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Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning's (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. We aimed to devise a prognostic model, leveraging MIMIC-IV data, to predict sepsis mortality and validate it in a Chinese teaching hospital. MIMIC-IV provided patient data, split into training and internal validation sets. Four ML models logistic regression (LR), support vector machine (SVM), deep neural networks (DNN), and extreme gradient boosting (XGBoost) were employed. Shapley additive interpretation offered early and interpretable mortality predictions. Area under the ROC curve (AUROC) gaged predictive performance. Results were cross verified in a Chinese teaching hospital. The study included 27,134 sepsis patients from MIMIC-IV and 487 from China. After comparing, 52 clinical indicators were selected for ML model development. All models exhibited excellent discriminative ability. XGBoost surpassed others, with AUROC of 0.873 internally and 0.844 externally. XGBoost outperformed other ML models (LR: 0.829; SVM: 0.830; DNN: 0.837) and clinical scores (Simplified Acute Physiology Score II: 0.728; Sequential Organ Failure Assessment: 0.728; Oxford Acute Severity of Illness Score: 0.738; Glasgow Coma Scale: 0.691). XGBoost's hospital mortality prediction achieved AUROC 0.873, sensitivity 0.818, accuracy 0.777, specificity 0.768, and F1 score 0.551. We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.
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BACKGROUND: Most Phalaenopsis cultivars have almost no aroma, with a few exceptions. Phalaenopsis presents significant challenges in fragrance breeding due to its weak aroma and low fertility. It is therefore necessary to identify the aroma components and key regulatory genes in Phalaenopsis cultivars like 'Orange Beauty', 'Brother Sara Gold', 'Purple Martin', 'H026', 'SK16', 'SX098', and 'SH51', to improve the aroma of the common Phalaenopsis. RESULTS: Floral aroma components were tested on nine Phalaenopsis species, using smell identification and headspace gas chromatography-mass spectrometry. The result showed that alcohols, esters, and alkenes were the key specific components in the different species and cultivar aromas and the aroma intensity and component content of cultivars with different colors were different. The main components of the floral aromas in Phalaenopsis were alcohols (including eucalyptol, linalool, citronellol, and 1-hexanol), esters (including hexyl acetate, leaf acetate, and dibutyl phthalate), alkenes (including pinene and sabinene) and arenes (like fluorene). The transcriptome of flowers in the bud stage and bloom stage of P. 'SH51' was sequenced and 5999 differentially expressed genes were obtained. The contributions of the phenylpropionic acid/phenyl ring compound and the terpene compound to the aroma were greater. Sixteen genes related to phalaenopsis aroma were found. TC4M, PAL, CAD6, and HR were related to phenylpropanoid synthesis pathway. SLS, TS10, and P450 were related to the synthesis pathway of terpenes. TS10 and YUCCA 10 were involved in tryptophan metabolism. CONCLUSION: This is the first report on the floral aroma components and regulatory genes in Phalaenopsis. The proposed method and research data can provide technical support for Phalaenopsis breeding. © 2024 Society of Chemical Industry.
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BACKGROUND: China experienced an overwhelming COVID-19 pandemic from middle December 2022 to middle January 2023 after lifting the zero-COVID-19 policy on December 7, 2022. However, the infection rate was less studied. We aimed to investigate the SARS-CoV-2 infection rate in children shortly after discontinuation of the zero-COVID-19 policy. METHODS: From February 20 to April 10, 2023, we included 393 children aged 8 months to less than 3 years who did not receive COVID-19 vaccination and 114 children aged 3 to 6 years who received inactivated COVID-19 vaccines based on the convenience sampling in this cross-sectional study. IgG and IgM antibodies against nucleocapsid (N) and subunit 1 of spike (S1) of SARS-CoV-2 (anti-N/S1) were measured with commercial kits (Shenzhen YHLO Biotech, China). RESULTS: Of the 393 unvaccinated children (1.5 ± 0.6 years; 52.2% boys), 369 (93.9%) were anti-N/S1 IgG positive. Of the 114 vaccinated children (5.3 ± 0.9 years; 48.2% boys), 112 (98.2%) were anti-N/S1 IgG positive. None of the unvaccinated or vaccinated children was anti-N/S1 IgM positive. The median IgG antibody titers in vaccinated children (344.91 AU/mL) were significantly higher than that in unvaccinated children (42.80 AU/mL) (P < 0.0001). The positive rates and titers of anti-N/S1 IgG had no significant difference between boys and girls respectively. CONCLUSION: Vast majority of children were infected with SARS-CoV-2 shortly after ending zero-COVID-19 policy in China. Whether these unvaccinated infected children should receive COVID-19 vaccine merits further investigation.
