Unveiling the Etiology of Urological Tumors: A Systematic Review of Mendelian Randomization Applications in Renal Cell Carcinoma, Bladder Cancer, and Prostate Cancer.

BACKGROUND: Our study aims to address two pivotal questions: "What are the recent advancements in understanding the etiology of urological tumors through Mendelian Randomization?" and "How can Mendelian Randomization be more effectively applied in clinical settings to enhance patient health outcomes in the future?" METHODS: In our systematic review conducted in April 2023, we utilized databases like PubMed and Web of Science to explore the influence of Mendelian Randomization in urological oncological diseases. We focused on studies published from January 2018, employing keywords related to urological tumors and Mendelian Randomization, supplemented with MeSH terms and manual reference checks. Our inclusion criteria targeted original research studies, while we excluded reports and non-relevant articles. Data extraction followed a PICO-based approach, and bias risk was independently evaluated, with discrepancies resolved through discussion. This systematic approach adhered to PRISMA guidelines for accuracy and thoroughness in reporting. RESULTS: From the initial 457 publications, we narrowed down to 43 full-text articles after screening and quality assessments. A deeper understanding of Mendelian Randomization can help us explore risk factors with a clear causal relationship to urological tumors. This insight may pave the way for future research in early diagnosis, treatment, and management of associated diseases. CONCLUSION: Our review underscores the value of MR in urogenital tumor research, highlighting its efficacy in establishing causality and its potential to clarify disease mechanisms. Despite challenges like large sample sizes and variant identification, MR offers new perspectives for understanding and managing these tumors, suggesting a trend towards more inclusive and diverse research approaches.

Basal metabolic rate and the risk of urolithiasis: a two-sample Mendelian randomization study.

OBJECTIVE: Few studies have investigated the impact of basal metabolic rate (BMR) on the development of urolithiasis, and the causal relationship is yet to be established. In this study, a two-sample Mendelian randomization (MR) analysis was utilized to identify the causal relationship between BMR and risk of urolithiasis. METHOD: Genetic instruments for BMR were drawn from a public genome-wide association study (GWAS). Summary dates on BMR and urolithiasis were obtained from a GWAS meta-analysis with sample sizes of 454,874 and 212,453, respectively. The inverse-variance weighted (IVW) method was provided as the main approach to estimate the causal relationship. The weighted-median method and the MR-Egger method were used as supplements to the IVW method. In addition, we conducted sensitivity analyses, including heterogeneity tests, pleiotropy tests and leave-one-out analysis, to assess the robustness of the outcomes. Furthermore, the funnel plot asymmetry was visually inspected to evaluate possible bias. RESULTS: The inverse-variance weighted data revealed that genetically predicted BMR significantly decreased the risk of urolithiasis [beta coefficient (beta): - 0.2366, odds ratio (OR): 0.7893, 95% confidence interval (CI) 0.6504-0.9579, p = 0.0166]. CONCLUSIONS: BMR has causal effects on urolithiasis in an MR study, and the risk of urolithiasis in patients with lower levels of BMR is higher.

The association between wet overactive bladder and consumption of tea, coffee, and caffeine: Results from 2005-2018 National Health and Nutrition Examination Survey.

