Quantitative genetic analysis of attractiveness of yeast products to Drosophila.

An attractive perfume is a complex mixture of compounds, some of which may be unpleasant on their own. This is also true for the volatile combinations from yeast fermentation products in vineyards and orchards when assessed by Drosophila. Here we used crosses between a yeast strain with an attractive fermentation profile and another strain with a repulsive one and tested flies responses using a T-maze. QTL analysis reveals allelic variation in four yeast genes, PTC6, SAT4, YFL040W, and ARI1, that modulated expression levels of volatile compounds (assessed by GC-MS) and in different combinations, generated various levels of attractiveness. The parent strain that is more attractive to Drosophila has repulsive alleles at two of the loci while the least attractive parent has attractive alleles. Behavioral assays using artificial mixtures mimicking the composition of odors from fermentation validated the results of GC-MS and QTL mapping, thereby directly connecting genetic variation in yeast to attractiveness in flies. This study can be used as a basis for dissecting the combination of olfactory receptors that mediate the attractiveness/repulsion of flies to yeast volatiles and may also serve as a model for testing the attractiveness of pest species such as Drosophila suzukii to their host fruit.

The potential up-regulation risk of 3' UTR SNP (rs10787760 G > A) for the VAX1 gene is associated with NSCLP in the northwest Chinese population.

AIMS: To investigate the association between single nucleotide polymorphisms (SNPs) in 3'UTR region of VAX1, SYT14 and PAX7 genes and the risk of non-syndromic cleft palate (NSCLP) in a northwest Chinese population. MAIN METHODS: A case-control study was conducted in 406 normal controls and 399 NSCLP patients. Using iMLDRTM genotyping technology, eight SNPs of three genes ((rs10787760, rs7086344 at VAX1), (rs1010113, rs851114, and rs485874 at PAX7), and (rs61820397, rs4609425, rs12133399 at SYT14)) were genotyped to investigate the differences in alleles and genotype distribution frequencies between NSCLP patients and healthy controls. RNA Folding Form software was used to predict RNA secondary structure and expression vectors were constructed to explore the function of the relevant SNP. The effect of SNP polymorphism of gene transcription and translation was assessed using qPCR and Western blot analysis. KEY FINDINGS: Among the eight SNPs of three genes, rs10787760 of VAX1 gene was found to be associated with an increased risk of NSCLP (OR = 1.341,CI = 1.004-1.790) and the GA genotype of rs10787760 increased the risk of cleft lip and/or palate (CL/P) about 1.42 times (p < 0.05), and carrying the A allele might increase the risk of NSCL/P in male (OR = 1.356, 95 % CI = 1.010-1.823). But there was no association observed with cleft palate only (CPO). Cell function experiments revealed that the G to A mutation in rs10787760 up-regulated GFP-VAX1 transcriptional level by 2.39 and 3.13 times in two cell lines respectively, and enhance the protein expression of the VAX1 gene further. RNA secondary structure study showed that the rs10787760 (G > A) had two different secondary structures in 3'UTR region. SIGNIFICANCE: The rs10787760 variant in the 3'UTR region of VAX1 gene is associated with CL/P in northwest Chinese population. We hypothesize that the machanism of it might be caused by the RNA differenct fold in the 3'UTR region caused by the polymorphism of the gene. LEVEL OF EVIDENCE: Original Reports.

The potassium transporter TaNHX2 interacts with TaGAD1 to promote drought tolerance via modulating stomatal aperture in wheat.

