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Highlights: The excellent performance of MoS2/PANI cathode is due to better conductivity and desolvation shielding. The self-charging zinc battery was successfully assembled that achieve deep self-discharge and long cycle life. The quasi solid state battery and battery module have excellent performance and practicability. Self-charging power systems collecting energy harvesting technology and batteries are attracting extensive attention. To solve the disadvantages of the traditional integrated system, such as highly dependent on energy supply and complex structure, an air-rechargeable Zn battery based on MoS2/PANI cathode is reported. Benefited from the excellent conductivity desolvation shield of PANI, the MoS2/PANI cathode exhibits ultra-high capacity (304.98 mAh g−1 in N2 and 351.25 mAh g−1 in air). In particular, this battery has the ability to collect, convert and store energy simultaneously by an air-rechargeable process of the spontaneous redox reaction between the discharged cathode and O2 from air. The air-rechargeable Zn batteries display a high open-circuit voltage (1.15 V), an unforgettable discharge capacity (316.09 mAh g−1 and the air-rechargeable depth is 89.99%) and good air-recharging stability (291.22 mAh g−1 after 50 air recharging/galvanostatic current discharge cycle). Most importantly, both our quasi-solid zinc ion batteries and batteries modules have excellent performance and practicability. This work will provide a promising research direction for the material design and device assembly of the next-generation self-powered system. [ABSTRACT FROM AUTHOR]

The article titled "Multi-objective optimization scheduling of integrated energy system interval under multiple uncertainty environment" by L. Zhao et al. has been retracted by the publisher due to concerns about compromised editorial handling and peer review processes, inappropriate references, and being out of scope for the journal. The authors did not respond to correspondence regarding the retraction. The online version of the article includes the retracted full text as Supplementary Information. [Extracted from the article]

Object: Disease progression is an important factor affecting the long-term survival in hepatocellular carcinoma (HCC). The progression-free survival (PFS) has been used as a surrogate endpoint for overall survival (OS) in many solid tumors. However, there were few models to predict the PFS in HCC patients. This study aimed to explore the prognostic factors that affect the PFS in HCC and establish an individualized prediction model. Methods: We included 2890 patients with hepatitis B-related HCC hospitalized at Beijing Ditan Hospital, Capital Medical University and randomly divided into training and validation cohort. Cox multivariate regression was used to analyze independent risk factors affecting the 1-year PFS of HCC, and an artificial neural networks (ANNs) model was constructed. C-index, calibration curve, and decision curve analysis were used to evaluate the performance of the model. Results: The median survival time was 26.2 m (95% CI: 24.08–28.32) and the 1-year PFS rate was 52.3% in whole study population. Cox multivariate regression showed smoking history, tumor number ≥ 2, tumor size ≥ 5 cm, portal vein tumor thrombus, WBC, NLR, γ-GGT, ALP, and AFP ≥ 400 ng/mL were risk factors for 1-year progression-free survival, while albumin and CD4 T cell counts were protective factors in HCC patients. A prediction model for 1-year PFS was constructed (https://lixuan.me/annmodel/myg-v3/). The ANNs model's ability to predict 1-year PFS had an area under the receiver operating characteristic curve (AUROC) of 0.866 (95% CI 0.848–0.884) in HCC patients, which was higher than predicted by TNM, BCLC, Okuda, CLIP, CUPI, JIS, and ALBI scores (p < 0.0001). In addition, the ANNs model could also estimate the probability of 1-year OS and presented a higher AUROC value, 0.877 (95% CI 0.858–0.895), than those other models. All patients were divided into high-, medium-, and low-risk groups, according to the ANNs model scores. Compared with the hazard ratios (HRs) of PFS and OS in low-risk group, those in the high-risk group were 26.42 (95% CI 18.74–37.25; p < 0.0001) and 11.26 (95% CI 9.11–13.93; p < 0.0001), respectively. Conclusion: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of progression-free survival in HCC during clinical practice. [ABSTRACT FROM AUTHOR]