A 43.5dB Gain Unipolar a-IGZO TFT Amplifier with Parallel Bootstrap Capacitor for Bio-signals Sensing Applications.

In this paper, a high gain amplifier with phase compensation loop is presented. A structure of parallel gate cross-coupled transistors to both ends of differential pair drain and source is designed to improves the load impedance, which obtains sufficient gain and further reduces power consumption. A novel capacitor bootstrap load circuit is proposed. The capacitor bootstrap topology is constructed by the drain source resistance of the transistor working in the cut-off region, where the gate source parasitic capacitor of the transistor is in parallel with the bootstrap capacitor rather than the existing series structure, thereby only a small bootstrap capacitor is required. By avoiding the use of large capacitors, chip area can be effectively reduced without compromising performance such as gain and bandwidth. The amplifier is fabricated using 10-µm n-type a-IGZO TFT technology. Measurement results show that the proposed amplifier achieves a voltage gain of 43.5dB and a common mode rejection ratio of 61.2dB while maintaining low power consumption. The amplifier also exhibits a -3dB bandwidth covering 0.4~2.1KHz, encompassing major bioelectric frequency bands. A real-time ECG signal was successfully captured using the fabricated TFT amplifier and gel electrodes. It has great potential in flexible sensing and acquisition applications such as electro cardiogram (ECG), electro encephalogram (EEG), pulse detection, and other wearable applications.

Efficacy and safety of tyrosine kinase inhibitors for advanced metastatic thyroid cancer: A systematic review and network meta-analysis of randomized controlled trials.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) have been approved for treating patients with clinically advanced metastatic thyroid cancer. However among the many TKIs, it remains unknown which regimen is the best choice for these patients. METHODS: We conducted a systematic review and network meta-analysis to compare the survival benefits and efficacy of the available first-line regimens. We conducted an active search for phase II, III, or IV randomized controlled trials (RCTs) in the PubMed, Embase, and Cochrane databases to compare the effects of at least 2 drugs in the systemic treatment of advanced or metastatic thyroid cancer up to May 2023. The network meta-analysis model was adjusted using Bayesian Network model. Twelve trials with 2535 patients were included in our meta-analysis. The overall survival (OS), progression-free survival (PFS), and serious adverse events (SAEs) were taken as reference indicators. We also performed subgroup analyses of OS and PFS in medullary thyroid cancer (MTC) and radioiodine-refractory differentiated thyroid cancer (RR-DTC) to explore the variations of TKIs in different groups. RESULTS: As a result, apatinib had the best effect on overall survival (OS) (hazards ratio [HR] = 0.42, 95% confidence interval [CI] = 0.18-0.98), lenvatinib 18 mg/d has the best effect on progression-free survival (PFS) (HR = 0.13, 95% CI = 0.064-0.27), and cabozantinib 60 mg/d has the best safety profile. CONCLUSIONS: Our network meta-analysis showed that we believe that cabozantinib has the potential to become a widely used drug in clinical practice.

SHMT2 promotes papillary thyroid cancer metastasis through epigenetic activation of AKT signaling.

Cancer cells alter their metabolism and epigenetics to support cancer progression. However, very few modulators connecting metabolism and epigenetics have been uncovered. Here, we reveal that serine hydroxymethyltransferase-2 (SHMT2) generates S-adenosylmethionine (SAM) to epigenetically repress phosphatase and tensin homolog (PTEN), leading to papillary thyroid cancer (PTC) metastasis depending on activation of AKT signaling. SHMT2 is elevated in PTC, and is associated with poor prognosis. Overexpressed SHMT2 promotes PTC metastasis both in vitro and in vivo. Proteomic enrichment analysis shows that AKT signaling is activated, and is positively associated with SHMT2 in PTC specimens. Blocking AKT activation eliminates the effects of SHMT2 on promoting PTC metastasis. Furthermore, SHMT2 expression is negatively associated with PTEN, a negative AKT regulator, in PTC specimens. Mechanistically, SHMT2 catalyzes serine metabolism and produces activated one-carbon units that can generate SAM for the methylation of CpG islands in PTEN promoter for PTEN suppression and following AKT activation. Importantly, interference with PTEN expression affects SHMT2 function by promoting AKT signaling activation and PTC metastasis. Collectively, our research demonstrates that SHMT2 connects metabolic reprogramming and epigenetics, contributing to the poor progression of PTC.

Identification and validation of key biomarkers associated with macrophages in nonalcoholic fatty liver disease based on hdWGCNA and machine learning.