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Antibodies, Viral , COVID-19 Vaccines , COVID-19 , Immunoglobulin G , SARS-CoV-2 , Humans , COVID-19/prevention & control , COVID-19/immunology , COVID-19/epidemiology , China/epidemiology , Child, Preschool , Male , Female , COVID-19 Vaccines/immunology , COVID-19 Vaccines/administration & dosage , Child , Antibodies, Viral/blood , SARS-CoV-2/immunology , Infant , Cross-Sectional Studies , Immunoglobulin G/blood , Immunoglobulin M/blood , Vaccination/statistics & numerical data , Spike Glycoprotein, Coronavirus/immunologyABSTRACT
Prunus zhengheensis, an extremely rare population of apricots, originated in warm South-East China and is an excellent material for genetic breeding. However, most apricots and two related species (P. sibirica, P. mandshurica) are found in the cold northern regions in China and the mechanism of their distribution is still unclear. In addition, the classification status of P. zhengheensis is controversial. Thus, we generated a high-quality haplotype-resolved genome for P. zhengheensis, exploring key genetic variations in its adaptation and the causes of phylogenetic incongruence. We found extensive phylogenetic discordances between the nuclear and organelle phylogenies of P. zhengheensis, which could be explained by incomplete lineage sorting. A 242.22-Mb pan-genome of the Armeniaca section was developed with 13 chromosomal genomes. Importantly, we identified a 566-bp insertion in the promoter of the HSFA1d gene in apricot and showed that the activity of the HSFA1d promoter increased under low temperatures. In addition, HSFA1d overexpression in Arabidopsis thaliana indicated that HSFA1d positively regulated plant growth under chilling. Therefore, we hypothesized that the insertion in the promoter of HSFA1d in apricot improved its low-temperature adaptation, allowing it to thrive in relatively cold locations. The findings help explain the weather adaptability of Armeniaca plants.
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Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.
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Neoplasm Recurrence, Local , Stomach Neoplasms , Stomach Neoplasms/pathology , Humans , Algorithms , Normal DistributionABSTRACT
Drought is a major stress affecting rice yields. Combining partial root-zone drying (PRD) and different nitrogen fertilizers reduces the damage caused by water stress in rice. However, the underlying molecular mechanisms remain unclear. In this study, we combined treatments with PRD and ammonia:nitrate nitrogen at 0:100 (PRD0:100) and 50:50 (PRD50:50) ratios or PEG and nitrate nitrogen at 0:100 (PEG0:100) ratios in rice. Physiological, transcriptomic, and metabolomic analyses were performed on rice leaves to identify key genes involved in water stress tolerance under different nitrogen forms and PRD pretreatments. Our results indicated that, in contrast to PRD0:100, PRD50:50 elevated the superoxide dismutase activity in leaves to accelerate the scavenging of ROS accumulated by osmotic stress, attenuated the degree of membrane lipid peroxidation, stabilized photosynthesis, and elevated the relative water content of leaves to alleviate the drought-induced osmotic stress. Moreover, the alleviation ability was better under PRD50:50 treatment than under PRD0:100. Integrated transcriptome and metabolome analyses of PRD0:100 vs PRD50:50 revealed that the differences in PRD involvement in water stress tolerance under different nitrogen pretreatments were mainly in photosynthesis, oxidative stress, nitrogen metabolism process, phytohormone signaling, and biosynthesis of other secondary metabolites. Some key genes may play an important role in these pathways, including OsGRX4, OsNDPK2, OsGS1;1, OsNR1.2, OsSUS7, and YGL8. Thus, the osmotic stress tolerance mediated by PRD and nitrogen cotreatment is influenced by different nitrogen forms. Our results provide new insights into osmotic stress tolerance mediated by PRD and nitrogen cotreatment, demonstrate the essential role of nitrogen morphology in PRD-induced molecular regulation, and identify genes that contribute to further improving stress tolerance in rice.
