CD24 affects the immunosuppressive effect of tumor-infiltrating cells and tumor resistance in a variety of cancers.

BACKGROUND: Cluster of differentiation 24 (CD24) is a highly glycosylated glycosylphosphatidylinositol (GPI)-anchored surface protein, expressed in various tumor cells, as a "don't eat me" signaling molecule in tumor immune. This study aimed to investigate the potential features of CD24 in pan-cancer. METHODS: The correlations between 22 immune cells and CD24 expression were using TIMER analysis. R package "ESTIMATE" was used to predict the proportion of immune and stromal cells in pan-cancer. Spearman's correlation analysis was performed to evaluate the relationships between CD24 expression and immune checkpoints, chemokines, mismatch repair, tumor mutation burden and microsatellite instability, and qPCR and western blot were conducted to assess CD24 expression levels in liver hepatocellular carcinoma (LIHC). In addition, loss of function was performed for the biological evaluation of CD24 in LIHC. RESULTS: CD24 expression was positively correlated with myeloid cells, including neutrophils and myeloid-derived suppressor cells, in various tumors, such as BLCA, HNSC-HPV, HNSC, KICH, KIRC, KIRP, TGCT, THCA, THYM, and UCEC. In contrast, anti-tumor NK cells and NKT cells showed a negative association with CD24 expression in BRCA-Her2, ESCA, HNSC-HPV, KIRC, THCA, and THYM. The top three tumors with the highest correlation between CD24 and ImmuneScore were TGCT, THCA, and SKCM. Functional enrichment analysis revealed CD24 expression was negatively associated with various immune-related pathways. Immune checkpoints and chemokines also exhibited inverse correlations with CD24 in CESC, CHOL, COAD, ESCA, READ, TGCT, and THCA. Additionally, CD24 was overexpressed in most tumors, with high CD24 expression in BRCA, LIHC, and CESC correlating with poor prognosis. The TIDE database indicated tumors with high CD24 expression, particularly melanoma, were less responsive to PD1/PD-L1 immunotherapy. Finally, CD24 knockdown resulted in impaired proliferation and cell cycle progression in LIHC. CONCLUSION: CD24 participates in regulation of immune infiltration, influences patient prognosis and serves as a potential tumor marker.

Surface albedo regulates aerosol direct climate effect.

Aerosols and Surface Albedo (SA) are critical in balancing Earth's energy budget. With the changes of surface types and corresponding SA in recent years, an intriguing yet unresolved question emerges: how does Aerosol Direct Radiative Effect (ADRE) and its warming effect (AWE) change with varying SA? Here we investigate the critical SA marking ADRE shift from negative to positive under varying aerosol properties, along with the impact of SA on the ADRE. Results show that AWE often occurs in mid-high latitudes or regions with high-absorptivity aerosols, with critical SA ranging from 0.18 to 0.96. Thinner and/or more absorptive aerosols more readily cause AWE statistically. In regions where the SA trend is significant, SA has decreased at -0.012/decade, causing a -0.2 ± 0.17 W/m²/decade ADRE change, with the most pronounced changes in the Northern Hemisphere during June-July. As SA declines, we highlight enhanced ADRE cooling or reduced AWE, indicating aerosols' stronger cooling, partly countering the energy rise from SA reduction.

Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT.

BACKGROUND AND PURPOSE: Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT. MATERIALS AND METHODS: This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification. RESULTS: The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model (P = .002) and the clinical features model (P = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model (P = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features (P = .028; P = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron (P < .001) and the clinical-radiomics model with logistic regression (P = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration. CONCLUSIONS: The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies.

Decoding Endothelial MPL and JAK2V617F Mutation: Insight Into Cardiovascular Dysfunction in Myeloproliferative Neoplasms.

BACKGROUND: Patients with JAK2V617F-positive myeloproliferative neoplasms (MPNs) and clonal hematopoiesis of indeterminate potential face a significantly elevated risk of cardiovascular diseases. Endothelial cells carrying the JAK2V617F mutation have been detected in many patients with MPN. In this study, we investigated the molecular basis for the high incidence of cardiovascular complications in patients with MPN. METHODS: We investigated the impact of endothelial JAK2V617F mutation on cardiovascular disease development using both transgenic murine models and MPN patient-derived induced pluripotent stem cell lines. RESULTS: Our investigations revealed that JAK2V617F mutant endothelial cells promote cardiovascular diseases under stress, which is associated with endothelial-to-mesenchymal transition and endothelial dysfunction. Importantly, we discovered that inhibiting the endothelial TPO (thrombopoietin) receptor MPL (myeloproliferative leukemia virus oncogene) suppressed JAK2V617F-induced endothelial-to-mesenchymal transition and prevented cardiovascular dysfunction caused by mutant endothelial cells. Notably, the endothelial MPL receptor is not essential for the normal physiological regulation of blood cell counts and cardiac function. CONCLUSIONS: JAK2V617F mutant endothelial cells play a critical role in the development of cardiovascular diseases in JAK2V617F-positive MPNs, and endothelial MPL could be a promising therapeutic target for preventing or ameliorating cardiovascular complications in these patients.

