Integration of multiomics analyses reveals unique insights into CD24-mediated immunosuppressive tumor microenvironment of breast cancer.

BACKGROUND: Tumor immunotherapy brings new light and vitality to breast cancer patients, but low response rate and limitations of therapeutic targets become major obstacles to its clinical application. Recent studies have shown that CD24 is involved in an important process of tumor immune regulation in breast cancer and is a promising target for immunotherapy. METHODS: In this study, singleR was used to annotate each cell subpopulation after t-distributed stochastic neighbor embedding (t-SNE) methods. Pseudo-time trace analysis and cell communication were analyzed by Monocle2 package and CellChat, respectively. A prognostic model based on CD24-related genes was constructed using several machine learning methods. Multiple quantitative immunofluorescence (MQIF) was used to evaluate the spatial relationship between CD24+PANCK+cells and exhausted CD8+T cells. RESULTS: Based on the scRNA-seq analysis, 1488 CD24-related differential genes were identified, and a risk model consisting of 15 prognostic characteristic genes was constructed by combining the bulk RNA-seq data. Patients were divided into high- and low-risk groups based on the median risk score. Immune landscape analysis showed that the low-risk group showed higher infiltration of immune-promoting cells and stronger immune reactivity. The results of cell communication demonstrated a strong interaction between CD24+epithelial cells and CD8+T cells. Subsequent MQIF demonstrated a strong interaction between CD24+PANCK+ and exhausted CD8+T cells with FOXP3+ in breast cancer. Additionally, CD24+PANCK+ and CD8+FOXP3+T cells were positively associated with lower survival rates. CONCLUSION: This study highlights the importance of CD24+breast cancer cells in clinical prognosis and immunosuppressive microenvironment, which may provide a new direction for improving patient outcomes.

Development of machine learning-based malignant pericardial effusion-related model in breast cancer: Implications for clinical significance, tumor immune and drug-therapy.

Background: Malignant pericardial effusion (MPE) is a common complication of advanced breast cancer (BRCA) and plays an important role in BRCA. This study is aims to construct a prognostic model based on MPE-related genes for predicting the prognosis of breast cancer. Methods: The BRCA samples are analyzed based on the expression of MPE-related genes by using an unsupervised cluster analysis method. This study processes the data by least absolute shrinkage and selection operator and multivariate Cox analysis, and uses machine learning algorithms to construct BRCA prognostic model and develop web tool. Results: BRCA patients are classified into three clusters and a BRCA prognostic model is constructed containing 9 MPE-related genes. There are significant differences in signature pathways, immune infiltration, immunotherapy response and drug sensitivity testing between the high and low-risk groups. Of note, a web-based tool (http://wys.helyly.top/cox.html) is developed to predict overall survival as well as drug-therapy response of BRCA patients quickly and conveniently, which can provide a basis for clinicians to formulate individualized treatment plans. Conclusion: The MPE-related prognostic model developed in this study can be used as an effective tool for predicting the prognosis of BRCA and provides new insights for the diagnosis and treatment of BRCA patients.

Changes in Body Mass Index Over Time in People With and Without HIV Infection.

Background: Excess weight gain is an important health concern among people with HIV (PWH) on antiretroviral therapy (ART). The extent to which ART contributes to body mass index (BMI) changes is incompletely understood. Methods: We conducted a retrospective study of PWH initiating ART and demographically matched people without HIV (PWoH). Data on baseline BMI (kg/m2; categorized as underweight/normal, overweight, or obese) and ART class (integrase strand transfer inhibitor [INSTI], non-nucleoside reverse transcriptase inhibitor [NNRTI], protease inhibitor [PI]) were obtained from electronic health records. BMI was evaluated longitudinally using piecewise linear splines in mixed effects models by HIV status, baseline BMI, and ART class. Models were adjusted for sociodemographics, comorbidities, and substance use. Results: The study included 8256 PWH and 129 966 PWoH (mean baseline age, 40.9 and 42.2 years, respectively; 88% men). In adjusted models, the average annual change in BMI in the first 2 years after ART initiation was 0.53 for PWH and 0.12 for PWoH (P < .001). BMI increases among PWH were observed for all ART classes: 0.69 for INSTIs, 0.69 for PIs, and 0.40 for NNRTIs vs 0.12 among PWoH. For PWH initiating INSTIs, BMI increases were observed regardless of baseline BMI. Overall BMI changes >2 years after ART initiation were similar by HIV status (0.02 average annual increase for PWH and PWoH). Conclusions: PWH initiating ART gained excess weight in the first 2 years, emphasizing the importance of monitoring weight and cardiometabolic health among ART-treated PWH.

