In diabetic wounds, hyperglycemia-induced cytotoxicity and impaired immune microenvironment plasticity directly hinder the wound healing process. Regulation of the hyperglycemic microenvironment and remodeling of the immune microenvironment are crucial. Here, we developed a nanozymatic functionalized regenerative microenvironmental regulator (AHAMA/CS-GOx@Zn-POM) for the effective repair of diabetic wounds. This novel construct integrated an aldehyde and methacrylic anhydride-modified hyaluronic acid hydrogel (AHAMA) and chitosan nanoparticles (CS NPs) encapsulating zinc-based polymetallic oxonate nanozyme (Zn-POM) and glucose oxidase (GOx), facilitating a sustained release of release of both enzymes. The GOx catalyzed glucose to gluconic acid and (H₂O₂), thereby alleviating the effects of the hyperglycemic microenvironment on wound healing. Zn-POM exhibited catalase and superoxide dismutase activities to scavenge reactive oxygen species and H₂O₂, a by-product of glucose degradation. Additionally, Zn-POM induced M1 macrophage reprogramming to the M2 phenotype by inhibiting the MAPK/IL-17 signaling diminishing pro-inflammatory cytokines, and upregulating the expression of anti-inflammatory mediators, thus remodeling the immune microenvironment and enhancing angiogenesis and collagen regeneration within wounds. In a rat diabetic wound model, the application of AHAMA/CS-GOx@Zn-POM enhanced neovascularization and collagen deposition, accelerating the wound healing process. Therefore, the regenerative microenvironment regulator AHAMA/CS-GOx@Zn-POM can achieve the effective conversion of a pathological microenvironment to regenerative microenvironment through integrated control of the hyperglycemic-immune microenvironment, offering a novel strategy for the treatment of diabetic wounds.

Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region‐based convolutional neural networks (Mask R‐CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R‐CNN and GANs in order to enhance the pixel‐wise classification. Secondly, k‐means clustering was used to lock the image aspect ratio, in order to get more essential anchors which can help boost the segmentation performance. Finally, we proposed a GAN Mask R‐CNN algorithm which achieved superior performance in comparison with the conventional Mask R‐CNN, Mask‐CNN, and k‐means algorithms in terms of the Dice similarity coefficient (DSC) and the MICCAI metrics. The proposed algorithm also achieved superior performance in comparison with ten state‐of‐the‐art algorithms in terms of six Boolean indicators. We hope that our work can be effectively used to optimize the segmentation and classification of liver anomalies.

Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60–0.88), 0.72 (95% CI 0.57–0.87) and 0.64 (95% CI 0.48–0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61–0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61–0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66–0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

The aim of the present study was to compare the safety and efficacy of cryoablation (CA) and microwave ablation (MWA) as treatments for non‑small cell lung cancer (NSCLC). Patients with stage IIIB or IV NSCLC treated with CA (n=45) or MWA (n=56) were enrolled in the present study. The primary endpoint was progression‑free survival (PFS); the secondary endpoints included overall survival (OS) time and adverse events (AEs). The median PFS times between the two groups were not significantly different (P=0.36): CA, 10 months [95% confidence interval (CI), 7.5‑12.4] vs. MWA, 11 months (95% CI, 9.5‑12.4). The OS times between the two groups were also not significantly different (P=0.07): CA, 27.5 months (95% CI, 22.8‑31.2 months) vs. MWA, 18 months (95% CI, 12.5‑23.5). For larger tumors (>3 cm), patients treated with MWA had significantly longer median PFS (P=0.04; MWA, 10.5 months vs. CA, 7.0 months) and OS times (P=0.04; MWA, 24.5 months vs. CA, 14.5 months) compared patients treated with CA. However, for smaller tumors (≤3 cm), median PFS (P=0.79; MWA, 11.0 months vs. CA, 13.0 months) and OS times (P=0.39; MWA, 30.0 months vs. CA, 26.5 months) between the two groups did not differ significantly. The incidence rates of AEs were similar in the two groups (P>0.05). The number of applicators, tumor size and length of the lung traversed by applicators were associated with a higher risk of pneumothorax and intra‑pulmonary hemorrhage in the two groups. Treatment with CA resulted in significantly less intraprocedural pain compared with treatment with MWA (P=0.001). Overall, the present study demonstrated that CA and MWA were comparably safe and effective procedures for the treatment of small tumors. However, treatment with MWA was superior compared with CA for the treatment of large tumors.

Non-healing diabetic wounds often culminate in amputation and mortality. The main pathophysiological features in diabetic wounds involve the accumulation of M1-type macrophages and excessive oxidative stress. In this study, we engineered a nano-enzyme functionalized hydrogel by incorporating a magnesium ion-doped molybdenum-based polymetallic oxide (Mg-POM), a novel bioactive nano-enzyme, into a GelMA hydrogel, to obtain the GelMA@Mg-POM system to enhance diabetic wound healing. GelMA@Mg-POM was crosslinked using UV light, yielding a hydrogel with a uniformly porous three-dimensional mesh structure. In vitro experiments showed that GelMA@Mg-POM extraction significantly enhanced human umbilical vein endothelial cell (HUVEC) migration, scavenged ROS, improved the inflammatory microenvironment, induced macrophage reprogramming towards M2, and promoted HUVEC regeneration of CD31 and fibroblast regeneration of type I collagen. In in vivo experiments, diabetic rat wounds treated with GelMA@Mg-POM displayed enhanced granulation tissue genesis and collagen production, as evidenced by HE and Masson staining. Immunohistochemistry demonstrated the ability of GelMA@Mg-POM to mitigate macrophage-associated inflammatory responses and promote angiogenesis. Overall, these findings suggest that GelMA@Mg-POM holds significant promise as a biomaterial for treating diabetic wounds.