Dendritic cell-cytokine-induced killer (DC-CIK) cell therapy has been experimentally implemented for enhancing anti-tumoral immunity in patients with hepatocellular carcinoma (HCC) undergoing postoperative transcatheter arterial chemoembolization (POTACE). We performed a retrospective study to evaluate the clinical efficacies of DC-CIK cell therapy and its correlations with several immune factors of the primary tumors. The overall survival time of HCC patients with HBV infection in the study group (POTACE plus DC-CIK cell therapy) was significantly longer than that of the control group (POTACE alone). The expression level of PD-L1 but not the tumor-infiltrated CD8 and CD4 T cells in the tumor tissues showed significant negative correlations with relapse-free survival (RFS) and overall survival (OS), which was also an independent prognostic factor for the five-years' suvival of patients with HCC receiving POTACE treatment. Furthermore, our study validated that PD-L1 expression was significantly inversely correlated with the survival time of HCC patients receiving POTACE plus DC-CIK cell therapy treatment. More importantly, DC-CIK cell therapy provided the best clinical benefits to HCC patients with the low PD-L1 expression receiving POTACE, which indicate that PD-L1 expression level can serve as a pivotal predictor for the therapeutic efficacy of DC-CIK cell therapy for HCC patients receiving POTACE treatment.

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In this paper, by using the general discrete Halanay inequalities, the techniques of inequalities and some other properties, we study the ultimate boundedness of a class of the discrete-time uncertain neural network systems and obtain several sufficient conditions to ensure the ultimate boundedness of discrete-time uncertain neural networks with leakage and time-varying delays. Finally numerical examples are given to verify the correctness of the conclusion.

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant’s continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah.

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