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BACKGROUND AND OBJECTIVES: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. METHODS: Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. RESULTS: A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. CONCLUSIONS: SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.
Subject(s)
Gene Expression Profiling , Single-Cell Analysis , Sequence Analysis, RNA/methods , Base Sequence , Least-Squares Analysis , Single-Cell Analysis/methods , Cluster Analysis , SoftwareABSTRACT
The formation of multi-pistil flowers reduces the yield and quality in Japanese apricot (Prunus mume). However, the molecular mechanism underlying the formation of multi-pistil flowers remains unknown. In the current study, overexpression of PmKNAT2/6-a, a class I KNOTTED1-like homeobox (KNOX) member, in Arabidopsis (Arabidopsis thaliana) resulted in a multi-pistil phenotype. Analysis of the upstream regulators of PmKNAT2/6-a showed that AGAMOUS-like 24 (PmAGL24) could directly bind to the PmKNAT2/6-a promoter and regulate its expression. PmAGL24 also interacted with Like Heterochromatin Protein 1 (PmLHP1) to recruit lysine trimethylation at position 27 on histone H3 (H3K27me3) to regulate PmKNAT2/6-a expression, which is indirectly involved in multiple pistils formation in Japanese apricot flowers. Our study reveals that the PmAGL24 transcription factor, an upstream regulator of PmKNAT2/6-a, regulates PmKNAT2/6-a expression via direct and indirect pathways and is involved in the formation of multiple pistils in Japanese apricot.
Subject(s)
Arabidopsis , Flowers , Gene Expression Regulation, Plant , Plant Proteins , Flowers/genetics , Flowers/metabolism , Plant Proteins/genetics , Plant Proteins/metabolism , Arabidopsis/genetics , Arabidopsis/metabolism , Transcription Factors/metabolism , Transcription Factors/genetics , Plants, Genetically Modified , Prunus/genetics , Prunus/metabolism , Prunus armeniaca/genetics , Prunus armeniaca/metabolism , Promoter Regions, Genetic/geneticsABSTRACT
Two new terpenoids were isolated from the branches and leaves of Rhododendron dauricum L., named as rhodayritions A (1) and B (2), together with five known compounds which were identified litseachromolaevane A (3), 11-αH-dihydrodehydrocostus lactone (4), (+)-9ß-hydroxyeudesma-4,11(13)-dien-12-al (5), macrostachyoside B (6) and aglaiabbreviatin E (7), respectively. The structures of isolated compounds were determined by UV, HR-ESI-MS, NMR analysis and X-Ray. Their neuroprotective activity was studied on serum deprivation-induced PC12 cells by the MTT method, compounds 1, 6, and 7 exhibited significant neuroprotective activity at 20 µΜ.
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Japanese apricot is an important subtropical deciduous fruit tree in China, widely distributed in different altitude areas. How does it adapt to the different temperature environments in these areas? In this study, we identified a low-temperature transcription factor PmCBF03 on chromosome 7 through adaptive analysis of populations at different altitudes, which has an early termination single nucleotide polymorphism mutation. There were two different types of variation, PmCBF03A type in high-altitude areas and PmCBF03T type in low-altitude areas. PmCBF03A gene increased the survival rate, Fv/Fm values, antioxidant enzyme activity, and expression levels of antioxidant enzyme genes, and reducing electrolyte leakage and accumulation of reactive oxygen species in transgenic Arabidopsis under low temperature and freezing stress. Simultaneously, PmCBF03A gene promoted the dormancy of transgenic Arabidopsis seeds than wild-type. Biochemical analysis demonstrated that PmCBF03A directly bound to the DRE/CRT element in the promoters of the PmCOR413, PmDAM6 and PmABI5 genes, promoting their transcription and enhanced the cold resistance and dormancy of the overexpressing PmCBF03A lines. While PmCBF03T gene is unable to bind to the promoters of PmDAM6 and PmABI5 genes, leading to early release of dormancy to adapt to the problem of insufficient chilling requirement in low-altitude areas.
