Making decisions on plant breeding programs require plant breeders to be able to test different breeding strategies by taking into account all the crucial factors affecting crop genetic improvement. Due to the complexity of the decisions, computer simulation serves as an important tool for researchers and plant breeders. This paper describes ADAM-plant, which is a computer software that models breeding schemes for self-pollinated and cross-pollinated crop plants using stochastic simulation. The program simulates a population of plants and traces the genetic changes in the population under different breeding scenarios. It takes into account different population structures, genomic models, selection (strategies and units) and crossing strategies. It also covers important features e.g., allowing users to perform genomic selection (GS) and speed breeding, simulate genotype-by-environment interactions using multiple trait approach, simulate parallel breeding cycles and consider plot sizes. In addition, the software can be used to simulate datasets produced from very complex breeding program in order to test new statistical methodology to analyze such data. As an example, three wheat-breeding strategies were simulated in the current study: (1) phenotypic selection, (2) GS, and (3) speed breeding with genomic information. The results indicate that the genetic gain can be doubled by GS compared to phenotypic selection and genetic gain can be further increased considerably by speed breeding. In conclusion, ADAM-plant is an important tool for comparing strategies for plant breeding and for estimating the effects of allocation of different resources to the breeding program. In the current study, it was used to compare different methodologies for utilizing genomic information in cereal breeding programs for selection of best-fit breeding strategy as per available resources. The goal of most plant breeding programs is to hybridize and select best elite lines or varieties with the best combination of desired characteristics, viz., yield-attributing traits, quality traits, and insect and pest resistance . The suc1, such as QuLine, QuHybrid, and QuMARs are capable to simulate simple to complex genetic models mimicking line breeding programs, including conventional selection, MAS and GS  and cross-pollinated crop plants and for application of new technologies such as speed breeding. It was developed from an existing computer program named ADAM, which can simulate a large range of breeding programs for animals using stochastic simulation . ADAM-pl(1)It allows users to simulate overlapping breeding cycles with a new cycle starting at each time step. In this paper, each time step represents a reproductive step with different actions such as selection, testing or replication being carried out at specific time steps. The simulation of overlapping breeding cycles allows early generations in one cycle to be used as parents in a new cycle before the parental cycle is finished.(2)It allows users to simulate different breeding strategy  for different stage of the breeding program. For instance, there are many options for different actions in each time step i.e., selected plants can be genotyped, phenotyped, reproduced and/or have germplasm stored.(3)It allows different units of selection. The selection unit can be population, within family, or entire family. The family can be full-sib family, half-sib family or defined by a group of parents  in an earlier generation.(4)It allows accounting for plot size (number of plants grown), that can be used for mimicking different planting density in different generations.This paper describes the simulation method and working process of ADAM-plant in different plant breeding applications with an emphasis on its main characteristics, component elements and computational performance using a couple of examples of wheat-breeding programs.An overview of ADAM-plant working pipeline is presented in Figure (1)The genetic model, which is the method used to generate breeding values, is specified by the user.(2)A founder population is simulated if genomic model with LD between QTLs and markers is used. This creates desired LD in the genome. This founder population is used as the basis for subsequent stages.(3)The selected population is generated. It initiates with a base population that is assumed unrelated based on the pedigree and followed by selection in subsequent generations. The user needs to specify the number of replicates of the selected population, selection strategy used for each selection stage and the type of propagation used for seed reproduction. If genomic model is used, the simulation of the selected population can initiate from the same or a unique founder population, depending on the choice made by the user.(4)The output variables are recorded and analyzed. Not all output variables are required in any simulation. The user specifies what to output  in each time step. These results are also summarized when more than one replicate have been obtained. If desired all the data generated on the molecular as well as phenotypic level in each time step can also be saved for further analysis.There are mainly two models available to generate breeding values for single and multiple traits.(1)Infinitesimal model, mimicking a polygenic makeup.(2)Genomic model with LD between QTLs and markers where individual QTL and markers are simulated.When a genomic model is used, it is necessary to simulate founder population or read in individual-level genome-wide SNP/sequence data either collected from breeding programs under investigation or simulated. This step is skipped when an infinitesimal model is used.Npat) and maternal (Nmat) founder plants, the number and length of chromosomes (Nchrom), the number of loci (Nloci) on each chromosome and the number of founder generations (founderNG). Expansion and contraction of the founder population are allowed in order to generate different LD structures e.g., long/short-range LD (NGfounder discrete generation producing (Npat + Nmat) offspring. In the first generation of founder population, a total of Nloci are evenly distributed across Nchrom genomes. The user specifies N so that every Nth locus harbors a QTL that coded for the trait under selection. The remaining loci are genetic markers. The user needs to specify the mutation rate, which is the probability of mutation occurring at each locus of genotype. Bi-allelic polymorphism at each locus is generated with this specified mutation rate. An additive effect for the mutant allele at each QTL is sampled from an exponential distribution. The additive effects are assumed to be negative probability 0.9 by default because the exponential distribution only generates positive values. The additive effects of the wild-type alleles are zero. Selection is introduced by culling and resampling 5% of plants with lowest TBV by default. The TBV of the ith plant in the founder population, gi , is calculated as nQTL is the number of QTLs across the genome, xij is the number of copies of the mutant allele that plant i inherited at jth QTL , and ig is the additive effect of mutant allele at jth QTL. Each offspring inherits marker and QTL alleles from their parents following Mendel’s rules allowing for recombination. ADAM-plant samples the number of crossovers from a Poisson distribution with mean number of crossovers equal to the length of the chromosomes in Morgan and then the crossovers are placed randomly along the chromosome. Alternatively, the user can also generate and store their genome of the founder population externally or use real genome and import them into ADAM-plant. The founder plants in generation founderNG or the stored genome are pooled for the subsequent sampling of base population.When a genomic model is used, a founder population is generated by default using a Fisher-Wright inheritance model in order to establish recombination-drift-mutation-selection equilibrium and create initial LD among markers and QTLs . The matrange LD . The plafounderNG of the founder population. For chromosome k (k = 1…Nchrom), two chromosomes are randomly sampled without replacement from the kth pool of chromosomes. The sampled chromosomes are replaced before the next base plant is sampled. When the plants in a base population are assumed inbred lines (self-pollinated), then the second haplotype of a base plant is set to be identical to the first haplotype. The user needs to define the genetic variance h2 , the user may have prior knowledge about heritability of plot phenotypes  from the first parental lines to the final selection of a new elite line, but other crops may have a different length of a breeding cycle. The base population in the first n - 1 cycles are established by randomly sampling two copies of the genome of all the base plants from the founder population. From cycle n onward, however, the base population is built using the germplasm stored from user-specified generation(s) in the previous cycles  of interest and number of plots grown per family in a user-specific selection stage. For instance, if there are three observations for a trait, then there are three plots for each family.The user needs to specify the economic values for each trait to calculate true aggregate-breeding value. The aggregate-breeding value is calculated by weighting TBV of each trait by its economic value. The user can also specify different economic values on each trait in each selection stage.Selection is carried out in each time step. Selection is carried out on single or multiple traits by threshold selection or truncation selection. Threshold selection is based on the phenotypic observation(s) with associated threshold. Truncation selection is based on one of the following criteria: phenotype, BLUP (based on phenotype and pedigree information), GBLUP, single-step GBLUP when only parts of plants are genotyped or phenotyped  or Bayest by maximizing the function tU:ADAM-plant also allows users to carry out optimum-contribution selection (OCS), which maximizes long-term genetic gain while constraining inbreeding by constraining the relationship among selected parents . Optimumc is a n vector  of genetic contributions to the new cohort and the number of matings allocated to each candidate is a linear function of these contributions, L is the generation interval, v is a k vector of expected relative contributions to future age-class, P is a n × k matrix of contributions to future age-class of plants in the current generation, A is a n × n matrix of additive–genetic relationships or genomic relationships The phased haplotypes and genotypes of the simulated individuals for each chromosome, the position and allele frequencies of the QTLs and the markers, and the pedigree can be stored for each generation and each time step. This yields the opportunity to create simulated datasets from full scale and complex breeding programs e.g., to test new evaluation methods or models and to test methods for estimation of population parameters etc., In addition, it enables the user to develop summary statistics currently not included in the standard set of output.(2)Estimated breeding values of every single plant can be stored if EBV is predicted for certain generation and time step.(3)The average TBV and phenotypes for each trait can be stored for each trait, for each generation and each time step.(4)Realized variance within- and between- families (families are specified by the user) can be calculated for each trait, for each generation and each time step.(5)Accuracy of selection can be recorded if selection is performed at certain generation and time step. If the selection unit is population or within family, the accuracy is either the correlation between TBV and EBV under selection on EBV or the correlation between TBV and phenotypes under PS. If selection is on entire families, the accuracy is calculated as the correlation between the sum of TBV of individuals in the family and the sum of EBV/phenotypes in the family.