is associated with regimens using lower dose concentrations and intermediate-to-long periods. This supports the notion that longer periods allow for the large amounts of Bla to degrade before the next dose, thus allowing each dose to be as effective as possible. (U-Y) High constitutive Bla production, IV-drip treatment. (U) Time course. The antibiotic was infused into the system over 3 time units, resulting in a sustained high concentration that led to a sustained decline in the population density. The Bla produced was insufficient to effectively degrade the antibiotic while it was being infused, so the population could not start to recover until after the infusion had ended. (V) Recovery time. A higher concentration of antibiotic was necessary to elicit a recovery time. Relative to the population constitutively producing Bla that was treated with an injection regimen, the IV-drip treatment produced longer recovery times. (W) Final density. IV-drip therapy results support the predictive power of the recovery time for populations constitutively producing high levels of Bla. Doses delivered at periods less than 1 recovery time clear the infection whereas doses delivered at periods longer than 1 recovery time fail. (X) Dose number. The fewest doses are required for regimens using high antibiotic concentrations delivered at intermediate-long periods. (Y) Total antibiotic delivered. The least amount of antibiotic was delivered for regimens delivering the intermediate concentrations of antibiotic at intermediate-to-long periods.time see . (S) Dos(PDF)Click here for additional data file.S4 Fig(A-B) Population recovers at periods < 1 recovery time. (A) Final Density. Populations can recover from an antibiotic applied at periods less than one recovery time if the bacteria are producing extreme levels of Bla and the antibiotic concentration is too weak to induce sufficient lysis (i.e. has a short recovery time). If the recovery time is too short, then the time between doses is too short for the Bla to return to a baseline level. As a result, the net Bla compounds with each subsequent dose, allowing populations to survive an antibiotic applied at periods less than one recovery time. (B). Time curves for cell density, Bla, and antibiotic concentration. Because each subsequent dose of antibiotic causes more cells to lyse and release Bla before the Bla from the previous dose is degraded, there is an increase in the base amount of Bla always present. As a result, the cells can clear the antibiotic faster on subsequent doses compared to the first dose. Consequentially, the observed recovery time from the multi-dose regimen is actually shorter than the expected response time calculated from the single dose. Here, A = 1.4 and period = 0.60 RT. (C-D) Population fails to recover at periods = 1 recovery time. (C) Final Density. Populations fail to recover from a low antibiotic concentration applied at periods less than one recovery time if the bacteria are producing low levels of Bla. Because the antibiotic concentration is low, the recovery time and its derived dosing periods are short. The time between doses is insufficient for the Bla to degrade enough of the antibiotic to recover. As a result, the antibiotic concentration compounds with each subsequent dose, preventing populations from recovering at periods greater than one recovery time. (D). Time curves for cell density, Bla, and antibiotic concentration. With each subsequent dose of antibiotic, more cells lyse and release Bla; however, the cells produce insufficient Bla to degrade the current dose of antibiotic before the next dose is delivered. As a result, the antibiotic accumulates with each subsequent dose and the cells are killed. Here, A = 0.63 and period = 1.33 RT.(PDF)Click here for additional data file.S5 Figpg ≪ Ng, Pl ≪ Nl), and, when the antibiotic concentration is low enough (a < 1σ), they are generated from and revert to a normal cell phenotype at the slow rates of Pκ and Nκ, respectively. Population with persisters that grow and lyse at rates 100 times more slowly than normal cells (A-E). (A) Time course. The bacteria lyse due to the antibiotic, release Bla to degrade the antibiotic, and then recover once the antibiotic concentration is low enough. At this point, persisters (dashed black line) are generated from and return to the normal cell population (solid black line). Here, the initial density of persisters and normal cells are 0.00001 and 0.1, respectively. (B) Recovery time. The recovery times are the same between the population containing persisters (black line) and the population containing no persisters (grey line) until. a0 > 0.3. From 0.3 < a0 < 26, both populations take longer to recover with increasing antibiotic concentration; however, the population containing persisters recovers slightly faster. When a0 > 26, then the population without persisters fails to recover, whereas the population with persisters is able to re-establish the population. (C) Final Density. After 100 doses of antibiotic, the final total density was measured. Periods greater than 1 recovery time resulted in the full recovery of the population; however, periods