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Arabidopsis , Prunus armeniaca , Prunus , Temperature , Fruit , Altitude , Prunus/genetics , Prunus/metabolism , Antioxidants/metabolism , Arabidopsis/geneticsABSTRACT
The co-existence of heavy metals and nitrate (NO3--N) pollutants in wastewater has been a persistent global concern for a long time. A strain LYF26, which can remove NO3--N, calcium (Ca(II)), and cadmium (Cd(II)) simultaneously, was isolated to explore the properties and mechanisms of synergistic contaminants removal. Different conditions (Cd(II) and Ca(II) concentrations and pH) were optimized by Zero-, Half-, and First-order kinetic analyses to explore the environmental parameters for the optimal effect of strain LYF26. Results of the kinetic analyses revealed that the optimal culture conditions for strain LYF26 were pH of 6.5, Cd(II) and Ca(II) concentrations of 3.00 and 180.00 mg L-1, accompanied by Ca(II), Cd(II), and NO3--N efficiencies of 53.10%, 90.03%, and 91.45%, respectively. The removal mechanisms of Cd(II) using strain LYF26 as a nucleation template were identified as biomineralization, lattice substitution, and co-precipitation. The differences and changes of dissolved organic matter during metabolism were analyzed and the results demonstrated that besides the involvement of extracellular polymeric substances in the precipitation of Cd(II) and Ca(II), the high content of humic acid-like species revealed a remarkable contribution to the denitrification process. This study is hopeful to contribute a theory for further developing microbially induced calcium precipitation used to treat complex polluted wastewater.
Subject(s)
Cadmium , Nitrates , Cadmium/metabolism , Nitrates/metabolism , Calcium , Kinetics , Pseudomonas/metabolism , Wastewater , Denitrification , Calcium, DietaryABSTRACT
Berberine acts via multiple pathways to alleviate fibrosis in various tissues and shows renoprotective effects. However, its role and underlying mechanisms in renal fibrosis remain unclear. Herein, we aimed to investigate the protective effects and molecular mechanisms of berberine against unilateral ureteric obstruction-induced renal fibrosis. The results indicated that berberine treatment (50 mg/kg/day) markedly alleviated histopathological alterations, collagen deposition and inflammatory cell infiltration in kidney tissue and restored mouse renal function. Mechanistically, berberine intervention inhibited NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome activation and the levels of the inflammatory cytokine IL-1ß in the kidneys of unilateral ureteric obstruction mice. In addition, berberine relieved unilateral ureteric obstruction-induced renal injury by activating adenosine monophosphate-activated protein kinase (AMPK) signalling and promoting fatty acid ß-oxidation. In vitro models showed that berberine treatment prevented the TGF-ß1-induced profibrotic phenotype of hexokinase 2 (HK-2) cells, characterized by loss of an epithelial phenotype (alpha smooth muscle actin [α-SMA]) and acquisition of mesenchymal marker expression (E-cadherin), by restoring abnormal fatty acid ß-oxidation and upregulating the expression of the fatty acid ß-oxidation related-key enzymes or regulators (phosphorylated-AMPK, peroxisome proliferator activated receptor alpha [PPARα] and carnitine palmitoyltransferase 1A [CPT1A]). Collectively, berberine alleviated renal fibrosis by inhibiting NLRP3 inflammasome activation and protected tubular epithelial cells by reversing defective fatty acid ß-oxidation. Our findings might be exploited clinically to provide a potential novel therapeutic strategy for renal fibrosis.