(6)Mean inbreeding computed based on pedigree information and, optionally, inbreeding based on IBD markers.A log file is written and updated as the program is running. The log files show the detailed information of simulation process  at each time step.The user decides what genetic variables are saved. Not all outputs are required in any simulation. When all the replicates are complete, the mean and the standard deviation of all the genetic variables are also written. Plots showing development in means and standard deviations are written to pdf files as well.http://adam.agrsci.dk.ADAM-Plant is written in Fortran 95. The program makes use of other programs for specific procedures: “Randlib90” to generate random numbers  and DMU In the next section, the current study showed three examples of commercial wheat-breeding programs using a finite-locus model. The founder population for these programs is established based on stored real dataset containing 988 individual genomes collected from a commercial plant breeding company . Data arFor breeding plan A, the current study simulated a 25-year commercial wheat-breeding program using phenotypic selection Figure . The gen(1)Parental lines P0: For the first seven cycles, 20 parental lines (P0) were randomly sampled from the founder population. For the remaining 18 cycles, the 20 P0 were sampled from the genotypes of selected F5, F6, and F7 in the previous cycles, assuming the germplasm of all the selected individual plants at these three generations were available. The 20 parental lines were allowed to be randomly crossed with each other, and the maximum times of crosses that one individual could be used were four. In total, a smaller subset of 50 out of possible 190 crosses were kept to produce F1.(2)F1: In total, 50 F1 were generated. In reality, a single cross can actually produce a number of F1 individuals that are genetically identical. Therefore, for simplicity, only a single F1 individual was simulated for each cross. These 50 F1 were self-pollinated to produce 10 seeds (F2) each. From F2 to F4, the families were derived by selfing from their common ancestors at F1 generation and were denoted as F1-families.(3)F2: Within family selection was conducted on F2. Within each of the 50 F1-families, out of 10 F2, the five highest-ranking individuals were selected based on BVP. The self-pollination of each selected F2 produced 30 seeds, resulting in 7500 F3 seeds in total.(4)F3: Entire family selection was conducted based on the YP performance of F3 individuals in each F1-families. Each F1-family was assumed planted in three plots, and therefore, three replicates were simulated. In total, out of 50 families, 45 families with highest-ranking YP were selected and were self-pollinated to produce 60 seeds (F4) per F1 family (20 F4 per plot).(5)F4: Within family selection was conducted for F4. Within each of the 45 F1-families, five highest-ranking individuals were selected based on BVP. So 225 F4 were selected in total. From now on, these F4 were used to create lines of single seed descent. Each F4 was self-pollinated to generate 20 seeds, resulting in 4500 F5. From F5 to F8, the selection were all based on the F4-lines.(6)F5: Line selection was conducted based on the YP performance of all F5 individuals in each F4-line. Only one replicate was simulated for each F4-line. In total, 75 out of 225 lines with highest YP were selected and were self-pollinated to produce 900 F6 per line (100 F6 per plot). The germplasm of the 75 selected lines were stored and potentially become parental lines for the next cycles.(7)F6: Line selection was conducted based on the total YA performance of all F6 individuals in each F4-line. Nine replicates were simulated for each F4-line, which means that each F6 line was grown in 9 plots. In total, 30 out of 75 lines with the highest YA were selected and were self-pollinated to produce 900 F7 per line (100 F7 per plot). The germplasm of the 30 selected lines were stored and they could potentially become parental lines for the next cycles.(8)F7: Line selection was conducted based on the total YA performance of all F7 individuals in each F4-line. Nine replicates were simulated for each F4-line, which means that each F7 line was grown in 9 plots. In total, 5 out of 30 lines with the highest YA were selected and were self-pollinated to produce 900 F8 per line (100 F8 per plot). The germplasm of the five selected lines were stored and could potentially become parental lines for the next cycles.Figure For breeding plan B, a 25-year commercial wheat-breeding program using GS was simulated. The first 10 years was used as a burn-in stage, where PS was used as in breeding plan A without GS. The breeding strategy for this burn-in stage was the same as in breeding plan A. In the last 15 years, the selection decisions in F1 to F3 was the same as in breeding plan A. The current study present the breeding strategy of breeding plan B for F4–F7 at year 11–25 where the difference existed between breeding plan A and B Figure :(1)F4: The breeding strategy of F4 for GS was the same as for PS with the exception that all the 225 individual F4 were assumed genotyped in each cycle. The genotyped germplasm of F4 with phenotypes for the targeted traits were added yearly to establish the reference population (growing by 225 genotypes per year) for the current cycle where the selection is performed Figure .(2)F5: Line selection was conducted based on the YP performance of all F5 individuals in each F4-line. Only one replicate was simulated for each F4-line. YP performance of all F5 individuals within each F4-line was recorded, so that there were 225 phenotypes. These 225 phenotypes and 225 F4 genotypes and existing reference population were used to estimate breeding values of each F4 using GBLUP model , so that(3)F6: Line selection was conducted for F6 based on the YP performance of all F5 individuals in each F4-line. Nine replicates was simulated, and therefore, each F4-line was grown in 9 plots. Similar to F5, total YA performance of all F6 individuals in each F4-line was recorded, so that there were 75 phenotypes. These 75 phenotypes and their corresponding F4 genotype were used for EBV estimation. In total, 30 out of 75 F6 lines with high GEBV were selected and were self-pollinated to produce 900 F7 per F4-line (100 F7 per plot). The germplasm of the 30 selected lines were stored and potentially become parental lines for the next cycles.(4)F7: Line selection was conducted based on the total YA performance of all F7 individuals in each F4-line. Nine replicates were simulated for each F4-line, which means that each F7 line was grown in 9 plots. Similar to F5 and F6, five out of 30 lines with the highest GEBV were selected and were self-pollinated to produce 900 F8 per line (100 F8 per plot). The germplasm of the five selected lines were stored and potentially become parental lines for the next cycles.The comparison of genetic gain from breeding plan A with that obtained from breeding plan B is given in Figure For breeding plan C, a 25-year speed-breeding program for wheat using GS was simulated. The breeding program was similar to breeding plan B with the exception that the first 4 generations (F1-F4) were achieved within 1 year instead of 4 years.The comparison of genetic gain from breeding plan C with that obtained from breeding plan A and B is given in Figure The rate of genetic gain after the burn-in stage was the highest in breeding plan C with SPB (0.28–0.46), which was 2–2.5 times higher than breeding plan A with PS and was 7–20% higher than Brreding plan B with GS Table . The totAll the three breeding plans were replicated 60 times and performed on a Linux server. The running time for each replicate was 2 h 15 ∼2 h 30 min for breeding plan A and B, and 6 h 40 min for breeding plan C.ADAM-plant is a computer software for stochastic simulation of plant breeding programs that utilize diverse genomic and phenotypic information. It was developed with a purpose of guiding breeding decisions in early planning and implementation phases. To maximize its utility, it was developed based on effective models of the genome and the breeding process with great flexibility. It can simulate easy to very complicated breeding programs that are similar to commercial breeding plans (with GS and/or speed breeding technologies), and the best breeding strategy can be identified. Based on the results from simulation experiments, breeders have the opportunity to optimize their breeding methodology, and use of resources  and greatly improve breeding efficiency.(1)There are a number of computer software packages available for simulating plant breeding programs. However, ADAM-plant has several advantages over the others: It allows users to simulate overlapping breeding cycles with the possibility of a new cycle starting at each time step. The simulation of overlapping breeding cycles allows information and elite genetic material transfer from one breeding cycle to another. It means that early generations in one cycle can be used as parents in a new cycle. This system resembles the procedure of actual commercial breeding programs for crop plants. Adam-Plant also enables simulation of breeding programs in competing companies, which use a proportion of parental lines from the other companies.(2)It allows simulation of speed breeding with flexibilities in defining generation intervals in order to test the effectiveness of selection in early generations and to quantify genetic progress and genetic variance using different designs for speed breeding. It is also possible to test the potential for integrating speed breeding with GS in accelerating the rate of genetic improvement in simulated crop breeding programs.(3)It allows storing the advanced germplasm in any generation and these germplasm can be used for later cycles. This function makes it more flexible for selection of parental lines.(4)There are more options for phenotyping, genotyping strategies, selection and mating strategies. For instance, optimum contribution selection or minimum co-ancestry mating can be carried out for cross-pollinated crops such as maize in order to constrain inbreeding while ensuring high genetic progress by managing the distribution of genetic contributions to the selected candidates.(5)It allows different units for selection. The selection unit can be population, within family, or entire family, in which the family can be defined with great flexibility e.g., a set of parents or a set of plants used in a poly-cross to create synthetics) in an earlier generation.