2 × 2 MIMO dual spiral octagonal prism liquid DRA with snake-shaped DGS for OAM multiplexing.

In this paper, we proposed a 2 × 2 multiple-input multiple-output (MIMO) dual spiral octagonal prism liquid dielectric resonator antenna (DRA) with snake-shaped defective ground structure (DGS) for space multiplexing of orbital angular momentum (OAM). The DRA element adopts an inner and outer nested dual spiral structure filled with 0.035 g/ml of brine outside and a cylinder filled with distilled water inside. The proposed MIMO antenna can generate resonance at 1.78-3.02 GHz and 4.01-7.73 GHz (S11≤-10 dB). The isolation among ports is below -20 dB at 2.6 GHz and below -40 dB at 5.1 GHz, which can effectively isolate the l = ±1 and l = ±3 modes' OAM waves through the snake-shaped DGS. The proposed MIMO antenna improves spectral efficiency by OAM spatial multiplexing with l = ±1 and l = ±3 modes' OAM, which improves the data transmission efficiency. The proposed MIMO antenna provides a novel, to the best of our knowledge, solution for wireless communications to improve spectral efficiency.

Multi-Shell Nanourchin-Integrated Dual Mode Lateral Flow Immunoassay for Sensitive and Rapid Detection of Clinical Cardiac Myosin-Binding Protein C.

Cardiac myosin-binding protein C (cMyBP-C) is a novel cardiac marker of acute myocardial infarction (AMI) and acute cardiac injuries (ACI). Construction of point-of-care testing techniques capable of sensing cMyBP-C with high sensitivity and precision is urgently needed. Herein, we synthesized an Au@NGQDs@Au/Ag multi-shell nanoUrchins (MSNUs), and then applied it in a colorimetric/SERS dual-mode immunoassay for detection of cMyBP-C. The MSNUs displayed superior stability, colorimetric brightness, and SERS enhancement ability with an enhanced factor of 5.4 × 109, which were beneficial to improve the detection capability of test strips. The developed MSNU-based test strips can achieve an ultrasensitive immunochromatographic assay of cMyBP-C in both colorimetric and SERS modes with the limits of detection as low as 19.3 and 0.77 pg/mL, respectively. Strikingly, this strip was successfully applied to analyze actual plasma samples with significantly better sensitivity, negative predictive value, and accuracy than commercially available gold test strips. Notably, this method possessed a wide range of application scenarios via combining with a color recognizer application named Color Grab on the smartphone, which can meet various needs of different users. Overall, our MSNU-based test strip as a mobile health monitoring tool shows excellent sensitivity, reproducibility, and rapid detection of the cMyBP-C, which holds great potential for the early clinic diagnosis of AMI and ACI.

Detection of MicroRNA-155 based on lambda exonuclease selective digestion and CRISPR/cas12a-assisted amplification.

In numerous malignancies, miRNA-155 is overexpressed and has oncogenic activity because it is one of the most efficient microRNAs for inhibiting apoptosis in human cancer cells. As a result, the highest sensitive detection of the miRNA-155 gene is a technological instrument that can enable early cancer screening. In this study, a miRNA-155 biosensor was created to create a hairpin probe that can bind to the miRNA-155 gene using lambda nucleic acid exonuclease, which can cut the 5' phosphorylated double strand, and by the DNA probe is recognized by the Cas12a enzyme, which then activates Cas12a to catalyze trans-cutting produces strong fluorescence. Research finding, the target concentration's logarithm and corresponding fluorescence intensity have a strong linear connection, and the limit of detection (LOD) of the sensing system was determined to be 8.3 pM. In addition, the biosensor displayed exceptional specificity, low false-positive signal, and high sensitivity in detecting the miRNA-155 gene in serum samples. This study's creation of a biosensor that has high sensitivity, good selectivity, and is simple to operate provides promising opportunities for research into biosensor design and early cancer detection.

