Additional file 1 of Differences between physician and patient preferences for cancer treatments: a systematic review

Supplementary Material

Data_Sheet_1_Two Nipped-B-Like Protein A (Nipbla) Gametologs in Chinese Tongue Sole (Cynoglossus semilaevis): The Identification of Alternative Splicing, Expression Pattern, and Promoter Activity Analysis.PDF

In mammals, the mutation of nipped-B-like protein (nipbl) leads to Cornelia de Lange Syndrome (CdLS), characterized by low birth weight, short stature, and structural abnormalities of the skeleton, heart, and gut. In Chinese tongue sole (Cynoglossus semilaevis), a typical marine fish exhibiting sexual size dimorphism, the nipbl homolog gene (nipped-B-like protein A (nipbla)) was also screened with female higher expression level by somatotropic and reproductive tissues’ transcriptomic analysis. In this study, two nipbla genes, namely, nipbla-w and nipbla-z, were identified from the W and Z chromosomes of C. semilaevis, respectively. Similar to other mammalian and fish species nipbl, the two homolog proteins of C. semilaevis contained two conserved domains, namely, cohesion_HEAT and Nipped-B_C. The phylogenetic tree analysis showed that these two nipbla gametolog proteins were first clustered together and then grouped with other fish species. At least two types of alternative splicing sites were observed in exon 12 of the nipbla-z gene, which produced nipbla-z-tv1 and nipbla-z-tv2. Also, the sex-biased expression patterns of different nipbla-w and nipbla-z transcripts in female and male tissues were revealed by quantitative PCR (qPCR). The highest expression level of nipbla-w was observed in female gonad. While nipbla-z-tv1 exhibited relatively high expression in the muscle, liver, gonad, and brain, nipbla-z-tv2 only showed its expression superiority in the muscle of male individuals. The promoter regions of nipbla genes were amplified, and their transcription activity was successfully verified by a dual-luciferase reporter system. After nipbla-w and nipbla-z knockdown in the brain cell lines by RNA interference, a series of growth-related genes were influenced, including Bone Morphogenetic Protein 4 (bmp4), Wnt Family Member 11 (wnt11), and Sprouty Related EVH1 Domain Containing 2 (spred2). The prediction of transcription factors suggested that c-Jun, sex-determining region Y (SRY), POU Class 1 Homeobox 1 (POU1F1a), myogenic differentiation antigen (MyoD), signal transducer and activator of transcription 5a (STAT5A), and nuclear factor I C (Nfic) might be the putative upstream regulatory factors for nipbla; among them, c-Jun has been verified to effectively regulate the transcriptional activity of nipbla. The identification of two nipbla genes provided important data for interpreting the sexual size dimorphism in C. semilaevis.</p

Data_Sheet_2_Two Nipped-B-Like Protein A (Nipbla) Gametologs in Chinese Tongue Sole (Cynoglossus semilaevis): The Identification of Alternative Splicing, Expression Pattern, and Promoter Activity Analysis.ZIP

In mammals, the mutation of nipped-B-like protein (nipbl) leads to Cornelia de Lange Syndrome (CdLS), characterized by low birth weight, short stature, and structural abnormalities of the skeleton, heart, and gut. In Chinese tongue sole (Cynoglossus semilaevis), a typical marine fish exhibiting sexual size dimorphism, the nipbl homolog gene (nipped-B-like protein A (nipbla)) was also screened with female higher expression level by somatotropic and reproductive tissues’ transcriptomic analysis. In this study, two nipbla genes, namely, nipbla-w and nipbla-z, were identified from the W and Z chromosomes of C. semilaevis, respectively. Similar to other mammalian and fish species nipbl, the two homolog proteins of C. semilaevis contained two conserved domains, namely, cohesion_HEAT and Nipped-B_C. The phylogenetic tree analysis showed that these two nipbla gametolog proteins were first clustered together and then grouped with other fish species. At least two types of alternative splicing sites were observed in exon 12 of the nipbla-z gene, which produced nipbla-z-tv1 and nipbla-z-tv2. Also, the sex-biased expression patterns of different nipbla-w and nipbla-z transcripts in female and male tissues were revealed by quantitative PCR (qPCR). The highest expression level of nipbla-w was observed in female gonad. While nipbla-z-tv1 exhibited relatively high expression in the muscle, liver, gonad, and brain, nipbla-z-tv2 only showed its expression superiority in the muscle of male individuals. The promoter regions of nipbla genes were amplified, and their transcription activity was successfully verified by a dual-luciferase reporter system. After nipbla-w and nipbla-z knockdown in the brain cell lines by RNA interference, a series of growth-related genes were influenced, including Bone Morphogenetic Protein 4 (bmp4), Wnt Family Member 11 (wnt11), and Sprouty Related EVH1 Domain Containing 2 (spred2). The prediction of transcription factors suggested that c-Jun, sex-determining region Y (SRY), POU Class 1 Homeobox 1 (POU1F1a), myogenic differentiation antigen (MyoD), signal transducer and activator of transcription 5a (STAT5A), and nuclear factor I C (Nfic) might be the putative upstream regulatory factors for nipbla; among them, c-Jun has been verified to effectively regulate the transcriptional activity of nipbla. The identification of two nipbla genes provided important data for interpreting the sexual size dimorphism in C. semilaevis.</p