(6)Adam-plant allows great flexibility in mating strategies as it allows crossing of individuals that are in different generations or in different selection units. For instance, ADAM-plant allows backcrossing by storing the germplasm of one of the parent and crossing this parent to its offspring in the generation where selection is performed, it allows three way cross by crossing an inbred line to an F1, it allows crossing of two single crosses that come from four separate inbred parents and it allows crossing between the individual plants within a pre-defined unit i.e., full-sib cross or half-sib cross.For the three examples presented, breeding plan A represented a traditional breeding program in wheat. Breeding plan B represented a modern breeding program, which is a combination of conventional breeding techniques and genomic tools leading to a new genomics-based plant breeding. Breeding plan C represented a new technology for rapid generation advance named “speed breeding,” which has been successfully deployed in bread wheat .The comparison between breeding plans A and B indicated that even adding 2000 genotypes per breeding cycle could result in 2 times higher genetic gain. Phenotypic selection resulted in higher genetic variance mainly due to less fixation of favorable alleles with less accurate selection. Compared with breeding plan 2, breeding plan 3 maintained more genetic variance as well as resulted in higher genetic gain because the parent lines were updated more rapidly. In the current study, real haplotypes were used as parental lines considering the real LD patterns in a commercial wheat-breeding program, and the schemes simulated were similar to current commercial breeding programs. Therefore, these results indicate that combining speed breeding with GS is a very promising tool in plant breeding. When the speed breeding and GS are considered, the initial facilities investment such as a growth chamber with appropriate supplemental lighting and temperature control capabilities can be substantial . HoweverADAM-plant is fast in generating simple breeding programs and genotype data. However, the time consumed can become significantly larger in particular when a large number of plants and complex breeding strategies are simulated. The computation time also depends on the utilization of genomic information and on the genetic model used. With polygenic model where no sequence data is generated, the running time is short even with complex selection steps and large number of seeds. When a very precise prediction is required with consideration of full genomic information, however, the time is markedly increased due to sampling of molecular information  and so on for each single plant. For example, one replicate of a 40 year – commercial GS breeding program with 40 overlapping breeding cycles takes around 23 h for running .The software was developed with modules, which makes it easy to be extended with new methodologies for example on how to utilize genomic information. Although the examples presented were wheat breeding programs, ADAM-plant can also be used for simulating many other plant species such as outcrossing crop maize and diploid ryegrass. In the future, the breeding program design for tetraploid plants will be integrated in the software. Dominance effects that are important for cross-pollinated crops and epistasis effects that are important for self-pollinated crops will also be integrated.In conclusion, ADAM-plant is a flexible and efficient computer software for stochastic simulation of breeding plans for crop plants. ADAM-plant simulate real commercial breeding program structures with parallel breeding cycles, GS and speed breedingfor self-pollinated and cross-pollinated plant crops. This makes ADAM-plant an important tool to compare breeding efficiencies and the improvement of performance from a wide range of selection strategies.HL and AS developed the software. HL performed the analyses and prepared the manuscript. BT, AS, JJ, FC, and JA helped to design the programs, participated in interpretation of results and revision of the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Clear-cell renal carcinoma constitutes over 90% of all cases of renal cancers. One of the least common locations of metastases of this type of cancer are skeletal muscles. We believe that this is the first case ever presented of renal clear-cell cancer metastasis to the extensor digitorum muscle. Our case should sensitize clinicians and radiologists dealing with this type of carcinoma to the abundance of metastases, their uncommon locations and frequently asymptomatic nature, which all makes them easy to overlook on physical and imaging examinations. Remote metastases and their atypical locations are characteristic for clear-cell renal carcinoma. They can be observed even after a very long time from removal of the primary lesion. Metastases to skeletal muscles are very rare and a single metastasis to a finger extensor is, according to the authors’ best knowledge and belief, the first case ever reported. Our patient presented with three uncommon metastases of clear-cell carcinoma—to the pancreas, thyroid and a finger extensor, which supports the unpredictable biology of this type of carcinoma.A 67-year-old male patient with a history of clear-cell renal carcinoma was admitted to the Department of Orthopaedics of the University Clinical Centre with suspected metastasis in the right forearm. MRI showed metastasis to the muscles of the forearm, while a core-needle biopsy confirmed the diagnosis of clear-cell renal carcinoma. The lesion was excised during a non-complicated surgery.We present a case of a 67-year-old male patient admitted to our tertiary hospital because of a 6-month history of painful right forearm secondary to suspected bony metastasis. The patient’s medical history included arterial hypertension, ischaemic heart disease, Type 2 diabetes and psoriasis. In 2011, he underwent radical nephrectomy owing to clear-cell renal carcinoma. In 2013, resection of the head of the pancreas was conducted owing to neoplastic metastasis. PET examination carried out in 2014 revealed clear-cell carcinoma metastasis to the right lobe of the thyroid and strumectomy was performed. Another physical examination revealed a palpable tumour of 6 × 4 cm in size on the right forearm.Clear-cell renal carcinoma frequently metastasizes to bones. A tender palpable tumour on the forearm is consistent with a lesion of a metastatic type.Differential diagnosis should include other neoplasms of soft tissues, which are probable to occur at this age such as chondrosarcomas, lipomas, liposarcomas or fibrosarcomas.An X-ray scan of the forearm revealed no significant anomaly in the projection of the coronoid process of the ulna .Further evaluation by MRI revealed a heterogeneous focal infiltration measuring 66 × 24 × 19  mm in size located in the extensor digitorum muscle of the right forearm with no additional lesions or involvement of the adjacent bones. The infiltration extended beyond the fascia of the muscle and penetrated 10 mm between the extensor carpi ulnaris and supinator. After administration of contrast medium the focus was significantly non-homogenously enhanced, which is typical for secondary lesions of renal cell carcinoma  and 3. CThe lesion was excised and the perioperative course was non-complicated. The metastatic nature of the lesion was confirmed after histopathological examination of the excised specimen . Eight m1 Clear-cell renal carcinoma can also be of genetic aetiology in the case of which it can occur in the course of von Hippel–Lindau disease with neoplasms located in the cerebellum, retina and adrenal medulla.The prevalence of malignant neoplasms of the kidney varies geographically, with the highest incidence in Europe and North America which ranges 2.9–15.2/100 000 in males and generally half of those cases in females. Risk factors for renal carcinoma include nicotinism, arterial hypertension, obesity, overuse of analgesics, polycystic kidneys and occupational exposure to toxic substances such as cadmium and asbestos.2 What is characteristic of this type of carcinomas are the uncommon locations of metastases such as salivary glands, heart, oesophagus or hard palate. Metastases to skeletal muscles are extremely rare. This is because of the high vascularization of the muscles, their extensive surface and production of lactic acid which suppresses tumour’s angiogenesis.3 Metastases to muscles are most frequently observed in the course of lung cancer.4 The prevalence of metastases of renal carcinoma to skeletal muscles is estimated at 0.4%.5 Their actual prevalence is probably much higher; however, it is difficult to assess owing to the fact that metastases to skeletal muscles can remain asymptomatic for a long time. Owing to their low prevalence they may be overlooked during preparation of radiological impression, even by highly experienced radiologists. The metastases are usually detected only when they reach a large size and start to exhibit symptoms.Owing to high vascularization, renal carcinomas show great ability to metastasize, mainly along the blood circulation system. Most prevalent are metastases to lungs (50%), lymph nodes (35%), liver (30%), bones (30%), adrenal glands (5.5%) and brain (2%).In literature, cases of metastases of the renal cell carcinoma to the following muscles have been described: deltoid, triceps brachii, biceps, brachioradialis, muscles of the scapula, trapezius, muscles of the abdominal wall, iliacus, iliofemoral muscle, gluteus maximus, gluteus medius, quadriceps femoris, biceps femoris, adductor magnus and sartorius.T2 weighted MRI images, a similar enhancement as primary tumor which can be homogenous, heterogeneous or peripheral and in USG they mostly appear as well-lobulated, heterogeneous, hypoechoic referred to muscles changes.Metastases of the renal cell carcinoma to the muscles are characterized by clearly increased signal intensity in 6 RCC muscle metastases are known only from case reviews, so there is no particular study which compares the treatments results or provides any treatment recommendations; however the survival rate in atypical metastases is comparable with lung metastases.7 Early detection of single metastases is crucial since it allows for application of surgical treatment. Excision of metastatic lesions improves the prognosis, increasing the 5 year survival rate by approximately 35–50%.8 A survival advantage from complete metastasectomy was also observed among patients with multiple, non-lung-only metastases, who had a 5 year survival rate of 32.5% with complete resection versus 12.4% without complete resection.9 There is also a potential benefit from integration of metastasectomy and immunotherapy, with the median survival rate of 4.7 years and 1.8 years of median time to progression.10 In patients treated with systemic combinational targeted and immunotherapy using tyrosine kinase inhibitors, interferon-α, interleukin 2 and 5FU or vinblastine, the mean progression-free survival was 8.1 months with overall survival 16.8 months.11 Because of the unpredictable character of RCC, it is impossible to provide for an accurate follow up in this group of patients, but careful clinical examination and control abdomen CT should be considered as most effective. CT including nephrographic phase is the most accurate method for observation of detected metastases and for monitoring of response to treatment. Patients with renal carcinoma should be observed for many years, since this type of cancer can metastasize even after 19 years from the moment of excision of the primary tumour.12Untreated patients with metastatic RCC have a median survival of 6 to 12 months and a 5 year survival rate of <20%.Clinicians and radiologists dealing with patients who suffer from clear-cell renal carcinoma should always bear in mind the unpredictable nature of this neoplasm and its high ability to metastasize to uncharacteristic locations such as skeletal musclesThe metastases are frequently asymptomatic.Early detection of metastases allows for surgical treatment and thus improves the prognosis.The informed consent was obtained to publish this case.


