Malignant tumors are complex systemic chronic diseases and one of the major causes of human mortality. Targeted therapy, chemotherapy, immunotherapy, and radiotherapy are examples of mainstream allopathic medicine treatments that effective for intermediate and advanced malignant tumors. The ongoing use of conventional allopathic medicine has resulted in adverse responses and drug resistance, which have hampered its efficacy. As an important component of complementary and alternative medicine, Chinese medicine has been found to have antitumor effects and has played an important role in enhancing the therapeutic sensitivity of mainstream allopathic medicine, reducing the incidence of adverse events and improving immune-related functions. The combined application of adjuvant Chinese medicine and mainstream allopathic medicine has begun to gain acceptance and is gradually used in the field of antitumor therapy. Traditional natural medicines and their active ingredients, as well as Chinese patent medicines, have been proven to have excellent therapeutic efficacy and good safety in the treatment of various malignant tumors. This paper focuses on the mechanism of action and research progress of combining the above drugs with mainstream allopathic medicine to increase therapeutic sensitivity, alleviate drug resistance, reduce adverse reactions, and improve the body’s immune function. To encourage the clinical development and use of Chinese herb adjuvant therapy as well as to provide ideas and information for creating safer and more effective anticancer medication combinations, the significant functions of Chinese herb therapies as adjuvant therapies for cancer treatment are described in detail.

Object detection is critical for autonomous vehicles. It enables safe and efficient autonomous driving. However, variations in weather conditions, such as night, fog, rain, and snow, introduce domain shifts that can lead to catastrophic forgetting in detection models, significantly degrading their performance in object detection tasks. This work introduces D-FusionNet, a model that combines data-level sensor fusion with domain incremental learning (Domain-IL) to improve detection performance under such conditions. By combining the complementary strengths of radar and camera sensors, and using Domain-IL to selectively retain features from different weather domains, D-FusionNet demonstrates improved accuracy in object detection, as validated on the RADIATE dataset. This approach effectively addresses catastrophic forgetting and increases object detection accuracy in adverse weather conditions, marking a significant advancement in autonomous vehicle technology.

Background. Diabetes mellitus (DM) belongs to the progressive and irreversible disease. With the development of the disease, the function of beta cells declines significantly. Current treatments cannot reverse the course of the disease. The role of traditional Chinese medicine (TCM) in the DM treatment has been recognized widely, while there are few long-term observation reports. In this study, we introduced a case of DM treated by classical prescription alone for 10 years, which would provide the reference for clinical practice. Case Presentation. A 64-year-old male complained of a 2-month history of dry mouth, frequent urination, and blurred vision and found increased blood glucose for 3 days. The patient’s glycated hemoglobin was 14.2%, fasting plasma glucose fluctuated at 12-15 mmol/L, and urinary albumin excretion rate (UAER) was 32.9 μg/min. The male was treated with 10 years of continuous classical prescription alone. After 3 months of TCM treatment, the patient’s blood glucose level decreased significantly and blurred vision symptoms improved. With continued TCM treatment, his UAER normalized. Subsequently, he continued outpatient consultation, and his TCM prescription was adjusted according to clinical symptoms. After 10 years of continuous TCM treatment, his blood glucose remained stable, urinary microalbumin quantitation showed no abnormalities, and blurred vision disappeared. Conclusions. This case provides specific treatment plans and effective references for long-term control of blood glucose, prevention and treatment of diabetes complications, delay of disease progression, and protection of impaired islet function in the treatment of diabetes with TCM. TCM may become a meaningful alternative DM treatment in the future.

Robust altitude tracking and attitude stabilization of a quadrotor helicopter (QH) operating in dynamic and uncertain environments remain challenging due to unknown external disturbances, time-varying parametric uncertainties, and practical actuator and sensor constraints. To address these challenges, this article proposes a neural-enhanced sliding-mode control (NE-SMC) framework that integrates a lightweight multilayer perceptron neural network (MLP-NN) with an SMC backbone within a unified adaptive control architecture. The neural module employs a sliding-mode-inspired weight-update law to enable real-time compensation for disturbances and uncertainties without requiring explicit disturbance models or prior knowledge of system parameters, thereby strengthening system-level robustness and adaptability. A rigorous Lyapunov-based analysis guarantees closed-loop stability and finite-time convergence of the tracking errors. Extensive high-fidelity simulations, including deterministic scenarios and a 100-run Monte Carlo study, are conducted under severe wind disturbances and turbulent conditions, up to 25% mass and inertia variations, actuator saturation, and sensor constraints. Comparative results demonstrate that the proposed NE-SMC achieves substantially improved tracking accuracy and robustness compared with standard SMC and conventional hybrid SMC (H-SMC) schemes. Specifically, the root-mean-square error (RMSE) is reduced by 83.3%–88.9% relative to standard SMC and by 75%–87.5% relative to conventional H-SMC. These results confirm that the proposed framework provides a robust, constraint-aware, and computationally efficient control solution suitable for real-time QH applications, aligning with the requirements of intelligent systems in complex, uncertain environments.