BACKGROUND & AIMS: Previous studies have reported an inconsistent relationship between overactive bladder (OAB) and the consumption of tea, coffee, and caffeine. Our study aims to determine these associations in a large and nationally representative adult sample. METHODS: This cross-sectional study included 15,379 participants from the 2005-2018 US National Health and Nutrition Examination Survey (NHANES) database. The outcome was the risk of wet OAB that was diagnosed when the OAB symptom score was ≥3 with urgent urinary incontinence and excluded other diseases affecting diagnosis. The exposures were the consumption of tea, coffee, and caffeine. Weighted logistic regression models were established to explore these associations by calculating odds ratios (OR) and 95% confidence intervals (CI), as did restricted cubic splines (RCS) used to analyze the nonlinear associations. RESULT: Of all the participants (n = 15,379), 2207 had wet OAB. Mean [SE] consumption of tea, total coffee, caffeinated coffee, decaffeinated coffee, and caffeine was 233.6 [15.7] g/day, 364.3 [15.5] g/day, 301.6 [14.9] g/day, 62.7 [7.9] g/day, 175.5 [6.6] mg/day in participants with wet OAB, respectively. In the fully adjusted model, compared to those without tea consumption, the high consumption of tea (>481 g/day) was associated with an increased risk of wet OAB (OR: 1.29; 95%CI: 1.01-1.64). Low decaffeinated coffee (0.001-177.6 g/day) had a negative association with the risk (OR: 0.66; 95%CI: 0.49-0.90). In the RCS analysis, tea consumption showed a positive linear association with the risk of wet OAB, and decaffeinated coffee showed a nonlinear relationship with the risk and had a turning point of 78 g/day in the U-shaped curve between 0 and 285 g/day. Besides, total coffee, caffeinated coffee, and caffeine consumption had no significant association with the risk. Interestingly, in the high tea consumption, participants with high total coffee consumption [>527.35 g/day, OR and 95%CI: 2.14(1.16-3.94)] and low caffeine consumption [0.1-74.0 mg/day, OR and 95%CI: 1.50(1.03-2.17)] were positively associated with the risk of wet OAB. CONCLUSION: High tea consumption was associated with the increased risk of wet OAB, especially intake together with high total coffee and low caffeine consumption, but no significant association with the single consumption of total coffee and caffeine. Low decaffeinated coffee was associated with a decreased risk of wet OAB. It is necessary to control tea intake when managing the liquid intake of wet OAB patients.

Classification method of traditional Chinese medicine compound decoction duration based on multi-dimensional feature weighted fusion.

This paper extends a text classification method utilizing natural language processing (NLP) into the field of traditional Chinese medicine (TCM) compound decoction to effectively and scientifically extend the TCM compound decoction duration. Specifically, a TCM compound decoction duration classification named TCM-TextCNN is proposed to fuse multi-dimensional herb features and improve TextCNN. Indeed, first, we utilize word vector technology to construct feature vectors of herb names and medicinal parts, aiming to describe the herb characteristics comprehensively. Second, considering the impact of different herb features on the decoction duration, we use an improved Term Frequency-Inverse Word Frequency (TF-IWF) algorithm to weigh the feature vectors of herb names and medicinal parts. These weighted feature vectors are then concatenated to obtain a multi-dimensional herb feature vector, allowing for a more comprehensive representation. Finally, the feature vector is input into the improved TextCNN, which uses k-max pooling to reduce information loss rather than max pooling. Three fully connected layers are added to generate higher-level feature representations, followed by softmax to obtain the final results. Experimental results on a dataset of TCM compound decoction duration demonstrate that TCM-TextCNN improves accuracy, recall, and F1 score by 5.31%, 5.63%, and 5.22%, respectively, compared to methods solely rely on herb name features, thereby confirming our method's effectiveness in classifying TCM compound decoction duration.

Hydrophilic, Porous, Fiber-Reinforced Collagen-Based Membrane for Corneal Repair.

Collagen membrane with outstanding biocompatibility exhibits immense potential in the field of corneal repair and reconstruction, but the poor mechanical properties limit its clinical application. Polycaprolactone (PCL) is a biodegradable polymer widely explored for application in corneal reconstruction due to its excellent mechanical properties, biocompatibility, easy processability, and flexibility. In this study, a PCL/collagen composite membrane with reinforced mechanical properties is developed. The membrane has a strong composite structure with collagen by utilizing a porous and hydrophilic PCL scaffold, maintaining its integrity even after immersion. The suture retention and mechanical tests demonstrate that compared with the pure collagen membrane, the prepared membrane has a greater tensile strength and twice the modulus of elasticity. Further, the suture retention strength is improved by almost two times. In addition, the membrane remains fully intact on the implant bed in an in vitro corneal defect model. Moreover, the membrane can be tightly sutured to a rabbit corneal defect, progressively achieve epithelialization, and remain unchanged during observation. Overall, the PCL/collagen composite membrane is a promising candidate as a suturable corneal restoration material in clinical keratoplasty.

Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA.