Attributes of Provider Referrals for Digital Mental Health Applications in an Integrated Health System, 2019-2021.

OBJECTIVE: This article describes trends and attributes associated with digital mental health application (DMHA) referrals from December 2019 through December 2021. METHODS: In total, 43,842 DMHA referrals for 25,213 unique patients were extracted from the electronic health record of a large, diverse, integrated health system. DMHAs were aggregated by type (cognitive-behavioral therapy [CBT] or mindfulness and meditation [MM]). Monthly referral patterns were described and categorized into mutually exclusive clusters (MM, CBT, or MM and CBT). Multinomial logistic regression and post hoc predicted probabilities were used to profile patient, clinical, and encounter attributes among referral clusters. RESULTS: DMHA referrals increased, reached equilibrium, and then began to decline over the 25-month observation period. Compared with the referral cluster average, MM-alone referrals were more likely to occur for patients who were ages ≥65, who were Hispanic or Asian, whose reason for visit concerned mental health, and who had a primary diagnosis of other anxiety disorders. CBT-alone referrals were more likely to occur for patients with a primary diagnosis of depression and less likely to occur for Hispanic patients. Combined MM and CBT referrals were more likely to occur for patients who were ages 18-30, whose reason for visit was "other," and who had a primary diagnosis of depression and were less likely to occur for Hispanic patients and those ages ≥65. CONCLUSIONS: Although this study demonstrates readiness to integrate DMHA referral into clinical workflows, observed variations in attributes of referral clusters support the need to further investigate provider decision making and whether referral patterns are optimal and sustainable.

Integrating single-cell and spatial transcriptomics reveals endoplasmic reticulum stress-related CAF subpopulations associated with chordoma progression.

BACKGROUND: With cancer-associated fibroblasts (CAFs) as the main cell type, the rich myxoid stromal components in chordoma tissues may likely contribute to its development and progression. METHODS: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, bulk RNA-seq, and multiplexed quantitative immunofluorescence (QIF) were used to dissect the heterogeneity, spatial distribution, and clinical implication of CAFs in chordoma. RESULTS: We sequenced here 72 097 single cells from 3 primary and 3 recurrent tumor samples, as well as 3 nucleus pulposus samples as controls using scRNA-seq. We identified a unique cluster of CAF in recurrent tumors that highly expressed hypoxic genes and was functionally enriched in endoplasmic reticulum stress (ERS). Pseudotime trajectory and cell communication analyses showed that this ERS-CAF subpopulation originated from normal fibroblasts and widely interacted with tumoral and immune cells. Analyzing the bulk RNA-seq data from 126 patients, we found that the ERS-CAF signature score was associated with the invasion and poor prognosis of chordoma. By integrating the results of scRNA-seq with spatial transcriptomics, we demonstrated the existence of ERS-CAF in chordoma tissues and revealed that this CAF subtype displayed the most proximity to its surrounding tumor cells. In subsequent QIF validation involving 105 additional patients, we confirmed that ERS-CAF was abundant in the chordoma microenvironment and located close to tumor cells. Furthermore, both ERS-CAF density and its distance to tumor cells were correlated with tumor malignant phenotype and adverse patient outcomes. CONCLUSIONS: These findings depict the CAF landscape for chordoma and may provide insights into the development of novel treatment approaches.

Cardiovascular-specific mortality and risk factors in colorectal Cancer patients: A cohort study based on registry data of over 500,000 individuals in the US.

BACKGROUND: Colorectal cancer (CRC) is one of the most common cancers worldwide, and recent studies have found that CRC patients are at increased risk for cardiovascular disease (CVD). This study aimed to investigate competing causes of death and prognostic factors among a large cohort of CRC patients and to describe cardiovascular-specific mortality in relation to the US standard population. METHODS: This registry-based cohort study identified patients diagnosed with CRC between 1973 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database in the US. Cumulative mortality functions, conditional standardized mortality ratios, and cause-specific hazard ratios were calculated. RESULTS: Of the 563,298 eligible CRC patients included in this study, 407,545 died during the follow-up period. CRC was the leading cause of death, accounting for 49.8% of all possible competing causes of death. CVD was the most common non-cancer cause of death, accounting for 17.8% of total mortality. This study found that CRC patients have a significantly increased risk of cardiovascular-specific mortality compared to the US standard population, with the risk increasing with age and extended survival time. CONCLUSION: This study highlights the need to develop multidisciplinary prevention and management strategies for CRC and CVD to improve CRC patients' survival and quality of life.