less than 1 recovery time appeared to suppress the population’s recovery. (D) Dose number. For each antibiotic concentration and period combination, the corresponding number of doses necessary to reduce the total population density to a critical threshold was calculated. The regimens using antibiotics associated with longer recovery times and longer periods require the fewest doses. (E) Total antibiotic delivered. Despite many different regimens requiring the same number of doses, the regimens could be differentiated by the total amount of antibiotic delivered. When comparing regimens with the same number of doses, the amount of antibiotics delivered would decrease as the dose concentration decreased. Population with persisters that grow and lyse at rates 1000 times more slowly than normal cells (F-J). (F) Time course. Similar to (A). (G) Recovery time. Similar to (B). (H) Final Density. Similar to general trend of (C), where final densities are high when using periods > 1 recovery time and are low otherwise. However, many of the low final densities achieved after 100 doses with periods < 1 recovery time are close to, but not exactly, 0. This means that once the regimen is completed and sufficient time has passed, the population will be able to regrow. (I) Dose number. For each antibiotic concentration and period combination, the corresponding number of doses necessary to reduce the total population density to a critical threshold was calculated. The regimens using antibiotics associated with longer recovery times and longest periods require the fewest doses. The regimens using lower concentrations of antibiotic (a0 < 10) and/or shorter dosing periods (period < 0.65 recovery time) were not successful at reducing the population below the threshold after 100 doses. This is due to the presence of persisters. (J) Total antibiotic delivered. The total amount of antibiotic delivered for each regimen was calculated and suggests that an injection based regimen would need very high concentrations of antibiotic to sufficiently reduce a population with persisters. Schematic for how a population with persisters responds to antibiotic. Black represents bacterial actions, blue represents Bla actions, and red represents antibiotic actions. Arrows denote induction or activation; T-lines indicate inhibition; the dashed arrow represents the ability for the model to simulate inducible or constitutive Bla production.Our model assumes that persisters form a small fraction of a population, grow and lyse at rates much slower than normal cells ((PDF)Click here for additional data file.S6 Fign1 and n2) with different levels of antibiotic resistance. n1 has the same parameter values as the base case from the homogeneous model; however, n2 has increased thresholds for antibiotic effects. Particularly, n2 requires higher concentrations of antibiotic to inhibit growth (σ5 = 5σ1) and induce lysis (σ6 = 5σ2). Although this model accounts for two distinct subpopulations, it could be extended to multiple populations displaying some degree of collective antibiotic tolerance. Subpopulation can switch between states (A-E). (A) Time course. Both subpopulations start at the same starting density (0.05) and start to lyse due to antibiotic. One subpopulation (n2) is more resistant than the other (n1), with thresholds for growth inhibition (σ5) and lysis (σ6) 5 times higher. Both populations contribute Bla to degrade the antibiotic. Once the antibiotic concentration is sufficiently low, then the subpopulations can start to recover. Since n1 and n2 can switch between states, n2 can help n1 recover under concentrations that would otherwise be lethal. (B) Recovery time curve. Because both populations still undergo the process of lysing before recovering, the dosing protocol based on recovery time is still applicable. When 0.3 < a0 < 2.3, both n1 and n2 are recovering at similar rates, thus the population as a whole recovers quickly. Once 2.3 < a0 < 26, then n2 recovers faster and determines the population level recovery time. When a0 < 26, then both subpopulations cannot recover. (C) Final density. Protocols using periods of less than one recovery time are effective at eliminating the population, regardless of antibiotic concentration. (D) Dose number. For each antibiotic concentration and period combination, the corresponding number of doses necessary to eliminate the population was calculated. The regimens using antibiotics associated with longer recovery times require the fewest doses; however, lower concentrations of antibiotic (10 < a0 < 26)can be effective if applied at period lengths of 0.10–0.50 period/RT. (E) Total antibiotic delivered. Despite many different regimens requiring the same number of doses to clear an infection, the regimens could be differentiated by the total amount of antibiotic delivered. When comparing regimens with the same number of doses, the amount of antibiotics delivered would decrease as the dose concentration decreased. Subpopulations cannot switch between states (F-J). (F) Time course. Same as in (A) except n1 and n2 do not switch back and forth. Thus the more resistant subpopulation, n2, is selected to recover. (G) Recovery time curve. Same as in (B). (H) Final density. Same as in (C). (D) Dose number. Same as in (D). (J) Total antibiotic delivered. Same as in (E). Schematic for a mixed population’s response to antibiotic. Black represents bacterial actions, blue represents Bla actions, and red represents antibiotic actions. Arrows denote induction or activation; T-lines indicate inhibition; the dashed arrow represents the ability for the model to simulate inducible or constitutive Bla production.The model represents a mixed population with equal starting densities of two subpopulations ((PDF)Click here for additional data file.