CD112 is an epithelial-to-mesenchymal transition-related and immunological biomarker in pan-cancer.

Background: The nectin adhesion molecule CD112, an important component of tumor progression, belongs to the nectin family. However, a comprehensive evaluation of its clinical relevance and mechanism in various cancers is yet to be conducted. Methods: This investigation fully examined the relationship between prognosis and CD112 expression. We clarified the function of CD112 in tumor immunity by employing The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. This involved examining its connections to tumor mutation burden (TMB), DNA methylation, tumor immune invasion, mismatch repair (MMR), microsatellite instability (MSI), and common immune checkpoint inhibitors (ICIs). Additionally, the impact of CD112 knockdown on cell function was examined in colorectal cancer (CRC) cell lines. Results: In the current study, we found malignant tissues express high levels of CD112, which was related to TMB, MMR, MSI, and DNA methylation. Survival analysis indicated that patients with high CD112 expression had an unfavorable prognosis more frequently. In addition, CD112 expression was negatively associated with infiltration levels of CD4 positive (CD4+) T cells, CD8 positive (CD8+) T cells, and T cells. Western blotting and pathway enrichment analysis showed that CD112 is significantly linked to epithelial-to-mesenchymal transition (EMT). Additionally, CRC cells migrate and proliferate less when CD112 was knocked down. CD112 expression was found to be negatively associated with anti-programmed cell death protein 1 (PD-1) and anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) treatment outcomes in patients. Conclusions: CD112 may act as a possible prognostic marker in immune therapy and may stimulate tumor growth by upregulating the EMT pathway.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm.

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species' potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen's kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively far from each other, may be lost due to the limitation of the receptive field of the convolution kernel. We aim to incorporate a graph to represent non-local information and improve the reconstructed images by using the Graph Convolutional Enhanced Self-Similarity (GCESS) network. METHODS: First, the image is reconstructed into the graph to extract the non-local self-similarity in the image. Second, GCESS uses spatial convolution and graph convolution to process the information in the image, so that local and non-local information can be effectively utilized. The network strengthens the non-local similarity between similar image patches while reconstructing images, making the reconstruction of structure more reliable. RESULTS: Experimental results on in vivo knee and brain data demonstrate that the proposed method achieves better artifact suppression and detail preservation than state-of-the-art methods, both visually and quantitatively. Under 1D Cartesian sampling with 4 × acceleration (AF = 4), the PSNR of knee data reached 34.19 dB, 1.05 dB higher than that of the compared methods; the SSIM achieved 0.8994, 2% higher than the compared methods. Similar results were obtained for the reconstructed images under other sampling templates as demonstrated in our experiment. CONCLUSIONS: The proposed method successfully constructs a hybrid graph convolution and spatial convolution network to reconstruct images. This method, through its training process, amplifies the non-local self-similarities, significantly benefiting the structural integrity of the reconstructed images. Experiments demonstrate that the proposed method outperforms the state-of-the-art reconstruction method in suppressing artifacts, as well as in preserving image details.

Rationale and design of a single-center randomized trial to compare the graft patency between the radial artery and the no-touch saphenous vein in coronary artery bypass grafting surgery (GRAFT-CAB Study).

BACKGROUND: Previous studies suggested only the radial artery and the No-touch (NT) technique were effective in reducing graft occlusion after coronary artery bypass grafting (CABG) surgery. However, there is no randomized trial comparing these 2 graft conduits. The optimum second conduit for CABG remains undetermined. MATERIALS AND METHODS: This study is a prospective, single-center randomized clinical trial, aiming to compare the graft patency between the radial artery and the NT vein graft. All patients undergoing isolated CABG with left internal mammary artery (LIMA) plus at least 2 additional grafts will be considered eligible. About 774 cases (516 in the radial artery group and 258 in the NT vein group) will be enrolled in over 1 to 2 years. Participants will be randomized and allocated to two bypass strategies: the LIMA plus 1 radial artery and 1 conventional vein graft, or the LIMA plus 2 NT vein grafts. The primary outcome is graft occlusion at 1 year after CABG evaluated by CT angiography. The secondary outcomes include graft occlusion at 3 and 5 years and major adverse cardiac or cerebrovascular events at 1, 3, and 5 years follow-ups. DISCUSSION: This study will define whether or not the NT vein has a lower graft occlusion rate than the radial artery in short and mid-term follow-ups, and provide new evidence for the second conduit choice in CABG surgery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06014047. Registered on October 15th, 2023.