A breast cancer classification and immune landscape analysis based on cancer stem-cell-related risk panel.

This study sought to identify molecular subtypes of breast cancer (BC) and develop a breast cancer stem cells (BCSCs)-related gene risk score for predicting prognosis and assessing the potential for immunotherapy. Unsupervised clustering based on prognostic BCSC genes was used to determine BC molecular subtypes. Core genes of BC subtypes identified by non-negative matrix factorization algorithm (NMF) were screened using weighted gene co-expression network analysis (WGCNA). A risk model based on prognostic BCSC genes was constructed using machine learning as well as LASSO regression and multivariate Cox regression. The tumor microenvironment and immune infiltration were analyzed using ESTIMATE and CIBERSORT, respectively. A CD79A+CD24-PANCK+-BCSC subpopulation was identified and its spatial relationship with microenvironmental immune response state was evaluated by multiplexed quantitative immunofluorescence (QIF) and TissueFAXS Cytometry. We identified two distinct molecular subtypes, with Cluster 1 displaying better prognosis and enhanced immune response. The constructed risk model involving ten BCSC genes could effectively stratify patients into subgroups with different survival, immune cell abundance, and response to immunotherapy. In subsequent QIF validation involving 267 patients, we demonstrated the existence of CD79A+CD24-PANCK+-BCSC in BC tissues and revealed that this BCSC subtype located close to exhausted CD8+FOXP3+ T cells. Furthermore, both the densities of CD79A+CD24-PANCK+-BCSCs and CD8+FOXP3+T cells were positively correlated with poor survival. These findings highlight the importance of BCSCs in prognosis and reshaping the immune microenvironment, which may provide an option to improve outcomes for patients.

A novel immune checkpoint-related signature for prognosis and immune analysis in breast cancer.

Breast cancer is one of the most prevailing forms of cancer globally. Immunotherapy has demonstrated efficacy in improving the overall survival of breast cancer. The aim of us was to formulate a novel signature predicated on immune checkpoint-related genes (ICGs) that could anticipate the prognosis and further analyze the immune status of patients with breast cancer. After acquiring data, we pinpointed the definitive ICGs for constructing the prognostic model of breast cancer. We constructed a novel prognostic model and created a fresh risk score called Immune Checkpoint-related Risk Score in breast cancer (ICRSBC). The nomogram was constructed to evaluate the accuracy of the model, and the new web-based tool was created to be more intuitive for predicting prognosis. We also investigated immunotherapy responsiveness and analyzed the tumor mutational burden (TMB) in ICRSBC subgroups. The ICRSBC was found to have significant correlations with the immune environment, immunotherapy responsiveness, and TMB. The expression levels of the 9 ICGs that construct the prognostic model and their promoter methylation levels are significantly different between breast cancer and normal tissues. Furthermore, the mutation profiles, the copy number alterations, and the levels of protein expression also exhibit marked disparities among the 9 ICGs. We have identified and validated a novel signature related to ICGs that is strongly associated with breast cancer progression. This signature enables us to create a risk score for prognosticating the survival and assessing the immune status of individuals affected by breast cancer.

Fracture Risk and Association With TDF Use Among People With HIV in Large Integrated Health Systems.

BACKGROUND: Greater decline in bone health among people with HIV (PWH) has been documented but fracture risk and the impact of specific antiretroviral therapy (ART) regimens remain unclear. SETTING: Retrospective analyses of electronic health record data from 3 US integrated health care systems. METHODS: Fracture incidence was compared between PWH aged 40 years or older without prior fracture and demographically matched people without HIV (PWoH), stratified by age, sex, and race/ethnicity. Multivariable Cox proportional hazards models were used to estimate fracture risk associated with HIV infection. The association of tenofovir disoproxil fumarate (TDF) use and fracture risk was evaluated in a subset of PWH initiating ART. RESULTS: Incidence of fracture was higher in PWH [13.6/1000 person-years, 95% confidence interval (CI): 13.0 to 14.3, n = 24,308] compared with PWoH (9.5, 95% CI: 9.4 to 9.7, n = 247,313). Compared with PWoH, the adjusted hazard ratio (aHR) for fracture among PWH was 1.24 (95% CI: 1.18 to 1.31). The association between HIV infection and fracture risk increased with age, with the lowest aHR (1.17, 95% CI: 1.10 to 1.25) among those aged 40-49 years and the highest aHR (1.89, 95% CI: 1.30 to 2.76) among those aged 70 years or older. Among PWH initiating ART (n = 6504), TDF was not associated with significant increase in fracture risk compared with non-TDF regimens (aHR: 1.18, 95% CI: 0.89 to 1.58). CONCLUSIONS: Among people aged 40 years or older, HIV infection is associated with increased risk of fractures. Bone health screening from the age of 40 years may be beneficial for PWH. Large cohort studies with longer follow-up are needed to evaluate TDF effect and the potential benefit of early screening.