Renewal of fear outside treatment context is a challenge for behavioral therapies. Prior studies suggest a social buffering effect that fear response is attenuated in the presence of social company. However, few studies have examined the role of social company in reducing fear renewal. Here, we used a Pavlovian fear conditioning procedure including acquisition, extinction and test stages to examine social buffering effect on fear memory renewal in male rats. The test context was manipulated to be either different from the extinction one in ABC model, or same as that in ACC model. All conditioned subjects underwent extinction individually in Experiment 1 but with a partner in Experiment 2. In test, both experiments manipulated social company  and context (ABC vs. ACC). Experiment 1 showed more freezing in ABC than in ACC model during the test-alone condition, indicating a fear renewal effect which, however, was absent during the test-accompanied condition. Also, accompanied subjects showed less freezing compared to alone subjects in the ABC model. In Experiment 2, animals showed a similar freezing in ABC and ACC models despite being tested alone, implying that social company offered at extinction disrupted fear renewal. Again, we observed reduced freezing in accompanied relative to alone subjects in the test. These results suggest that social company is effective in disrupting fear renewal after leaving treatment context. Posttraumatic stress disorder (PTSD) is characterized by the intrusive flashback of fear memories or difficulty in fear memory extinction . ExposurTo simulate PTSD and explore its therapies in the laboratory setting, the Pavlovian fear conditioning paradigm is often used with animal models . In the Accumulating evidence shows that mere presence of conspecific partner, whether familiar or unfamiliar, is able to reduce one’s stress-related response in both human and animal subjects . This phFor human studies, the psychotherapists require PTSD patients and their family members to receive therapeutic training together, in order to optimize the intervention effects for PTSD symptoms . This imTo address this issue, the present study used both ABC and ACC model to isolate a fear renewal effect. ACC, instead of ABB, is used in order to equate the test context during both conditions. Consequently, differential freezing among the ABC and ACC model is not attributable to physical differences in the test context, as all tests are conducted in an identical context. ABC model is used due to its close resemblance to the situation of fear memory renewal of PTSD patients in real life, in that novel contexts outside of the extinction one are common and the original context for fear acquisition is difficult to replicate . We usedBoth experiments were conducted in strict accordance with the recommendations of “Regulations for the Administration of Affairs Concerning Experimental Animals,” the State Science and Technology Commission, China. All animal procedures were approved by the animal care and use committee at Southwest University, China. 64 male Sprague-Dawley rats  including 52 subjects and 12 partners were purchased from the Institute of Traditional Medicine, Chongqing, China. Animals were housed in pairs in transparent cages (47 cm × 32 cm × 21cm) with corn-cob granule for bedding in a room maintained on a 12-h light/dark cycle (lights on at 8:00 a.m.) and were allowed to freely access food and water in their home cages. In each cage, two rats were assigned to be either subject or partner , which ensured unfamiliarity between the subject and the accompanying rats. All the accompanying rats were manipulated to be unfamiliar to the subjects in this study, in order to control for the possible amplification of social buffering effect by familiarity . All ratFour identical and standard rodent conditioning observation chambers  were used in the experiments. The chambers consisted of aluminum  and Plexiglas . Digital video cameras were mounted on top of each chamber to videotape rats’ behavior. A speaker was mounted outside the wall of each chamber and was used for delivery of white noise.Context A was a semi-circular chamber made by placing a curved plastic board into the standard rodent conditioning chamber. The floor of each chamber consisted of 18 stainless steel rods (5 mm diameter) spaced 1.6 cm apart. The rods were wired to a shock source for delivery of footshocks. The white light within the chambers was provided for illumination and the experimental room was dark. Stainless steel pans were placed underneath the grid floors and the chambers were sprayed with 2% acetic acid before animals were placed into it.2 with a transparent plastic PVC partition. There are 9 holes (2 cm diameter) in transparent plastic board for subject and partner to communicate by visual, olfactory or restricted tactile modalities (Context B was an opaque cask (25 cm diameter and 23 cm height) with a transparent cover inside the standard rodent conditioning chamber. A hole (10 cm diameter) was designed in the cover, to facilitate the rats’ breathing. Context B did not have stainless steel rods on the floor. To protect subjects’ activity or freezing from being disturbed by partners when subjects received the white noise (CS), all casks were approximately bisected into 250-cmdalities . Olfactodalities . The par2 with a transparent plastic PVC partition. Similarly, there are 9 holes (2 cm diameter) in transparent plastic board for subject and partner to communicate by visual, olfactory or restricted tactile modalities. The partner was placed on one side of the box before subject entered in it. The white light within the chamber was provided for illumination and the experimental room was bright. The chambers were sprayed and cleaned with 2% isoamyl alcohol before animals were placed into it.Context C was formed by the standard rodent conditioning chamber, with the floor of each consisting of 16 stainless steel rods alternate in thickness. To protect subjects’ activity or freezing from being disturbed by the partners when subjects received the white noise (CS), all chambers were bisected into approximately 360-cmIn Experiment 1, there were three experimental phases: fear conditioning, extinction, and test. For fear conditioning (Day 1), each time a maximum of 4 subjects were transported to context A in pairs in their home cages, which were covered with a white trash bag. All the subjects received auditory fear conditioning which consisted of 4 trials of 30 s, 80 dB, white-noise  co-terminating with a 1 s, 0.5 mA foot shock . The white noise started 3 min after subjects were placed in context A. After the final trial was finished, the animals were immediately returned to the homecage and the shock grids and floor trays were cleaned. Partners stayed in the feeding room with no foot shock.Figure 1A). In the ACC condition, subjects were transported to context C in pairs in their homecages covered with a black trash bag. All the subjects, irrespective of condition, received 20 white-noise  in the absence of footshock, beginning 3 min after placement in their respective context without social company. After the final trial was finished, the animals were immediately returned to the homecage.On Day 2 and Day 3, subjects were transported to context B in pairs in their homecages that were covered with a yellow trash bag in the ABC condition (∗ 2 design (context ∗ company): ACC-Test alone (n = 8), ACC-Test accompanied (n = 7), ABC-Test alone (n = 7), and ABC-Test accompanied (n = 6).On Day 4, subjects were transported to context C in pairs in their homecages that were covered with a black trash bag. All the subjects received renewal test with or without partner. The accompanying rats were unfamiliar to the subjects. The subject and the accompanying rats were neither housed together nor had any physical contacts before experiment. The test consisted of 6 trials of white-noise  in the absence of footshock, beginning 3 min after placement in the context C. The animals were returned to the homecage immediately after the final trial was finished. The test occurred either in the extinction context (ACC) or in a novel context (ABC). This yields a total of 4 groups in a 2 ∗ 2 design (context ∗ company): ACC-Test alone (n = 6), ACC-Test accompanied (n = 7), ABC-Test alone (n = 6), and ABC-Test accompanied (n = 5) .Experiment 2 had the same procedure as described above with the exception that all the subjects underwent extinction training in the presence of a naïve unconditioned partner . It also yields a total of 4 groups in a 2 Post hoc comparisons with Bonferroni method were performed after a significant omnibus F-ratio was detected. Student’s t-test was used to investigate the difference between EXT.1 ACC-test alone and EXT.2 ACC-test alone groups. The first block of two trials during test was chosen as the key indicator of fear renewal. The sample size of both experiments was determined by the principle  recommended by E = 24) and 24 rats (E = 20) were used for Experiment 2, in order to obtain an E value no less than 20. Post hoc power analysis via G∗power indicated that the current sample size of two experiment (5–8 rats for each condition) is sufficient to obtain a reliable statistical power for the key results reported in this study . Statistical significance was accepted at p < 0.05, two-tailed.Freezing was defined as an immobile posture and was measured during the 30-s period after the onset of each CS . We calculated the percentage of time spent in a freezing posture with respect to the 30-s period in every CS trial during the acquisition phase. All of the freezing was recorded and analyzed with digital video cameras by using commercially available software . The averaged freezing of every 4 CS was calculated as a block for the extinction and the averaged freezing of every 2 CS as a block for the test. Freezing during fear conditioning was analyzed via two-way repeated ANOVA with Group as the between-subjects factor and Trial as the within subjects factor. After this, animals were equally split into groups  based on their freezing level in the acquisition phase, ensuring a similar level of freezing before extinction. Freezing during extinction was analyzed via a three-way mixed-design ANOVA with Time  as a repeated factor while Company and Context as two between-subjects factors. Baseline freezing in the test was analyzed via a two-way ANOVA with Company and Context as between-subjects factors. Baseline freezing was measured in the first 3 min before the onset of the first CS during test . FreezinFigure 1B shows that the subjects’ freezing during acquisition was affected by the trial  or significant interaction effect between group and trial .In Experiment 1, Figure 1C. The data from the first and the last block were used for statistical analysis. A 2 (Context) ∗ 2 (Company) ∗ 2 (Block) mixed-design ANOVA of freezing during extinction revealed a significant main effect of Block  and Company . There was no significant Context and Block interaction , Context by Company interaction , Context by Block interaction , or Context by Block by Company . These data suggest that the extinction was successful, and the extinction effect was similar across the four samples before the test.Freezing averaged in each of the 10 blocks during extinction is shown in Figure 1D shows the freezing in the baseline. A 2 (Company) ∗ 2 (Context) ANOVA of the baseline freezing showed a main effect of Company  or significant interaction between Company and Context . This suggests that the accompanied groups had less freezing than the alone groups during the baseline.After extinction, rats were tested for fear renewal in the same context as extinction (“ACC” design) or shifted out of the extinction context . Figure 1E shows the freezing in the first block of the test, as the first test block has proven to exhibit the strongest fear renewal effect  = 9.20; p < 0.001; F = 9.16; p = 0.006; F = 9.33; p < 0.001; Post hoc comparisons indicates that subjects in ABC model showed a significantly higher freezing than subjects in ACC model from block 1 to block 3 , while this effect was most pronounced in block 1 (p < 0.001). This indicates that ABC model induced a robust fear renewal effect. There was a significant main effect of Company, with the