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Objectives: To comprehensively analyze the copper metabolism in Breast cancer, we established a prognostic signature for breast cancer (BC) related to copper metabolism. Methods: Copper metabolism-related genes were sourced from previous literatures and were selected by the Univariate Cox regression. Cu-enrichment scores were calculated via ssGSEA. Differentially expressed genes were identified with limma between high and low Cu-enrichment scores group, then we used the Random Survival Forest and LASSO to build the CuScore for BC. Kaplan-Meier analysis, ROC curves, and Cox regression were used to evaluate CuScore. Genomic mutations were analyzed with GISTIC. Immune cells were examined using ESTIMATE, ssGSEA and TIMER. Enrichment analysis used clusterProfiler and GSVA. The GDSC database and oncoPredict package analyzed chemotherapeutic sensitivity. MMP13 was selected for in vitro assays. Results: Four copper metabolism-related genes (UBE2D2, SLC31A1, ATP7A, and MAPK1) with prognostic value were identified. Higher expression levels of these genes were associated with higher Cu-enrichment scores, a factor of malignancy in breast cancer. Among 115 differentially expressed genes, 19 prognostic genes were identified, with three (CEACAM5, MMP13, and CRISP3) highlighted by Random Survival Forest and LASSO. Higher CuScores correlated with worse prognoses and were effective in predicting breast cancer outcomes. CuScore and metastasis were independent prognostic factors. Tumor-infiltrating immune cells were associated with lower CuScores. GO-GSEA analysis indicated six immune-related pathways might be regulated by CuScore. Patients with higher CuScores had lower TMB and were more sensitive to Sapitinib and LCL161, while those with lower CuScores might respond better to anti-PD1 therapy. High MMP13 expression in breast cancer was linked to malignancy, affecting cell proliferation and migration. Conclusion: The identified copper metabolism-related gene signature has the potential to predict prognosis and guide clinical treatment for BC. Among these genes, MMP13 may act as a malignant factor in BC.

A 9-10-bit adjustable and energy-efficient switching scheme for SAR ADC with one-LSB common-mode voltage variation is proposed. Based on capacitor-splitting technology and common-mode conversion techniques, the proposed switching scheme reduces the DAC switching energy by 96.41% compared to the conventional scheme. The low complexity and the one-LSB common-mode voltage offset of this scheme benefit from the simultaneous switching of the reference voltages of the capacitors corresponding to the positive array and the negative array throughout the entire reference voltage switching process, and the reference voltage of each capacitor in the scheme does not change more than two voltages. The post-layout result shows that the ADC achieves the 54.96 dB SNDR, the 61.73 dB SFDR, and the 0.67 μw power consumption with the 10-bit mode and the 48.33 dB SNDR, the 54.17 dB SFDR, and the 0.47 μw power consumption with the 9-bit mode in a 180 nm process with a 100 kS/s sampling frequency.

Acute myeloid leukemia (AML) is a complex mixed entity composed of malignant tumor cells, immune cells and stromal cells, with intra-tumor and inter-tumor heterogeneity. Single-cell RNA sequencing enables a comprehensive study of the highly complex tumor microenvironment, which is conducive to exploring the evolutionary trajectory of tumor cells. Herein, we carried out comprehensive analyses of aggrephagy-related cell clusters based on single-cell sequencing for patients with acute myeloid leukemia. A total of 11 specific cell types (T, NK, CMP, Myeloid, GMP, MEP, Promono, Plasma, HSC, B, and Erythroid cells) using t-SNE dimension reduction analysis. Several aggrephagy-related genes were highly expressed in the 11 specific cell types. Using Monocle analysis and NMF clustering analysis, six aggrephagy-related CD8+ T clusters, six aggrephagy-related NK clusters, and six aggrephagy-related Mac clusters were identified. We also evaluated the ligand-receptor links and Cell–cell communication using CellChat package and CellChatDB database. Furthermore, the transcription factors (TFs) of aggrephagy-mediated cell clusters for AML were assessed through pySCENIC package. Prognostic analysis of the aggrephagy-related cell clusters based on R package revealed the differences in prognosis of aggrephagy-mediated cell clusters. Immunotherapy of the aggrephagy-related cell clusters was investigated using TIDE algorithm and public immunotherapy cohorts. Our study revealed the significance of aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for AML.

A hybrid energy-efficient, area-efficient, low-complexity switching scheme in SAR ADC for biosensor applications is proposed. This scheme is a combination of the monotonic technique, the MSB capacitor-splitting technique, and a new switching method. The MSB capacitor-splitting technique, as well as the reference voltage Vaq allow for more options for reference voltage conversion, resulting in higher area savings and higher energy efficiency. In a capacitor array, the circuit performs unilateral switching during all comparisons except for the second and last two comparisons, reducing the difficulty in designing the drive circuit. The proposed switching scheme saves 98.4% of the switching energy and reduces the number of unit capacitors by 87.5% compared to a conventional scheme. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low-sampling error-sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrated that the proposed SAR ADC achieves 61.51 dB SNDR, 79.21 dB SFDR and consumes 0.278 μW of power in a 180 nm process with a 1 V power supply, a full swing input signal frequency of 23.33 kHz, and a sampling rate of 100 kS/s.