Physiologically Based Pharmacokinetic Modeling for Prediction of 5-FU Pharmacokinetics in Cancer Patients with Hepatic Impairment After 5-FU and Capecitabine Administration.

PURPOSE: 5-fluorouracil (5-FU) and its prodrug capecitabine are commonly prescribed anti-tumor medications. We aimed to establish physiologically based pharmacokinetic (PBPK) models of capecitabine-metabolites and 5-FU-metabolites to describe their pharmacokinetics in tumor and plasma of cancer patients with liver impairment. METHODS: Models including the cancer compartment were developed in PK-Sim® and MoBi® and evaluated by R programming language with 25 oral capecitabine and 18 intravenous 5-FU studies for cancer patients with and without liver impairment. RESULTS: The PBPK models were constructed successfully as most simulated Cmax and AUClast were within two-fold error of observed values. The simulated alterations of tumor 5-FU Cmax and AUClast in cancer patients with severe liver injury compared with normal liver function were 1.956 and 3.676 after oral administration of capecitabine, but no significant alteration was observed after intravenous injection of 5-FU. Besides, 5-FU concentration in tumor tissue increases with higher tumor blood flow but not tumor size. Sensitivity analysis revealed that dihydropyrimidine dehydrogenase (DPD) and other metabolic enzymes' activity, capecitabine intestinal permeability and plasma protein scale factor played a vital role in tumor and plasma 5-FU pharmacokinetics. CONCLUSIONS: PBPK model prediction suggests no dosage adaption of capecitabine or 5-FU is required for cancer patients with hepatic impairment but it would be reduced when the toxic reaction is observed. Furthermore, tumor blood flow rate rather than tumor size is critical for 5-FU concentration in tumor. In summary, these models could predict pharmacokinetics of 5-FU in tumor in cancer patients with varying characteristics in different scenarios.

MiR-183-5p promotes renal cell carcinoma metastasis by targeting TET1.

Ten-eleven translocation 1 (TET1) is a member of the DNA demethylase family that regulates the methylation level of the genome. Dysregulation of TET1 in renal cell carcinoma (RCC) may be associated with RCC progression, but the mechanism of TET1 down-regulation in RCC is not yet known. MiR-183-5p is up-regulated in various tumor tissues and acts as an oncogene. We used Transwell and wound healing assays to test cell invasion and migration. To investigate DNA methylation, we used dot blot, which indicates TET1 enzyme activity. We verified the binding of miR-183-5p and TET1 3'-UTR (untranslated region) using dual-luciferase reporter assay. Our study demonstrated, for the first time, that miR-183-5p can directly repress TET1 expression in RCC. We observed a significant decrease in TET1 expression in RCC specimens, as reported in the literature, and a significant decrease in the concentration of 5hmC in RCC. By aligning the microRNA with a database and using the luciferase reporter gene method, we found that miR-183-5p can inhibit luciferase activity by binding to 453-459 bp of TET1 3'-UTR, leading to inhibition of TET1 expression. Furthermore, down-regulation of TET1 inhibited miR-200c expression and promoted RCC cell invasion and migration. Our findings suggest that in RCC, increased expression of miR-183-5p inhibits the expression of TET1, which in turn inhibits the expression of miR-200c and E-cadherin, both of which are associated with cell adhesion. This leads to the promotion of cell invasion and migration.

Unveiling tumor immune evasion mechanisms: abnormal expression of transporters on immune cells in the tumor microenvironment.

