Correlation analysis of vaginal microecology and different types of human papillomavirus infection: a study conducted at a hospital in northwest China.

Background: Vaginal microecology has a definite influence on human papillomavirus (HPV) infection and clearance, but the specific correlation is still controversial. This research aimed to investigate the differences in the vaginal microenvironment of different types of HPV infection and also provide data supporting clinical diagnosis and treatment. Methods: According to strict inclusion and exclusion criteria, the case data of 2,358 female patients who underwent vaginal microecology and HPV-DNA tests at the same time in the Department of Obstetrics and Gynecology of the First Affiliated Hospital of Xi'an Jiaotong University from May 2021 to March 2022 were retrospectively analyzed. The population was divided into two groups: an HPV-positive group and an HPV-negative group. HPV-positive patients were further classified into HPV16/18-positive group and HPV other subtypes positive group. The vaginal microecology of HPV-infected patients was analyzed using the chi-square test, Fisher's exact test, and logistic regression. Results: Among the 2,358 female patients, the HPV infection rate was 20.27% (478/2,358), of which the HPV16/18 infection rate was 25.73% (123/478), and the HPV other subtypes infection rate was 74.27% (355/478). The difference in HPV infection rates between the age groups was statistically significant (P < 0.01). The prevalence of mixed vaginitis was 14.37% (339/2,358), with bacterial vaginosis (BV) paired with aerobic vaginitis (AV) accounting for the majority (66.37%). The difference in HPV infection rates among mixed vaginitis was not statistically significant (P > 0.05). The prevalence of single vaginitis was 24.22% (571/2,358), with the most frequent being vulvovaginal Candidiasis (VVC; 47.29%, 270/571), and there was a significant difference in HPV infection rates among single vaginitis (P < 0.001). Patients with BV had a higher risk of being positive for HPV16/18 (OR: 1.815, 95% CI: 1.050-3.139) and other subtypes (OR: 1.830, 95% CI: 1.254-2.669). Patients with Trichomoniasis were at higher odds of other HPV subtype infections (OR: 1.857, 95% CI: 1.004-3.437). On the contrary, patients with VVC had lower odds of becoming infected with other HPV subtypes (OR: 0.562, 95% CI: 0.380-0.831). Conclusion: There were disparities in HPV infection among different age groups; therefore, we should pay attention to the prevention and treatment of susceptible individuals. BV and Trichomoniasis are linked to HPV infection; hence, restoring the balance of vaginal microecology could assist in the prevention of HPV infection. As a protective factor for other HPV subtype infections, VVC may provide new insights into the development of immunotherapeutic therapies.

Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.

Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky-Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS-SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R 2 = 0.741-0.852), while the SVR and PLSR models performed similarly (R 2 = 0.733-0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R 2 was 0.849-0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R 2 increased by 14.39%, and the R 2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.

Therapeutic Potential of Patient iPSC-Derived iMelanocytes in Autologous Transplantation.

Induced pluripotent stem cells (iPSCs) are a promising melanocyte source as they propagate indefinitely and can be established from patients. However, the in vivo functions of human iPSC-derived melanocytes (hiMels) remain unknown. Here, we generated hiMels from vitiligo patients using a three-dimensional system with enhanced differentiation efficiency, which showed characteristics of human epidermal melanocytes with high sequence similarity and involved in multiple vitiligo-associated signaling pathways. A modified hair follicle reconstitution assay in vivo showed that MITF+PAX3+TYRP1+ hiMels were localized in the mouse hair bulb and epidermis and produced melanin up to 7 weeks after transplantation, whereas MITF+PAX3+TYRP1- hiMelanocyte stem cells integrated into the bulge-subbulge regions. Overall, these data demonstrate the long-term functions of hiMels in vivo to reconstitute pigmented hair follicles and to integrate into normal regions for both mature melanocytes and melanocyte stem cells, providing an alternative source of personalized cellular therapy for depigmentation.