accompanied subjects showing reduced freezing compared to the alone subjects .l effect . For theF = 5.42; p = 0.03; post hoc comparisons revealed that the difference between accompanied and alone conditions was not significant in ACC model (p = 0.74); while the accompanied group showed significantly less freezing compared to the alone group in ABC model (p = 0.002). On the other hand, the alone subjects showed a higher percent of freezing in the ABC vs. ACC conditions (p = 0.002), while the accompanied subjects showed a similar level of freezing across ABC and ACC conditions (p = 0.64). The above company by context interaction was unaffected by block, shown by the non-significant company ∗ context ∗ block interaction .More importantly, there was a significant interaction between Company and Context  or significant interaction between Group and Trial .In Experiment 2, Figure 2C. A 2 (Context) ∗ 2 (Company) ∗ 2 (Block) mixed-design ANOVA of freezing during extinction revealed a significant main effect of Block  and Company  were non-significant. There was no significant Context and Block interaction , Context by Company interaction , Company by Block interaction , and no significant Context by Block by Company interaction . These results suggest that the extinction was successful, and the extinction effect was similar across the four samples before the test.The averaged freezing from block 1 to 10 in the extinction is depicted in Figure 2D shows the freezing in the baseline. The 2 (Company) ∗ 2 (Context) ANOVA of baseline freezing showed no main effect of Company  or Context . Also, there was no significant interaction between Company and Group . This suggests that distinct from that in Experiment 1, the baseline freezing in Experiment 2 was similar across the four groups when the extinction was performed with company .After extinction, rats were tested for fear renewal in the same context as extinction (“ACC”) or shifted out of the extinction context (“ABC”). Figure 2E shows the freezing data for the first test block. For the freezing analysis in the test stage, we observed significant main effects of Company , and no significant Company by Context , or Company by Context by Block interaction . These data suggest that social company offered to the extinction stage disrupted the robust fear memory renewal effect as observed in Experiment 1.There was no significant main effect of Context . Thus, removal of partner in the test-alone condition of ACC model in Experiment 2 did not significantly induce fear renewal.In order to examine whether test alone in ACC of Experiment 2 induces freezing due to removal of partner in test compared to extinction context, we compared the test-alone freezing of ACC model between Experiment 1 and Experiment 2 by the Student’s Giving social company at different stage in two experiments, the current study focuses on how social company modulates fear memory renewal elicited by contextual updating. Fear memory renewal was operationally defined by the contrast of freezing rate between ABC and ACC model. Experiment 1 manipulated social company at the test rather than the extinction stage, to examine how social company in the test phase alone may alter fear memory renewal. The results firstly showed that rats exhibited more freezing in ABC than in ACC model, which is consistent with previous finding of robust fear memory renewal in the ABC model . Then, wImportantly, the accompanied group showed significantly less freezing than alone group during test in ABC model, which implies that social company during test could suppress fear memory renewal from context updating. This is consistent with prior reports that the presence of a conspecific, whether familiar or unfamiliar to the subject, ameliorated conditioned fear responses in behavioral  and physiological  measures when the animal was tested after a fear conditioning procedure . HoweverAs described above, Experiment 1 showed a clear social buffering effect on fear memory renewal when social company was offered to the test stage. However, the practical implication of this finding is limited. This is due to the fact that the rehabilitated patients in the real-life settings may experience various new situations after leaving a specific therapeutic situation, as stated above. This prompted Experiment 2 that focused on whether social company given to the extinction stage may suppress the fear renewal effect in a novel context. The results showed that the subjects’ freezing in the test stage was no longer significantly different between ABC and ACC model, implying that social company given to the extinction stage disrupted the robust fear memory renewal effect.Figure 1E). It is still necessary to conduct confirmatory research manipulating Extinction Company in parallel with Context in the test-alone condition with a larger sample size. This allows a more rigid statistical examination of whether removal of partner from extinction to test indeed elicits no effect of fear renewal.However, one may question that in Experiment 2, the lack of higher freezing from ACC to ABC model in the test-alone condition does not necessarily reflect social buffering effect due to providing company to the extinction stage. Instead, it may reflect enhanced freezing in the ACC model due to removal of company from the extinction to the test stage. This possibility should be considered as prior studies indicated that context includes not only external physical environment but also internal cognitive context . HoweverAnother issue worth noting is that the current findings appear discrepant with those by More importantly, driven by the research purpose of how the effects of extinction training on fear response may be moderated by social company, Prior studies in protection from extinction showed that pairing an inhibitory CS (a predictor for the absence of the US) with a fear-eliciting cue during extinction impedes extinction, leading to a return of fear response when the inhibitory CS is removed in the test . In ExpeTo the best of our knowledge, previous studies have provided social company at different time points of fear conditioning, to examine how social company influences fear response, such as pre-fear conditioning  or in feJY, MY, and XW performed the experiments. WC helped in experimental design. JY and MY analyzed the data. JY, MY, and YX wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Collaborative care can support the treatment of depression in people with long-term conditions, but long-term benefits and costs are unknown.To explore the long-term (24-month) effectiveness and cost-effectiveness of collaborative care in people with mental-physical multimorbidity.A cluster randomised trial compared collaborative care  with usual care for depression alongside diabetes and/or coronary heart disease. Depression symptoms were measured by the symptom checklist-depression scale (SCL-D13). The economic evaluation was from the perspective of the English National Health Service.191 participants were allocated to collaborative care and 196 to usual care. At 24 months, the mean SCL-D13 score was 0.27  lower in the collaborative care group alongside a gain of 0.14  quality-adjusted life-years (QALYs). The cost per QALY gained was £13 069.In the long term, collaborative care reduces depression and is potentially cost-effective at internationally accepted willingness-to-pay thresholds.None. Collaborative care integrates physical and mental healthcare. It is more effective than usual care for managing depression over the long term in people with coexisting mental and physical health conditions . This is the first long-term evaluation of collaborative care for managing mental–physical multimorbidity in a UK setting. Additionally, collaborative care is good value for money over the long term and has the potential to deliver important health gains at levels generally considered to be cost-effective.,Multimorbidity refers to the presence of two or more long-term conditions, which can include combinations of physical and mental symptoms. Multimorbidity is a critical burden on health systems, associated with increased mortality, reduced quality of life and increased use of unscheduled care.,The COINCIDE trial evaluated the clinical effectiveness of collaborative care over a short-term period (4 months); collaborative care was associated with a significantly greater improvement in depression symptoms compared with usual care (effect size: 0.30).The evaluation was a two-arm, cluster randomised, controlled trial in the north-west of England. Participating general practices held electronic registers of patients with diabetes and/or coronary heart disease and were assigned a unique identifier. A total of 459 general practices across the north-west of England were invited to participate and 39 were allocated in a phased approach across the region ; three practices withdrew before participants were recruited. Designated staff based remotely at the Clinical Trials Unit of the Christie National Health Service (NHS) Foundation Trust  provided a central randomisation service. A computerised random number generator was used. The first six practices recruited were allocated at a ratio of 1:1 at random, to either the collaborative care or usual care arm. Subsequent practices were then allocated by minimisation (with a probability weighting of 0.8),,Staff from the National Institute for Health's Mental Health Research Network searched clinical databases at participating practices for patients with a record of diabetes and/or coronary heart disease. Patients who met these eligibility criteria were sent a postal invitation followed by a reminder letter 3 weeks later; non-responders to the reminder postal invitation were telephoned. Patients were screened for depressive symptoms The National Research Ethics Service Committee North West – Preston  granted ethical approval, and research governance approvals were granted by participating primary care trusts. Informed consent was obtained in writing from all participants before data collection.,Participants attending general practitioner (GP) practices allocated to the collaborative care arm received up to eight face-to-face sessions of brief psychological therapy delivered by a case manager over 3 months.The primary outcome was self-reported depression severity on the 13 depression items of the Symptom Check List-90 scale  24 months after randomisation.For economic analyses, we used the UK NHS and personal social services perspective in line with NICE guidance for economic evaluations of healthcare interventions.,Use of healthcare services was collected by asking participants to report their total number of visits to different healthcare professionals in these categories: in-patient admission, out-patient, day patient , accident and emergency, and primary/ community care . Direct costs were also estimated for the intervention. Data on the resources used to provide the intervention only were collected from activity logs completed by PWPs and practice nurses who delivered care to participants in the collaborative care arm. The costs of training PWPs were also included in the primary economic analysis. We based PWP costs on NHS Agenda for Change salary band four  and included employer National Insurance and pension contributions plus capital, administrative and managerial costs. We calculated cost per hour using standard working time assumptions,The primary measure of cost-effectiveness was cost per quality-adjusted life-year (QALY).n = 450), allowing for 20% attrition and an intra-practice correlation of 0.06. Average recruitment in the first 11 practices was <15 patients per practice. We therefore increased the total number of clusters from 30 to 36, with a target of 10 patients