Background: As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment. Methods: We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candidate genes to obtain the signature genes of RIRI. Receiver operating characteristic (ROC) analysis was conducted to measure the predictive ability of the signature genes. Kaplan‒Meier analysis was used for association analysis between signature genes and graft survival. Verifying the expression of signature genes in the ischemia cell model. Results: A total of 117 DEGs were screened out. Subsequently, RF, Lasso regression analysis, SVM-RFE and WGCNA identified 17, 25, 18 and 74 candidate genes, respectively. Finally, 3 signature genes (DUSP1, FOS, JUN) were screened out through the intersection of candidate genes. ROC analysis suggested that the 3 signature genes could well diagnose and predict RIRI. Kaplan‒Meier analysis indicated that patients with low FOS or JUN expression had a longer OS than those with high FOS or JUN expression. Finally, we validated using the ischemia cell model that compared to the control group, the expression level of JUN increased under hypoxic conditions. Conclusions: Three signature genes (DUSP1, FOS, JUN) offer a good prediction for RIRI outcome and may serve as potential therapeutic targets for RIRI intervention, especially JUN. The prediction of graft survival by FOS and JUN may improve graft survival in patients with RIRI.

A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation.

In this study, we aimed to identify transplantation tolerance (TOL)-related gene signature and use it to predict the different types of renal allograft rejection performances in kidney transplantation. Gene expression data were obtained from the Gene Expression Omnibus (GEO) database, differently expressed genes (DEGs) were performed, and the gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted. The machine learning methods were combined to analyze the feature TOL-related genes and verify their predictive performance. Afterward, the gene expression levels and predictive performances of TOL-related genes were conducted in the context of acute rejection (AR), chronic rejection (CR), and graft loss through heatmap plots and the receiver operating characteristic (ROC) curves, and their respective immune infiltration results were also performed. Furthermore, the TOL-related gene signature for graft survival was conducted to discover gene immune cell enrichment. A total of 25 TOL-related DEGs were founded, and the GO and KEGG results indicated that DEGs mainly enriched in B cell-related functions and pathways. 7 TOL-related gene signature was constructed and performed delightedly in TOL groups and different types of allograft rejection. The immune infiltration analysis suggested that gene signature was correlated with different types of immune cells. The Kaplan-Meier (KM) survival analysis demonstrated that BLNK and MZB1 were the prognostic TOL-related genes. Our study proposed a novel gene signature that may influence TOL in kidney transplantation, providing possible guidance for immunosuppressive therapy in kidney transplant patients.

The Association between Dietary Sugar Intake and Nephrolithiasis: Results from National Health and Nutrition Examination Survey 2007-2018.

BACKGROUND: Dietary sugar intake is gradually considered a risk factor for many diseases. A sugary diet was positively associated with risk of nephrolithiasis, but the specific relationships remain undefined. OBJECTIVES: To determine associations between risk of nephrolithiasis and dietary sugar intake. METHODS: This cross-sectional study involved 21,590 participants based on the National Health and Nutrition Examination Survey from 2007 to 2018. Amounts of dietary sugar intake (g/d) were the main exposure, including total sugar intake, added sugar intake, and food sources. Associations were analyzed by logistic regression models and restricted cubic splines using complex weighted designs. RESULTS: Weighted mean intake [standard error] of total sugar and added sugar were 111.2 [2.0] g/d and 73.7 [1.9] g/d in participants with nephrolithiasis, respectively. In the fully adjusted regression model, compared to those in quartile 1, the population in quartile 4 of total sugar intake showed a significant risk of nephrolithiasis [odds ratio (OR): 1.23; 95% confidence interval (CI): 1.00-1.51]; OR for added sugar intake was 1.56 (95% CI: 1.25-1.94). The risks of nephrolithiasis increased steadily when total sugar and added sugar intake exceeded ∼150 g/d and 63 g/d in restricted cubic spline analyses, respectively. The highest sugar intake from beverages was associated with an increased risk of nephrolithiasis (OR for total sugar: 1.36; 95% CI: 1.07-1.72; OR for added sugar: 1.37; 95% CI: 1.09-1.73). Added sugar intake from meat, egg, and oil was significantly associated with risk of nephrolithiasis (quartile 4, OR: 1.22; 95% CI: 1.02-1.47), whereas total sugar intake from dairy products was in reverse (quartile 4, OR: 0.67; 95% CI: 0.54-0.82). CONCLUSIONS: Total and added sugar intake, sugar intake from beverages, and added sugar intake from meat, egg, and oil were associated with an increased risk of nephrolithiasis, whereas total sugar intake from dairy products was negatively associated.

The investigation of the ultrafast excited state deactivation mechanisms for coumarin 307 in different solvents.