Drought is a major global challenge in agriculture that decreases crop production. γ-Aminobutyric acid (GABA) interfaces with drought stress in plants; however, a mechanistic understanding of the interaction between GABA accumulation and drought response remains to be established. Here we showed the potassium/proton exchanger TaNHX2 functions as a positive regulator in drought resistance in wheat by mediating cross-talk between the stomatal aperture and GABA accumulation. TaNHX2 interacted with glutamate decarboxylase TaGAD1, a key enzyme that synthesizes GABA from glutamate. Furthermore, TaNHX2 targeted the C-terminal auto-inhibitory domain of TaGAD1, enhanced its activity, and promoted GABA accumulation under drought stress. Consistent with this, the tanhx2 and tagad1 mutants showed reduced drought tolerance, and transgenic wheat with enhanced TaNHX2 expression had a yield advantage under water deficit without growth penalty. These results shed light on the plant stomatal movement mechanism under drought stress and the TaNHX2-TaGAD1 module may be harnessed for amelioration of negative environmental effects in wheat as well as other crops.

Source-specified atmospheric age distribution of black carbon and its impact on optical properties over the Yangtze River Delta.

Black carbon (BC) exerts a profound and intricate impact on both air quality and climate due to its high light absorption. However, the uncertainty in representing the absorption enhancement of BC in climate models leads to an increased range in the modeled aerosol climate effects. Changes in BC optical properties could result either from atmospheric aging processes or from variations in its sources. In this study, a source-age model for identifying emission sources and aging states presented by University of California at Davis/California Institute of Technology (UCD/CIT) was used to simulate the atmospheric age distribution of BC from different sources and to quantify its impact on the optical properties of BC-containing particles. The results indicate that regions with greater aged BC concentrations do not correspond to regions with higher BC emissions due to atmospheric transport. High concentrations of aged BC are found in northern Yangtze River Delta (YRD) regions during summer. The chemical compositions of particles from different sources and with different atmospheric ages differ significantly. BC and primary organic aerosols (POA) are dominating in Traffic-dominated source while other components dominate in Industry-dominated source. As the atmospheric age increases, the mass fraction of secondary inorganic aerosols rises. Compared to the original model, the simulated mass absorption cross section of BC particles in the source-age model decreases while the single scattering albedo increases. This compensates for ~11 % of the overestimation of the simulated BC direct radiative forcing. Our study highlights that incorporating atmospheric age and source information into models can greatly improve the estimation of optical properties of BC-containing particles and deepen our understanding of their climate effects.

Programming crystallization kinetics of self-assembled DNA crystals with 5-methylcytosine modification.

Self-assembled DNA crystals offer a precise chemical platform at the ångström-scale for DNA nanotechnology, holding enormous potential in material separation, catalysis, and DNA data storage. However, accurately controlling the crystallization kinetics of such DNA crystals remains challenging. Herein, we found that atomic-level 5-methylcytosine (5mC) modification can regulate the crystallization kinetics of DNA crystal by tuning the hybridization rates of DNA motifs. We discovered that by manipulating the axial and combination of 5mC modification on the sticky ends of DNA tensegrity triangle motifs, we can obtain a series of DNA crystals with controllable morphological features. Through DNA-PAINT and FRET-labeled DNA strand displacement experiments, we elucidate that atomic-level 5mC modification enhances the affinity constant of DNA hybridization at both the single-molecule and macroscopic scales. This enhancement can be harnessed for kinetic-driven control of the preferential growth direction of DNA crystals. The 5mC modification strategy can overcome the limitations of DNA sequence design imposed by limited nucleobase numbers in various DNA hybridization reactions. This strategy provides a new avenue for the manipulation of DNA crystal structure, valuable for the advancement of DNA and biomacromolecular crystallography.

CYP7A1 Gene Induction via SHP-Dependent or Independent Mechanisms can Increase the Risk of Drug-Induced Liver Injury Independently or in Synergy with BSEP Inhibition.

