. Lymph node harvest in colon cancer specimens depends on tumour factors, patients and doctors, but foremost on specimen handling, APMIS 2011; 119: 127-34.There are good indications that the number of lymph nodes found in the specimen after resections for colon cancer somehow has a bearing on prognosis. Many factors have been reported in the literature to influence lymph node retrieval. We wanted to assess these closer with special focus on the pathology handling process in our own practice. A range of international literature was reviewed to study what has been found to influence lymph node harvest. A questionnaire was sent to 13 renowned national and international institutions to explore their handling of the colon cancer specimens to obtain a histological diagnosis. A retrospective, hospital audit was undertaken to examine if the number of lymph nodes and staging after examinations of the specimens varied between individual pathologists. In the literature, tumour and patient characteristics, as well as the surgeon and the pathologist, are found to be influential, but it is difficult to ascertain which ones are truly essential. Fat solvents were found by several to increase the lymph node yield, although some also opposed this finding. Our questionnaire showed some variations in the routines of each Department. A junior pathologist was more likely to inspect the specimen first hand and not more than half employed specific lymph node detection strategies while three of 13 did not seek a minimum number of lymph nodes. Still every department had implemented a standard procedure for such examinations. The internal audit showed without doubt that the devotion of the pathologist secured significantly more lymph nodes from the specimen and this may also have detected more stage III cancers. Several tumour and individual patient characteristics, surgical approach and specimen handling may influence lymph node yield and theoretically, TNM staging. Our investigation specifically suggests that tissue handling by pathologists may be a prominent factor in lymph node harvest from colon cancer specimens.
A total of 362 abstracts were retrieved following the search. Of these 325 did not meet the inclusion criteria and were excluded, leaving 37 studies to be included in the review. Among these, 36 were case series and one a small randomised non-blinded study Conclusions: The existing quality of evidence for risks and benefits of the operation is very low. It is unclear what the complication frequencies are after the reconstruction, and what effect on quality-of-life the operation has. Prospective studies of high quality are needed to evaluate the health effects and risks with NAC reconstruction.
Knowledge on dynamic interactions in microbiota is pivotal for understanding the role of bacteria in the gut. We herein present comprehensive dynamic models of the horse cecal microbiota, which include short-chained fatty acids, carbohydrate metabolic networks, and taxonomy. Dynamic models were derived from time-series data in a crossover experiment in which four cecum-cannulated horses were fed a starch-rich diet of hay supplemented with barley (starch intake 2 g kg−1 body weight per day) and a fiber-rich diet of only hay. Cecal contents were sampled via the cannula each h for 24 h for both diets. We observed marked differences in the microbial dynamic interaction patterns for Fibrobacter succinogenes, Lachnospiraceae, Streptococcus, Treponema, Anaerostipes, and Anaerovibrio between the two diet groups. Fluctuations and microbiota interactions were the most pronounced for the starch rich diet, with Streptococcus spp. and Anaerovibrio spp. showing the largest fluctuations. Shotgun metagenome sequencing revealed that diet differences may be explained by modular switches in metabolic cross-feeding between microbial consortia in which fermentation is linked to sugar alcohols and amino sugars for the starch-rich diet and monosaccharides for the fiber-rich diet. In conclusion, diet may not only affect the composition of the cecal microbiota, but also dynamic interactions and metabolic cross-feeding.
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