The intramolecular charge transfer (ICT) and twisted intramolecular charge transfer (TICT) processes of coumarin 307 (C307) in different solvents were investigated by performing steady-state/time-resolved transient absorption spectroscopic and steady-state photoluminescence spectroscopic characterizations in combination with time-dependent density functional theoretical calculation (TDDFT). The study unveiled the remarkable influence of solvent polarity and the strength of intermolecular hydrogen bonds formed between the solutes and solvents on the relaxation dynamics of the electronic excited state. Significantly, the emergence of the TICT state was observed in polar solvents, specifically dimethylformamide (DMF) and dimethyl sulfoxidemethanol (DMSO), owing to their inherent polarity as well as the enhanced intermolecular hydrogen bonding interactions. Interestingly, even in a weak polar solvent such as methanol (MeOH), the TICT state was also observed due to the intensified hydrogen bonding effects. Conversely, nonpolar solvents, exemplified by 1,4-dioxane (Diox), resulted in the stabilization of the ICT state due to the augmented N-H⋯O hydrogen bonding interactions. The experimental findings were corroborated by the computational calculations, thus ensuring the reliability of the conclusions drawn. Furthermore, schematic diagrams were presented to illustrate the mechanisms underlying the excited-state deactivation. Overall, this investigation contributes valuable mechanistic insights and provides a comprehensive understanding of the photochemical and photophysical properties exhibited by coumarin dyes.

A comprehensive analysis of FBN2 in bladder cancer: A risk factor and the tumour microenvironment influencer.

Bladder cancer (BLCA) is a common and difficult-to-manage disease worldwide. Most common type of BLCA is urothelial carcinoma (UC). Fibrillin 2 (FBN2) was first discovered while studying Marfan syndrome, and its encoded products are associated with elastin fibres. To date, the role of FBN2 in BLCA remains unclear. The authors first downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The patients were divided into high FBN2 expression and low FBN2 expression groups, and the survival curve, clinical characteristics, tumour microenvironment (TME), and immune cell differences were analysed between the two groups. Then, the differentially expressed genes (DEGs) were filtered, and functional enrichment for DEGs was performed. Finally, chemotherapy drug susceptibility analysis based on the high and low FBN2 groups was conducted. The authors found upregulated expression of FBN2 in BLCA and proved that FBN2 could be an independent prognostic factor for BLCA. TME analysis showed that the expression of FBN2 affects several aspects of the TME. The upregulated expression of FBN2 was associated with a high stromal score, which may lead to immunosuppression and be detrimental to immunotherapy. In addition, the authors found that NK cells resting, macrophage M0 infiltration, and other phenomena of immune cell infiltration appeared in the high expression group of FBN2. The high expression of FBN2 was related to the high sensitivity of some chemotherapy drugs. The authors systematically investigated the effects and mechanisms of FBN2 on BLCA and provided a new understanding of the role of FBN2 as a risk factor and TME influencer in BLCA.

A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma.

BACKGROUND: As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated. METHODS: The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores. RESULTS: A total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC. CONCLUSIONS: We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

Characteristic Genes and Immune Infiltration Analysis for Acute Rejection after Kidney Transplantation.

Background: Renal transplantation can significantly improve the survival rate and quality of life of patients with end-stage renal disease, but the probability of acute rejection (AR) in adult renal transplant recipients is still approximately 12.2%. Machine learning (ML) is superior to traditional statistical methods in various clinical scenarios. However, the current AR model is constructed only through simple difference analysis or a single queue, which cannot guarantee the accuracy of prediction. Therefore, this study identified and validated new gene sets that contribute to the early prediction of AR and the prognosis prediction of patients after renal transplantation by constructing a more accurate AR gene signature through ML technology. Methods: Based on the Gene Expression Omnibus (GEO) database and multiple bioinformatic analyses, we identified differentially expressed genes (DEGs) and built a gene signature via LASSO regression and SVM analysis. Immune cell infiltration and immunocyte association analyses were also conducted. Furthermore, we investigated the relationship between AR genes and graft survival status. Results: Twenty-four DEGs were identified. A 5 gene signature (CPA6, EFNA1, HBM, THEM5, and ZNF683) were obtained by LASSO analysis and SVM analysis, which had a satisfied ability to differentiate AR and NAR in the training cohort, internal validation cohort and external validation cohort. Additionally, ZNF683 was associated with graft survival. Conclusion: A 5 gene signature, particularly ZNF683, provided insight into a precise therapeutic schedule and clinical applications for AR patients.

