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Background Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation. Methods We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance. Results The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength. Conclusion Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.

Background Since December 2019, the 2019 coronavirus disease (COVID-19) has expanded to cause a worldwide outbreak that more than 600,000 people infected and tens of thousands died. To date, the clinical characteristics of COVID-19 patients in the non-Wuhan areas of Hubei Province in China have not been described. Methods We retrospectively analyzed the clinical characteristics and treatment progress of 91 patients diagnosed with COVID-19 in Jingzhou Central Hospital. Results Of the 91 patients diagnosed with COVID-19, 30 cases (33.0%) were severe and two patients (2.2%) died. The severe disease group tended to be older (50.5 vs. 42.0 years; p  = 0.049) and have more chronic disease (40% vs. 14.8%; p  = 0.009) relative to mild disease group. Only 73.6% of the patients were quantitative polymerase chain reaction (qPCR)-positive on their first tests, while typical chest computed tomography images were obtained for each patient. The most common complaints were cough ( n  = 75; 82.4%), fever ( n  = 59; 64.8%), fatigue ( n  = 35; 38.5%), and diarrhea ( n  = 14; 15.4%). Non-respiratory injury was identified by elevated levels of aspartate aminotransferase ( n  = 18; 19.8%), creatinine ( n  = 5; 5.5%), and creatine kinase ( n  = 14; 15.4%) in laboratory tests. Twenty-eight cases (30.8%) suffered non-respiratory injury, including 50% of the critically ill patients and 21.3% of the mild patients. Conclusions Overall, the mortality rate of patients in Jingzhou was lower than that of Wuhan. Importantly, we found liver, kidney, digestive tract, and heart injuries in COVID-19 cases besides respiratory problems. Combining chest computed tomography images with the qPCR analysis of throat swab samples can improve the accuracy of COVID-19 diagnosis.

Background A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. Methods We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. Results The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8–65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. Conclusions GOHI—subject to rigorous validation—would represent the world’s first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge. Graphical Abstract

The glymphatic system (GS) is a novel defined brain-wide perivascular transit network between cerebrospinal fluid (CSF) and interstitial solutes that facilitates the clearance of brain metabolic wastes. The complicated network of the GS consists of the periarterial CSF influx pathway, astrocytes-mediated convective transport of fluid and solutes supported by AQP4 water channels, and perivenous efflux pathway. Recent researches indicate that the GS dysfunction is associated with various neurological disorders, including traumatic brain injury, hydrocephalus, epilepsy, migraine, and Alzheimer’s disease (AD). Meanwhile, the GS also plays a pivotal role in the pathophysiological process of stroke, including brain edema, blood–brain barrier (BBB) disruption, immune cell infiltration, neuroinflammation, and neuronal apoptosis. In this review, we illustrated the key anatomical structures of the GS, the relationship between the GS and the meningeal lymphatic system, the interaction between the GS and the BBB, and the crosstalk between astrocytes and other GS cellular components. In addition, we contributed to the current knowledge about the role of the GS in the pathology of stroke and the role of AQP4 in stroke. We further discussed the potential use of the GS in early risk assessment, diagnostics, prognostics, and therapeutics of stroke.

The chemical structure of donors and acceptors limit the power conversion efficiencies achievable with active layers of binary donor-acceptor mixtures. Here, using quaternary blends, double cascading energy level alignment in bulk heterojunction organic photovoltaic active layers are realized, enabling efficient carrier splitting and transport. Numerous avenues to optimize light absorption, carrier transport, and charge-transfer state energy levels are opened by the chemical constitution of the components. Record-breaking PCEs of 18.07% are achieved where, by electronic structure and morphology optimization, simultaneous improvements of the open-circuit voltage, short-circuit current and fill factor occur. The donor and acceptor chemical structures afford control over electronic structure and charge-transfer state energy levels, enabling manipulation of hole-transfer rates, carrier transport, and non-radiative recombination losses. Efficiency of organic solar cells is determined by the physical properties of donors and acceptors in bulk heterojunction film. The authors optimise quaternary blends to realize a double cascading energy level alignment enabling efficient carrier dissociation and transport, achieving 18% efficiency.

Lithium‐ion batteries (LIBs) have been widely used in the field of portable electric devices because of their high energy density and long cycling life. To further improve the performance of LIBs, it is of great importance to develop new electrode materials. Various transition metal oxides (TMOs) have been extensively investigated as electrode materials for LIBs. According to the reaction mechanism, there are mainly two kinds of TMOs, one is based on conversion reaction and the other is based on intercalation/deintercalation reaction. Recently, hierarchically nanostructured TMOs have become a hot research area in the field of LIBs. Hierarchical architecture can provide numerous accessible electroactive sites for redox reactions, shorten the diffusion distance of Li‐ion during the reaction, and accommodate volume expansion during cycling. With rapid research progress in this field, a timely account of this advanced technology is highly necessary. Here, the research progress on the synthesis methods, morphological characteristics, and electrochemical performances of hierarchically nanostructured TMOs for LIBs is summarized and discussed. Some relevant prospects are also proposed. Hierarchical nanostructures have been extensively investigated in the field of lithium‐ion batteries because they can provide numerous accessible electroactive sites, shorten the ion diffusion pathway, and accommodate volume expansion. Research progress on hierarchically nanostructured transition metal oxides as electrode materials for lithium‐ion batteries is summarized and evaluated.