MET amplification, are predictive of response to Met-specific therapy in preclinical models. Immunohistochemistry (IHC) of formalin-fixed paraffin-embedded (FFPE) tissues is currently used to select for ‘high Met’ expressing tumors for Met inhibitor trials. IHC suffers from antibody non-specificity, lack of quantitative resolution, and, when quantifying multiple proteins, inefficient use of scarce tissue.Overexpression of Met tyrosine kinase receptor is associated with poor prognosis. Overexpression, and particularly MET gene copy number (GCN)/nucleus or MET/CEP7 ratio by fluorescence in situ hybridization (FISH).After describing the development of the Liquid-Tissue-Selected Reaction Monitoring-mass spectrometry (LT-SRM-MS) Met assay, we evaluated the expression level of Met in 130 FFPE gastroesophageal cancer (GEC) tissues. We assessed the correlation of SRM Met expression to IHC and mean 418TEFTTALQR426 as the optimal SRM peptide. Limits of detection (LOD) and quantitation (LOQ) for this peptide were 150 and 200 amol/µg tumor protein, respectively. The assay demonstrated excellent precision and temporal stability of measurements in serial sections analyzed one year apart. Expression levels of 130 GEC tissues ranged . IHC did not correlate well with SRM  nor FISH GCN . A Met SRM level of ≥1500 amol/µg was 100% sensitive (95% CI 0.69–1) and 100% specific (95% CI 0.92–1) for MET amplification.Proteomic mapping of recombinant Met identified MET tumors. These results demonstrate a novel clinical tool for efficient tumor expression profiling, potentially leading to better informed therapeutic decisions for patients with GEC.The Met SRM assay measured the absolute Met levels in clinical tissues with high precision. Compared to IHC, SRM provided a quantitative and linear measurement of Met expression, reliably distinguishing between non-amplified and amplified  MET gene amplification and consequent overexpression is an ‘oncogenic driver’ in a subset (∼5%) of gastric and esophageal adenocarcinomas MET mutations have been rarely reported in various hereditary and sporadic cancers including gastroesophageal adenocarcinomas (GEC) MET GCN and Met over-expression MET amplification Hepatocyte growth factor receptor (HGFR), commonly known as Met, is a membrane receptor that possesses tyrosine kinase activity IHC analysis of formalin-fixed paraffin-embedded (FFPE) tissue sections is routinely used for measuring Met expression in patient samples. While IHC has proven useful over the past several decades, mass spectrometry (MS) methods that measure the absolute levels of proteins with high specificity for clinical application are rapidly emerging Liquid-Tissue-SRM has been developed which is a MS-based technology platform that measures the absolute abundance of targeted proteins in patient-derived FFPE tissue MET gene copy number (GCN) and MET/CEP7 ratio as determined by fluorescence in situ hybridization (FISH). Finally, we sought to define a SRM expression level cut-off that would optimally distinguish between Met expression consistent with MET gene amplification as compared to expression without MET gene amplification.In this study we present a Liquid Tissue-SRM assay for measuring the absolute abundance (amol/µg) of Met expression in FFPE tumor tissues ungoing laser microdissection. We tested the reproducibility across sample replicates as well as between different instrument platforms and operators. We also tested Liquid-Tissue-SRM Met levels in serial tissue sections re-analyzed one year after the initial testing; lack of temporal reproducibility is a recognized limitation of IHC. We then applied the developed assay to measure Met levels in 130 GEC FFPE tumors , and results were correlated with Met expression as either an H-score or as a binary (positive/negative) score as determined by IHC, as well as 418TEFTTALQR426 provided the most reproducible peak heights, retention times, chromatographic ion intensities, clean elution profile, and distinctive/reproducible transition ion ratios; therefore, this peptide was selected for clinical assay development. SRM transitions used for the quantification of Met was selected based on the representative MS fragmentation spectrum for peptide 418TEFTTALQR426 (2 = 0.9998 (y = 0.9627x+80.025) for the assay standard curve when all 8 spiking concentration points were included (2 = 0.9989 (y = 1.1391x–7.7041) when excluding the highest point of 25000 amol (inset). The standard curve showed linearity and low variations over the 2 orders of magnitude