Roles of DNA damage repair and precise targeted therapy in renal cancer (Review).

The primary subtypes of renal cell carcinoma (RCC) include clear cell, papillary and chromophobe RCC. RCC occurs often due to loss of von Hippel­Lindau (VHL) and accumulation of lipids and glycogen, and RCC cells may exhibit sensitivity to the disruption of normal metabolism or homologous recombination gene defect. Although the application of molecular­targeted drugs (tyrosine kinase inhibitors) and immune checkpoint inhibitors has been recommended for the treatment of advanced RCC, more targets of DNA damage repair (DDR) signaling pathway involved in the synthetic lethal effect have been investigated. However, although achievements has been made in the exploration of the roles of DDR genes on RCC progression, their association has not been systematically summarized. Poly (ADP­ribose) polymerase (PARP) 1 inhibitors are used in tumors with BRCA1/2 DNA repair­associated mutations. PARP family enzymes perform post­translational modification functions and participate in DDR and cell death. Inhibitors of PARP, ataxia telangiectasia mutant gene and polymerase θ serve key roles in the treatment of specific RCC subtypes. PARP1 may serve as an important biological marker to predict the therapeutic effect of immune checkpoint inhibitors and evaluate the prognosis of patients with ccRCC with polybromo 1 mutation. Therefore, the roles of DDR pathway on RCC progression or treatment may hold promises for the treatment of certain specific types of RCC.

A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer.

BACKGROUND: Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC. METHODS: The Cancer Genome Atlas (TCGA) provided RNA expression profiles and clinical information of BC samples, and GSEA Molecular Signatures database provided 1171 inflammation-related genes. IRRlncRNAs were identified using Pearson correlation analysis. After that, consensus clustering was performed to form molecular subtypes. After performing least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, a risk model constructed based on the prognostic IRRlncRNAs was validated in an independent cohort. Kaplan-Meier (KM) analysis, univariate and multivariate Cox regression, clinical stratification analysis, and time-dependent receiver operating characteristic (ROC) curves were utilized to assess clinical effectiveness and accuracy of the risk model. In clusters and risk model, functional enrichment was investigated using GSEA and GSVA, and immune cell infiltration analysis was demonstrated by ESTIMATE and CIBERSORT analysis. RESULTS: A total of 174 prognostic IRRlncRNAs were confirmed, and 406 samples were divided into 2 clusters, with cluster 2 having a significantly inferior prognosis. Moreover, cluster 2 exhibited a higher ESTIMATE score, immune infiltration, and PD-L1 expression, with close relationships with the inflammatory response. Further, 12 IRRlncRNAs were identified and applied to construct the risk model and divide BC samples into low-risk and high-risk groups successfully. KM, ROC, and clinical stratification analysis demonstrated that the risk model performed well in predicting prognosis. The risk score was identified as an independently significant indicator, enriched in immune, cell cycle, and apoptosis-related pathways, and correlated with 9 immune cells. CONCLUSION: We developed an inflammatory response-related subtypes and steady prognostic risk model based on 12 IRRlncRNAs, which was valuable for individual prognostic prediction and stratification and outfitted new insight into inflammatory response in BC.

Construction of A Novel Ferroptosis-related Prognostic Risk Signature for Survival Prediction in Clear Cell Renal Cell Carcinoma Patients.

PURPOSE: Targeted ferroptosis is a reliable therapy to inhibit tumor growth and enhance immunotherapy. This study generated a novel prognostic risk signature based on ferroptosis-related genes (FRGs), and explored the ability in clinic for clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: The expression profile of mRNA and FRGs for ccRCC patients were exacted from The Cancer Genome Atlas (TCGA) database. A ferroptosis-related prognostic risk signature was constructed based on univariable and multivariable Cox-regression analysis. Kaplan-Meier (KM) survival curves and receiver operating characteristic (ROC) curves were performed to access prognostic value of riskscore. A nomogram integrating riskscore and clinical features was established to predict overall survival (OS). Based on differentially expressed genes between high- and low-OS groups with 5-year OS, function enrichment analyses and single-sample gene set enrichment analysis (ssGSEA) were investigated to immune status. RESULTS: A 9-FRGs prognostic risk signature was constructed based on 37 differentially expressed FRGs. ROC and KM curves showed that riskscore has excellent reliability and predictive ability; Cox regression disclosed the riskscore as an independent prognosis for ccRCC patients. Then, the C-index and calibration curve demonstrated the good performance of nomogram in training and validation cohort, and its predictive ability better than other features. Immune-related biological processes were enriched by function enrichment analysis, and the immune-related cells and functions were differential by ssGSEA between high- and low-OS groups. CONCLUSION: Our study identified and verified a novel 9-FRGs prognostic signature and nomogram to predict OS, providing a novel sight to explore targeted therapy of ferroptosis for ccRCC.

