Zhiwei Chen

A DataMatrix (DM) code is an automatic identification barcode based on a combination of coding and image processing. Traditional DM code sampling methods are mostly based on simple segmentation and sampling of a DM code. However, the obtained DM code images often have problems such as wear, corrosion, geometric distortion, and strong background interference in practical scenarios. To improve decoding ability in complex environments, a DM code recognition method based on coarse positioning of images is proposed. The two-dimensional barcode is first converted into a one-dimensional waveform using a projection algorithm. Then, the spacing between segmentation lines is predicted and corrected using an exponential weighted moving average model for adaptive grid division. Finally, the local outlier factor algorithm and local weighted linear regression algorithm are applied to predict and binarize the gray level values, converting the DM code image into a data matrix. The experimental results show that this method effectively handles problems like blurring, wear, corrosion, distortion, and background interference. Compared to popular DM decoding libraries like libdmtx and zxing, it demonstrates better resolution, noise resistance, and distortion tolerance.

Reactive oxygen species (ROS) plays important roles in living organisms. While ROS is a double-edged sword, which can eliminate drug-resistant bacteria, but excessive levels can cause oxidative damage to cells. A core–shell nanozyme, CeO₂@ZIF-8/Au, has been crafted, spontaneously activating both ROS generating and scavenging functions, achieving the multi-faceted functions of eliminating bacteria, reducing inflammation, and promoting wound healing. The Au Nanoparticles (NPs) on the shell exhibit high-efficiency peroxidase-like activity, producing ROS to kill bacteria. Meanwhile, the encapsulation of CeO₂ core within ZIF-8 provides a seal for temporarily limiting the superoxide dismutase and catalase-like activities of CeO₂ nanoparticles. Subsequently, as the ZIF-8 structure decomposes in the acidic microenvironment, the CeO₂ core is gradually released, exerting its ROS scavenging activity to eliminate excess ROS produced by the Au NPs. These two functions automatically and continuously regulate the balance of ROS levels, ultimately achieving the function of killing bacteria, reducing inflammation, and promoting wound healing. Such innovative ROS spontaneous regulators hold immense potential for revolutionizing the field of antibacterial agents and therapies.

The intelligent responsive drug delivery system has great application potential in cancer precision therapy. Although many antitumor methods have been developed based on drug delivery systems, most of them yet suffer from poor antitumor efficiency. In this project, a near-infrared and pH dual-response multimodal collaborative platform for diagnosis and treatment (PCN-DOX@PDA) was constructed. We used PCN-600 as a vehicle loaded with antineoplastic drugs and polydopamine (PDA). Under 633 nm laser irradiation, the ligand tetrakis(4-carboxyphenyl)porphyrin (TCPP) in PCN-600 can generate singlet oxygen (¹O₂) and kill tumor cells. PDA is used as photothermal agent of PTT. PCN-DOX@PDA achieves the intelligent release of antitumor drugs by responding to the weak acidity of the tumor microenvironment and thermal stimulation generated by NIR irradiation. In addition, since the central ion of PCN is Fe³⁺, PCN-DOX@PDA realizes the diagnosis and treatment of tumors through magnetic resonance imaging-mediated tumor chemotherapy and photothermal and photodynamic synergistic therapy. This triple synergistic strategy showed excellent biocompatibility and antitumor ability in in vivo experiments on a 4T1 tumor-bearing mouse model, indicating that PCN-DOX@PDA has a good development prospect in the field of precision cancer therapy and diversified biomedical applications.

Nanozymes with peroxidase-like activity have great application potential in combating pathogenic bacterial infections and are expected to become an alternative to antibiotics. However, the near-neutral pH and high glutathione (GSH) levels in the bacterial infection microenvironment severely limit their applications in antibacterial therapy. In this work, a metal–organic framework (MOF)-based cascade catalytic glutathione-depleting system named MnFe₂O₄@MIL/Au&GOx (MMAG) was constructed. The MMAG cascade-catalyzed glucose to provide H⁺ and produces a large amount of toxic reactive oxygen species. In addition, MMAG consumed GSH, which can result in bacterial death more easily. Systematic antibacterial experiments illustrated that MMAG has superior antibacterial effects on both Gram-positive bacteria and Gram-negative bacteria.

The abuse of antibiotics makes bacterial infection an increasingly serious global health threat. Reactive oxygen species (ROS) are the ideal alternative antibacterial approach for quick and effective sterilization. Although various antibacterial strategies based on ROS have been developed, many of them are still limited by insufficient antibacterial efficiency. Here, we have developed an acid-enhanced dual-modal antibacterial strategy based on zeolitic imidazolate frameworks-8 (ZIF8) -derived nanozyme. ZIF8, which can release Zn²⁺, is chosen as the carrier to integrate glucose oxidase (GOx) and gold nanoparticles (Au NPs) which can produce ROS via a cascade catalytic reaction. Thus, the bactericidal capability of ROS and Zn²⁺ have been integrated. More importantly, gluconic acid, a “by-product” of the catalytic reaction, can generate an acidic environment to promote both the ROS-producing and Zn²⁺-releasing, enhancing the overall antibacterial performance further. This triple-synergistic strategy exhibits extraordinary bactericidal ability at a low dosage of 4 μg/mL (for S. aureus) and 8 μg/mL (for E. coli), which shows a great potential of MOF-derived nanozyme for efficient bacterial eradication and diverse biomedical applications.

Background: Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages. NMR users need to flexibly integrate rapidly developing NMR applications with emerging technologies. Scripting systems offer open environments for NMR users to write custom programs. However, existing scripting systems have limited capabilities for both extending the functionality of NMR software’s non-script main program and using advanced native script libraries to support specialized application domains (e.g., biomacromolecules and metabolomics). Therefore, it is essential to design a novel scripting system to address both of these needs. Result: Here, a novel NMR scripting system named SpinSPJ is proposed. It works as a plug-in in the Java based NMR spectrometer software SpinStudioJ. In the scripting system, both Java based NMR methods and original CPython based libraries are supported. A module has been developed as a bridge to integrate the runtime environments of Java and CPython. The module works as an extension in the CPython environment and interacts with Java via the Java Native Interface. Leveraging this bridge, Java based instrument control and data processing methods of SpinStudioJ can be called with the CPython style. Compared with traditional scripting systems, SpinSPJ better supports both extending the non-script main program and implementing advanced NMR applications with a rich variety of script libraries. NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython native libraries. Conclusion: SpinSPJ offers a user-friendly environment to implement custom functionality leveraging its powerful basic NMR and rich CPython libraries. NMR applications with emerging technologies can be easily integrated. The scripting system is free of charge and can be downloaded by visiting http://www.spinstudioj.net/spinspj.

Multidimensional NMR spectroscopy provides a powerful tool for structure elucidation and dynamic analysis of complex samples, particularly for biological macromolecules. Multidimensional sparse sampling effectively accelerates NMR experiments while an efficient reconstruction method is generally required for unraveling spectra. Various reconstruction methods were proposed for pure Fourier NMR (only involving chemical shifts and J couplings detection). However, reconstruction concerned with Laplace-related NMR (i.e., involving relaxation or diffusion detection) is more challenging due to its ill-posed property. The existing Laplace-related NMR sparse sampling reconstruction methods suffer from poor resolution and possible artifacts in the resulting spectra owing to the pitfalls of the optimization algorithms. Herein, we propose a general approach for fast high-resolution reconstruction of multidimensional sparse sampling NMR, including pure Fourier, mixed Fourier–Laplace, and pure Laplace NMR, benefiting from the comprehensive sparse constraint and effective optimization algorithm and thus showing the promising prospects of multidimensional NMR.