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Background Treatment delays have frequently been observed in cancer patients. Whether the treatment delays would impair the survival of patients with nasopharyngeal carcinoma (NPC) is still unclear. Methods The data was derived from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Patients were divided into groups of timely treatment (adjustedHazard ratio (aHR) = 0.86, 95%CI: 0.74–0.99, p = 0.043) and in advanced stage (aHR = 0.85, 95%CI: 0.72-1.00, p = 0.049) NPC patients. Similar results were also observed in the CSS (aHR = 0.84, 95%CI: 0.71–0.98, p = 0.030 in whole stage patients and aHR = 0.83, 95%CI: 0.70–0.99, p = 0.038 in advanced stage patients). Conclusions Our results revealed that treatment delays might not impair the survival of NPC patients. Whether intermediate treatment delays could improve the clinical outcomes of NPC patients need further validation.

pH sensing plays a key role in the life sciences as well as the environmental, industrial, and agricultural fields. Carbon nanodots (C-dots) with small size, low toxicity, and excellent stability hold great potential in pH sensing as nanoprobes due to their intrinsic pH-sensitive photoluminescence (PL). Nonetheless, the undesirable sensitivity and response range of C-dot PL toward pH cannot meet the requirements of practical applications, and the unclear pH-sensitive PL mechanism makes it difficult to control their pH sensitivity. Herein, the quantitative correlation of pH-sensitive PL with specific surface structures of C-dots is uncovered for the first time, to our best knowledge. The association of carboxylate and H+ increases the ratio of nonradiation to radiation decay of C-dots through excited-state proton transfer, resulting in the decrease of PL intensity. Meanwhile, the dissociation of α-H in β-dicarbonyl forming enolate increases the extent of delocalization of the C-dots conjugated system, which induces the PL broadening to the red region and a decreasing intensity. Based on the understanding of the pH-sensitive PL mechanism, the pH-sensitive PL of C-dots can be switched by quantitative modulation of carboxyl and β-dicarbonyl groups to achieve a desirable pH response range with high sensitivity. This work contributes to a better understanding of the pH-sensitive PL of C-dots and therefore presents an effective strategy for controllably tuning their pH sensitivity, facilitating the rational design of C-dot-based pH sensors.

Additional file 1: Table S1. Nursery diet composition (as fed). Table S2. Growing and finishing diet composition (as fed). Table S3. List of age-associated bacterial features identified by LEfSe. Fig. S1. Longitudinal dynamics of alpha diversities (chao1, observed_features, and Shannon) of the gut microbiota in recipient pigs gavage fed with PBS with 20% glycerol (Con), fecal microbiota from a nursery pig with low (NL) or high body weight (NH), fecal microbiota from a growing pig with low (GL) or high body weight (GH), and fecal microbiota from a finishing pig with low (FL) or high body weight (FH) at weaning (a). Alpha diversities (chao1, observed_features, and shannon) of the six donors (b). Fig. S2. Bray-Curtis dissimilarity-based PCOA plots of gut microbial communities from donors and recipient pigs on d25, 35, 49, 63, and 174. Points represent the gut microbiota in recipient pigs gavage fed with PBS with 20% glycerol (Con/yellow), fecal microbiota from a nursery pig with low (NL/light red) or high body weight (NH/dark red), fecal microbiota from a growing pig with low (GL/light green) or high body weight (GH/dark green), and fecal microbiota from a finishing pig with low (FL/light purple) or high body weight (FH/dark purple) at weaning (a). Donors are yellow color filled triangles (light BW) and squares (heavy BW). Fig. S3. The top 50 estimated age (EMA)-associated features identified by the regression-based random forest. Fig. S4. Boxplots present the estimated microbiota ages (EMA) of the gut microbiota in recipient pigs on d 32, 35, 49, 63, and 104 (a). Distributions of EMA values from each group on d35 and 49 (b). The relative abundances of potential modulated bacterial members associated with EMA on d35 and 49 by fecal microbial transplantations (c). Fig. S5. Boxplots showing the relative abundances of BW-associated bacterial features at each treatment (a. d63 and 104; b. d35; c. d49). The scatter plots with regression line showing correlations of these features with body weight (BW) on each day. Box color represents different FMT groups (Con/blue: vehicle group; NL/light red: nursery stage microbiota from a light BW donor; NH/dark red: nursery stage microbiota from a heavy BW donor; GL/light green: growing stage microbiota from a light BW donor; GH/dark green: growing stage microbiota from a heavy BW donor; FL/light red: finishing stage microbiota from a light BW donor; FH/dark red: finishing stage microbiota from a heavy BW donor). Fig. S6. Abundances of the predicted metabolic pathways in the recipient fecal microbiotas that were gavage fed either a placebo or growing stage microbiotas from a heavy body weight donor (GH) on d104. Fig. S7. Abundances of the influenced metabolic pathways predicted in the recipient microbiotas that were gavage fed growing stage microbiotas from both light (GL) and heavy body weight donors (GH) on d49. Fig. S8. Abundances of the influenced metabolic pathways predicted in the recipient microbiotas that were gavage fed either placebo or growing stage microbiotas from a heavy body weight donor (GH) on d49.