Isoflurane increases Tau-PS262 levels in brain tissues of AD Tg mice.

<p><i>a.</i> Isoflurane anesthesia (lanes 2 and 4) increases Tau-PS262 levels as compared to the control condition (lanes 1 and 3) in the brain tissues of AD Tg mice at six hours after the isoflurane anesthesia. <i>b.</i> Quantification of the Western blot shows that isoflurane anesthesia (black bar, **P = 0.0069) increases Tau-PS262 levels as compared to the control condition (white bar). <i>c.</i> Isoflurane anesthesia (lanes 2 and 4) increases Tau-PS262 levels as compared to the control condition (lanes 1 and 3) in brain tissues of AD Tg mice at 12 hours after the isoflurane anesthesia. <i>d.</i> Quantification of the Western blot shows that isoflurane anesthesia (black bar, **P = 0.0016) increases Tau-PS262 levels as compared to the control condition (white bar). <i>e.</i> Isoflurane anesthesia (lanes 2, 4 and 6) increases Tau-PS262 levels as compared to the control condition (lanes 1, 3 and 5) in brain tissues of AD Tg mice at 24 hours after the isoflurane anesthesia. <i>f.</i> Quantification of the Western blot shows that isoflurane anesthesia (black bar, *P = 0.044) increases Tau-PS262 levels as compared to the control condition (white bar). We have averaged results from three independent experiments. AD, Alzheimer’s disease, Tg, transgenic. N = 3.</p

Aβ42 levels in the primary neurons from WT and AD Tg mice.

<p>Extracellular Aβ42 levels are higher in the primary neurons from AD Tg mice than those in the primary neurons from WT mice. N = 6.</p><p>Aβ, β-amyloid protein; WT, wild-type; AD, Alzheimer’s disease; Tg, transgenic.</p

Isoflurane increases Tau-PS262 levels in WT and AD Tg mice primary neurons.

<p><i>a.</i> Isoflurane treatment (lanes 4 and 5) increases Tau-PS262 levels as compared to the control condition (lanes 1 to 3) in WT mice primary neurons. <i>b.</i> Quantification of the Western blot shows that isoflurane treatment (black bar, *P = 0.0162) increases Tau-PS262 levels as compared to the control condition (white bar). <i>c.</i> Isoflurane treatment (lane 4) increases Tau-PS262 levels as compared to the control condition (lanes 1 to 3) in AD Tg mice primary neurons. <i>d.</i> Quantification of the Western blot shows that isoflurane treatment (black bar, *P = 0.014) increases Tau-PS262 levels as compared to the control condition (white bar) in AD Tg mice primary neurons. <i>e.</i> The baseline levels of Tau-PS262 levels in AD Tg mice primary neurons (lanes 7 to 9) are higher than those in WT mice primary neurons (lanes 1 to 3); and isoflurane treatment (lanes 10 to 12) induces a greater increase in Tau-PS262 levels in AD Tg mice primary neurons than in WT mice primary neurons (lanes 4 to 6). <i>f.</i> Quantification of the Western blot shows that there is a higher baseline Tau-PS262 levels in AD Tg mice primary neurons (gray bar) than WT mice primary neurons (white bar): **P = 0.0014; and isoflurane induces a greater increase in Tau-PS262 levels in AD Tg mice primary neurons (net bar) than in WT mice primary neurons (black bar): **P = 0.0038. We have averaged results from 6 to 12 independent experiments. WT, wild-type, AD, Alzheimer’s disease, Tg, transgenic. N = 6–12.</p

Table_7_Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.docx

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.</p

Table_8_Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.xlsx

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.</p

Gallium Electrocrystallization Process on SUS316 Cathode in Alkaline Solution

The negative reduction potential of gallium leads to the occurrence of the hydrogen evolution reaction (HER) during the process of gallium electrowinning. However, accurately measuring the partial current of the gallium electrodeposition reaction (GER) using the chronoamperometry (CA) test is not possible. To address this issue, a model capable of separating the partial current of the GER and HER from the total current was employed, and the CA curves were fitted at different potentials. This approach allowed the researchers to determine the relevant electrochemical parameters. The results indicated that the measured value of the current efficiency was in agreement with the fitted value, indicating the reliability of the model. The nondimensional forms of the partial contribution of GER were compared with the Scharifker–Hills model to determine the nucleation mechanism. The model showed that the GER transitions from instantaneous nucleation to progressive nucleation as the potential increases, which was further confirmed by the microscopic morphology at different electrodeposition times. In summary, this study offers a promising solution for accurately measuring the partial current of GER during gallium electrodeposition

Table_9_Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.xlsx

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.</p

Table_4_Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.xlsx

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.</p

Table_3_Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.xlsx

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.</p

Image_5_Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.tif

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.</p