Intestinal Flora Changes Induced by a High-Fat Diet Promote Activation of Primordial Follicles through Macrophage Infiltration and Inflammatory Factor Secretion in Mouse Ovaries.

Obesity induced by a high-fat diet (HFD) leads to the excessive consumption of primordial follicles (PFs) in the ovaries. There is systemic chronic inflammation under HFD conditions, but no previous studies have explored whether there is a certain causal relationship between HFD-induced chronic inflammation and the overactivation of PFs. Here, we showed that HFD causes disorders of intestinal microflora in mice, with five Gram-negative bacteria showing the most profound increase at the genus level compared to the normal diet (ND) groups and contributes to the production of endotoxin. Endotoxin promotes M1 macrophage infiltration in the ovaries, where they exhibit proinflammatory actions by secreting cytokines IL-6, IL-8, and TNFα. These cytokines then boost the activation of PFs by activating Signal Transducer and Activator of Transcription 3 (STAT3) signaling in follicles. Interestingly, transplantation of the HFD intestinal microflora to the ND mice partly replicates ovarian macrophage infiltration, proinflammation, and the overactivation of PFs. Conversely, transplanting the ND fecal microbiota to the HFD mice can alleviate ovarian inflammation and rescue the excessive consumption of PFs. Our findings uncover a novel and critical function of gut microbes in the process of PF overactivation under HFD conditions, and may provide a new theoretical basis for the microbial treatment of patients with premature ovarian insufficiency caused by HFD.

Study on the Estimation of Forest Volume Based on Multi-Source Data.

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.

Vaccinating women previously treated for human papillomavirus-related cervical precancerous lesions is highly cost-effective in China.

Background: The 2021 Chinese Expert Consensus on the Clinical Application of the Human Papillomavirus (HPV) Vaccine recommended vaccination for women who previously received ablative or excisional treatment for high-grade squamous intraepithelial lesion (HSIL). This study evaluates the cost-effectiveness of HPV vaccination in women previously treated for cervical precancerous lesions. Methods: We used a Markov model to simulate the disease progression of both low- and high-risk HPV subtypes. We followed a cohort of 100,000 women aged 18-45 years who received treatment for cervical precancerous lesions for a lifetime (80 years). We used the Incremental Cost-Effectiveness Ratios (ICER) with a 5% discount rate to measure the cost-effectiveness of nine vaccination strategies, including a combination of HPV bivalent (HPV-2), quadrivalent (HPV-4) and nonavalent vaccine (HPV-9), each with three vaccination doses (one-, two- and three-dose). We conducted one-way sensitivity analysis and probabilistic sensitivity analysis. We followed the CHEERS 2022 guidelines. Results: Compared to the status quo, the nine vaccination strategies would result in $3.057-33.124 million incremental cost and 94-1,211 incremental quality-adjusted life-years (QALYs) in 100,000 women previously treated for cervical precancerous lesions. Three vaccination strategies were identified on the cost-effectiveness frontier. In particular, ICER for one-dose HPV-4 vaccination was US$10,025/QALY compared to the status quo (no vaccination); ICER for two-dose HPV-4 vaccination was US$17,641//QALY gained compared to one-dose HPV-4 vaccination; ICER for three-dose HPV-4 vaccination was US$27,785/QALY gained compared with two-dose HPV-4 vaccination. With a willingness-to-pay of three times gross domestic product per capita (US$37655), three-dose HPV-4 vaccination was the most cost-effective vaccination strategy compared with the lower-cost non-dominated strategy on the cost-effectiveness frontier. A probabilistic sensitivity analysis confirmed a 99.1% probability of being cost-effective. If the cost of the HPV-9 is reduced to 50% of the current price, three-dose HPV-9 vaccination would become the most cost-effective strategy. Discussion: Three-dose HPV-4 vaccination is the most cost-effective vaccination strategy for women treated for precancerous cervical lesions in the Chinese setting.

What Is the Impact of Early and Subsequent Epidemic Characteristics on the Pre-delta COVID-19 Epidemic Size in the United States?