concentration range tested. The chromatographic profile of the light and heavy versions of the peptide TEFTTALQR are shown in m/z 533.78 and 538.78, respectively and three fragment ions per precursor were chosen for the identification and quantification of Met. The fragment ions for the light and heavy peptides and their corresponding optimized collision energy are m/z 588.35 (19V)/689.39 (20V)/836.42 (19V) and 598.35 (19V)/699.39 (20V)/846.47 (19V), respectively.For development of the Met Liquid Tissue-SRM assay, multiple peptides obtained from a tryptic digest of recombinant Met were measured using MS. The resulting three candidate peptides, NLNSVSVPR, GDLTIANLGTSEGR, and TEFTTALQR, were then extensively screened in multiple formalin-fixed cell lines and FFPE clinical samples. The peptide To test assay precision, Met was measured in 9 human NSCLC and 11 human GEC FFPE tissues. The precision study was conducted on two different LC-MS systems (System R and System S) operated by two different scientists. Five of the nine NSCLC samples and four out of eleven GEC tissues showed Met levels above the LOD. The levels of Met in these tissues ranged from 275.5–2401.2 amol/µg. The CVs for these measurements ranged from 1.5–17.7%. A plot Figure 32) between the two sets of measurements was 0.998 when all five cell lines were compared, and 0.716 when excluding MKN45, a MET gene amplified line with extremely high expression. ( inset). Overall there was good correlation of the measurements provided by Liquid Tissue-SRM and ECL.The levels of Met in five different cell lines  were measured using both Liquid Tissue-SRM and electrochemiluminescence (ECL) immunoassay. The results obtained using the two methods are plotted against each other in  inset table). The correlation coefficient between these two groups of samples was 0.893, showing that the Liquid Tissue-SRM process provides reproducible results for FFPE samples that have been previously sectioned up to 13 months prior to analysis.IHC is known to be very sensitive to preanalytical variables, including fixation time, warm and cold ischemia time, and the age of tissue sections. To test the robustness the SRM assay, Met protein expression was measured on tissues dissected from sections cut from ten NSCLC tumor blocks, where the first microdissection/analysis occurred immediately after sectioning and the subsequent microdissection/analysis from the adjacent serial sections occurred 13 months later. A plot comparing the Met levels detected in these paired tissues is shown in In a separate experiment, the levels of EGFR in A431 xenografts fixed for various lengths of time (4 hour-1 week) ranged from 2–3.01 fmol/ug; similarly, EGFR expressions ranged from 2.3–3.0 fmol/ug when compared between immediate and 1 hour-delay fixation. The CVs for these measurements ranged from 3.7–24%. This demonstrated the excellent assay robustness regardless of fixation time (4 hour–1 week), as well as prefixation cold ischemia (1 hour immediately before fixation).Table S1). The Met levels in the tumors were above the LOD in 45/130 (34.6%) samples with the levels in these samples ranging from 215.88 to 4669.5 amol/µg  . We first assessed 22 GEC cell lines and 1 lymphoblast control line for MET GCN by FISH, and Met expression by SRM . Met levels were detected in 16/22 (72.7%) GEC lines, and ranged from 150 to 6290 amol/µg. FISH confirmed three previously reported MET amplified GEC lines  MET amplification status. This preliminary cut-off was therefore used to evaluate 30 GEC tissue samples prospectively.Met SRM results were compared with those of MET FISH in a subset (n = 30) of the total 130 GEC cohort (Table S3). Twenty-three samples (23/30) had mean MET GCN per nucleus >2. Among these elevated GCN samples, 7 tumors (23%) had MET gene amplification (ratio MET:CEP7 ≥2 and GCN≥4); the high rate of MET gene amplification was due to intentional enrichment in this 30 sample cohort with patients having previously established MET amplification by FISH. The Met SRM result for the same 30 cases is plotted against MET:CEP7 ratio (red) or MET GCN (blue) (2) between the two sets of measurements in all 30 samples were 0.651 for SRM and MET:CEP7 ratio, and 0.774 for SRM and mean MET GCN per nucleus (figure not shown). While Sample 24 had a MET:CEP7 ratio of 1.18, it demonstrated high polysomy with a mean 7.35 copies of MET per nucleus, corresponding with higher expression compared to low polysomy/disomy samples, but lower than MET amplified samples. Sample 23 represented a primary gastric cancer; only ∼20–30% of the tumor cells revealed clustered gene amplification (MET:CEP7 ratio 14.93) (sample 23a), while the remainder of tumor cells were low polysomic (sample 23b) . Although the gene amplified subclonal area demonstrated a mean 41.8 copies of MET per nucleus, the remaining non-amplified tumor cells were also microdissected during processing for MS analysis based tumor cells observed by H&E staining. This tumor molecular heterogeneity, therefore, likely led to a lower ‘diluted’ SRM Met read. This explanation was verified by examining the metastatic lymph node of this patient (Sample 23c), where