per practice, giving 79% power to detect an effect of 0.4 under the same assumptions. The revised target sample was therefore 360 patients.We used an intention-to-treat approach for all clinical and cost-effectiveness analyses as per the original data analysis plan shared with the Data Monitoring and Ethics Committee. All analyses were conducted with Stata (Release 13). The trial originally had 80% power (two-sided α = 0.05) to detect a difference between groups on the primary outcome at follow-up, equivalent to a standardised effect size of 0.4, for which we required 15 practices per arm and 15 patients per practice , level of limitation of daily activities at baseline, use of antidepressants or antianxiety drugs , GP practice list size and GP practice area deprivation (the latter two as ‘design factors’). Multiple imputation was used to estimate missing scale scores and other data values at both baseline and follow-up. Thirty imputed data-sets were generated with chained equations, including covariates as listed above. As part of the imputation model, missing SCL-D13 values were restricted to between 0 and 4, in line with the possible questionnaire responses. To assess sensitivity of the results to multiple imputation, we conducted secondary analyses on complete cases, first with the same covariates as the main analysis and second with only baseline depression score included as a covariate. A further The base–case (primary) economic analysis calculated incremental cost-effectiveness ratios (ICERs); accordingly, no parametric statistical tests of differences in mean costs or outcomes were conducted. Net QALYs  were estimated by a linear regression model, and net costs were estimated by a generalised linear regression model with a log link and gamma family to allow for the non-normal distribution of costs. Regression models were adjusted for baseline variables identified through stepwise regression, using the following measures: World Health Organization Quality of Life Instrument,https://doi.org/10.1192/bjp.2018.70. For the 24-month assessment, 69% of the original sample had a cost recorded for at least one category of healthcare and 69% had responded to at least one item on the EQ-5D-5L. All available  cost and outcome data for a particular participant was used to impute missing data. The number of imputed data-sets was chosen for pragmatic reasons: it was felt that this represented a balance between robustness and the computational burden of conducting the bootstrapping procedure on imputed data.Missing data on costs and EQ-5D-5L utility scores were imputed five times with a chained-equation procedure. Costs were imputed by category and utility by individual EQ-5D-5L domain rather than as totals, so that all available data were used to inform the imputed values. The pattern of available cost and utility data across the different assessments are summarised in supplementary Table 1, available at Sensitivity analyses assessed the effect of design and analysis choices on the cost-effectiveness of collaborative care. These were an alternative method for utility/QALY estimation ,n = 211) of participants lived in areas ranked as highly deprived (index of multiple deprivation score ≥30). A total of 25% of participants were in paid employment. Half of participants were prescribed antidepressant or antianxiety medication at baseline. Full details about the delivery of the intervention have been previously reported.19 practices were randomised to collaborative care and 20 to usual care. We identified 387 patients with depression and heart disease and/or diabetes and invited them to a baseline assessment. Follow-up data on 350  participants were collected at 4 months between 18 November 2012 and 4 October 2013, and follow-up data on 272 participants  were collected at 24 months between 18 May 2014 and 4 June 2015  diagram). Characteristics of participating practices and participants are reported in For the primary outcome measure, depression scores were available at 24 months for 62% of participants allocated to the collaborative care arm and 74% allocated to usual care. The primary measure of health benefit for the economic evaluation was completed by 61% of collaborative care participants and 68% of usual care participants at 24 months. Across the categories of healthcare utilisation, 70–83% of participants had complete data at 4 months and 64–68% at 24 months. However, when combined into a total cost, 34% of the collaborative care group and 40% of the usual care had data for all of the categories at both time points. An additional summary of available cost and utility data is reported in supplementary Table 2.The unadjusted difference in mean SCL-D13 scores between baseline and first follow-up showed an improvement in both groups  . When coP = 0.014) in participants who received collaborative care compared with those who received usual care. This difference is equal to a standardised mean difference of −0.35 , using the baseline pooled s.d. for SCL-D13. Collaborative care was also found to be significantly more effective than usual care at 24 months in all sensitivity analyses, although there was very little difference in the estimated coefficients between models . For participants with complete cost data at all time points, the mean cost across all categories of healthcare was higher among those randomised to collaborative care; the cost of healthcare resources used was higher across all categories except day patient hospital visits and emergency department visits (see supplementary Table 3).Unadjusted mean health state index scores show that the usual care group worsened overall between baseline and 24 months ; £1777  over 24 months, although this difference is not statistically significant. Participants receiving collaborative care accrued significantly more QALYs over 24 months than those receiving usual care . The bootstrapped estimates of net costs and QALYs are shown on a cost-effectiveness plane in The cost per additional QALY gained from collaborative care  is £13 069. The probability of collaborative care being cost-effective is 0.75 if decision makers are willing to pay £20 000 to gain one QALY. If decision makers are willing to pay £30 000 to gain one QALY, the probability that collaborative care is more cost-effective than usual care is 0.92. This is shown in the cost-effectiveness acceptability curve in v. 0.136), resulting in a slightly higher ICER (£15 063/QALY v. £13 069/QALY). When only participants with complete cost and QALY data were included in the analysis, net costs were higher and net QALYs lower than the primary (base–case) analysis. This resulted in an ICER of £38 032 per QALY at which collaborative care would be unlikely to be more cost-effective than usual care (probability 0.42 at a willingness-to-pay threshold of £30 000). When the measure of health benefit was the number of people showing a clinical response in terms of depression symptoms . Collaborative care was also associated with superior QALY gains compared with usual care  in this context.Collaborative care for depression in the context of multimorbidity is clinically and cost-effective over the long term. Not only were the treatment effects of collaborative care maintained over 24 months, but they marginally exceeded those reported at 4 months ).This is the first long-term evaluation of collaborative care for managing mental–physical multimorbidity in a UK primary care setting. The net QALY gain observed in the COINCIDE trial at 24 months was greater than that observed in a previous economic evaluation of collaborative care in UK primary care.An important difference between the economic model and observed data is the estimated net cost. Net costs were notably higher using data observed within-trial, meaning that the additional healthcare resources used by participants randomised to collaborative care was greater than expected. Participants in the collaborative care arm used almost £1800 more of healthcare resources over 24 months  than those receiving usual care. By comparison, in the Clinical and Cost Effectiveness of Collaborative Care for Depression in UK Primary Care Trial (CADET) the cost of healthcare resources over 12 months were almost identical between usual and collaborative care treatment groups.The COINCIDE trial was a large, pragmatic trial conducted across a wide geographical area in the north-west of England that included a population with high levels of multimorbidity, disability and deprivation. This makes it particularly relevant to the management of mental–physical multimorbidity, which is more prevalent in younger adults from deprived regions.Our economic analysis conforms to the high standards of analysis and reporting expected of cost-effectiveness analyses of health interventions . However, data was not captured at baseline regarding use of health services before the intervention so it is not possible to determine or adjust for any underlying differences between the treatment groups.Although multiple imputation of missing data reduced the potential for bias associated with missing data, the robustness of any imputation method declines as the level of missing data increases, reducing the validity and reliability of the analyses. Data from baseline and 24 months only were included in the clinical effectiveness analysis, and there was little difference in the change in depression score or effect size between the complete case analysis and the analysis after multiple imputation. The attrition rate was higher among participants in the collaborative care group (37.7%) compared with usual care (26.0%). We have explored key characteristics between participants with and without complete data (results not shown) and found only that in the usual care group, the mean age was higher (66 years) for those with incomplete data for the primary outcome measure than those with complete data (59 years). There were no other differences. Age was adjusted for in all analyses and included in the multiple imputation model, therefore this is unlikely to have unduly biased our results.or utility data; however, the proportion of participants with data for both was lower. As described in the Methods section, all available data were used to inform the imputed values and the role of multiple imputation can be thought of as filling in the blanks. A post hoc comparison of the characteristics of the participants with complete/incomplete economic data showed that only ethnicity was significantly different: there was a higher proportion of black and minority ethnic participants with complete data than incomplete data (see supplementary Table 2). This was surprising because for the participants who did not return the 24-month postal questionnaire, only English speakers were able to complete it over the telephone (because of limited resources). The cost-effectiveness results were somewhat sensitive to missing data. The direction of the effect on costs and QALYs were the same, but the cost to gain one QALY was higher in the complete case analysis.The economic evaluation included cost and utility data from baseline, 4 months and 24 months, and so there was greater scope for data to be missing. The proportion of participants with complete cost or utility data for all three time points  was lower than at the final time point (24 months) alone, suggesting that missing data items rather than loss to follow-up contributed to the level of missing data. Almost 70% of participants had complete cost ,Data on depression symptoms (SCL-D13), health status (EQ-5D-5L) and healthcare resource utilisation were self-reported by participants. Although pragmatic, self-reported methods are potentially prone to recall bias over a long-term follow-up. This is especially true in relation to capturing healthcare utilisation, particularly among those who use a large number of different healthcare services. However, use of costly services (e.g. an in-patient admission or major surgery) are notable events and likely to be recalled even over a long period.,A strong case for routinely using collaborative care as a framework of care for depression was made by the CADET trial.In conclusion, the need for cost-effective interventions for multimorbidity is of paramount importance in high-, middle-, and low-income regions faced with the burden of managing ageing and/or deprived populations with complex health needs. Despite the limitations of this analysis, it can be concluded that collaborative care is clinically effective (with a moderate effect size) and cost-effective over the long term, as a treatment for people with depression in the context of multimorbidity. In addition to existing evidence, findings from the COINCIDE trial send a strong signal to clinicians and commissioners in primary care that collaborative care merits implementation to manage the effect of mental–physical multimorbidity over the long term.