Drug-induced liver injury (DILI) is a frequent cause of clinical trial failures during drug development. While inhibiting bile salt export pump (BSEP) is a well-documented DILI mechanism, interference with genes related to bile acid (BA) metabolism and transport can further complicate DILI development. Here, the effects of twenty-eight compounds on genes associated with BA metabolism and transport were evaluated, including those with discontinued development or use, boxed warnings, and clean labels for DILI. The study also included rifampicin and omeprazole, pregnane X receptor and aryl hydrocarbon receptor ligands, and four mitogen-activated protein kinase kinase (MEK1/2) inhibitors. BSEP inhibitors with more severe DILI, notably pazopanib and CP-724714, significantly upregulated the expression of 7 alpha-hydroxylase (CYP7A1), independent of small heterodimer partner (SHP) expression. CYP7A1 expression was marginally induced by omeprazole. In contrast, its expression was suppressed by mometasone (10-fold), vinblastine (18-fold), hexachlorophene (2-fold), bosentan (2.1-fold), and rifampin (2-fold). All four MEK1/2 inhibitors that show clinical DILI were not potent BSEP inhibitors but significantly induced CYP7A1 expression, accompanied by a significant SHP gene suppression. Sulfotransferase 2A1 and BSEP were marginally upregulated, but no other genes were altered by the drugs tested. Protein levels of CYP7A1 were increased with the treatment of CYP7A1 inducers and decreased with obeticholic acid, an farnesoid X receptor ligand. CYP7A1 inducers significantly increased bile acid (BA) production in hepatocytes, indicating the overall regulatory effects of BA metabolism. This study demonstrates that CYP7A1 induction via various mechanisms can pose a risk for DILI, independently or in synergy with BSEP inhibition, and it should be evaluated early in drug discovery. SIGNIFICANCE STATEMENT: Kinase inhibitors, pazopanib and CP-724714, inhibit BSEP and induce CYP7A1 expression independent of small heterodimer partner (SHP) expression, leading to increased bile acid (BA) production and demonstrating clinically elevated drug-induced liver toxicity. MEK1/2 inhibitors that show BSEP-independent drug-induced liver injury (DILI) induced the CYP7A1 gene accompanied by SHP suppression. CYP7A1 induction via SHP-dependent or independent mechanisms can pose a risk for DILI, independently or in synergy with BSEP inhibition. Monitoring BA production in hepatocytes can reliably detect the total effects of BA-related gene regulation for de-risking.

Spacer-Programmed Two-Dimensional DNA Origami Assembly.

Two-dimensional (2D) DNA origami assembly represents a powerful approach to the programmable design and construction of advanced 2D materials. Within the context of hybridization-mediated 2D DNA origami assembly, DNA spacers play a pivotal role as essential connectors between sticky-end regions and DNA origami units. Here, we demonstrated that programming the spacer length, which determines the binding radius of DNA origami units, could effectively tune sticky-end hybridization reactions to produce distinct 2D DNA origami arrays. Using DNA-PAINT super-resolution imaging, we unveiled the significant impact of spacer length on the hybridization efficiency of sticky ends for assembling square DNA origami (SDO) units. We also found that the assembly efficiency and pattern diversity of 2D DNA origami assemblies were critically dependent on the spacer length. Remarkably, we realized a near-unity yield of ∼98% for the assembly of SDO trimers and tetramers via this spacer-programmed strategy. At last, we revealed that spacer lengths and thermodynamic fluctuations of SDO are positively correlated, using molecular dynamics simulations. Our study thus paves the way for the precision assembly of DNA nanostructures toward higher complexity.

Deep learning infers clinically relevant protein levels and drug response in breast cancer from unannotated pathology images.