A systematic review of antibiotics and antibiotic resistance genes (ARGs) in mariculture wastewater: Antibiotics removal by microalgal-bacterial symbiotic system (MBSS), ARGs characterization on the metagenomic.

Antibiotic residues in mariculture wastewater seriously affect the aquatic environment. Antibiotic Resistance Genes (ARGs) produced under antibiotic stress flow through the environment and eventually enter the human body, seriously affecting human health. Microalgal-bacterial symbiotic system (MBSS) can remove antibiotics from mariculture and reduce the flow of ARGs into the environment. This review encapsulates the present scenario of mariculture wastewater, the removal mechanism of MBSS for antibiotics, and the biomolecular information under metagenomic assay. When confronted with antibiotics, there was a notable augmentation in the extracellular polymeric substances (EPS) content within MBSS, along with a concurrent elevation in the proportion of protein (PN) constituents within the EPS, which limits the entry of antibiotics into the cellular interior. Quorum sensing stimulates the microorganisms to produce biological responses (DNA synthesis - for adhesion) through signaling. Oxidative stress promotes gene expression (coupling, conjugation) to enhance horizontal gene transfer (HGT) in MBSS. The microbial community under metagenomic detection is dominated by aerobic bacteria in the bacterial-microalgal system. Compared to aerobic bacteria, anaerobic bacteria had the significant advantage of decreasing the distribution of ARGs. Overall, MBSS exhibits remarkable efficacy in mitigating the challenges posed by antibiotics and resistant genes from mariculture wastewater.

Precisely Self-Cooperative Nanoassembly Enables Photothermal/Ferroptosis Synergistic Tumor Eradication.

Ferroptosis is identified as a potential target for anticancer therapy. However, most conventional ferroptosis inducers not only fail to trigger intracellular lipid peroxidation storm, but are also prone to cause ferroptosis-related toxicity through off-target destruction of intracellular antioxidant defense systems. Therefore, a potent and highly tumor-specific ferroptosis induction modality is desired. Herein, a self-cooperative nanomedicine for imaging-guided photothermal ferrotherapy, which is fabricated based on molecular nanoassembly (NA) of DiR (a photothermal probe) and ferrocene (Fc, a reactant of the Fenton reaction), is elaborately exploited. DiR-elicited hyperthermia induces both photothermal therapy (PTT) and a significant acceleration of the kinetics of the Fc-involved Fenton reaction, collaboratively causing a lipid peroxidation storm in tumor cells. In turn, plenty of lipid peroxides boost PTT through the downregulation of heat shock protein 90. As expected, such a self-cooperative NA demonstrates synergetic tumor eradication in the 4T1 breast tumor-bearing mice xenograft model. This study offers a novel nanotherapeutic paradigm for precise multimodal cancer therapy.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

High-reliability infrared broadband thin-film polarizing beam splitter with ZnSe compensation layers.

Thin-film polarizing beam splitters (PBSs) fulfill a pivotal role in laser beam splitting, modulation, shaping and isolation. In this study, a high-reliability infrared broadband thin-film PBS was developed. To correct for tensile stress in Ge/YbF3 multilayer coatings, ZnSe compensation layers were incorporated in the multilayer design. The effects of different symmetrical periods on the spectral properties of the infrared PBS were systematically discussed. The infrared PBS operated at 45° and in the long-wave infrared (LWIR) band. Using the percent of optical extrema monitoring (POEM) strategy combined with the high-temperature optical constants (HTOC) of Ge film, the infrared PBS was precisely fabricated on ZnSe substrates. Subsequently, the spectral performance and film reliability of the infrared PBS were carefully characterized. Specifically, the transmittance of p-polarization surpassed 96%, while the extinction ratio exceeded 100:1 within the 10.6 ± 0.15 µm band. The infrared PBS demonstrated commendable environmental reliability, in addition to exhibiting excellent spectral characteristics.

Echelon grating refractive index sensor.

There are few reports on optical refractive index sensors that have both high resonant-wavelength resolution (RWR) and high refractive index sensitivity (RIS). Herein, based on an echelon grating, we design a refractive index sensor that combines the two advantages together. The principal fringe of echelon grating has a small full width at half maximum and a good signal-to-noise ratio, leading to a high RWR. The wavefront splitting interference makes the sensor have high RIS. The large free spectral range (FSR) of the principal fringes expands the dynamic range of the sensor. The experimentally realized RWR, RIS, and FSR are 2 × 10-2 nm, 1.14 × 104 nm/RIU (RIU: refractive index unit), and 130 nm, respectively. The detection limit of refractive index is 1.59 × 10-6 RIU. The dynamic range of the sensor is 1.14 × 10-2 RIU. In addition, there are schemes to improve RWR and RIS, which can further reduce the detection limit of refractive index. The echelon grating refractive index sensor features low detection limit, low cost, high stability, and good robustness.

GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning.

RNA modification, N4-acetylcytidine (ac4C), is enzymatically catalyzed by N-acetyltransferase 10 (NAT10) and plays an essential role across tRNA, rRNA, and mRNA. It influences various cellular functions, including mRNA stability and rRNA biosynthesis. Wet-lab detection of ac4C modification sites is highly resource-intensive and costly. Therefore, various machine learning and deep learning techniques have been employed for computational detection of ac4C modification sites. The known ac4C modification sites are limited for training an accurate and stable prediction model. This study introduces GANSamples-ac4C, a novel framework that synergizes transfer learning and generative adversarial network (GAN) to generate synthetic RNA sequences to train a better ac4C modification site prediction model. Comparative analysis reveals that GANSamples-ac4C outperforms existing state-of-the-art methods in identifying ac4C sites. Moreover, our result underscores the potential of synthetic data in mitigating the issue of data scarcity for biological sequence prediction tasks. Another major advantage of GANSamples-ac4C is its interpretable decision logic. Multi-faceted interpretability analyses detect key regions in the ac4C sequences influencing the discriminating decision between positive and negative samples, a pronounced enrichment of G in this region, and ac4C-associated motifs. These findings may offer novel insights for ac4C research. The GANSamples-ac4C framework and its source code are publicly accessible at http://www.healthinformaticslab.org/supp/.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Mitochondrial KATP channel-mediated autophagy contributes to angiotensin II-induced vascular dysfunction in mice.

BACKGROUND AND AIM: The present study aimed to investigate whether the mitochondrial KATP channel contributes to angiotensin II (Ang II)-induced vascular dysfunction, the development of hypertension, and atherosclerosis. METHODS AND RESULTS: ApoE (-/-) mice fed a high-fat diet were chronically infused with Ang II for eight weeks and concomitantly treated with losartan (ARB), apocynin, or 5-hydroxy decanoate (5-HD), or 3-methyladenine (3-MA). Systolic blood pressure was measured, and pathological changes of aortic or liver tissue were observed. Nitric oxide (NO), superoxide dismutase 2 (SOD2) levels and vasorelaxation rate were measured, and protein and mRNA expressions were examined by western blot and RT-PCR. Ang II-induced development of hypertension was suppressed not only by ARB, and apocynin but also by 5-HD or 3-MA. Ang II infusion decreased aortic NO production and relaxation, as well as SOD2 activity in liver, which were improved by all treatments. In addition, Ang II-induced activation of autophagy was suppressed by 5-HD in aortic tissue, furthermore, Ang II increases the atherosclerotic index in plasma and exacerbates the development of atherosclerosis by increases of fat deposition in the aorta and liver. Lipid metabolism-related mRNA expressions (LXR-α, LDLR, SRBI, Acca, and FASN) were changed by Ang II. Similarly, not only ARB, and apocynin, but also 5-HD and 3-MA suppressed Ang II-induced these changes. CONCLUSIONS: Our present findings evidence that mitochondrial KATP channel-mediated autophagy contributes to Ang II-induced vascular dysfunction, development of hypertension, and atherosclerosis.

Research on accurate pipetting complementation model for high-throughput molecular detection platform.

The incidence of infectious diseases has risen in recent years, leading to a significant surge in the demand for medical molecular detection. High-throughput molecular detection platforms play a crucial role in facilitating rapid and efficient molecular detection. Among the various techniques employed in high-throughput molecular detection, microliquid transfer stands out as one of the most frequently utilized methods. However, ensuring the accuracy of liquid transfer poses a challenge due to variations in the physical and chemical properties of different samples and reagents. In this study, a pipetting complementation model was developed specifically for the serum, paraffin oil, and throat swabs. The aim was to enhance the transfer accuracy of diverse liquids in the context of high-throughput molecular detection, ultimately ensuring detection reliability and stability. The experimental findings revealed notable improvements in pipetting accuracy after compensating for the three liquids. In particular, the pipetting error rates decreased by 52.5, 96, and 71.4% for serum, paraffin oil, and throat swabs, respectively. These results underscore the model's effectiveness in providing reliable support for the precise transfer of liquids on the high-throughput molecular detection platform.