Table_3_An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.xlsx

PurposeTo establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.MethodsWe have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with ResultsThe areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.ConclusionsBased on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.</p

Table_1_An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.xlsx

PurposeTo establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.MethodsWe have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with ResultsThe areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.ConclusionsBased on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.</p

Table_4_An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.xlsx

PurposeTo establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.MethodsWe have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with ResultsThe areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.ConclusionsBased on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.</p

Image_1_Physiological and Molecular Responses in the Gill of the Swimming Crab Portunus trituberculatus During Long-Term Ammonia Stress.pdf

Ammonia is a common environmental stressor encountered during aquaculture, and is a significant concern due to its adverse biological effects on vertebrate and invertebrate including crustaceans. However, little information is available on physiological and molecular responses in crustaceans under long-term ammonia exposure, which often occurs in aquaculture practices. Here, we investigated temporal physiological and molecular responses in the gills, the main ammonia excretion organ, of the swimming crab Portunus trituberculatus following long-term (4 weeks) exposure to three different ammonia nitrogen concentrations (2, 4, and 8 mg l–1), in comparison to seawater (ammonia nitrogen below 0.03 mg l–1). The results revealed that after ammonia stress, the ammonia excretion and detoxification pathways were initially up-regulated. These processes appear compromised as the exposure duration extended, leading to accumulation of hemolymph ammonia, which coincided with the reduction of adenosine 5′-triphosphate (ATP) and adenylate energy charge (AEC). Considering that ammonia excretion and detoxification are highly energy-consuming, the depression of these pathways are, at least partly, associated with disruption of energy homeostasis in gills after prolonged ammonia exposure. Furthermore, our results indicated that long-term ammonia exposure can impair the antioxidant defense and result in increased lipid peroxidation, as well as induce endoplasmic reticulum stress, which in turn lead to apoptosis through p53-bax pathway in gills of the swimming crab. The findings of the present study further our understanding of adverse effects and underlying mechanisms of long-term ammonia in decapods, and provide valuable information for aquaculture management of P. trituberculatus.</p

Table_2_An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.xlsx

PurposeTo establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.MethodsWe have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with ResultsThe areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.ConclusionsBased on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.</p

DataSheet_1_An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.docx

PurposeTo establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.MethodsWe have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with ResultsThe areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.ConclusionsBased on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.</p

Table_5_An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study.xlsx

PurposeTo establish a more convenient ovarian reserve model with anti-Müllerian hormone (AMH) level and age (the AA model), with blood samples taken at any time in the menstrual cycle.MethodsWe have established this AA model for predicting ovarian reserve using the AMH level and age. The outcome variable was defined as poor ovarian response (POR) with ResultsThe areas under the receiver operating characteristic curve for the training, inner, and external validation sets were 0.862, 0.843, and 0.854 respectively. The main effects of AMH level and age contributing to the prediction of POR were 95.3% and 1.8%, respectively. The incidences of POR increased with its predicted probability in both the model building and in external validation datasets, indicating its stability. An online website-based tool for assessing the score of ovarian reserve (http://121.43.113.123:9999) has been developed.ConclusionsBased on external validation data, the AA model performed well in predicting POR, and was more cost-effective and convenient than our previous published models.</p