The computational pathology has been demonstrated to effectively uncover tumor-related genomic alterations and transcriptomic patterns. Although proteomics has indeed shown great potential in the field of precision medicine, few studies have focused on the computational prediction of protein levels from pathology images. In this paper, we assume that deep learning-based pathological features imply the protein levels of tumor biomarkers that are indicative of prognosis and drug response. For this purpose, we propose wsi2rppa, a weakly supervised contrastive learning framework to infer the protein levels of tumor biomarkers from whole slide images (WSIs) in breast cancer. We first conducted contrastive learning-based pre-training on tessellated tiles to extract pathological features, which are then aggregated by attention pooling and adapted to downstream tasks. We conducted extensive evaluation experiments on the TCGA-BRCA cohort (1978 WSIs of 1093 patients with protein levels of 223 biomarkers) and the CPTAC-BRCA cohort (642 WSIs of 134 patients). The results showed that our method achieved state-of-the-art performance in tumor diagnostic tasks, and also performed well in predicting clinically relevant protein levels and drug response. To show the model interpretability, we spatially visualized the WSIs colored the tiles by their attention scores, and found that the regions with high scores were highly consistent with the tumor and necrotic regions annotated by a 10-year experienced pathologist. Moreover, spatial transcriptomic data further verified that the heatmap generated by attention scores agrees greatly with the spatial expression landscape of two typical tumor biomarker genes. In predicting the response to drug trastuzumab treatment, our method achieved a 0.79 AUC value which is much higher than the previous study reported 0.68. These findings showed the remarkable potential of computational pathology in the prediction of clinically relevant protein levels, drug response, and clinical outcomes.

Rapid diagnosis of systemic lupus erythematosus by Raman spectroscopy combined with spiking neural network.

Multiple organs are affected by the autoimmune inflammatory connective tissue disease known as systemic lupus erythematosus (SLE). If not diagnosed and treated in a timely manner, it can lead to nephritis and damage to the blood system in severe cases, resulting in the patient's death. Therefore, correct and timely diagnosis and treatment are essential for patients. In this study, a framework based on neural network algorithm and Raman spectroscopy technique was established to diagnose SLE patients. Firstly, we pre-processed the obtained Raman data by three methods: baseline correction, smoothing processing and normalization methods, before using it as input for the model, and then ANN, ResNet and SNN classification models were established. The respective classification accuracies for SLE patients were 89.61%, 85.71%, and 95.65% for the three models, with corresponding AUC values of 0.8772, 0.8100, and 0.9555. The results of the experimental indicate that SNN possesses a good classification effect, and the number of model parameters is only 525,826, which is 414,221 less than that of ResNet model. Since the network only uses 0 and 1 to transmit information, and only has basic operations such as summation, compared with the second-generation artificial neural network, which simplifies the product operation of floating point numbers into multiple addition operations, the network has low energy consumption and is suitable for embedding portable Raman spectrometer for clinical diagnosis. This research highlights the significant potential for quick and precise SLE patient discrimination offered by Raman spectroscopy in conjunction with spiking neural networks.

Small Extracellular Vesicles Laden Oxygen-Releasing Thermosensitive Hydrogel for Enhanced Antibacterial Therapy against Anaerobe-Induced Periodontitis Alveolar Bone Defect.

Periodontitis is a bacterially induced chronic destructive inflammatory disease that leads to irreversible destruction of the tooth supporting structure, including connective tissue destruction, bone resorption, and even tooth loss. Until now, there has been no effective treatment to repair inflammatory bone loss in periodontitis. Recently, small extracellular vesicles (sEVs) emerged as the essential paracrine factors of mesenchymal stem cells (MSCs) that mediated tissue regeneration. However, limitations of antimicrobial activity associated with the use of sEVs have led to the urgency of new alternative strategies. Currently, we investigated the potential of a biocompatible oxygen-releasing thermosensitive hydrogel laded with sEVs secreted by bone marrow MSCs (BMMSCs) for the alveolar bone defect in periodontitis. The hydrogel composed of different polymers such as chitosan (CS), poloxamer 407 (P407), and cross-linked hyaluronic acid (c-HA) conglomerating is a kind of nanoporous structure material. Then, the gel matrix further encapsulated sEVs and calcium peroxide nanoparticles to realize the control of sEVs and oxygen release. Furthermore, ascorbic acid was added to achieve the REDOX equilibrium and acid-base equilibrium. The experiments in vivo and in vitro proved its good biocompatibility and effectively inhibited the growth of the periodontal main anaerobe, relieved periodontal pocket anaerobic infections, and promoted the periodontal defect regeneration. Therefore, this finding demonstrated that it was a promising approach for combating anaerobic pathogens with enhanced and selective properties in periodontal diseases, even in other bacteria-induced infections, for future clinical application.

