Digestible (DE), metabolizable (ME), and net (NE) energy values of 61 diets were measured in 45-kg growing Large White boars. Net energy was calculated as energy retained at an average ME intake equivalent to 540 kcal/kg BW.60 plus fasting heat production estimated from data of the present experiment as 179 kcal/kg BW.60. Retained energy was measured as the difference between ME intake and heat production obtained in respiration chambers. The amounts of DE digested before the end of the ileum (DEi) and in the hindgut (DEh) were also measured for each diet. Regression equations for predicting dietary NE content from digestible nutrient levels or from DE or ME and chemical characteristics or from chemical composition only were calculated. Efficiencies of utilization of ME for NE (k, %) were also obtained. The mean k value for the 61 diets was 74% (range: 69 to 77). Digestible nutrients were used differently for NE: k values varied from approximately 60% for digestible CP or digestible cell wall fractions to 82% for starch and 90% for digestible ether extract. Accordingly, k for ME associated with DEh was lower than ME from DEi (58 vs 76%). Equations for predicting NE content are proposed. Their applicability, the comparison with other available NE prediction equations, and the effects of energy system on diet formulation are discussed.

Tumor-infiltrating immune cells (TIICs) play essential roles in cancer development and progression. However, the association of TIICs with prognosis in colorectal cancer (CRC) patients remains elusive. Infiltration of TIICs was assessed using ssGSEA and CIBERSORT tools. The association of TIICs with prognosis was analyzed in 1,802 CRC data downloaded from the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/) databases. Three populations of TIICs, including CD66b+ tumor-associated neutrophils (TANs), FoxP3+ Tregs, and CD163+ tumor-associated macrophages (TAMs) were selected for immunohistochemistry (IHC) validation analysis in 1,008 CRC biopsies, and their influence on clinical features and prognosis of CRC patients was analyzed. Prognostic models were constructed based on the training cohort (359 patients). The models were further tested and verified in testing (249 patients) and validation cohorts (400 patients). Based on ssGSEA and CIBERSORT analysis, the correlation between TIICs and CRC prognosis was inconsistent in different datasets. Moreover, the results with disease-free survival (DFS) and overall survival (OS) data in the same dataset also differed. The high abundance of TIICs found by ssGSEA or CIBERSORT tools can be used for prognostic evaluation effectively. IHC results showed that TANs, Tregs, TAMs were significantly correlated with prognosis in CRC patients and were independent prognostic factors (PDFS ≤ 0.001; POS ≤ 0.023). The prognostic predictive models were constructed based on the numbers of TANs, Tregs, TAMs (C-indexDFS&OS = 0.86; AICDFS = 448.43; AICOS = 184.30) and they were more reliable than traditional indicators for evaluating prognosis in CRC patients. Besides, TIICs may affect the response to chemotherapy. In conclusion, TIICs were correlated with clinical features and prognosis in patients with CRC and thus can be used as markers.

Digestible (DE), metabolizable (ME), and net (NE) energy values of seven diets were measured in castrated male pigs of 45 (Stage 1), 100 (Stage 2), or 150 (Stage 3) kg BW. Diets were prepared from a basal diet supplemented with cornstarch, or sucrose, or a protein mixture (referred to here as protein), or rapeseed oil, or a mixture of fibrous ingredients (referred to as fiber), or rapeseed oil+fiber. Diets were fed at similar levels (x maintenance) at the three stages. Heat production at different feeding levels, as measured by indirect calorimetry, allowed calculation of energy retained by each pig (equal to ME intake minus heat production) and an estimate of fasting heat production of all pigs (360 kcal/kg BW.42). Net energy intake was then calculated for each pig as retained energy plus 360 x BW.42. The amounts of DE digested before the end of the ileum (DEi) and in the hindgut (DEh) were measured. Formulation of diets allowed calculation of energy values of the ingredients added to the basal diet. Digestibility and metabolizability of diets increased significantly from Stages 1 to 3, with higher variations for low-energy diets. The NE:ME ratio (k, %) and dietary NE content were not affected (P > .05) by stage of growth. On average, k was 75%, with higher values for diets containing rapeseed oil or starch (77%) and lower estimates for the fiber diet (72%). As a consequence, k was approximately 90, 82, 80, 72, and 60% for rapeseed oil, cornstarch, sucrose, protein, and fiber, respectively. These values are consistent with the lower k value for ME from DEh (57 vs 78% for DEi). Present data confirm that the hierarchy between feeds is dependent on the energy system (DE vs ME vs NE) and that the NE concept is superior in predicting the "true" energy value. The present results combined with previous ones show that, under practical conditions, the same NE prediction equations based on digestible nutrient contents, or preferably DE or ME contents, can be applied at all stages of growth in pigs. However, attention should be paid to factors such as BW or feeding level that affect digestibility and metabolizability of feeds markedly. The effects are the most important for ingredients.

No abstract

The 5-methylcytosine (m5C) RNA methyltransferase NSUN2 is involved in the regulation of cell proliferation and metastasis formation and is upregulated in multiple cancers. However, the biological significance of NSUN2 in gastric cancer (GC) and the modification of NSUN2 itself have not been fully investigated. Here, we analyzed the expression level of NSUN2 in tissue microarrays containing 403 GC tissues by immunohistochemistry. NSUN2 was upregulated in GC, and that it was a predictor of poor prognosis. NSUN2 promotes the proliferation, migration, and invasion of GC cells in vitro. We also demonstrated that small ubiquitin-like modifier (SUMO)-2/3 interacts directly with NSUN2 by stabilizing it and mediating its nuclear transport. This facilitates the carcinogenic activity of NSUN2. Furthermore, m5C bisulfite sequencing (Bis-seq) in NSUN2-deficient GC cells showed that m5C-methylated genes are involved in multiple cancer-related signaling pathways. PIK3R1 and PCYT1A may be the target genes that participate in GC progression. Our findings revealed a novel mechanism by which NSUN2 functions in GC progression. This may provide new treatment options for GC patients.