the hypothesis of possible clonal selection of the MET amplified cells from the primary tumor to the lymph node was evaluated. The metastatic lymph node (Sample 23c) did indeed reveal a mean of 15.33 MET copies per nucleus (ratio MET:CEP7 6.31) with 100% of the scored tumor cells having elevated MET GCN. The SRM detected a high amount of Met (3836 amol/µg) .We performed N (blue) Figure 7MET:CEP7 ratio showed high correlation R2 = 0.898; SRM and MET GCN per nucleus had R2 = 0.829  and SRM Met measurement. SRM identified GEC lines and samples having MET amplification with 100% (10/10) sensitivity (95% CI 0.69–1) and 100% (43/43) specificity (95% CI 0.92–1).Overall, the pooled data from the cell lines and GEC tissues (using 23c) demonstrated high concordance between Figure S3), which under-appreciated the expression intensity level differences between MET amplified and non-amplified lines. We then assessed the subset (n = 44) of the total 130 GEC cohort having both Met IHC and SRM. A comparison between the two in terms of any expression revealed a correlation coefficient R2 = 0.54 . Using an H-score of ≥10 and SRM level of ≥LOD (150 amol/ug) to define ‘Met+’, there was high discordance between the two assays . Using an H-score of ≥150 and FISH ratio of ≥2 with GCN≥4 as ‘amplified’, there was discordance between the two assays (2 = 0.48 (FISH ratio) and R2 = 0.51 (FISH GCN) . Using FISH as the gold standard, IHC identified 6 of 8 FISH amplified tumors resulting in a sensitivity of 75% (95% CI 0.35–0.97). IHC identified tumors with H-score ≥150 in 5 FISH negative samples resulting in specificity of 78.3% (95% CI 0.56–0.83). In summary, using an IHC H-Score of ≥150 to discern MET amplification status resulted in a sensitivity of 75% and specificity of 78%. Altering the H-score cut-off could increase the sensitivity at the expense of specificity, or vice versa. H-score was out-performed by SRM in the ability to accurately discern MET amplification status in this study.We assessed a subset of 31 GEC samples with both Met IHC and Table S6). A stronger correlation of SRM with FISH MET/CEP7 ratio (R2 = 0.89) was observed compared to SRM with IHC H-Score (R2 = 0.68) (MET/CEP7 ratio (R2 = 0.89) compared to IHC H-Score with FISH (R2 = 0.53) ( B).We assessed the correlation of IHC, SRM and FISH in 24 GEC FFPE tissues that had been tested using all three methods .Archival FFPE sections are extremely valuable resources for translational clinical studies and for the validation of clinical utility for companion diagnostics. However, IHC analysis of previously cut tissue sections is limited, deeming its usefulness, reliability, and clinical significance on archival FFPE slides compromised over time. There was observed decrease in the reactivity for p53 in FFPE tissue sections that had been cut from a paraffin block and stored at room temperature for only two months Discordant result provided by older slides is a major concern, particularly due to the increasing importance of retrospective diagnostic tissue analysis. It is estimated that more than one billion FFPE tissue blocks are archived in tissue banks worldwide. Since the first description of formalin fixation and storage was described more than a century ago, this approach has become the technique of choice for preservation of tissue. Most importantly, in many cases previously cut sections are the only form in which remaining archived FFPE tissue is stored. Archiving the tissue should enable retrospective biomarker studies of various cancers; however, the temporal stability of IHC epitopes is unsatisfactory, with unreliable results. RNA expression analysis via next-generation sequencing (NGS) is also subject to wide variability, often lacking concordance with protein expression even in the best of circumstances, let alone when evaluating aged FFPE tissue sections Simultaneous IHC analyses of multiple samples concurrently using tissue microarrays (TMAs) can be used to increase assay throughput, yet will often analyze tissues that have been stored for several weeks/months after sectioning from their paraffin block. Adjusting the IHC protocols for fresh or stored slides  may offer a potential solution; however, this strategy eliminates the study’s consistency and introduces the presence of potentially undetectable artifacts that may arise by altering the protocol for a selected group of sections. Using different antibodies has also been suggested as a possible remedy to this problem. Different antibodies are known to provide highly variant results, even when used on identical slides In our study we showed that Liquid Tissue-SRM results obtained at least one year apart of cut tissue sections are highly reproducible, providing a more robust platform for conducting both proteomic biomarker studies on archival samples. The robustness of Liquid Tissue-SRM results compared to IHC results is most likely due to the fact that the SRM analyte is a linear tryptic peptide instead of a three-dimensional epitope. As such, the SRM assay is less susceptible to preanalytical handling issues by heating the tissue to 95 degrees, followed by trypsinization. In addition to the age