BACKGROUND: NAFLD has attracted increasing attention because of its high prevalence and risk of progression to cirrhosis or even hepatocellular carcinoma. Therefore, research into the root causes and molecular indicators of NAFLD is crucial. METHODS: We analyzed scRNA-seq data and RNA-seq data from normal and NAFLD liver samples. We utilized hdWGCNA to find module-related genes associated with the phenotype. Multiple machine learning algorithms were used to validate the model diagnostics and further screen for genes that are characteristic of NAFLD. The NAFLD mouse model was constructed using the MCD diet to validate the diagnostic effect of the genes. RESULTS: We identified a specific macrophage population called NASH-macrophages by single-cell sequencing analysis. Cell communication analysis and Pseudo-time trajectory analysis revealed the specific role and temporal distribution of NASH-macrophages in NAFLD. The hdWGCNA screening yielded 30 genes associated with NASH-macrophages, and machine learning algorithms screened and obtained two genes characterizing NAFLD. The immune infiltration indicated that these genes were highly associated with macrophages. Notably, we verified by RT-qPCR, IHC, and WB that MAFB and CX3CR1 are highly expressed in the MCD mouse model and may play important roles. CONCLUSIONS: Our study revealed a macrophage population that is closely associated with NAFLD. Using hdWGCNA analysis and multiple machine learning algorithms, we identified two NAFLD signature genes that are highly correlated with macrophages. Our findings may provide potential feature markers and therapeutic targets for NAFLD.

Associations between genetically predicted sex and growth hormones and facial aging in the UK Biobank: a two-sample Mendelian randomization study.

Background: Aging is an inescapable process, but it can be slowed down, particularly facial aging. Sex and growth hormones have been shown to play an important role in the process of facial aging. We investigated this association further, using a two-sample Mendelian randomization study. Methods: We analyzed genome-wide association study (GWAS) data from the UK Biobank database comprising facial aging data from 432,999 samples, using two-sample Mendelian randomization. In addition, single-nucleotide polymorphism (SNP) data on sex hormone-binding globulin (SHBG) and sex steroid hormones were obtained from a GWAS in the UK Biobank [SHBG, N = 189,473; total testosterone (TT), N = 230,454; bioavailable testosterone (BT), N = 188,507; and estradiol (E2), N = 2,607)]. The inverse-variance weighted (IVW) method was the major algorithm used in this study, and random-effects models were used in cases of heterogeneity. To avoid errors caused by a single algorithm, we selected MR-Egger, weighted median, and weighted mode as supplementary algorithms. Horizontal pleiotropy was detected based on the intercept in the MR-Egger regression. The leave-one-out method was used for sensitivity analysis. Results: SHBG plays a promoting role, whereas sex steroid hormones (TT, BT, and E2) play an inhibitory role in facial aging. Growth hormone (GH) and insulin-like growth factor-1 (IGF-1) levels had no significant effect on facial aging, which is inconsistent with previous findings in vitro. Conclusion: Regulating the levels of SHBG, BT, TT, and E2 may be an important means to delay facial aging.

Construction and validation of a PANoptosis-related lncRNA signature for predicting prognosis and targeted drug response in thyroid cancer.

Thyroid cancer (TC) is the most prevalent malignancy of the endocrine system. PANoptosis, a newly discovered cell death pathway, is of interest in tumor research. However, the relationship between PANoptosis-related lncRNAs (PRlncRNAs) and TC remains unclear. The study aimed to develop a prognostic model based on PRlncRNAs in TC. Gene expression data of PANoptosis-associated genes and clinical information on TC from The Cancer Genome Atlas (TCGA) database were analyzed by Pearson correlation analysis, univariate/multivariate Cox analysis, and Lasso Cox regression analysis. A PRlncRNA signature was constructed and used to develop a nomogram to predict overall survival (OS). We further explored the correlation between the risk score and tumor immune microenvironment, immune checkpoints, and drug sensitivity. Moreover, we verified the expression and biological function of lncRNAs in TC cell lines. Finally, seven PRlncRNAs were used to construct a prognostic model for predicting the OS of TC patients. We found that the risk score was associated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. In addition, we screened for drugs that high- or low-risk TC groups might be sensitive to. Quantitative real-time polymerase chain reaction (qRT-PCR) results showed differential expression of four PRlncRNAs (GAPLINC, IDI2-AS1, LINC02154, and RBPMS-AS1) between tumor and normal tissues. Besides, a GEO database (GSE33630) was used to verify the expression differences of PRLncRNAs in THCA tissues and normal tissues. Finally, RBPMS-AS1 was found to inhibit the proliferation and migration of TC cells. In conclusion, we developed a PANoptosis-related lncRNA prognostic risk model that offers a comprehensive understanding of TME status in patients with TC and establishes a foundation for the choice of sensitive medications and immunotherapy.

