To enhance the energy density and safety of lithium metal batteries, the research team designed a ternary composite electrolyte additive system PAFE. By leveraging the synergistic coordination between Al(EtO)₃, FEC, and PFPN, an in-situ polymerization reaction occurs at the electrode surface, forming a uniform solid electrolyte interphase. This interphase simultaneously mitigates lattice stress in the Ni-rich cathode and suppresses dendrite formation at the lithium metal anode, significantly improving cycling stability and safety under 4.7 V.

In a paper published in Polymer Science & Technology, an international team of scientists

has, for the first time, introduced oligoethylene glycol side chains into A-A type polymers containing BNBP units, designing a polar side chain-functionalized organic boron polymer, PBN-OEG. The introduction of polar ethylene glycol side chains improves the miscibility between the host material and small molecule dopants, exhibiting a more efficient n-type small molecule doping level compared to the control material PBN-alkyl, which only contains alkyl side chains. Consequently, PBN-OEG possesses superior thermoelectric properties, with an optimal electrical conductivity of up to 1.95 S cm^-1 and a maximum power factor of up to 4.7 μW m^-1 K^-2. Furthermore, the oligoethylene glycol side chains promote the swelling of PBN-OEG films in aqueous electrolyte solutions, facilitating the ionic transport of hydrated cations. Therefore, PBN-OEG can be used as a channel material for organic field-effect transistors (OECTs), achieving a large volumetric capacitance (C*) of 97.7 F cm^-3 and a high figure of merit (μC*) of 2.6 F cm^-1 V^-1 s^-1. This study demonstrates the potential of n-type BNBP-based OMIEC materials in the fields of organic thermoelectric transistors (OTEs) and OECTs. This study is led by Jian Liu (Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China) and Jun Liu (Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China).

The increase in precision agriculture has promoted the development of picking robot technology, and the visual recognition system at its core is crucial for improving the level of agricultural automation. This paper reviews the progress of visual recognition technology for picking robots, including image capture technology, target detection algorithms, spatial positioning strategies and scene understanding. This article begins with a description of the basic structure and function of the vision system of the picking robot and emphasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural environment. Subsequently, various image processing techniques and vision algorithms, including color image analysis, three-dimensional depth perception, and automatic object recognition technology that integrates machine learning and deep learning algorithms, were analyzed. At the same time, the paper also highlights the challenges of existing technologies in dynamic lighting, occlusion problems, fruit maturity diversity, and real-time processing capabilities. This paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of picking. At the same time, this paper also introduces innovative research, such as the application of convolutional neural networks (CNNs) for accurate fruit detection and the development of event-based vision systems to improve the response speed of the system. At the end of this paper, the future development of visual recognition technology for picking robots is predicted, and new research trends are proposed, including the refinement of algorithms, hardware innovation, and the adaptability of technology to different agricultural conditions. The purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricultural robotics, including current achievements, existing challenges and future development prospects.

In a paper published in Journal of Geo-information Science, a group of researchers pioneered a new paradigm by leveraging large language models (LLMs) in constructing typhoon disaster knowledge graphs (KGs), transforming fragmented data into structured disaster intelligence. Simultaneously, these KGs are fused into LLMs to achieve intelligent knowledge services, advancing contextualized and intelligent disaster response systems.

In recent years, the field of clinical laboratory medicine has witnessed remarkable advancements, driven by technological innovations, interdisciplinary research, and the growing demand for precision diagnostics. As Co-Editor-in-Chief, I am pleased to introduce LabMed Discovery (LMD), a new open-access, peer-reviewed journal dedicated to facilitating scholarly communication and fostering innovation in laboratory medicine, in vitro diagnostics, and emerging diagnostic technologies.

LabMed Discovery is proudly sponsored by Ruijin Hospital and Shanghai Jiao Tong University and serves as the official journal of the College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine. With a clear mission to “lead the innovation of laboratory medicine technology and promote international exchanges in medical technology,” LMD aspires to be a leading platform for global researchers and clinicians to disseminate cutting-edge research, novel diagnostic methodologies, and transformative clinical applications.

