Additional file 1 of Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data

Supplementary Material 1: Table S1 All feature codes and comments in datasets. Table S2 Classification Performance of 15 models for predicting chlamydia in male populations. Table S3 Classification Performance of 15 models for predicting genital herpes in male populations. Table S4 Classification Performance of 15 models for predicting genital warts in male populations. Table S5 Classification Performance of 15 models for predicting gonorrhea in male populations. Table S6 Classification Performance of 15 models for predicting STIs in male populations. Table S7 Classification Performance of 15 models for predicting chlamydia in female populations. Table S8 Classification Performance of 15 models for predicting genital herpes in female populations. Table S9 Classification Performance of 15 models for predicting genital warts in female populations. Table S10 Classification Performance of 15 models for predicting gonorrhea in female populations. Table S11 Classification Performance of 15 models for predicting HPV in female populations. Table S12 Classification Performance of 15 models for predicting STIs in female populations. Figure S1. The CatBoost classifiers for predicting chlamydia(A), genital herpes(B), genital warts(C), gonorrhea(D), and overall STIs(E) based on the confusion matrix in male populations. Figure S2. The CatBoost classifiers for predicting chlamydia(A), genital herpes(B), genital warts(C), gonorrhea(D), HPV(E) and overall STIs(F) based on the confusion matrix in female populations. Figure S3. The CatBoost classifiers for predicting chlamydia(A), genital herpes(B), genital warts(C), gonorrhea(D), and overall STIs(E) based on the ROC plots in male populations. Figure S4. The CatBoost classifiers for predicting chlamydia(A), genital herpes(B), genital warts(C), gonorrhea(D), HPV(E) and overall STIs(F) based on the ROC plots in female population

MicroRNAs Enhance Keratinocyte Proliferative Capacity in a Stem Cell-Enriched Epithelium

<div><p>MicroRNAs are critical regulators of stem cell behavior. The miR-103/107 family is preferentially expressed in the stem cell-enriched corneal limbal epithelium and plays an important role in coordinating several intrinsic characteristics of limbal epithelial stem cells. To elucidate further the mechanisms by which miRs-103/107 function in regulating limbal epithelial stem cells, we investigate the global effects of miRs-103/107 on gene expression in an unbiased manner. Using antagomirs-103/107, we knocked down endogenous miRs-103/107 in keratinocytes and conducted an mRNA profiling study. We show that miRs-103/107 target mitogen-activated protein kinase kinase kinase 7 (MAP3K7) and thereby negatively regulate the p38/AP-1 pathway, which directs epithelial cells towards a differentiated state. Pharmacological inhibition of p38 increases holoclone colony formation, a measure of proliferative capacity. This suggests that the negative regulation of p38 by miRs-103/107 contributes to enhanced proliferative capacity, which is a hallmark of stem cells. Since miRs-103/107 also promote increased holoclone colony formation by regulating JNK activation through non-canonical Wnt signaling, we believe that this microRNA family preserves “stemness” by mediating the crosstalk between the Wnt/JNK and MAP3K7/p38/AP-1 pathways.</p></div

MiRs-103/107 negatively regulate p38/AP-1 pathway in HLEKs via directly targeting MAP3K7.

<p>(A) HLEKs were treated with Ir-antago, Antago-103 or Antago-107 for 24 hours; then lysates were immunoblotted against phospho-p38 (p-p38), p38, phosphor-c-Jun (p-c-Jun), c-Jun and GAPDH. (B) Schematic diagrams of miR-103/107 binding sites in the 3’UTR region of MAP3K7 mRNA. Bold: seed sequences. (C) Screening the interaction of MAP3K7 with miR-103/107 using the psiCHECKTM-2 vector harboring 3’ untranslated region (UTR) of MAP3K7. Constructs were co-transfected with either miR-control, or miR-107 into cells. Twenty four hour after transfection, firefly and renilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System. N: precursor microRNA control, 107: precursor microRNA-107. T test was performed. *p<0.05. (D) Immunoblotting of endogenous total MAP3K7, phospho-MAP3K7 and GAPDH following over-expression of either pre-miR-negative control (N), pre-miR-103(103), or pre-miR-107(107). (E) HLEKs were treated with Ir-antago, Antago-103 or Antago-107 for 24 hours and lysates were immunoblotted for total MAP3K7, phospho-MAP3K7 and GAPDH. Numbers below the panels represent the normalized expression signal of proteins.</p

Functional Annotation Clustering analysis for known target genes of miRs-103/107.

<p>The known target gene list was exported from miRTarBase. The clustering analysis was performed by DAVID Functional Annotation Bioinformatics Resources v6.7. The rank is based on the Enrichment score, which represents mean p-value (in –log scale).</p

MiRs-103/107 negatively regulate AP-1 in HLEKs.

<p>Real time qPCR analysis of KLF6, PPP1R15A, JunD, c-Jun, c-Maf, NOXA, HMOX1, KLF2, and TXNIP levels in HLEKs that were treated with Ir-antago, Antago-103, or Antago-107. Values are median with range of four independent experiments. The significance of the differences between 3 groups was tested by non-parametric one-way ANOVA. *p<0.05.</p

Networking analysis of miR-103/107-regulated genes.

<p>This network consists only of the seed nodes and of their direct interactions. Seed nodes included: (i) the differentially expressed genes in antagomir-103/107-treated HLEKs and (ii) miRs-103/107’s direct target genes that involve in stem cell maintenance. The genes in the box are known direct targets of miRS-103/107.</p

Expressions of limbal preferred microRNAs during postnatal eye development.

<p>MicroRNA qPCR analysis of miR-328, miR-103, miR-107, miR-350, miR-99a, let7e, miR-199a-3p, miR-199b*, and miR-342-3p levels in limbal epithelium at postnatal day 3, 7, 14, and 60. Values are means ± SD of three independent experiments.</p

Functional Annotation Clustering analysis for differentially expressed genes in antago-103/107 treated HLEKs when compared with Ir-antago treated HLEKs.

<p>The clustering analysis was performed by DAVID Functional Annotation Bioinformatics Resources v6.7. The rank is based on the Enrichment score, which represents mean p-value (in –log scale).</p

Fig 6 -

(a) Lateral skin temperature curve when moxa-burning temperature changes. (b) Vertical skin temperature curve when moxa-burning temperature changes.</p

The optimal solutions under different RPDIs.

During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.</div