Endovascular treatment of anterior inferior cerebellar artery aneurysms: a single-center experience and review of 33 patients.

OBJECTIVE: Aim of the present article was the demonstration of the institutional experience with the endovascular management of the anterior inferior cerebellar artery (AICA) aneurysms in order to propose a treatment algorithm. METHODS: Clinical data were obtained from 33 patients with 37 AICA aneurysms who had been surgically treated at the authors' hospital between 2010 and 2022. The patients' medical records, imaging data, and follow-up outcomes were retrospectively analyzed. RESULTS: All 33 patients (10 males, 23 females; mean age 54.88±12.49 years) underwent endovascular therapy for AICA aneurysms. The most common chief complaints were headache (87.9%), nausea and vomiting (57.6%), and alteration of consciousness (27.3%). 31 patients experienced subarachnoid hemorrhage (SAH). Regarding the AICA aneurysm location, 23 aneurysms were found at the right side of AICA in DSA images, and there were 6, 9, 16, 6 aneurysms in segments A1-A4, respectively. Coiling (59.5%), Onyx embolization (29.7%), coiling-combined Onyx embolization (5.4%), non-intervention (5.4%) were chosen in the surgical strategy. The length of follow-up was 8.09±5.05 months, and 84.8% of the patients had favorable modified Rankin Scale (mRS) scores. The complete occlusion rates were 94.6%. Postoperative complications occurred in 4 cases (12.1 %), including new neurological deficit in 3 cases and cerebral infarction in 1 case. 1 patient died after follow-up because of the severe pneumonia. Poor initial Hunt and Hess grade (HHG) (p=0.007) was the risk factor for unfavorable clinical outcome. The rupture status (p=0.025) and the location (p=0.021) of the AICA aneurysms are statistically significant in determining which operation strategy to be chosen. Coiling had an advantage over Onyx embolization (P=0.001) in parent artery preservation (PAP). CONCLUSIONS: In this study, an algorithm for the treatment of AICA aneurysms was proposed based on the clinical status of the patients before treatment, the anatomical factors of AICA and the technical conditions of EVT. To our knowledge, this is the first study to report more than 30 cases of AICA aneurysms that had been treated by EVT and to advocate a treatment algorithm. EVT of AICA aneurysms is an optional strategy, but decisions are made based on the specific condition, anatomical location and other factors.

Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.

Background: The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). Methods: In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney U-test and independent sample t-test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, normalized iodine concentration (NIC) and NZeff] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER40keV, NIC, and CER70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801-0.935). Conclusions: The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.

Polysaccharide of Asparagus cochinchinensis (Lour.) Merr regulates macrophage immune response and epigenetic memory through TLR4-JNK/p38/ERK signaling pathway and histone modification.

BACKGROUND: Innate immune memory of macrophages is closely linked to histone modifications. While various studies have demonstrated that the polysaccharide of Asparagus cochinchinensis (Lour.) Merr (ACMP), extracted through alcohol-alkali extraction, enhances macrophages' non-specific immune function; no literature currently addresses whether ACMP's regulatory effect is related to innate immune memory and histone modification. PURPOSE: This study aims to investigate if ACMP induces innate immune memory emergence in macrophages via pattern recognition receptor (PRR). STUDY DESIGN: After co-incubating different doses of ACMP with RAW264.7 cells and BMDM cells, we observed changes in signaling pathways related to PRR and assessed the presence of innate immune memory phenomenon in the cells. METHODS: We observed the morphological characteristics of the ACMP using a scanning electron microscope, infrared spectrum, and HPLC pre-column derivatization method. We used q-PCR, Western blot, RNA-seq, and CUT&Tag-seq methods to examine ACMP's regulation of macrophage immune response and innate immune memory and explored its specific mechanism. RESULTS: ACMP, primarily composed of Man, GlcN, Rha, Fuc, GalA, Xyl, Glc, Gal, Ara, and, exhibited a molar ratio of each monosaccharide (1.41: 0.35: 0.49: 0.18: 1.00: 97.12: 0.36: 3.58: 1.14). ACMP regulated immunological function in macrophages through the TLR4-MAPK-JNK/p38/ERK pathway. ACMP induced elevated levels of chromosomal H3K4me1, enhancing TNF-α, IL-1ß, and other genes' responsiveness, allowing macrophages to develop innate immune memory to ACMP stimulation. CONCLUSION: This study first time demonstrates that ACMP regulates immunological function through the TLR4-MAPK-JNK/ERK/p38 signaling pathway, distinct from prior reports. ACMP induces innate immune memory in macrophages in response to its immune stimulation by promoting increased H3K4me1 on chromosomes. This mechanism may be crucial in how plant polysaccharides regulate macrophages and the body's immune function.