The tumor microenvironment (TME) is a crucial driving factor for tumor progression and it can hinder the body's immune response by altering the metabolic activity of immune cells. Both tumor and immune cells maintain their proliferative characteristics and physiological functions through transporter-mediated regulation of nutrient acquisition and metabolite efflux. Transporters also play an important role in modulating immune responses in the TME. In this review, we outline the metabolic characteristics of the TME and systematically elaborate on the effects of abundant metabolites on immune cell function and transporter expression. We also discuss the mechanism of tumor immune escape due to transporter dysfunction. Finally, we introduce some transporter-targeted antitumor therapeutic strategies, with the aim of providing new insights into the development of antitumor drugs and rational drug usage for clinical cancer therapy.

Site-specific detection of circulating tumor DNA methylation in biological samples utilizing phosphorothioated primer-based loop-mediated isothermal amplification.

DNA methylation, a kind of epigenetic alteration, plays a vital role in tumorigenesis and offers a new class of targets for cancer treatment. DNA hypermethylation at the E-Box site (CACGTG, -288 bp) in the SLC22A2 promoter was related to multidrug resistance of renal cell carcinoma (RCC), which can provide the target for both treatment and monitoring. Herein, we developed a novel phosphorothioated primer based loop-mediated isothermal amplification (PS-LAMP) assay to detect circulating tumor DNA (ctDNA) methylation levels in E-Box sites in tumor tissue, urine, and plasma samples from patients with RCC. Bisulfite treatment converted methylated/unmethylated discrepancy to a single base discrepancy (C/U). PS-LAMP amplified the templates to a tremendous amount. One-step strand displacement (OSD) probe provided single base resolution in amplified products and finally realized the specific site methylation detection. Our proposed method provided a linear range from 0% to 100% for methylation levels and was available in samples at low concentrations (102 copies/µL). Visually observable colorimetric detection can be achieved by incorporating the OSD probe with gold nanoparticles (AuNP). Our assay performed better than traditional methods in biological samples with low ctDNA concentration. Further, we found a potential consistency of methylation levels between tumor tissue and plasma sample from the same patient (Spearman's ρ = 0.886, P = 0.019, n = 6). In general, this work provides a PS-LAMP assay combining OSD probes for site-specific methylation detection in various biological samples, offering a method for noninvasive detection.

Network pharmacology analysis reveals potential targets and mechanisms of proton pump inhibitors in breast cancer with diabetes.

Breast cancer and diabetes are significant health challenges, and effective treatments for both diseases are lacking. Proton pump inhibitors (PPIs) have demonstrated anticancer and hypoglycemic effects, but their mechanisms of action are not yet fully understood. We used the GeneCards and PharmMapper databases to identify therapeutic targets for diabetes,  breast cancer and PPIs. We identified common targets and constructed a regulatory network of diseases and drugs using the STRING database and Cytoscape software. We also explored the binding between small molecule ligands and protein receptors using Discovery Studio software. We identified 33 shared targets for breast cancer, diabetes, and PPIs including lansoprazole, omeprazole, and pantoprazole, which play a critical role in fatty acid transport, insulin resistance, apoptosis, and cancer-related signaling pathways. Our findings demonstrated that PPIs had a strong affinity for AKT1 and MMP9. This study provides insights into the mechanisms of action of PPIs in breast cancer and diabetes and identifies AKT1 and MMP9 as critical targets for future drug development. Our findings highlight the potential of PPIs as a novel therapeutic approach for these challenging diseases.

Investigation of fatty acid metabolism-related genes in breast cancer: Implications for Immunotherapy and clinical significance.

Breast cancer (BRCA) is a major global health issue, characterized by high mortality and low early diagnosis rates. The tumor immune microenvironment (TME) of BRCA is closely linked to fatty acid metabolism (FAM). This study aimed to identify FAM-related subtypes in BRCA based on gene expression and clinical data from the Cancer Genome Atlas (TCGA) database. The study found two distinct FAM-related subtypes, each with unique immune characteristics and prognostic implications. A FAM-related risk score prognostic model was developed and validated using TCGA and International Cancer Genome Consortium (GEO) cohorts, showing potential clinical applications for chemotherapy and immunotherapy. Additionally, a nomogram was established to facilitate clinical use of the risk score. These results highlight the significant correlation between FAM genes and TME in BRCA, and demonstrate the potential clinical utility of the FAM-related risk score in informing treatment decisions for BRCA patients.

High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon's liver dataset, and the BTCV multi-organ segmentation dataset.

Studies on the interaction of five triazole fungicides with human renal transporters in cells.

