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Iron is an essential trace metal for almost all organisms, including human; however, oxidative stress can easily be caused when iron is in excess, producing toxicity to the human body due to its capability to be both an electron donor and an electron acceptor. Although there is a strict regulation mechanism for iron homeostasis in the human body and brain, it is usually inevitably disturbed by genetic and environmental factors, or disordered with aging, which leads to iron metabolism diseases, including many neurodegenerative diseases such as Alzheimer’s disease (AD). AD is one of the most common degenerative diseases of the central nervous system (CNS) threatening human health. However, the precise pathogenesis of AD is still unclear, which seriously restricts the design of interventions and treatment drugs based on the pathogenesis of AD. Many studies have observed abnormal iron accumulation in different regions of the AD brain, resulting in cognitive, memory, motor and other nerve damages. Understanding the metabolic balance mechanism of iron in the brain is crucial for the treatment of AD, which would provide new cures for the disease. This paper reviews the recent progress in the relationship between iron and AD from the aspects of iron absorption in intestinal cells, storage and regulation of iron in cells and organs, especially for the regulation of iron homeostasis in the human brain and prospects the future directions for AD treatments.

Background Cardiac surgery–associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence–based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI. Methods A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model. Results Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772–0.898), whereas the AUC (0.843, 95% CI 0.778–0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model. Conclusions In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

Resistance to chemotherapy is a major challenge for the treatment of patients with colorectal cancer (CRC). Previous studies have found that microRNAs (miRNAs) play key roles in drug resistance; however, the role of miRNA‐373‐3p (miR‐375‐3p) in CRC remains unclear. The current study aimed to explore the potential function of miR‐375‐3p in 5‐fluorouracil (5‐FU) resistance. MicroRNA‐375‐3p was found to be widely downregulated in human CRC cell lines and tissues and to promote the sensitivity of CRC cells to 5‐FU by inducing colon cancer cell apoptosis and cycle arrest and by inhibiting cell growth, migration, and invasion in vitro. Thymidylate synthase (TYMS) was found to be a direct target of miR‐375‐3p, and TYMS knockdown exerted similar effects as miR‐375‐3p overexpression on the CRC cellular response to 5‐FU. Lipid‐coated calcium carbonate nanoparticles (NPs) were designed to cotransport 5‐FU and miR‐375‐3p into cells efficiently and rapidly and to release the drugs in a weakly acidic tumor microenvironment. The therapeutic effect of combined miR‐375 + 5‐FU/NPs was significantly higher than that of the individual treatments in mouse s.c. xenografts derived from HCT116 cells. Our results suggest that restoring miR‐375‐3p levels could be a future novel therapeutic strategy to enhance chemosensitivity to 5‐FU. Resistance to chemotherapy is a major challenge for the treatment of patients with colorectal cancer (CRC). Our results suggest that the restoration of microRNA‐375‐3p levels could be a future novel therapeutic strategy to modulate and enhance chemosensitivity to 5‐fluorouracil treatment in CRC.

Seawater electrolysis using renewable electricity offers an attractive route to sustainable hydrogen production, but the sluggish electrode kinetics and poor durability are two major challenges. We report a molybdenum nitride (Mo 2 N) catalyst for the hydrogen evolution reaction with activity comparable to commercial platinum on carbon (Pt/C) catalyst in natural seawater. The catalyst operates more than 1000 hours of continuous testing at 100 mA cm −2 without degradation, whereas massive precipitate (mainly magnesium hydroxide) forms on the Pt/C counterpart after 36 hours of operation at 10 mA cm −2 . Our investigation reveals that ammonium groups generate in situ at the catalyst surface, which not only improve the connectivity of hydrogen-bond networks but also suppress the local pH increase, enabling the enhanced performances. Moreover, a zero-gap membrane flow electrolyser assembled by this catalyst exhibits a current density of 1 A cm −2 at 1.87 V and 60 o C in simulated seawater and runs steadily over 900 hours. Efficient catalysts for seawater electrolysis are crucial for sustainable hydrogen production but struggle with slow kinetics and low durability. Here, the authors report a molybdenum nitride catalyst that in situ generates ammonium groups, enhancing both performance and stability in natural seawater.

