Xiaoyong Hu’s research while affiliated with Peking University and other places

With the rising global incidence of cancer and the limitations of traditional treatment methods, the integration of three-dimensional (3D) printing technology with drug delivery systems offers a promising solution for precision medicine. 3D printing, with its high flexibility and precise production control, allows for the accurate modulation of drug delivery systems, particularly in terms of targeted delivery and controlled drug release rates, thus significantly enhancing therapeutic efficacy and reducing side effects. This review focuses on the applications of various drug delivery forms, such as microneedle patches, implants, and tablets, in the treatment of cancers including breast cancer, melanoma, osteosarcoma, cervical cancer, colorectal cancer, and prostate cancer. Furthermore, the review explores the synergistic effects of combination therapies, such as photothermal therapy, chemotherapy, and immunotherapy, within 3D-printed drug delivery systems, and assesses their potential in addressing tumor recurrence, drug resistance, and treatment-related side effects. Despite the substantial promise of 3D printing technology in cancer treatment, challenges remain in material selection, process optimization, and production standardization. As technology continues to evolve and multidisciplinary collaborations deepen, 3D printing is expected to play an increasingly significant role in the future of precision medicine and personalized cancer therapy.

Dynamically encircling exceptional points in non‐Hermitian systems leads to counterintuitive chiral mode conversions, which provides a new platform for implementing information processing. Previous research has shown that the position of the starting point determines whether the dynamics are chiral or non‐chiral. Photonic devices that simultaneously realize chiral and non‐chiral mode converters are yet to be investigated. In this letter, a four‐state non‐Hermitian waveguide array with two exceptional points is presented and the dynamical encirclement of each exceptional point and double exceptional points, which possess chiral and non‐chiral dynamics is studied. Encircling a single exceptional point and both exceptional points gives the same outcome but a different encircling process. During non‐chiral dynamics, the initial states are not on the Riemann sheet that forms the EPs, encircling both exceptional points produces more branch cuts than encircling a single exceptional point. An asymmetric–symmetric multimode switch at telecommunication wavelengths is demonstrated. Chiral mode converter and non‐chiral mode converter can be implemented simultaneously on one photonic device. The research enriches the exploration of the physics associated with multiple exceptional points in non‐Hermitian multistate systems and inspires wide applications in on‐chip optical systems.

Integrated photonic chips hold substantial potential in optical communications, computing, light detection and ranging, sensing, and imaging, offering exceptional data throughput and low power consumption. A key objective is to build a monolithic on-chip photonic system that integrates light sources, processors and photodetectors on a single chip. However, this remains challenging due to limitations in materials engineering, chip integration techniques and design methods. Perovskites offer simple fabrication, tolerance to lattice mismatch, flexible bandgap tunability and low cost, making them promising for hetero-integration with silicon photonics. Here we propose and experimentally realize a near-infrared monolithic on-chip photonic system based on a perovskite/silicon nitride photonic platform, developing nano-hetero-integration technology to integrate efficient light-emitting diodes, high-performance processors and sensitive photodetectors. Photonic neural networks are implemented to perform photonic simulations and computer vision tasks. Our network efficiently predicts the topological invariant in a two-dimensional disordered Su–Schrieffer–Heeger model and simulates nonlinear topological models with an average fidelity of 87%. In addition, we achieve a test accuracy of over 85% in edge detection and 56% on the CIFAR-10 dataset using a scaled-up architecture. This work addresses the challenge of integrating diverse nanophotonic components on a chip, offering a promising solution for chip-integrated multifunctional photonic information processing.

TMEM16A, a key calcium-activated chloride channel, is crucial for many physiological and pathological processes such as cancer, hypertension, and osteoporosis, etc. However, the regulatory mechanism of TMEM16A is poorly understood, limiting the discovery of effective modulators. Here, we unveil an allosteric gating mechanism by presenting a high-resolution cryo-EM structure of TMEM16A in complex with a channel inhibitor that we identified, Tamsulosin, which is resolved at 2.93 Å. Tamsulosin wedges itself into a pocket within the extracellular domain of TMEM16A, surrounded by α1-α2, α5-α6, and α9-α10 loops. This binding stabilizes a transient preopen conformation of TMEM16A, which is activated by Ca ²⁺ ions while still preserving a closed pore to prevent Cl ⁻ permeation. Validation of this binding site through computational, electrophysiological, and functional experiments, along with site-directed mutagenesis, confirmed the pivotal roles of the pocket-lining residues R605 and E624 on α5-α6 loop in modulating Tamsulosin binding and pore activity. Tamsulosin induces significant positional shifts in extracellular loops, particularly the α5-α6 loop, which moves toward the extracellular exit of the pore, leading to noticeable structural rearrangements in pore-lining helices. The hinges induced by P595 in α5 and G711 in α7 introduce flexibility to the transmembrane helices, orienting Y593 to collaborate with I641 in effectively gating the preopening pore. Notably, Tamsulosin demonstrates significant antiosteoporotic effects by inhibiting TMEM16A, suggesting potential for its repurposing in new therapeutic indications. Our study not only enhances our understanding of the gating mechanism of TMEM16A inhibition but also facilitates structure-based drug design targeting TMEM16A.

Quantum transport in materials describes the behavior of particles at the quantum level. Topological materials exhibit nontrivial transport properties with topological invariants, leading to the emergence of protected states that are immune against disorders at the material boundaries. In many real-world materials, especially those with anisotropic crystal structures, the transport properties can vary significantly along different directions within the material bulk. Here, we experimentally observe counterintuitive quantum transport phenomena in anisotropic topological insulators with controllable anisotropy and disorder, implemented on a programmable topological photonic chip. We examine phase transition from the topological phase to the Anderson phase, between which a new quasi-diffusive phase emerges. Anisotropic topological transport demonstrates unconventional superior robustness in the bulk mode compared to the edge mode, in the presence of disorder and loss in realistic systems. Peculiar topological transport with sophisticated gradient anisotropy, emulating stretched topological materials, occurs at the gradient domain wall that can be reconfigured. Our findings provide fresh insights into the intricate interplay between anisotropy within the bulk and robustness at the boundary of topological materials, which could lead to advancements in the field of topological material science and the development of topological devices with tailored functionalities.

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing

Continuous development of photonic crystals (PCs) over the last 30 years has carved out many new scientific frontiers. However, creating tunable PCs that enable flexible control of geometric configurations remains a challenge. Here we present a scheme to produce a tunable plasma photonic crystal (PPC) ‘kaleidoscope’ with rich diversity of structural configurations in dielectric barrier discharge. Multi-freedom control of the PPCs, including the symmetry, dielectric constant, crystal orientation, lattice constant, topological state, and structures of scattering elements, has been realized. Four types of lattice reconfigurations are demonstrated, including transitions from periodic to periodic, disordered to ordered, non-topological to topological, and striped to honeycomb Moiré lattices. Furthermore, alterations in photonic band structures corresponding to the reconstruction of various PPCs have been investigated. Our system presents a promising platform for generating a PPC ‘kaleidoscope’, offering benefits such as reduced equipment requirements, low cost, rapid response, and enhanced flexibility. This development opens up new opportunities for both fundamental and applied research. Graphical Abstract