The widespread use of triazole fungicides in agricultural production poses a potential risk to human health. This study investigates the interaction of five triazole fungicides, i.e., tebuconazole, triticonazole, hexaconazole, penconazole, and uniconazole with human renal transporters, including OAT1, OAT3, OCT2, OCTN1, OCTN2, MATE1, MATE2-K, MRP2, MDR1, and BCRP, using transgenic cell models. For uptake transporters, triticonazole was the substrate of OAT1 and OAT3 and the inhibitor of OCT2. Tebuconazole and penconazole inhibited OCTN2 (100 µM), while tebuconazole, triticonazole, hexaconazole, penconazole, and uniconazole inhibited MATE1 (100 µM). Tebuconazole and hexaconazole inhibited MATE2-K (100 µM). All five triazole fungicides were not substrates or strong inhibitors of MRP2, MDR1, and BCRP efflux transporters. Penconazole inhibited OCT2 with IC50 = 1.12 µM. Penconazole and uniconazole inhibited MATE1 with IC50 = 0.94 µM and 0.87 µM. Tebuconazole and hexaconazole inhibited MATE2-K with IC50 = 0.96 µM and 1.04 µM, indicating that triazole fungicides may inhibit renal drug transporter activity at low concentrations. Triticonazole was transported by OAT1 and OAT3, and the Km values of triticonazole were 5.81 ± 1.75 and 47.35 ± 14.27, respectively. Tebuconazole and uniconazole were transported by OAT3, and the Km values of tebuconazole and uniconazole were 30.28 ± 7.18 and 87.61 ± 31.70, respectively, which may induce nephrotoxicity.

NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search.

Neural architecture search (NAS) adopts a search strategy to explore the predefined search space to find superior architecture with the minimum searching costs. Bayesian optimization (BO) and evolutionary algorithms (EA) are two commonly used search strategies, but they suffer from being computationally expensive, challenging to implement, and exhibiting inefficient exploration ability. In this article, we propose a neural predictor guided EA to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is a BO acquisition function for which we design a graph-based uncertainty estimation network as the surrogate model. The second predictor is a graph-based neural network that directly predicts the performance of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP, respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Experimental results on five NAS search spaces indicate that NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-NP achieving state-of-the-art performance on four of the five search spaces.

A duplex-specific nuclease (DSN) and catalytic hairpin assembly (CHA)-mediated dual amplification method for miR-146b detection.

A novel method for detecting miRNA has been developed using a combination of duplex-specific nuclease signal amplification (DSNSA) and a catalytic hairpin assembly (CHA). In this work, a biotinylated trigger release (BTR) probe with a biotin group at the 3'-end and a CHA reaction sequence trigger as an initiator (catalyst I) at the 5'-end was designed to hybridize target miRNA. The DSN enzyme was introduced to initiate the DSNSA. The miRNA was released to consume more BTR probes and amplify the signals. Subsequently, streptavidin-coated magnetic beads (SA-MBs) were added to the DSNSA reaction solution to remove excess BTR probes that did not hybridize with miRNA, which would then separate BTR probes and catalyst-I, to ensure detection with high selectivity and sensitivity. The catalyst-I remaining in the solution could trigger the CHA reaction to enable signal amplification in the second step. The developed method exhibits a sensitive detection limit and excellent selectivity in identifying a high sequence homology among family members.

Simulation study of inverse diffusion counterbalance method for super-resolution ion mobility spectrometry.

Ion mobility spectrometer (IMS) is a powerful chemical composition analysis tool working at atmospheric pressure that can be used to separate complex samples and study molecular structures. Resolution is a key parameter for evaluating the performance of IMS. However, for the pulsed sampling technique used by drift tube IMS, there is an upper limit to the resolution due to the diffusion between ions and the drift gas. In this work, an inverse diffusion counterbalance method is proposed to break the resolution limit. The method is inspired by the stimulated emission depletion (STED). In optical microscopy systems, STED is used to break the optical diffraction limit by a ring of depleted light to counteract diffraction effects of the excited light. We modified this strategy and applied it to an IMS system for counteracting the diffusion effect of the pulsed ion packet. The method can increase the resolution up to 1.55 times through theoretical analysis, and the improvement is verified by simulations. The simulation results find that the initial width of the ion packet has an influence on the effectiveness of the method, and the narrower the initial width, the better the effect. The proposed inverse counterbalance strategy may also be applied to other spectral analysis instruments to break the resolution limit.