In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Example Input: Context: Women diagnosed with pregnancy-associated breast cancer postpartum have a worse prognosis, stage for stage, than other women with breast cancer. The time of breast involution is tumor promotional. The extracellular matrix protein tenascin-C is upregulated during involution in animal models and promotes breast cancer progression. It interacts with transforming growth factor (TGF)β, which also is involved in breast involution and breast cancer progression. Little is known about the expression of tenascin-C during human breast involution, nor its relationship to TGFβ. The purpose of this study was to investigate the expression of tenascin-C throughout lactation, as well as its relationship to TGFβ1 and TGFβ2.', 'Three milk samples from 25 lactating women (transitional, whole, and wean) were collected, separated into components (cells, fat, and skim), and the skim fraction analyzed for total protein, tenascin-C, TGFβ1, and TGFβ2. Tenascin-C, TGFβ1, and TGFβ2 were detectable in all milk samples.', 'Highest tenascin-C levels on average were found in whole milk, whereas highest mean TGFβ1 and TGFβ2 levels were in wean milk. Wean samples on average had higher levels of both TGFβ1 (26%) and TGFβ2 (>500%) than matched transitional milk samples. Tenascin-C levels in wean milk were associated with nursing length (p = 0.048). Combining all three milk collection time points, tenascin-C exhibited a weak inverse correlation with TGFβ1 and TGFβ2 (p < 0.1). The inverse correlation at the wean time point was stronger for TGFβ2 than -1 (-0.37 versus -0.25). Tenascin-C, a protein known to promote breast cancer progression, is expressed throughout lactation.\Question: Does expression of the Extracellular Matrix Protein Tenascin-C vary During Lactation?
Example Output: The inverse correlation with TGFβ2 in wean milk suggests a possible interaction during breast involution, which should be further investigated.

Example Input: Context: Poultry remains a major source of foodborne bacterial infections. A variety of additives with presumed anti-microbial and/or growth-promoting effects are commonly added to poultry feed during commercial grow-out, yet the effects of these additives on the gastrointestinal microbial community (the GI microbiome) as the bird matures remain largely unknown. Here we compared temporal changes in the cecal microbiome to the effects of formic acid, propionic acid, and medium-chain fatty acids (MCFA) added to feed and/or drinking water.', 'Cecal bacterial communities at day of hatch (n\u2009=\u20095 birds), 7d (n\u2009=\u200932), 21d (n\u2009=\u200927), and 42d (n\u2009=\u200936) post-hatch were surveyed using direct 454 sequencing of 16S rRNA gene amplicons from each bird in combination with cultivation-based recovery of a Salmonella Typhimurium marker strain and quantitative-PCR targeting Clostridium perfringens. Treatment effects on specific pathogens were generally non-significant. S. Typhimurium introduced by oral gavage at day of hatch was recovered by cultivation from nearly all birds sampled across treatments at 7d and 21d, but by 42d, S. Typhimurium was only recovered from ca. 25% of birds, regardless of treatment. Sequencing data also revealed non-significant treatment effects on genera containing known pathogens and on the cecal microbiome as a whole. In contrast, temporal changes in the cecal microbiome were dramatic, highly significant, and consistent across treatments. At 7d, the cecal community was dominated by three genera (Flavonifractor, Pseudoflavonifractor, and a Lachnospiracea sequence type) that accounted for more than half of sequences. By 21d post-hatch, a single genus (Faecalibacterium) accounted for 23-55% of sequences, and the number of Clostridium 16S rRNA gene copies detected by quantitative-PCR reached a maximum.\Question: Are successional changes in the chicken cecal microbiome during 42 days of growth independent of organic acid feed additives?
Example Output: Over the 42 d experiment, the cecal bacterial community changed significantly as measured by a variety of ecological metrics and increases in the complexity of co-occurrence networks. Management of poultry to improve animal health, nutrition, or food safety may need to consider the interactive effects of any treatments with the dramatic temporal shifts in the taxonomic composition of the cecal microbiome as described here.

Example Input: Context: Microsatellite instability is a property of most tumors occurring in the context of hereditary nonpolyposis colon cancer. Instability also occurs in 10%-15% of apparently sporadic colorectal cancers, and it has been hypothesized that this instability may indicate a genetic predisposition to colonic cancer. This study evaluated whether there is a clinically useful association between colon cancer instability and a family history of cancer.', 'Colon cancer cases (n = 188) from a population-based study were evaluated for microsatellite instability with 10 polymerase chain reaction primer sets. Instability results were compared with family history and other clinical and biological characteristics.', 'Microsatellite instability was found in 16.5% of tumors. It was predominantly a feature of right-sided tumors (P = 0.003) and was associated with the youngest and oldest ages at diagnosis (P = 0.01). Instability was not associated with family history of cancer, sex of the individual, or the glutathione-S-transferase mu 1 null genotype.\Question: Is microsatellite instability in human colonic cancer a useful clinical indicator of familial colorectal cancer?
Example Output:
Although some very small, and as yet undefined, proportion of colon cancer may be caused by inherited mutations leading to microsatellite instability, tumoral instability by itself is not a marker for familiality and should not be considered as evidence for an inherited syndrome.