Table_2_A Systems Analysis of the Relationships Between Anemia and Ischemic Stroke Rehabilitation Based on RNA-Seq Data.XLS

Ischemic stroke (IS) is one of the main causes of morbidity and disability worldwide due to its complex mechanism. Anemia was characterized as a risk factor of IS because the direct connection between central nervous system, blood supply, and tissue oxygen delivery. As the key oxygen-carrying molecule in the blood, hemoglobin (Hb) may be decisive in the destiny of penumbral area or influence the brain recovery and neurologic function, which could finally affect the outcome of IS. However, more detailed information on the expression levels of Hb related genes were still lacking possibly because the concentration of Hb was determined by the genes’ expression several hours ago, which may make the research more difficult to perform. This time gap between gene expressions and protein concentration could make these genes predictive bio-markers for IS outcome. In this study, we choose 28 IS patients, of which 12 were suffering from anemia. Statistical analysis results showed that the outcomes of the patients were different when dividing them into two groups characterized by Hb concentration. 2 sex and age matched patients were first chosen to perform RNA-seq analysis on, on two occasions at two different time points, after which the Hb counts were tested at least 24 h after sequencing. Results showed that the outcome of anemia patients was poor compared with non-anemia patients. Two other patients were then chosen for analysis which excluded the coincidence of other factors. The results showed that the low value of Hb under 13 g/dL in men were closely related to the poor outcome of IS patients. Differently expressed Hb related genes were tested and six genes were shown to be positively correlated with the recovery degree of IS patients: ELANE, FGF23, HBB, PIEZO1, RASA4, and PRTN3. Gene CPM was shown to be negatively correlated with clinical outcomes. All of the seven genes were validated to be related to strokes using real-time PCR or literature searches. Taken together, these genes could be considered as new predictors for the recovery of IS patients.</p

Image_4_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_3_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_2_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_1_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_5_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Additional file 1 of Associations between apolipoprotein B and bone mineral density: a population-based study

Additional file 1: Table S1. The association between apolipoprotein B and bone mineral density before imputation

Image_3_Clinical study on sequential treatment of severe diarrhea irritable bowel syndrome with precision probiotic strains transplantation capsules, fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules.tif

ObjectiveTo study the effect of precision probiotic strains transplantation capsules on diarrhea irritable bowel syndrome compared with fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules.MethodsTwo patients with severe irritable bowel syndrome were treated with precision probiotic strains transplantation capsules, fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules in sequence. IBS-SSS, IBS-QoL, GSRS, stool frequency, stool character, degree of abdominal pain, GAD-7, and PHQ9 scores of patients at 0, 2, 4, 6, 8, 10, and 12 weeks of treatment were monitored and recorded, and stool samples were collected for metagenomics and metabolomics.ResultsIt was found that the IBS-SSS score of patient case 1 decreased by 175 points and that of patient case 2 decreased by 100 points after treatment of precision probiotic strains transplantation capsules. There was no significant decrease after fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules were used. At the same time, compared with fecal microbiota transplantation and live combined bacillus subtilis and enterococcus faecium capsules, the IBS QoL, stool frequency, stool character, degree of abdominal pain and GAD-7 score of patient case 1 improved more significantly by the precision probiotic strains transplantation capsules. And the stool frequency and stool character score of patient case 2 decreased more significantly. Intestinal microbiota also improved more significantly after the precise capsule transplantation treatment. And we found Eubacterium_ Eligens showed the same change trend in the treatment of two patients, which may play a role in the treatment.Conclusionprecision probiotic strains transplantation capsules is more beneficial to improve the intestinal microbiota of patients than microbiota transplantation capsule and live combined bacillus subtilis and enterococcus faecium capsules, so as to better alleviate clinical symptoms. This study provides a more perfect and convenient therapeutic drugs for the treatment of IBS.</p

Table_1_Clinical study on sequential treatment of severe diarrhea irritable bowel syndrome with precision probiotic strains transplantation capsules, fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules.docx

ObjectiveTo study the effect of precision probiotic strains transplantation capsules on diarrhea irritable bowel syndrome compared with fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules.MethodsTwo patients with severe irritable bowel syndrome were treated with precision probiotic strains transplantation capsules, fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules in sequence. IBS-SSS, IBS-QoL, GSRS, stool frequency, stool character, degree of abdominal pain, GAD-7, and PHQ9 scores of patients at 0, 2, 4, 6, 8, 10, and 12 weeks of treatment were monitored and recorded, and stool samples were collected for metagenomics and metabolomics.ResultsIt was found that the IBS-SSS score of patient case 1 decreased by 175 points and that of patient case 2 decreased by 100 points after treatment of precision probiotic strains transplantation capsules. There was no significant decrease after fecal microbiota transplantation capsules and live combined bacillus subtilis and enterococcus faecium capsules were used. At the same time, compared with fecal microbiota transplantation and live combined bacillus subtilis and enterococcus faecium capsules, the IBS QoL, stool frequency, stool character, degree of abdominal pain and GAD-7 score of patient case 1 improved more significantly by the precision probiotic strains transplantation capsules. And the stool frequency and stool character score of patient case 2 decreased more significantly. Intestinal microbiota also improved more significantly after the precise capsule transplantation treatment. And we found Eubacterium_ Eligens showed the same change trend in the treatment of two patients, which may play a role in the treatment.Conclusionprecision probiotic strains transplantation capsules is more beneficial to improve the intestinal microbiota of patients than microbiota transplantation capsule and live combined bacillus subtilis and enterococcus faecium capsules, so as to better alleviate clinical symptoms. This study provides a more perfect and convenient therapeutic drugs for the treatment of IBS.</p

Tumor Microenvironment Activated Membrane Fusogenic Liposome with Speedy Antibody and Doxorubicin Delivery for Synergistic Treatment of Metastatic Tumors

Metastasis is the principal event leading to breast cancer death. Discovery of novel therapeutic approaches that are specific in targeting tumor metastasis factors while at the same time are an effective treatment of the tumor is urgently required. S100A4 protein is a key player in promoting metastasis and sequestrating the effect of tumor-suppressor protein p53. Here, a tumor microenvironment activated membrane fusogenic liposome was prepared to deliver rapidly anti-S100A4 antibody and doxorubicin into the cytoplasm directly in a fusion-dependent manner in order to bypass the cellular endocytosis to avoid the inefficient escape and degradation in the acidic endosome. After intracellular S100A4 blockage with anti-S100A4 antibody, the cytoskeleton of breast cancer 4T1 cells was rearranged and cell motility was suppressed. In the meantime, the antitumor effect of doxorubicin was enormously enhanced by reversing the effect of S100A4 on the sequestration of tumor-suppressor protein p53. Importantly, both local growth and metastasis of 4T1 cells were inhibited in a xenograft mouse model. Together, the speedy delivery of antibody and doxorubicin into cytoplasm based on a new membrane fusogenic liposome was an innovative approach for metastatic breast cancer treatment