of cut sections, we have also assessed time of fixation (4 hour to 1 week) and cold ischemia (1 hour) prior to fixation. In each case, SRM results were similarly not impacted by these factors.We then demonstrated a strong correlation with ECL expression and SRM for Met in FFPE cell lines. Next, analysis of 130 GEC samples using Liquid Tissue-SRM showed that Met at any level was detected in approximately 35% of the tumors. This rate of Met expression is similar to other studies, where Met ‘overexpression’ by IHC ranged from 23.7% to 70% MET gene amplification have been shown to be both prognostic and predictive in preclinical and early phase clinical trials MET amplified tumors (eg. TKIs) while other inhibitors may be more efficacious in MET non-amplified tumors (eg. HGF ligand antibodies). Therefore, a multiplex analysis using SRM, including Met and other relevant proteins, that is able to provide a linear expression level of Met, as well as validated cut-off levels consistent with gene amplification, would be an ideal ‘one-time’ clinical test for economical use of scarce tissue. Moreover, the level of gene amplification and consequent level of overexpression may also be important with respect to prognostic and predictive benefit, as recently was shown for HER2/Her2 MET GCN and MET/CEP7 ratio in cell lines and FFPE tissues, while IHC poorly correlated with SRM and FISH analyses. We demonstrated that ≥1500 amol/µg of Met had excellent (100%) sensitivity and specificity in all samples evaluated to detect MET FISH amplification, and further validation of this cut-off is ongoing prospectively in an independent large patient tumor cohort. One case (23a) was initially determined to be negative for Met expression by SRM and IHC, as well as non-amplified by FISH, NGS, and array comparative genomic hybridization (aCGH) (data not shown). However, a lymph node metastasis of this patient was shown to be highly amplified by both SRM and FISH, suggesting that the primary tumor harbored a subclonal population of MET amplified cells which seeded the lymph node metastasis. This intra-tumoral molecular heterogeneity underscores the need to molecularly profile lesions (metastases) that will ultimately become the most detrimental to the patient. This occurred in only 1 of 7 (15%) identified MET amplified FFPE samples; the true rate at which this observed tumor evolution occurs with respect to MET amplification is an area of active research in our laboratory. Efforts to biopsy metastatic lesions for molecular profiling may help limit biomarker misclassification due to intra-patient tumor molecular heterogeneity through space MET amplified tissues, was observed with high polysomy samples (eg sample 24), representing a subset of samples having expression levels less than 1500 amol/µg but more ∼1000 amol/µg. Thus, ‘gene dosage’ clearly corresponded with SRM-MS Met expression levels in the cell lines and FFPE samples evaluated. Prospective evaluation of the prognostic and predictive properties of each of the observed expression groups  will be valuable.Gene copy number (GCN) elevation, and more specifically MET with corresponding clinical relevance (prognostic and/or predictive) in a timely, tissue-economic, and cost-efficient manner will prevail.One limitation of this study is the use of a polyclonal anti-MET antibody for IHC testing. An ongoing study evaluating currently available monoclonal anti-MET antibody companion diagnostics will evaluate the performance of those antibodies compared to SRM and FISH assays. Another limitation is the current lack of correlation of the three assays  with clinical outcome data (prognostic or predictive); this analysis is also ongoing. Despite a promising randomized phase II trial MET amplification as a surrogate for ‘Met-addicted’ tumors. Additionally, the ‘GEC-plex’ is incorporated as an integral diagnostic test, along with targeted NGS, to prospectively assign GEC patients into predefined molecular subsets in the planned PANGEA clinical trial As biomarker discoveries and the establishment of their clinical relevance continue, it will be critical that the companion diagnostic assays be accurate but also expandable. While assays used to determine biomarker expression will be performed using many different technologies , this expansion must not only preferably include the ability to multi-plex additional important analytes within the assay All research involving human tissue samples was approved by the University of Chicago Review Board (IRB16146B and IRB16294A) and conducted in accordance with these protocols and the Declaration of Helsinki. Written informed consent from the donor or next of kin was obtained in accordance with the tissue procurement protocols.2 containing approximately 45,000 malignant cells was microdissected by laser microdissection from each FFPE cancer specimen and cultured cell lines.Tumor tissue was obtained from patients with gastroesophageal cancer (GEC) from the University of Chicago  dating between 1999 and 2013 (N = 130). Of the samples evaluated, 63% were from curative intent resection specimens of the primary tumor, 23% were from endoscopic biopsy of the