An iPhone 6 using the Avenza software for capturing horizontal positions was employed to understand relative positional accuracy in an urban environment, during two seasons of the year, two times of day, and two perceived WiFi usage periods. On average, time of year did not seem to influence the average error observed in horizontal positions when GPS-only (no WiFi) capability was enabled, nor when WiFi was enabled. Observations of average horizontal position error only seemed to improve with time of day (afternoon) during the leaf-off season. During each season and during each time of day, horizontal position error seemed to improve in general during perceived high WiFi usage periods (when more people were present). Overall average horizontal position accuracy of the iPhone 6 (7–13 m) is consistent with the general accuracy levels observed of recreation-grade GPS receivers in potential high multi-path environments. Smartphones have become ubiquitous tools of the human race, as millions of people now go about their days with small GPS-capable computers in their hands or pockets. The majority of current research involving smartphone GPS capabilities focuses on transportation or directional uses , 2, 3, 4Modern smartphones are equipped with Assisted GPS (A-GPS) capability. A-GPS uses smartphone networks in combination with a GPS antenna to increase the speed of determining or fixing position . A-GPS pMixed results can be found in the literature concerning the accuracy of smartphone GPS services. In a rather early study, using GPS enabled iPhones, iPods, an iPad and an app used by an insurance company in Switzerland, von Watzdorf and Michahelles  found avIn addition to the previously-noted studies, Zandbergen  found thPseudotsuga menziesii) stand, and an area with no canopy cover). The average error for GPS collection ranged between 1 and 4 m in open areas, between approximately 1 and 7 m under a moderately dense canopy, and approximately 3 and 11 m under a closed forest canopy. In each forest condition, the average error was less than the manufacture’s assessment of 15 to 20 m accuracy. Within a dense Douglas-fir and western hemlock (Tsuga heterophylla) forest in Oregon, Wing and Eklund [In contrast to the study of smartphones, more research has been conducted on the accuracy of traditional GPS receivers under forested conditions. Wing et al.  investigd Eklund , measured Eklund  evaluateIn a mixed deciduous-coniferous forest, Tomaštík et al.  evaluateThe University of Georgia, founded in 1785, is located in Athens, Georgia (USA). The nearly 800-acre campus serves as an educational facility for approximately 40,000 students. PAWS-Secure is the name of the WiFi network accessible to students, faculty, and staff, and this system is managed by the University’s Department of Enterprise Information Technology Services. The WiFi network is accessible in all buildings and green spaces on campus. The University of Georgia also has a system of survey monuments, benchmarks placed to identify surveyed points. Dispersed across the campus, the positions of these were surveyed and measured by the University Architects. The system contains 212 survey monuments of which six were selected for use in this study . Of the In an effort to describe the landscape surrounding each survey cap, a zone around each survey monument was created using a 30 m buffering process in a geographic information system (GIS). This 30 m buffer was chosen to represent an area twice as large as the largest mean error (14.15 m) from data collection across all survey monuments. A 2018 campus wide tree inventory provided by the University of Georgia’s Warnell School of Forestry and Natural Resources , was useChionanthus virginicus), southern magnolia (Magnolia grandiflora), northern white cedar , tulip poplar (Liriodendron tulipifera), Chinese elm (Ulmus parviflora), willow oak (Quercus phellos), and red maple (Acer rubrum). Land cover within the 30 m was dominated by trees (30%), grass (37%), and sidewalks (22%).Point (monument) 1 was located in the middle of a quad, with buildings on all sides and a network of sidewalks and grassy areas throughout. From Point 1, there is a three story building approximately 40 m to the northeast, approximately 60 m to the southeast is another three story building, approximately 40 m to the west is a four story building, and approximately 50 m to the southwest is a second four story building. Within the 30 m buffer, are thirty-nine trees that range in height between 2 and 22 m. Tree species within the buffer include fringe tree (Pinus echinata), eastern redcedar (Juniperus virginiana), flowering dogwood (Cornus florida), water oak (Quercus nigra), willow oak, swamp chestnut oak (Quercus michauxii), red maple, and eastern redbud (Cercis canadensis).Point 2 was located in a relatively open area, at the convergence of multiple walkways and adjacent to a road. The land cover of the polygon buffer is 40% sidewalk and roadway, 28% tree cover, and 31% grass. From Point 2, there is a three story building approximately 35 m to the northeast from the monument. Around 50 m from the monument to the northwest is a two story building, and 45 m to the southeast is a three story building. Within the 30 m sample buffer, there are thirty-three trees ranging in height from 2 to 23 m tall. The tree species included shortleaf pine (Quercus lyrata). An additional 24 m tall overcup oak is approximately 15 m from the monument. In total there are eight trees within the 30 m sample buffer. Other species include nuttall oak  and pin oak . Heights of the trees here range from 2 m to 14 m. The monument is also approximately 120 m from the university’s basketball coliseum. The land cover near the survey marker is dominated by a combination of sidewalks, roadways, and a parking lot (44%). Nearly one-third of the buffer contained tree cover (29%) and low vegetation like small shrubs (24%).Point 3 was located at the intersection of two streets and approximately 30 m from a three story building, the monument is located in close proximity (approximately 3 m) to an 18 m tall overcup oak (Quercus virginiana) and Yoshino cherry (Prunus x yedoensis), within the 30 m sample buffer around this monument ranging in height between 6 m and 14 m. Approximately 40 m to the north of the monument is a three story building. To the southeast, approximately 35 m from the monument, is a two story building.Point 4 was located at an intersection of two streets. The dominant land cover surrounding the survey monument is comprised of sidewalk and roadway (58%). Both grassy areas (22%) and tree cover (16%) were in close proximity to the sample point along with a minimal amount of low vegetation (4%). There were two trees, live oak (Ilex ‘Nellie R. Stevens’), trident maple (Acer buercerianum), Texas redbud (Cercis texensis), Okame cherry (Prunus x incamp ‘Okame’), laurel oak (Quercus laurifolia), water oak, and pin oak. The majority of the land cover near the point was tree cover (47%) in addition to grassy areas (22%), sidewalks and roadways (17%), and a small amount of low vegetation (6%), and building (8%).Point 5 was located in a sidewalk adjacent to a street. Within the 30 m buffer around this monument, there are seventeen trees ranging in height from 2 to 30 m. The monument is located within 10 m of the largest tree in the sample with the tree crown extending over the monument during the leaf-on period. Tree species around this monument include pin oak, Nellie Stevens holly (Pinus elliotti), willow oak, tulip poplar, European hornbeam (Carpinus betulus), and a fringe tree, which range in height from 2 to 20 m.Point 6 was located inside a parking lot median. Only 6% of the area in proximity to the survey monument is comprised of low vegetation and grassy areas. The predominant land cover is sidewalks and roadways (52%) and tree cover (36%). The closest building to the monument is approximately 30 m away, and the building is three stories tall. Within the 30 m sample buffer around this monument, there are fifty-seven trees, including eastern red cedars, two willow oaks, eighteen flowering dogwoods, slash pine (https://www.avenza.com/avenza-maps/) on an iPhone 6. One trip entailed visiting each of the six points once in a clockwise or counter-clockwise order, as opposed to zig-zagging from point to point. For instance, if a trip began at Point 1 the next point visited would either be Point 4 or Point 2. Similarly, if the starting point for a trip was Point 5, the next point visited would be either Point 6 or Point 3. In order to randomize the sampling process, the starting point of each trip was randomized along with the direction of travel (clockwise or counter-clockwise). Trips were also separated by the time of day for sample collection. Samples were classified as morning (AM) samples collected between 8:00 AM EST and 11:59 AM eastern (USA) standard time, and afternoon (PM) samples collected between 12:00 PM EST and 5:00 PM. The timing of trips was divided into high and low activity times with respect to human activity on campus. High activity included days when classes were in session. Low activity included days when classes were not in session  or during the summer when the number of students taking classes was quite lower. Every effort was made to avoid collecting data when major sporting events occurred during what would normally be classified as low activity sampling periods. Finally, samples were collected during leaf-on and leaf-off periods. All leaf-on GPS collection was conducted between May 2017 and November 2017. All leaf-off GPS collection was conducted during two time periods: 1) from December 2016 to March 2017 and 2) March 2018. For the second time period, all data collection was completed during the month of March. During each data collection period, each point was visited 20 times. In total, there were eight separate data collection periods:A static horizontal position was recorded at each survey cap during one trip using LBS and the Avenza Maps app  as the data associated with the monuments. One aim of this research endeavor was to determine whether using WiFi affected the horizontal position accuracy of the GPS data. Additionally, we were interested in whether an increased number of potential WiFi-users on campus had any impact on the horizontal position accuracy during data collection. Finally, we wanted to quantify the amount of horizontal position error a user could expect when collecting GPS points in an urban environment using an iPhone 6. For this, Euclidean distances were determined between the monument position and the location of each horizontal position