Chronic kidney disease (CKD) has been recognized as an increasingly serious public health problem globally over the decades. Accumulating evidence has shown that the incidence rate of cancer was relatively higher in CKD patients than that in general population, which, mechanistically, may be related to chronic inflammation, accumulation of carcinogenic compounds, oxidative stress, impairment of DNA repair, excessive parathyroid hormone and changes in intestinal microbiota, etc. And in patients with cancer, regardless of tumor types or anticancer treatment, it has been indicated that the morbidity and incidence rate of concomitant CKD was also increased, suggesting a complex inter-relationship between CKD and cancer and arousing increasing attention from both nephrologists and oncologists. This narrative review focused on the correlation between CKD and cancer, and underlying molecular mechanisms, which might provide an overview of novel interdisciplinary research interests and the potential challenges related to the screening and treatment of CKD and cancer. A better understanding of this field might be of help for both nephrologists and oncologists in the clinical practice.

Diabetic kidney disease (DKD) is one of the major microvascular complications of diabetes mellitus, with relatively high morbidity and mortality globally but still in short therapeutic options. Over the decades, a large body of data has demonstrated that oxidative stress, inflammatory responses, and hemodynamic disorders might exert critical influence in the initiation and development of DKD, whereas the delicate pathogenesis of DKD remains profoundly elusive. Recently, long non-coding RNAs (lncRNAs), extensively studied in the field of cancer, are attracting increasing attentions on the development of diabetes mellitus and its complications including DKD, diabetic retinopathy, and diabetic cardiomyopathy. In this review, we chiefly focused on abnormal expression and function of lncRNAs in major resident cells (mesangial cell, endothelial cell, podocyte, and tubular epithelial cell) in the kidney, summarized the critical roles of lncRNAs in the pathogenesis of DKD, and elaborated their potential therapeutic significance, in order to advance our knowledge in this field, which might help in future research and clinical treatment for the disease.

Fyn, a member of the Src family of tyrosine kinases, is a key regulator in cytoskeletal remodeling in a variety of cell types. Recent studies have demonstrated that Fyn is responsible for nephrin tyrosine phosphorylation, which will result in polymerization of actin filaments and podocyte damage. Thus detailed involvement of Fyn in podocytes is to be elucidated. In this study, we investigated the potential role of Fyn/ROCK signaling and its interactions with paxillin. Our results presented that high glucose led to filamentous actin (F-actin) rearrangement in podocytes, accompanied by paxillin phosphorylation and increased cell motility, during which Fyn and ROCK were markedly activated. Gene knockdown of Fyn by siRNA showed a reversal effect on high glucose-induced podocyte damage and ROCK activation; however, inhibition of ROCK had no significant effects on Fyn phosphorylation. These observations demonstrate that in vitro Fyn mediates high glucose-induced actin cytoskeleton remodeling of podocytes via promoting ROCK activation and paxillin phosphorylation.

Temporal and spatial resolution of satellite images are coarse and cannot provide the real-time, meter-scale resolution monitoring required in many applications, such as precision agriculture. Since high resolution thermal infrared data provide one means to observe canopy temperature variance, we developed an algorithm (three-temperature model, 3T) to estimate transpiration rate at meter-scale pixels and detected transpiration variation for soybean under different upper irrigation limits: No irrigation, 35% of field capacity (FC), 55% of FC, and 75% of FC, denoted as W0, W1, W2, and W3, respectively. The spatial patterns of the transpiration rate indicated that heterogeneity is common in farmland. Transpiration rates in the wet treatment (i.e., W3) were consistently higher than that in the dry treatment (i.e., W0). Transpiration rates reached peak values at around 12:30–14:30 and most of values showed that W3 > W2 > W1 > W0, with 0.91 mm/h, 0.89 mm/h, 0.79 mm/h, and 0.62 mm/h during the reproductive period, respectively. In general, the transpiration rate of soybean increased with increasing irrigation quantities. With a higher irrigation total, soil water content increased gradually, and then the transpiration rate increased. Although land surface temperature decreased by only 8.57 K (Kelvin), 6.33 K, and 5.47 K, respectively, the transpiration rate increased by 78%, 60%, and 40%, respectively, for the W3, W2, and W1 treatment compared with the W0 treatment. The magnitude of transpiration change is greater than that of canopy temperature, but both parameters are strongly interrelated with each other through a non-linear correlation. Heterogeneity of canopy leaf temperature and transpiration is mainly due to physical and biological interactions. Understanding transpiration rate and canopy temperature heterogeneity under different irrigation treatments can not only help in scheduling irrigation, but also in enhancing water utilization efficiency in irrigated agriculture. The real-time monitoring of crop transpiration at meter-scale is of great importance for large irrigation systems, especially for precision irrigation, and will have great application prospects in the near future.