primary tumor via upper endoscopy, 7% were from intraoperative biopsy of peritoneal carcinomatosis, and 7% from ultrasound-guided core needle biopsy of metastatic lesions . Surgically resected NSCLC tumor tissue (N = 19) was provided by Dr. Ignacio Wistuba and Dr. Jaime Rodriguez-Canales (MD Anderson Cancer Center). For measurement of SRM Met abundance, tissue sections or FFPE cultured cell sections (10 µm) were cut from blocks onto Director slides and deparaffinized for microdissection as described previously The human GEC lines were obtained and cultured as previously described 2+/Mg2+ deficient PBS, cells were collected and pelleted. Cells were resuspended in fibrinogen from human plasma  in complete media and clotted using thrombin (bovine origin) . After overnight formalin fixation, the cell clots were paraffin embedded and mounted on slides in 4µm sections for IHC, as described below.Cells were collected and prepared in clots according to the procedure developed by Dr. Elizabeth Hyjek in the Department of Pathology at the Unversity of Chicago (unpublished) for formalin fixation and paraffin embedding for use in immunohistochemical (IHC) studies. After washing cells in Cahttp://blast.ncbi.nlm.nih.gov/Blast.cgi), and sequence analysis using Phosphosite.org. Light (TEFTTALQR) and heavy  versions of this peptide were synthesized to develop and perform the assay . SRM transitions used for the quantification of the light Met peptide were 533.78/588.35 (y5), 689.39 (y6), and 836.42 (y7) (Q1/Q3) and the transitions used for the heavy internal standard were 538.78/598.35 (y5), 699.39 (y6), and 846.47 (y7) (Q1/Q3).For development of the Liquid Tissue-SRM assay, recombinant Met protein (UniProtKB accession number P08581) was digested with trypsin and the resultant peptides analyzed using a TSQ Vantage triple quadrupole mass spectrometer  equipped with a nanoAcquityLC system . Software programs Pinpoint 1.0 and Xcalibur 2.1  were used to identify the optimal tryptic peptides for SRM analysis based on reproducible peak heights, retention times, chromatographic ion intensities, and distinctive/reproducible transition ion ratios. Peptides containing methionine or cysteine-residues were excluded due to their propensity to undergo unpredictable oxidation. The peptide TEFTTALQR, comprising residues 418–426 within the protein’s extracellular domain, was found to be unique to Met by comparing this sequence to the entire human proteome using the BLASTP function within the BLAST search engine (The following mass spectrometer conditions were used for the SRM assays: Q1(FWHM); 0.4, Q3(FWHM):0.7, dwell time; 10 ms. Peptides were loaded onto a 75 µm inner diameter (i.d.)×1 cm IntegraFrit  column and separated analytically using a 100 µm i.d.×12 cm PicoFrit  column slurry packed in-house with Jupiter Proteo reversed phase particles . Peptides were eluted into the mass spectrometer using the following gradient: load onto pre-column for 3 min with buffer A (0.1% formic acid) at a flow rate of 5 ul/min and eluted with buffer B  using a step gradient at 800 nl/min. Buffer B was increased from 1–9% (2 min), 9–15% (6 min), 15–25% (4 min), 25–50% (2 min), and 50–95% (1 min). Finally, the column was cleaned with buffer B for 3 min and equilibrated with buffer A for 13.5 min.−18 mol)  added to the proteome extracted from human breast epithelial SK-BR-3 cells (ATCC HTB-30). Light peptide was added to the eight distinct lysates prepared from formalin-fixed SK-BR-3 cells to provide a final concentration ranging from 150 to 25000 amol (10−18 mol) Table 1.The absolute abundance of SRM Met in each sample was calculated by measuring the area under the curve (AUC) for the endogenous and heavy standard peptide. The concentration of endogenous Met peptide was calculated using the following formula:*Summation of peak area for 3 transitions from endogenous or heavy peptide.**Quantity of spiked heavy internal standard (amol) injected.***Quantity of total protein injected.Light and heavy AUCs were exported from Pinpoint 1.1. Correlation linear regression coefficients were estimated using Excel. Patient samples were analyzed in triplicate, and results were charted as mean ± standard deviation (represented by error bars).MET amplification were determined using STATA 12.1 software.Confidence intervals of sensitivity and specificity for identifying To demonstrate the precision of the assay, nine human NSCLC and eleven human GEC FFPE tissues were analyzed independently by two different operators on each of two platforms (“System R” and “System S”) on different days. “System R” was comprised of a nanoAcquity LC coupled to a TSQ Vantage mass spectrometer and “System S” was comprised of a Thermo Easy nLC II coupled to a separate TSQ Vantage mass spectrometer. Each sample was run in triplicate on both systems.The concentrations of Met in five cell lines  were measured using both Liquid-Tissue-SRM and an electrochemiluminescence (ECL) immunoassay. For the Liquid-Tissue-SRM study, the cells were cultured and formalin-fixed (FF) for 5 minutes. Lysates were extracted and tryptically digested from the FF cells using the Liquid-Tissue protocol and