recorded.Statistical tests were employed to determine whether positional error from GPS-only and WiFi-enabled data were significantly different. Using nineteen collection categories , the folo: The horizontal position errors of the GPS-only and WiFi data are not distributed differently.H1: The horizontal position errors of the GPS-only and WiFi data are distributed differently.HThe sets of static horizontal position errors of the nineteen different data collection scenarios were tested for normality using a Shapiro-Wilk test. We found that the sets of data were generally not normally distributed. Therefore, a non-parametric test for statistical significance was required for statistical analysis . Using tFinally, local weather data variables were compiled for the period of time during which the sampling cycle was completed. Meteorological data was recorded for the time period of each trip using reports from nearby Athens Ben Epps Airport. Weather variables included air temperature (°F), relative humidity (%), barometric pressure (inches), wind speed (mph), and condition . Pearson's correlation coefficient was used to determine whether these weather characteristics were correlated with the horizontal position accuracy of the data collected. Separately, a multivariate regression analysis was performed to identify the influence of weather conditions on positional error. Regression analysis was chosen because it allowed for an analytical method for incorporating categorical data. To do so, each weather condition was converted into a dummy variable containing values of 0 (the weather condition was not recorded at the time of data collection) or 1 (the weather condition was recorded at the time of data collection). Each weather condition served as an independent variable in the regression analysis with a total of 5 independent variables.In examining all horizontal position error derived during the GPS-only data collection effort, the minimum positional error was 0.05 m compared to a maximum error of 99.7 m. The RMSE for all data collected with the iPhone in GPS-only mode was about 9.9 m. On average, time of year did not seem to influence the average error observed in horizontal positions when GPS-only capability was assumed. In terms of overall performance, some improvement was observed in RMSE during the leaf-off period, but not in every case . Points 3 and 5 seemed to have the highest RMSE during the leaf-on period, and were joined by Point 1 during the leaf-off period . It shouOn average, time of year also did not seem to influence the average error observed in horizontal positions when WiFi capability was enabled. Again, Points 3 and 5 seemed to have the highest RMSE during the leaf-on period, joined by Point 1 during the leaf-off period . And as p < 0.05, p < 0.05). However, we could not reject the null hypothesis when only considering the data collected from a single sample point. The error was also statistically significantly different when considering all sets of observations of horizontal position error observed during AM, Leaf-off, and high data collection periods High AM and High PM (separately) data collection periods. Sample Point 1 was the only point where there were many instances of detection of significant differences among the GPS-only and WiFi-enabled data. While we cannot be completely certain what is causing the significant differences at this sample point, Point 1 is unique in that it is surrounded on each side by multistory buildings which may be increasing the positional error when GPS-only data was collected. However, each of these buildings house WiFi access points which may lead to a stronger WiFi signal at this point reducing the positional error when the WiFi is enabled.Across all of the combinations of data collection conditions examined, there were only 12 (out of 133) instances where the sets of horizontal position error were statistically significantly different (n = 76) between 5 and 10 m than any other error range. The error distributions at Point 4 and Point 5 were very similar between WiFi and GPS-only sampling with the majority of error falling between 2 to 10 m at Point 4 and ranging from 2 m to more than 20 m at Point 5. The frequency of horizontal position error at Point 6 was most prominent between the 0 and 2 m and 2 to 5 m ranges for both GPS-only and WiFi data collection.In examining the frequency of the error across all points, interesting patterns emerge . For insIn an effort to identify what might be causing the horizontal error, a correlation between the percent land cover within the 30 m sample buffer and the error was derived . A moderWhile understanding the amount of horizontal error between a survey monument and a position collected by the iPhone 6 is useful, knowing the predominant direction of that error may also be important. In examining the directional error, the angle between survey monuments and positions determined by the iPhone was calculated, and some general patterns emerged. For example, directional error at Point 1 using GPS-only data predominantly ranged in cardinal direction from south to north with a majority of directional error occurring in a west to northwest direction under all data collection conditions , yet dur2 = -0.0009) and weather condition . Similarly, when WiFi was enabled positional error was also not related (adjusted R2 = -0005) to current weather conditions.Finally, almost uniformly, there was generally weak to no correlation between horizontal position error of the positions determined by the iPhone, and temperature, barometric pressure, wind speed, and relative humidity during the data collection effort . The resWhen considering the static horizontal position error from all data points collected, the error observed in GPS-only data was significantly different from the error observed in the WiFi- enabled data. Further, data collected in the morning, and data collected during high WiFi use periods also indicated that GPS-only data and WiFi-enabled data had significantly different levels of horizontal position error. During only the leaf-off season were similar significant differences observed between GPS-only and WiFi-enabled data. These observations, while not significant at every data collection point, suggest that on average, enabling the iPhone to use WiFi signals to augment the determination of horizontal positions will lead to higher quality positional information. While it was unclear how extensive the WiFi services were utilized by the iPhone, the opportunity to use these services affected positional accuracy. Interestingly, nearby buildings may have influenced the direction of error observed, due likely to multipathed signals from either the GPS satellite constellation or the WiFi signal emitting devices.One pattern became evident when interpreting the results for data collected when the WiFi was enabled: the average positional error was greater around Point 5 than all other test points, regardless of leaf-on or leaf-off conditions, and morning or afternoon data collection efforts, the error was most pronounced at this point. Some of this could likely be explained by multipath conditions or a simple deterioration in GPS signals. However, this survey monument was located across a two-lane street from a multistory hotel and convention center and under a large tree. Comparatively, Points 2 and 6 were frequently the data collection points with the lowest RMSE, particularly during WiFi data collection periods. During GPS-only collection, Point 6 had low positional error compared to data collected at other survey monuments. Each of these monuments (Points 2 and 6) were located in relatively open areas, and thus this may indicate that what plays the largest role in smartphone GPS data accuracy may be proximity to multistory structures, rather than increased activity on a nearby WiFi network or the presence of nearby trees. Further, our work has provided results that are similar to those provided by Garnett and Stewart , Weaver This observational study is one of the first of its kind to examine the positional accuracy of horizontal positions determined by a smartphone during high and low human activity, and during two different seasons of the year (influencing the amount and presence of nearby tree canopies). Many of the influential factors could not be closely controlled by the study team; therefore, numerous samples were collected over each survey monument during random times of the day to understand on average the level of positional error one might expect. While protocols for data collection seem reasonable , some of the uncontrollable aspects of the data collection process included the amount of human activity , and the status and condition of the WiFi network managed by the university. Persistent efforts were employed to acquire information on the status of the network during the data collection periods, yet we were unable to acquire metrics regarding the WiFi signal status around the test course due to the university not allowing us access to these metrics. As a result, our observations should reflect average performance of the smartphone under average WiFi operating conditions.This study could be complemented by further studies that focus on some of the limitations we observed. For example, we were unable to sufficiently understand why horizontal position accuracy improved during periods of time when WiFi usage was high. Given our lack of access to the technical specifications of the WiFi network, perhaps management of the network during high use periods contributed to this result. Additional research that incorporates a measurement of the strength of the WiFi signal at each sample point would be useful. Further, the results we observed were highly variable around each sample point, perhaps due to the heterogeneous nature of the urban environment . A complementary study to better understand the impact of the spatial arrangement of features, similar to that of Bettinger and Merry  that wasThe horizontal position error associated with GPS positions determined by a smartphone is often assumed negligible by ordinary users of the technology. However, as smartphones are used more often for data collection purposes, perhaps during crowd sourcing data collection exercises or the capture of positional information through various smartphone apps, this concern may need more attention. Our study has shown that the overall average horizontal position error of the iPhone 6 is in the 7–13 m range, depending on conditions, which is consistent with the general accuracy levels observed of recreation-grade GPS receivers in potential high multipath environments. It seemed in our study that the time of year did not influence the average horizontal position error observed when GPS-only parameters were assumed, or when WiFi was enabled. Our observations of average horizontal position error only seemed to improve with time of day (afternoon) during the leaf-off season. Interestingly, horizontal position error seemed to improve in general during perceived high WiFi usage periods (when more people were present) within each season and during each time of day most prominently in the afternoon. In general, directional error was consistent at each data collection point during both GPS-only and WiFi collection. The most pronounced instance of directional error occurred at Point 5 in a west to northwest direction. Data collection may have been subjected to multipath issues at some of the data collection points. We saw moderate correlation between the presence of buildings and positional error during both GPS-only and WiFi-enabled data collection. Finally, weather conditions had little to no influence on the accuracy of data collected.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.