Tannic acid alleviates 3-nitropropionic acid-induced ovarian damage in Brandt's vole (Lasiopodomys brandtii).

Tannic acid (TA) is a polyphenol with antioxidant properties present in various plants. In this study, we explored the protective effect of TA against ovarian oxidative stress in Brandt's voles and its underlying mechanism. At various doses, 3-nitropropionic acid (3-NPA) was intraperitoneally injected into Brandt's voles to simulate ovarian oxidative stress. Thereafter, various doses of TA were intragastrically administered to examine the protective effect of TA against 3-NPA-induced ovarian damage. Changes in inflammation, autophagy, apoptosis, and oxidative stress-related factors were investigated through various biochemical and histological techniques. Ovarian oxidative stress was successfully induced by the intraperitoneal administration of 12.5 mg/kg 3-NPA for 18 days. As a result, the ovarian coefficient decreased and ovarian tissue fibrosis was induced. TA treatment effectively alleviated the increase in luteinizing hormone and follicle-stimulating hormone levels; the decrease in estradiol, progesterone, and anti-Müllerian hormone levels; and the decline in fertility induced by 3-NPA. Compared to that in the 3-NPA group, TA decreased the expression of autophagy-related proteins beclin-1 and LC3, as well as the level of apoptosis. It also activated the AKT/mTOR signaling pathway, downregulated PTEN and p-NF-κB expression, and upregulated Nrf2 expression. In conclusion, our findings indicate that TA could inhibit autophagy via the regulation of AKT/mTOR signaling, suppressing oxidative damage and inflammatory responses through Nrf2 to alleviate 3-NPA-induced ovarian damage. Collectively, the current findings highlight the protective effects of TA in Brandt's vole, where it promotes the maintenance of normal ovarian function.

Accelerated Variational Quantum Eigensolver.

The problem of finding the ground state energy of a Hamiltonian using a quantum computer is currently solved using either the quantum phase estimation (QPE) or variational quantum eigensolver (VQE) algorithms. For precision ε, QPE requires O(1) repetitions of circuits with depth O(1/ε), whereas each expectation estimation subroutine within VQE requires O(1/ε^{2}) samples from circuits with depth O(1). We propose a generalized VQE algorithm that interpolates between these two regimes via a free parameter α∈[0,1], which can exploit quantum coherence over a circuit depth of O(1/ε^{α}) to reduce the number of samples to O(1/ε^{2(1-α)}). Along the way, we give a new routine for expectation estimation under limited quantum resources that is of independent interest.

Area disease estimation based on sentinel hospital records.

BACKGROUND: Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased. METHODS AND FINDINGS: A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospital's records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators. CONCLUSIONS: The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Kriging's inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimator's limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.