reagents. After addition of the Met internal standard, the samples were analyzed using SRM as described above. For the ECL immunoassay measurements, lysates were prepared from unfixed cells. ECL immunoassay of Met content was performed as described in Tissue sections were cut from 10 NSCLC tumor blocks and microdissection via laser microdissection performed to collect cancer cells. Liquid Tissue lysates were immediately prepared from dissected cells from all three sections according to the manufacturer’s recommendations . Total protein content for each Liquid Tissue lysate was measured using a Micro BCA assay . For SRM-MS analysis, 1 µg of the total protein extracted via laser microdissection from the tumor was injected onto the column. The MS and chromatography conditions were identical to those described above. Each sample was analyzed in triplicate. Approximately 13 months later, dissection was performed on the serial tissue sections and Liquid-Tissue lysates were prepared from each microdissected cell population. Met levels were measured by SRM-MS as described above.Liquid-Tissue lysates were prepared from GEC tumors microdissected from 130 FFPE tissues according to the manufacturer’s recommendations . Total protein content for each Liquid-Tissue lysate was measured using a Micro BCA assay , blinded to IHC and FISH results. After addition of 5 fmol of heavy isotopically labeled internal standard to the sample, 1 µg of the total protein was injected onto the column. The MS and chromatography conditions were as described above. Each GEC Liquid-Tissue lysate was analyzed in triplicate.MET/CEP7 probe mixture containing homebrewed MET DNA  and alpha-satellite DNA clone pZ7.5 (centromere enumeration probe for chromosome 7) labeled with SpectrumOrange and the SpectrumGreen dUTPs, respectively, using the Nick Translation Kit  MET gene copy number (GCN) per nucleus, the mean CEP7 GCN per nucleus and the MET/CEP7 ratio were reported. FISH MET gene amplification in this study was defined as both MET/CEP7 ratio ≥2 and GCN≥4. Both criteria were required to be met to rule out samples with MET/CEP7 ratio ≥2 merely due to isolated loss of CEP7. These criteria were intended to be more stringent than using only the ratio, and were based on previous reports for MET GCN assessment and clinical correlation Dual-color FISH assay was conducted, scored and interpreted on GEC cell lines and FFPE GEC tissues as previously described CEP7, centromere enumeration probe for chromosome 7) ) labeled with SpectrumOrange and the SpectrumGreen dUTPs respectively using the Nick Translation Kit .Alpha-satellite DNA clone pZ7.5  was also performed at higher cut-offs, where positive (MET+) was defined as any staining intensity ≥1+ in i) ≥25% of tumor cells , or ii) ≥50% of tumor cells , as each of these is currently being assessed in ongoing clinical trials as potential IHC scoring systems of predictive benefit to anti-Met therapy.Figure S1MET amplified cell lines are red bars and double starred (**).Quantification of Met SRM-MS (amol/ug protein of sample loaded) for 22 GEC lines and a lymphoblast control line (GM15677*). (PDF)Click here for additional data file.Figure S2MET FISH. The primary tumor (top row) was comprised of 75% IHC Met negative cells (GEC23b) and 25% Met positive (GEC23a), which correlated with FISH gene copy number (insets). The metastatic lymph node  showed Met expression in 100% of cells, and tumor cells were all amplified by MET/CEP7 ratio >2 (Met IHC low and high power). (see Correlation of IHC Met levels in the heterogeneous tissue sample #23 with (PDF)Click here for additional data file.Figure S3MET amplified lines  the difference of Met expression with non-amplified lines is under-appreciated by IHC, in contrast to levels observed with Mass Spectrometry (SRM-MS) (Met IHC (bottom rows) for gastroesophageal cancer (GEC) cell line paraffin embedded pellets. For (SRM-MS) . RON tyr(PDF)Click here for additional data file.Figure S4Correlation of Met levels using Liquid Tissue-SRM and Met H-score by IHC in 44 GEC tumors. Inset: comparison of SRM and Met H-score in 8 GEC tumors where Met expression level ≥1500 amol/mg.(PDF)Click here for additional data file.Figure S5MET gene amplification by FISH in 31 GEC tumors. The left y-axis (blue diamond) represents the MET copy number per nucleus and the right y-axis (red square) indicates MET:CEP7 ratio.Correlation of Met H-score and (TIF)Click here for additional data file.Table S1Levels of SRM Met observed in 130 gastroesophageal cancer FFPE tissues.(DOCX)Click here for additional data file.Table S2MET GNC detected by FISH in GEC cell lines.Met protein level detected by SRM and (DOCX)Click here for additional data file.Table S3MET GNC detected by FISH in 30 GEC FFPE tissues.Met protein level detected by SRM and (DOCX)Click here for additional data file.Table S4Met expression by IHC and SRM in 44 GEC FFPE tissues.(DOCX)Click here for additional data file.Table S5MET GCN by FISH in 31 GEC tissues.Met expression by IHC and (DOCX)Click here for additional data file.Table S6MET GCN by FISH in 24 GEC tissues.Met expression by IHC, SRM and (DOCX)Click here for additional data file.