Improved prediction of hourly PM2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model.

Accurate prediction of particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) is important for environmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques. In this study, we propose a deep learning model for short-term PM2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial correlation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant information is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM2.5 concentrations for the next hour. The experimental results show that the predicted PM2.5 concentrations are consistent with observation, with correlation coefficient (R2) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 µg/m3. Additionally, to better understand the factors affecting PM2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM2.5 and its influencing factors. By identifying the main factors affecting PM2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE). The deep learning model established in this study provide a valuable tool for air quality management and public health protection.

Ganoderma lucidum spore oil synergistically enhances the function of cyclophosphamide in the prevention of breast cancer metastasis.

BACKGROUND: Ganoderma lucidum ( G . lucidum ) is a traditional Chinese herbal medicine that has shown potential as an alternative adjuvant therapy for cancer patients. However, the mechanisms and adjuvant therapeutic effects of G . lucidum in cancer treatment remain unclear. METHODS: In this work, G . lucidum spore oil (GanoOil), a newly developed oily G . lucidum spore extract was used to investigate the mechanisms and adjuvant therapeutic effects of GanoOil in conjunction with the chemotherapeutic drug cyclophosphamide (CTX) for preventing breast cancer metastasis. RESULTS: In the model of lung metastasis, orally administered GanoOil increased the population of CD8 + T cells and interleukin (IL)-6 cytokine levels in mouse blood, whereas also enhancing the activity of natural killer cells in the spleen. Furthermore, the combination of GanoOil and CTX effectively suppressed the lung metastasis of circulating breast cancer cells, alleviated CTX-induced weight loss, and reduced the ratio of lung and spleen weight to body weight in mice. Moreover, high concentrations of GanoOil exhibited no significant toxicity or side effects in both in vitro and in vivo experiments. CONCLUSION: In conclusion, GanoOil is a safe drug that can enhance immune activity in mice to achieve therapeutic effects on cancer, and can also synergistically inhibit tumor metastasis with CTX.

Programmable Atom-Like Nanoparticle Reporters for High-Precision Urinalysis of In Situ Membrane Proteins.

The expression of disease-specific membrane proteins (MPs) is a crucial indicator for evaluating the onset and progression of diseases. Urinalysis of in situ MPs has the potential for point-of-care disease diagnostics, yet remains challenging due to the lack of molecular reporter to transform the expression information of in situ MPs into the measurable urine composition. Herein, a series of tetrahedral DNA frameworks (TDFs) are employed as the cores of programmable atom-like nanoparticles (PANs) to direct the self-assembly of PAN reporters with defined ligand valence and spatial distribution. With the rational spatial organization of ligands, the interaction between PAN reporters and MPs exhibits superior stability on cell-membrane interface under renal tubule-mimic fluid microenvironment, thus enabling high-fidelity conversion of MPs expression level into binding events and reverse assessment of in situ MP levels via measurement of the renal clearance efficiency of PAN reporters. Such PAN reporter-mediated signal transformation mechanism empowers urinalysis of the onset of acute kidney injury at least 6 h earlier than the existing methods with an area under the curve of 100%. This strategy has the potential for urinalysis of a variety of in situ membrane proteins.