A Non-Resonant Piezoelectric-Electromagnetic-Triboelectric Hybrid Energy Harvester for Low-Frequency Human Motions.

With the rapid development of wireless communication and micro-power technologies, smart wearable devices with various functionalities appear more and more in our daily lives. Nevertheless, they normally possess short battery life and need to be recharged with external power sources with a long charging time, which seriously affects the user experience. To help extend the battery life or even replace it, a non-resonant piezoelectric-electromagnetic-triboelectric hybrid energy harvester is presented to effectively harvest energy from low-frequency human motions. In the designed structure, a moving magnet is used to simultaneously excite the three integrated energy collection units (i.e., piezoelectric, electromagnetic, and triboelectric) with a synergistic effect, such that the overall output power and energy-harvesting efficiency of the hybrid device can be greatly improved under various excitations. The experimental results show that with a vibration frequency of 4 Hz and a displacement of 200 mm, the hybrid energy harvester obtains a maximum output power of 26.17 mW at 70 kΩ for one piezoelectric generator (PEG) unit, 87.1 mW at 500 Ω for one electromagnetic generator (EMG) unit, and 63 µW at 140 MΩ for one triboelectric nanogenerator (TENG) unit, respectively. Then, the generated outputs are adopted for capacitor charging, which reveals that the performance of the three-unit integration is remarkably stronger than that of individual units. Finally, the practical energy-harvesting experiments conducted on various body parts such as wrist, calf, hand, and waist indicate that the proposed hybrid energy harvester has promising application potential in constructing a self-powered wearable system as the sustainable power source.

A 7-gene signature predicts the prognosis of patients with bladder cancer.

The biomarkers have an important guiding role in prognosis and treatment of patients with bladder cancer (BC). The aim of the present study was to identify and evaluate a prognostic gene signature in BC patients. The gene expression profiles of BC samples and the corresponding clinicopathological data were downloaded from GEO and TCGA. The differentially expressed genes (DEGs) were identified by R software. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct the prognostic score model. A nomogram was established with the identified prognostic factors to predict the overall survival rates of BC patients. The discriminatory and predictive capacity of the nomogram was evaluated based on the concordance index (C-index), calibration curves and decision curve analysis (DCA). A 7-gene signature (KLRB1, PLAC9, SETBP1, NR2F1, GRHL2, ANXA1 and APOL1) was identified from 285 DEGs by univariate and LASSO Cox regression analyses. Univariate and multivariate Cox regression analyses showed that age, lymphovascular invasion, lymphatic metastasis, metastasis and the 7-gene signature risk score was an independent predictor of BC patient prognosis. A nomogram that integrated these independent prognostic factors was constructed. The C-index (0.73, CI 95%, 0.693-0.767) and calibration curve demonstrated the good performance of the nomogram. DCA of the nomogram further showed that this model exhibited good net benefit. The combined 7-gene signature could serve as a biomarker for predicting BC prognosis. The nomogram built by risk score and other clinical factors could be an effective tool for predicting the prognosis of patients with BC.

Factors influencing the performance of rapid SARS-CoV-2 antigen tests under field condition.