It is still uncertain how the epidemic characteristics of COVID-19 in its early phase and subsequent waves contributed to the pre-delta epidemic size in the United States. We identified the early and subsequent characteristics of the COVID-19 epidemic and the correlation between these characteristics and the pre-delta epidemic size. Most (96.1% (49/51)) of the states entered a fast-growing phase before the accumulative number of cases reached (30). The days required for the number of confirmed cases to increase from 30 to 100 was 5.6 (5.1−6.1) days. As of 31 March 2021, all 51 states experienced at least 2 waves of COVID-19 outbreaks, 23.5% (12/51) experienced 3 waves, and 15.7% (8/51) experienced 4 waves, the epidemic size of COVID-19 was 19,275−3,669,048 cases across the states. The pre-delta epidemic size was significantly correlated with the duration from 30 to 100 cases (p = 0.003, r = −0.405), the growth rate of the fast-growing phase (p = 0.012, r = 0.351), and the peak cases in the subsequent waves (K1 (p < 0.001, r = 0.794), K2 (p < 0.001, r = 0.595), K3 (p < 0.001, r = 0.977), and K4 (p = 0.002, r = 0.905)). We observed that both early and subsequent epidemic characteristics contribute to the pre-delta epidemic size of COVID-19. This identification is important to the prediction of the emerging viral infectious diseases in the primary stage.

PADI2-Catalyzed MEK1 Citrullination Activates ERK1/2 and Promotes IGF2BP1-Mediated SOX2 mRNA Stability in Endometrial Cancer.

Peptidylarginine deiminase II (PADI2) converts positively charged arginine residues to neutrally charged citrulline, and this activity has been associated with the onset and progression of multiple cancers. However, a role for PADI2 in endometrial cancer (EC) has not been previously explored. This study demonstrates that PADI2 is positively associated with EC proregression. Mechanistically, PADI2 interacting and catalyzing MEK1 citrullination at arginine 113/189 facilitates MEK1 on extracellular signal-regulated protein kinases 1/2 (ERK1/2) phosphorylation, which activates insulin-like growth factor-II binding protein 1 (IGF2BP1) expression. Furthermore, RNA immunoprecipitation (RIP) and RNA stability analyses reveal that IGF2BP1 binds to the m6A sites in SOX2-3'UTR to prevent SOX2 mRNA degradation. Dysregulation of IGF2BP1 by PADI2/MEK1/ERK signaling results in abnormal accumulation of oncogenic SOX2 expression, therefore supporting the malignant state of EC. Finally, PADI2 gene silencing, inhibiting MEK1 citrullination by PADI2 inhibitor, or mutation of MEK1 R113/189 equally inhibits EC progression. These data demonstrate that PADI2-catalyzed MEK1 R113/189 citrullination is a critical diver for EC malignancies and suggest that targeting PADI2/MEK1 can be a potential therapeutic approach in patients with EC.

Early characteristics of the COVID-19 outbreak predict the subsequent epidemic scope.

OBJECTIVES: The mostly-resolved first wave of the COVID-19 epidemic in China provided a unique opportunity to investigate how the initial characteristics of the COVID-19 outbreak predict its subsequent magnitude. METHODS: We collected publicly available COVID-19 epidemiological data from 436 Chinese cities from 16th January-15th March 2020. Based on 45 cities that reported >100 confirmed cases, we examined the correlation between early-stage epidemic characteristics and subsequent epidemic magnitude. RESULTS: We identified a transition point from a slow- to a fast-growing phase for COVID-19 at 5.5 (95% CI, 4.6-6.4) days after the first report, and 30 confirmed cases marked a critical threshold for this transition. The average time for the number of confirmed cases to increase from 30 to 100 (time from 30-to-100) was 6.6 (5.3-7.9) days, and the average case-fatality rate in the first 100 confirmed cases (CFR-100) was 0.8% (0.2-1.4%). The subsequent epidemic size per million population was significantly associated with both of these indicators. We predicted a ranking of epidemic size in the cities based on these two indicators and found it highly correlated with the actual classification of epidemic size. CONCLUSIONS: Early epidemic characteristics are important indicators for the size of the entire epidemic.