A Prognostic Model of Genetic Markers for Triple-Negative Breast Cancer Based on Machine Learning and Bioinformatics Analysis.

Triple negative breast cancer (TNBC) that has low survival rate and prognosis due to its heterogeneity and lack of reliable molecular targets for effective targeted therapy. Therefore, finding new biomarkers is crucial for the targeted treatment of TNBC. The experimental data from the Cancer Genome Atlas database (TCGA).First, key genes associated with TNBC prognosis were screened and used for survival analysis using a single-factor COX regression analysis combined with three algorithms: LASSO, RF and SVM-RFE. Multi-factor COX regression analysis was then used to construct a TNBC risk prognostic model. Four key genes associated with TNBC prognosis were screened as TENM2, OTOG, LEPR and HLF. Among them, OTOG is a new biomarker. Survival analysis showed a significant effect of four key genes on OS in TNBC patients (P<0.05). The experiment showed that four key genes could provide new ideas for targeting therapy for TNBC patients and improved prognosis and survival.

Novel Strategies for Angiogenesis in Tissue Injury: Therapeutic Effects of iPSCs-Derived Exosomes.

Regeneration after tissue injury is a dynamic and complex process, and angiogenesis is necessary for normal physiological activities and tissue repair. Induced pluripotent stem cells are a new approach in regenerative medicine, which provides good model for the study of difficult-to-obtain human tissues, patient-specific therapy, and tissue repair. As an innovative cell-free therapeutic strategy, the main advantages of the treatment of induced pluripotent stem cells (iPSCs)-derived exosomes are low in tumorigenicity and immunogenicity, which become an important pathway for tissue injury. This review focuses on the mechanism of the angiogenic effect of iPSCs-derived exosomes on wound repair in tissue injury and their potential therapeutic targets, with a view to providing a theoretical basis for the use of iPSCs-derived exosomes in clinical therapy.

Decreased lipid levels in adult with congenital heart disease: a systematic review and Meta-analysis.

BACKGROUND: Metabolic disorders were a health problem for many adults with congenital heart disease, however, the differences in metabolic syndrome-related metabolite levels in adults with congenital heart disease compared to the healthy population were unknown. METHODS: We collected 18 studies reporting metabolic syndrome-associated metabolite levels in patients with congenital heart disease. Data from different studies were combined under a random-effects model using Cohen's d values. RESULTS: The results found that the levels of total cholesterol (Cohen's d -0.68, 95% CI: -0.91 to -0.45), high-density lipoprotein cholesterol (Cohen's d -0.63, 95% CI: -0.89 to -0.37), and low-density lipoprotein cholesterol (Cohen's d -0.32, 95% CI: -0.54 to -0.10) were significantly lower in congenital heart disease patients compared with controls. Congenital heart disease patients also had a lower body mass index (Cohen's d -0.27, 95% CI: -0.42 to -0.12) compared with controls. On the contrary, congenital heart disease patients had higher levels of hemoglobin A1c (Cohen's d 0.93, 95% CI: 0.17 to 1.70) than controls. Meanwhile, there were no significant differences in triglyceride (Cohen's d 0.07, 95% CI: -0.09 to 0.23), blood glucose (Cohen's d -0.12, 95% CI: -0.94 to 0.70) levels, systolic (Cohen's d 0.07, 95% CI: -0.30 to 0.45) and diastolic blood pressure (Cohen's d -0.10, 95% CI: -0.39 to 0.19) between congenital heart disease patients and controls. CONCLUSIONS: The lipid levels in patients with congenital heart disease were significantly lower than those in the control group. These data will help in the health management of patients with congenital heart disease and guide clinicians. PROSPERO REGISTRATION NUMBER: CRD42022228156.