We systematically investigate the quasinormal modes of thick branes in f ( R ) gravity by numerically solving the Schrödinger-like perturbation equation of gravitational perturbations. To ensure reliability of the results, we employ three complementary methods: the asymptotic iteration method, direct integration of the wave equation, and time-domain numerical evolution. We analyze how model parameters influence the shape of the effective potential of gravitational perturbations and find that the structure of the potential barrier plays a significant role in shaping the quasinormal frequency spectrum. The results obtained from the three methods exhibit strong consistency, thereby ensuring the reliability of the calculations. In particular, real parts of the quasinormal frequencies exhibit an approximately arithmetic progression, suggesting that quasi-localized states can be understood as resonances between the barriers.

Recent advances in nonlinear optics have revolutionized integrated photonics, providing on-chip solutions to a wide range of new applications. Currently, state of the art integrated nonlinear photonic devices are mainly based on dielectric material platforms, such as Si 3 N 4 and SiO 2 . While semiconductor materials feature much higher nonlinear coefficients and convenience in active integration, they have suffered from high waveguide losses that prevent the realization of efficient nonlinear processes on-chip. Here, we challenge this status quo and demonstrate a low loss AlGaAs-on-insulator platform with anomalous dispersion and quality ( Q ) factors beyond 1.5 × 10 6 . Such a high quality factor, combined with high nonlinear coefficient and small mode volume, enabled us to demonstrate a Kerr frequency comb threshold of only ∼36 µW in a resonator with a 1 THz free spectral range, ∼100 times lower compared to that in previous semiconductor platforms. Moreover, combs with broad spans (>250 nm) have been generated with a pump power of ∼300 µW, which is lower than the threshold power of state-of the-art dielectric micro combs. A soliton-step transition has also been observed for the first time in an AlGaAs resonator. Despite larger nonlinear coefficients, waveguide losses have prevented using semiconductors instead of dielectric materials for on-chip frequency-comb sources. By significantly reducing waveguide loss, ultra-low-threshold Kerr comb generation is demonstrated in a high- Q AlGaAs-on-insulator microresonator system.

The flexibility in structural design of organic semiconductors endows organic solar cells (OSCs) not only great function-tunabilities, but also high potential toward practical application. In this work, simple non-fused-ring electron acceptors are developed through two-step synthesis from single aromatic units for constructing efficient OSCs. With the assistance of non-covalent interactions, these rotatable non-fused acceptors (in solution) allow transiting into planar and stackable conformation in condensed solid, promoting acceptors not only feasible solution-processability, but also excellent film characteristics. As results, decent power conversion efficiencies of 10.27% and 13.97% can be achieved in single and tandem OSCs consisting of simple solution-cast blends, in which the fully unfused acceptors exhibit exceptionally low synthetic complexity index. In addition, the unfused acceptor and its based OSCs exhibit promising stabilities under continuous illumination. Overall, this work reveals valuable insights on the structural design of simple and effective electron acceptors with great practical perspectives. Non-fullerene electron acceptors have pushed the efficiency of organic solar cells up to 15% but they all contain fused rings and are inconvenient to synthetic access. Here Yu et al. develop fully unfused acceptors featuring non-covalent intramolecular interactions, high efficiencies and high stability.

The etiology of major depressive disorder (MDD), the leading cause of worldwide disability, is unknown. The neurogenic hypothesis proposes that MDD is linked to impairments of adult neurogenesis in the hippocampal dentate gyrus (DG), while the effects of antidepressants are mediated by increased neurogenesis. However, alterations in neurogenesis and endophenotypes are not always causally linked, and the relationship between increased neurogenesis and altered behavior is controversial. To address causality, we used chemogenetics in transgenic mice to selectively manipulate activity of newborn DG neurons. Suppressing excitability of newborn neurons without altering neurogenesis abolish the antidepressant effects of fluoxetine. Remarkably, activating these neurons is sufficient to alleviate depression-like behavior and reverse the adverse effects of unpredictable chronic mild stress. Our results demonstrate a direct causal relationship between newborn neuronal activity and affective behavior. Thus, strategies that target not only neurogenesis but also activity of newborn neurons may lead to more effective antidepressants. It is unclear if there is a causal link between increased neurogenesis and altered affective behaviors in major depressive disorders. Here, the authors show that selectively suppressing the excitability of newborn neurons, without altering neurogenesis, abolishes the antidepressant effects of fluoxetine.