BACKGROUND: Globally, real-time reverse transcription-polymerase chain reaction (rRT-PCR) is the reference detection technique for SARS-CoV-2, which is expensive, time consuming, and requires trained laboratory personnel. Thus, a cost-effective, rapid antigen test is urgently needed. This study evaluated the performance of the rapid antigen tests (RATs) for SARS-CoV-2 compared with rRT-PCR, considering different influencing factors. METHODS: We enrolled a total of 214 symptomatic individuals with known COVID-19 status using rRT-PCR. We collected and tested paired nasopharyngeal (NP) and nasal swab (NS) specimens (collected from same individual) using rRT-PCR and RATs (InTec and SD Biosensor). We assessed the performance of RATs considering specimen types, viral load, the onset of symptoms, and presenting symptoms. RESULTS: We included 214 paired specimens (112 NP and 100 NS SARS-CoV-2 rRT-PCR positive) to the analysis. For NP specimens, the average sensitivity, specificity, and accuracy of the RATs were 87.5%, 98.6%, and 92.8%, respectively, when compared with rRT-PCR. While for NS, the overall kit performance was slightly lower than that of NP (sensitivity 79.0%, specificity 96.1%, and accuracy 88.3%). We observed a progressive decline in the performance of RATs with increased Ct values (decreased viral load). Moreover, the RAT sensitivity using NP specimens decreased over the time of the onset of symptoms. CONCLUSION: The RATs showed strong performance under field conditions and fulfilled the minimum performance limit for rapid antigen detection kits recommended by World Health Organization. The best performance of the RATs can be achieved within the first week of the onset of symptoms with high viral load.

DNA Methylation Data-Based Classification and Identification of Prognostic Signature of Children With Wilms Tumor.

Background: As an epigenetic alteration, DNA methylation plays an important role in early Wilms tumorigenesis and is possibly used as marker to improve the diagnosis and classification of tumor heterogeneity. Methods: Methylation data, RNA-sequencing (RNA-seq) data, and corresponding clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The prognostic values of DNA methylation subtypes in Wilms tumor were identified. Results: Four prognostic subtypes of Wilms tumor patients were identified by consensus cluster analysis performed on 312 independent prognostic CpG sites. Cluster one showed the best prognosis, whereas Cluster two represented the worst prognosis. Unique CpG sites identified in Cluster one that were not identified in other subtypes were assessed to construct a prognostic signature. The prognostic methylation risk score was closely related to prognosis, and the area under the curve (AUC) was 0.802. Furthermore, the risk score based on prognostic signature was identified as an independent prognostic factor for Wilms tumor in univariate and multivariate Cox regression analyses. Finally, the abundance of B cell infiltration was higher in the low-risk group than in the high-risk group, based on the methylation data. Conclusion: Collectively, we divided Wilms tumor cases into four prognostic subtypes, which could efficiently identify high-risk Wilms tumor patients. Prognostic methylation risk scores that were significantly associated with the adverse clinical outcomes were established, and this prognostic signature was able to predict the prognosis of Wilms tumor in children, which may be useful in guiding clinicians in therapeutic decision-making. Further independent studies are needed to validate and advance this hypothesis.

Competitive endogenous RNA (ceRNA) regulation network of lncRNAs, miRNAs, and mRNAs in Wilms tumour.

BACKGROUND: Competitive endogenous RNAs (ceRNAs) have revealed a new mechanism of interaction between RNAs. However, an understanding of the ceRNA regulatory network in Wilms tumour (WT) remains limited. METHODS: The expression profiles of mRNAs, miRNAs and lncRNAs in Wilms tumour samples and normal samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. The EdgeR package was employed to identify differentially expressed lncRNAs, miRNAs and mRNAs. Functional enrichment analyses via the ClusterProfile R package were performed, and the lncRNA-miRNA-mRNA interaction ceRNA network was established in Cytoscape. Subsequently, the correlation between the ceRNA network and overall survival was analysed. RESULTS: A total of 2037 lncRNAs, 154 miRNAs and 3609 mRNAs were identified as differentially expressed RNAs in Wilms tumour. Of those, 205 lncRNAs, 26 miRNAs and 143 mRNAs were included in the ceRNA regulatory network. The results of Gene Ontology (GO) analysis revealed that the differentially expressed genes (DEGs) were mainly enriched in terms related to response to mechanical stimuli, transcription factor complexes, and transcription factor activity (related to RNA polymerase II proximal promoter sequence-specific DNA binding). The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in pathways related to the cell cycle. The survival analysis results showed that 16 out of the 205 lncRNAs, 1 out of 26 miRNAs and 5 out of 143 mRNAs were associated with overall survival in Wilms tumour patients (P < 0.05). CONCLUSIONS: CeRNA networks play an important role in Wilms tumour. This finding might provide effective, novel insights for further understanding the mechanisms underlying Wilms tumour.