Background Drought stress has great negative effects on the plant growth and development. The tolerance of plants to such abiotic stress is triggered by complicated and multilayered signaling pathways to restore cellular homeostasis and to promote survival. The WRKY family is one of the largest transcription factor families in higher plants, and has been well recognized for the roles in regulating plants tolerance to abiotic and biotic stress. However, little is known about how the WRKY genes regulate drought resistance in cotton. Results In this work, we identified the WRKY transcription factor GhWRKY1-like from upland cotton as a positive regulator of tolerance to drought that directly manipulates abscisic acid (ABA) biosynthesis. Overexpression of GhWRKY1-like in Arabidopsis constitutively activated ABA biosynthesis genes, signaling genes, responsive genes and drought related maker genes, and led to enhanced tolerance to drought. Further analysis has shown that GhWRKY1-like can interact with “W-box” cis-elements of the promoters of AtNCED2 , AtNCED5 , AtNCED6 and AtNCED9 which are essential enzymes for ABA biosynthesis, and promotes the expression of those target genes. Conclusions In summary, our findings suggest that GhWRKY1-like may act as a positive regulator in Arabidopsis tolerance to drought via directly interacting with the promoters of AtNCED2 , AtNCED5 , AtNCED6 and AtNCED9 to promote ABA biosynthesis.

Background Sepsis is a severe syndrome of organ dysfunction that often leads to cardiac dysfunction and endangers life. The role of mitochondrial aldehyde dehydrogenase 2 (ALDH2) in LPS-induced myocardial injury is unclear. The purpose of this study was to assess the role of ALDH2 in lipopolysaccharide (LPS)-induced myocardial injury and the regulatory mechanism and to identify potential therapeutic strategies for treating this condition. Methods An in vivo model was established by 12 h of LPS (10 mg/kg, intraperitoneal injection) stimulation, and an in vitro model was generated by stimulating H9C2 cells with LPS (10 μg/ml) for 12 h. We then used the ALDH2 activator Alda-1 and the ALDH2 inhibitor daidzin to assess their effects on LPS-induced cardiac injury. Cardiac function in mice was evaluated by using cardiac ultrasound. We used various methods to evaluate inflammation, apoptosis, and oxidative stress, including ELISA, flow cytometry, JC-1 staining, Western blotting, and DCFH-DA staining. Additionally, we used a small interfering RNA (siRNA) to knock down cyclic GMP-AMP synthase (cGAS) to further investigate the relationship between ALDH2 and cGAS in LPS-induced cardiac injury. Results LPS-induced cardiac dysfunction and increased the levels of the cardiac injury markers creatine kinase-MB (CKMB) and lactate dehydrogenase (LDH) in vivo. This change was accompanied by an increase in reactive oxygen species (ROS) levels, which exacerbated the oxidative stress response and regulated apoptosis through cleaved caspase-3, BAX, BCL-2. The expression of inflammatory cytokines such as IL-6/IL-1β/TNF-α was also upregulated. However, these effects were reversed by pretreatment with Alda-1 via the inhibition of cGAS/stimulator of interferon genes (STING) signaling pathway. Interestingly, LPS, Alda-1 and daidzin altered the activity of ALDH2 but did not regulate its protein expression. Knocking down cGAS in H9C2 cardiomyocytes alleviated LPS-induced cardiac inflammation, apoptosis, and ROS production and weakened the synergistic effect of daidzin. Conclusion We demonstrated that ALDH2 alleviated LPS-induced cardiac dysfunction, inflammation, and apoptosis through the cGAS/STING signaling pathway, thereby protecting against LPS-induced cardiac injury. This study identifies a novel therapeutic approach for treating sepsis-induced cardiomyopathy (SIC).

This paper addresses the energy management control problem of solar power generation system by using the data-driven method. The battery-supercapacitor hybrid energy storage system is considered to smooth the power fluctuation. A new model-free control method is utilized in the stand-alone photovoltaic DC-microgrid to provide the power to meet the demand load, while guaranteeing the DC bus voltage is stable. Furthermore, the proposed data-based power management control strategy only needs I/O data. Numerical simulations with real data verify the effectiveness of the proposed method.

The performance of perovskite photovoltaics is fundamentally impeded by the presence of undesirable defects that contribute to non-radiative losses within the devices. Although mitigating these losses has been extensively reported by numerous passivation strategies, a detailed understanding of loss origins within the devices remains elusive. Here, we demonstrate that the defect capturing probability estimated by the capture cross-section is decreased by varying the dielectric response, producing the dielectric screening effect in the perovskite. The resulting perovskites also show reduced surface recombination and a weaker electron-phonon coupling. All of these boost the power conversion efficiency to 22.3% for an inverted perovskite photovoltaic device with a high open-circuit voltage of 1.25 V and a low voltage deficit of 0.37 V (a bandgap ~1.62 eV). Our results provide not only an in-depth understanding of the carrier capture processes in perovskites, but also a promising pathway for realizing highly efficient devices via dielectric regulation. Performance of perovskite photovoltaics is greatly affected by undesirable defects that contribute to non-radiative losses. Here, the authors mitigate these losses by doping perovskite with KI to alter the dielectric response, thus defect capturing probability, resulting in inverted device with PCE of 22.3% and low voltage loss.