MicroRNAs (miRNAs) are endogenous single-stranded non-coding RNAs approximately 22 ​nt in length, which play a regulatory role in various biological processes by targeting and inhibiting protein-coding gene transcripts. Among the wide range of miRNAs, miR-668 exhibits abnormal expression patterns in different diseases, including head and neck squamous cell carcinoma (HNSC), hepatocellular carcinoma (HCC), breast cancer (BrC), ischemic acute kidney injury (ischemic AKI), myocardial infarction (MI), ischemic stroke (IS), Parkinson’s disease (PD), smoking-related interstitial fibrosis (SRIF), premature aging, and non-segmental vitiligo (NSV), among others. The expression of miR-668 is regulated by various upstream factors, including other non-coding RNAs and endogenous proteins. Functionally, miR-668 targets 6 protein-coding genes (SDF-1, Dvl1, BTG2, MTFP1, FHIT, and NFKBIA) and is involved in 3 signaling pathways: the Drp signaling pathway, NF-κB signaling pathway, and NLRP3 signaling pathway. Notably, miR-668 has contrasting effects depending on the context; it can promote the development and metastasis of HCC and induce radiation resistance in BrC cells, while also promoting aging and inhibiting HNSC progression in human keratinocytes. Furthermore, miR-668 exhibits anti-apoptotic effects, alleviates ischemia/reperfusion injury, improves intracerebral expansion, relieves hydrocephalus, and has prognostic value in gastric cancer (GC), HCC, and BrC, with high expression predicting poor outcomes. Notably, the cancer drugs melatonin and Pterostilbene (Ptero) exert their therapeutic effects by modulating miR-668. Future studies should focus on validating the reliability of miR-668 as a biomarker and elucidating the specific mechanisms through which miR-668 participates in cancer therapy.

Objective: Paper cups are mostly used for consuming beverages. These cups have an interior laminated hydrophobic thin film coated with polyethene and copolymers. During association with hot water or beverages ions like nitrite, chloride, fluoride and microplastic particles were released into water. Deposition of these particles has serious health effects in human. So, the objectives of the present study are to evaluate the degradation of the microplastic particles as a result of exposure to hot water. Methods: Different characterization techniques like UV-Vis spectroscopy, scanning electron microscopy, Gas chromatography-mass spectroscopy, Fourier transform infrared spectroscopy and Inductively coupled plasma optical emission spectroscopy are used to identify microplastic particles. Information about microplastic content in such samples is limited, yet most of the analytical methods used are very time-consuming. Results: Sample preparation and detection require sophisticated analytical tools and eventually require high user knowledge. This research shown that microplastics can leach from the lining of disposable cups into the beverage within minutes of contact with hot liquids. GCMS report shows that Phthalates compound and chemical exposure affect the endocrine system which interferes with hormone function. When exposed to hot water, the plastic coatings start to break down and the compounds like Diethyl phthalate, Di-2-ethylhexyl-adipate, Butylated hydroxytoluene, Benzotriazole, Styrene, Vinyl chloride were found in the hot water. Some scientists have shown that certain bacterial enzymes can hydrolyze and degrade the microplastic compound. Conclusions: This research discusses paper cup microplastic compounds and the binding affinity of detoxification enzyme interaction have been discussed in silico. However, this research recommends to opting for reusable cups made of glass, steel, and safer materials like soil-made cup.

A groundbreaking study led by Prof. Bo Peng at Fudan University demonstrates that microglia replacement effectively halts the progression of adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP), a fatal neurodegenerative disease caused by mutations in the microglia-specific gene CSF1R. Published in Science on July 10, 2025, this research replaced defective microglia with healthy ones, halting the disease progression and significantly improving neurological function in both mouse models and human clinical trials. This first-in-human microglia replacement therapy marks the first clinically effective treatment for ALSP, highlighting the therapeutic potential of microglia replacement strategies for other neurological disorders.

Myopenia, a condition marked by abnormal muscle loss, affects people across all age groups. Though linked to various diseases, myopenia appears to have distinct origins and clinical features in rheumatoid arthritis (RA). Researchers have now examined its underlying mechanisms in unprecedented detail, revealing how RA drives muscle wasting. Their findings offer critical insights into age-specific presentations of myopenia and highlight emerging opportunities to improve care and functional outcomes for patients living with rheumatoid arthritis.