Identification and immunological characterization of lipid metabolism-related molecular clusters in nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is now the major contributor to chronic liver disease. Disorders of lipid metabolism are a major element in the emergence of NAFLD. This research intended to explore lipid metabolism-related clusters in NAFLD and establish a prediction biomarker. METHODS: The expression mode of lipid metabolism-related genes (LMRGs) and immune characteristics in NAFLD were examined. The "ConsensusClusterPlus" package was utilized to investigate the lipid metabolism-related subgroup. The WGCNA was utilized to determine hub genes and perform functional enrichment analysis. After that, a model was constructed by machine learning techniques. To validate the predictive effectiveness, receiver operating characteristic curves, nomograms, decision curve analysis (DCA), and test sets were used. Lastly, gene set variation analysis (GSVA) was utilized to investigate the biological role of biomarkers in NAFLD. RESULTS: Dysregulated LMRGs and immunological responses were identified between NAFLD and normal samples. Two LMRG-related clusters were identified in NAFLD. Immune infiltration analysis revealed that C2 had much more immune infiltration. GSVA also showed that these two subtypes have distinctly different biological features. Thirty cluster-specific genes were identified by two WGCNAs. Functional enrichment analysis indicated that cluster-specific genes are primarily engaged in adipogenesis, signalling by interleukins, and the JAK-STAT signalling pathway. Comparing several models, the random forest model exhibited good discrimination performance. Importantly, the final five-gene random forest model showed excellent predictive power in two test sets. In addition, the nomogram and DCA confirmed the precision of the model for NAFLD prediction. GSVA revealed that model genes were down-regulated in several immune and inflammatory-related routes. This suggests that these genes may inhibit the progression of NAFLD by inhibiting these pathways. CONCLUSIONS: This research thoroughly emphasized the complex relationship between LMRGs and NAFLD and established a five-gene biomarker to evaluate the risk of the lipid metabolism phenotype and the pathologic results of NAFLD.

Identification of ferroptosis-related genes and predicted overall survival in patients with burns.

Introduction: Burns are a common trauma associated with considerable mortality and morbidity. Although a lot is known regarding burns' pathogenesis, the involvement of ferroptosis is uncertain. Here, we aimed to explore vital ferroptosis-related genes and molecules in burns, through bioinformatics analysis, to uncover new effective therapeutic targets. Methods: The FerrDb database was used to acquire ferroptosis-related genes and GSE19743 was downloaded from Gene Expression Omnibus (GEO), a dataset with analysis of control and burned individuals. Hub genes were selected with Cytoscape software, and Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Cox proportional hazard function and Kaplan-Meier survival analyses were implemented to screen prognosis-related genes. Additionally, the miRWalk database was used to acquire the miRNAs relevant to our hub genes function and analyzed for enrichment. Result: We identified 64 differentially expressed genes and through the intersection with ferroptosis-related genes, 10 were selected as hub genes. GO analysis revealed that the hub genes' most enriched activities were response to oxidative stress, pyridine-containing compound metabolic processes, and reactive oxygen species metabolic processes. KEGG pathways' analysis showed that these overlapped genes were enriched in several pathways, namely, in VEGF signaling. Furthermore, the molecular miRNA functions significantly enriched were signal transduction and cell communication, namely, the biological pathways of the glypican pathway and the ErbB receptor signaling network. SLC40A1 and GPT2 genes were found to be associated with overall survival, suggesting an important role in burn prognosis. Discussion: This study may improve our understanding of the underlying burn mechanisms and provide a new direction for the prevention of poor outcomes, advances in burns treatment, and drug development.

A Delay-Cell-Controlled VCO Design for Unipolar Single-Gate Enhancement-Mode TFT Technologies.

This work outperforms the previous literatures by proposing a delay-cell-controlled voltage control oscillator (VCO) design for common unipolar, single-gate, and enhancement-mode thin-film transistor (TFT) technologies. A design example with InZnO TFTs is simulated to verify the proposed design. The design example has a 500 µW power consumption, 0.7 mm2 area, 3.8 kHz-8 kHz output frequency range, 600 Hz/V tuning sensitivity, and 4% maximum linear error. This design may have the potential to be used for flexible, low cost, and moderate speed sensor readout interfaces.