Recent years have witnessed a few attempts of vision transformers for single image super-resolution (SISR). Since the high resolution of intermediate features in SISR models increases memory and computational requirements, efficient SISR transformers are more favored. Based on some popular transformer backbone, many methods have explored reasonable schemes to reduce the computational complexity of the self-attention module while achieving impressive performance. However, these methods only focus on the performance on the training platform (e.g., Pytorch/Tensorflow) without further optimization for the deployment platform (e.g., TensorRT). Therefore, they inevitably contain some redundant operators, posing challenges for subsequent deployment in real-world applications. In this paper, we propose a deployment-friendly transformer unit, namely UFONE (i.e., UnFolding ONce is Enough), to alleviate these problems. In each UFONE, we introduce an Inner-patch Transformer Layer (ITL) to efficiently reconstruct the local structural information from patches and a Spatial-Aware Layer (SAL) to exploit the long-range dependencies between patches. Based on UFONE, we propose a Deployment-friendly Inner-patch Transformer Network (DITN) for the SISR task, which can achieve favorable performance with low latency and memory usage on both training and deployment platforms. Furthermore, to further boost the deployment efficiency of the proposed DITN on TensorRT, we also provide an efficient substitution for layer normalization and propose a fusion optimization strategy for specific operators. Extensive experiments show that our models can achieve competitive results in terms of qualitative and quantitative performance with high deployment efficiency.

A growing body of evidence shows that vasculogenic mimicry (VM) is closely related to the invasion and metastasis of many tumor cells. Although the estrogen receptor (ER) can promote initiation and progression of renal cell carcinoma (RCC), how the downstream biomolecules are involved, and the detailed mechanisms of how ER expression is elevated in RCC remain to be further elucidated. Here, we discovered that long noncoding RNA (LncRNA)-SERB is highly expressed in tumor cells of RCC patients. We used multiple RCC cells and an in vivo mouse model for our study, and results indicated that LncRNA-SERB could boost RCC VM formation and cell invasion in vitro and in vivo. Although a previous report showed that ERβ can affect the VM formation in RCC, it is unclear which factor could upregulate ERβ. This is the first study to show LncRNA-SERB can be the upstream regulator of ERβ to control RCC progression. Mechanistically, LncRNA-SERB may increase ERβ via binding to the promoter area, and ERβ functions through transcriptional regulation of zinc finger E-box binding homeobox 1 (ZEB1) to regulate VM formation. These results suggest that LncRNA-SERB promotes RCC cell VM formation and invasion by upregulating the ERβ/ZEB1 axis and that therapeutic targeting of this newly identified pathway may better inhibit RCC progression.

Background: Renal cell carcinoma (RCC), one of the top 10 causes of cancer death, is responsible for more than 90% of all cases of primary renal cancer worldwide. Follicular dendritic cell-secreted protein (FDC-SP) specifically binds to activated B cells and regulates the generation of antibodies. It is also thought to promote cancer cell invasion and migration, which could help with tumor metastases. This study aimed to assess the efficacy of FDC-SP in the diagnosis and prognosis of RCC and to investigate the relationship between immune infiltration in RCC and these outcomes. Results: RCC tissues had significantly higher levels of FDC-SP protein and mRNA than normal tissues. The high level of FDC-SP expression was linked to the T stage, histological grade, pathological stage, N stage, M stage, and OS event. Functional enrichment analysis identified the major pathways that were enriched as immune response regulation, complement, and coagulation. Immunological checkpoints and immune cell infiltration were observed to substantially correlate with the levels of FDC-SP expression. FDC-SP expression levels showed the ability to precisely distinguish high-grade or high-stage renal cancer (area under the curve (AUC) = 0.830, 0.722), and RCC patients with higher FDC-SP expression levels had worse prognoses. The AUC values for one-, two-, and five-year survival rates were all greater than 0.600. Moreover, the FDC-SP expression is an independent predictive biomarker of OS in RCC patients. Conclusion: FDC-SP may be a prospective therapeutic target in RCC as well as a possible diagnostic and prognostic biomarker associated with immune infiltration.

Glucose is a source of energy for daily activities of the human body and is regarded as a clinical biomarker, due to the abnormal glucose level in the blood leading to many endocrine metabolic diseases. Thus, it is indispensable to develop simple, accurate, and sensitive methods for glucose detection. However, the current methods mainly depend on natural enzymes, which are unstable, hard to prepare, and expensive, limiting the extensive applications in clinics. Herein, we propose a dual-mode Cu₂O nanoparticles (NPs) based biosensor for glucose analysis based on colorimetric assay and laser desorption/ionization mass spectrometry (LDI MS). Cu₂O NPs exhibited excellent peroxidase-like activity and served as a matrix for LDI MS analysis, achieving visual and accurate quantitative analysis of glucose in serum. Our proposed method possesses promising application values in clinical disease diagnostics and monitoring.

Nutcracker syndrome (NCS) arises from extrinsic compression of the left renal vein (LRV) between the superior mesenteric artery and the abdominal aorta. Extravascular stenting (EVS) has emerged as a minimally invasive alternative to historical operations and endovascular stents. We report a single-center series spanning 2010 to 2025 and propose a standardized, reproducible framework that couples intraoperative process quality with objective postoperative hemodynamic targets. We retrospectively analyzed 22 consecutive NCS patients treated with laparoscopic or robot-assisted EVS. We standardized five intraoperative steps (five-in-a-row: fibrotic-ring resection, proper length tailoring/placement, sufficient superior mesenteric artery mobilization, complete division of LRV tributaries, stable anterior fixation). Postoperative duplex ultrasound metrics included aortomesenteric (AM) LRV peak systolic velocity and the AM/hilum peak systolic velocity (PSV) ratio. Thresholds were determined by receiver operating characteristic-Youden index; performance was summarized at fixed cutoffs, with bootstrap for the ratio and exploratory OR/AND combinations. Complete success was achieved in 18 of 22 patients (81.8%). Data-driven analysis identified a postoperative AM PSV of ≤72 cm/s as the primary attainment threshold, yielding a sensitivity of 1.00, specificity of 0.75, accuracy of 0.95, and area under the receiver operating characteristic curve (AUC) of ≈0.917. The AM/hilum ratio showed a Youden-optimal cutoff of ≈1.90 (clinically ≈2.0) with an AUC of ≈0.56, supporting its role as a sensitivity/replicability metric rather than a standalone gatekeeper. OR and AND combinations demonstrated expected trade-offs; a simple 0/1/2 composite score achieved an AUC of ≈0.78. The five-in-a-row checklist was concordant with attaining the AM-PSV target on Doppler ultrasound examination. Laparoscopic or robot-assisted EVS is a safe, feasible, and effective option for NCS. We a propose postoperative AM PSV of ≤72 cm/s as a unified, reproducible primary quantitative end point, with an AM/hilum ratio of ≈2.0 as a secondary, replicability-oriented metric. Integrating these targets with a standardized five-in-a-row checklist establishes a process-outcome loop that enhances procedural reproducibility and supports sustained symptom relief over the available follow-up.