Subject(s)
Berberine , Kidney Diseases , Ureteral Obstruction , Mice , Animals , Ureteral Obstruction/complications , Ureteral Obstruction/drug therapy , Berberine/pharmacology , Berberine/therapeutic use , Berberine/metabolism , Inflammasomes/metabolism , NLR Family, Pyrin Domain-Containing 3 Protein/metabolism , AMP-Activated Protein Kinases/metabolism , Kidney Diseases/drug therapy , Kidney Diseases/etiology , Kidney Diseases/prevention & control , Kidney , Transforming Growth Factor beta1/metabolism , Inflammation/pathology , Fibrosis , Fatty Acids/metabolism , Fatty Acids/pharmacology , Fatty Acids/therapeutic useABSTRACT
Background: Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis. Methods: In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score. Results: The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set. Conclusion: The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.
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Partial root-zone drying (PRD) is an effective water-saving irrigation strategy that improves stress tolerance and facilitates efficient water use in several crops. It has long been considered that abscisic acid (ABA)-dependent drought resistance may be involved during partial root-zone drying. However, the molecular mechanisms underlying PRD-mediated stress tolerance remain unclear. It's hypothesized that other mechanisms might contribute to PRD-mediated drought tolerance. Here, rice seedlings were used as a research model and the complex transcriptomic and metabolic reprogramming processes were revealed during PRD, with several key genes involved in osmotic stress tolerance identified by using a combination of physiological, transcriptome, and metabolome analyses. Our results demonstrated that PRD induces transcriptomic alteration mainly in the roots but not in the leaves and adjusts several amino-acid and phytohormone metabolic pathways to maintain the balance between growth and stress response compared to the polyethylene glycol (PEG)-treated roots. Integrated analysis of the transcriptome and metabolome associated the co-expression modules with PRD-induced metabolic reprogramming. Several genes encoding the key transcription factors (TFs) were identified in these co-expression modules, highlighting several key TFs, including TCP19, WRI1a, ABF1, ABF2, DERF1, and TZF7, involved in nitrogen metabolism, lipid metabolism, ABA signaling, ethylene signaling, and stress regulation. Thus, our work presents the first evidence that molecular mechanisms other than ABA-mediated drought resistance are involved in PRD-mediated stress tolerance. Overall, our results provide new insights into PRD-mediated osmotic stress tolerance, clarify the molecular regulation induced by PRD, and identify genes useful for further improving water-use efficiency and/or stress tolerance in rice.
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Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.
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Algorithms , Artificial Intelligence , Machine Learning , Environment , MutationABSTRACT
COVID-19 is now often moderate and self-recovering, but in a significant proportion of individuals, it is severe and deadly. Determining whether individuals are at high risk for serious disease or death is crucial for making appropriate treatment decisions. We propose a computational method to estimate the mortality risk for patients with COVID-19. To develop the model, 4,711 reported cases confirmed as SARS-CoV-2 infections were used for model development. Our computational method was developed using ensemble learning in combination with a genetic algorithm. The best-performing ensemble model achieves an AUCROC (area under the receiver operating characteristic curve) value of 0.7802. The best ensemble model was developed using only 10 features, which means it requires less medical information so that the diagnostic cost may be reduced while the prognostic time may be improved. The results demonstrate the robustness of the used method as well as the efficiency of the combination of machine learning and genetic algorithms in developing the ensemble model.
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There has been great interest in the enzymatic cascade amplification strategy for the electrochemical detection of circulating tumor cells (CTCs). In this work, we designed a highly efficient enzymatic cascade reaction based on a multiwalled carbon nanotubes-chitosan (MWCNTs-CS) composite for detection of CTCs. A high electrochemical effective surface area was obtained for a MWCNTs-CS-modified glassy carbon electrode (GCE) for loading glucose oxidase (GOD), as well as a high loading rate and high electrical activity of the enzyme. As a 'power source', the MWCNTs-CS composites provided a strong driving power for horseradish peroxidase (HRP) on the surface of polystyrene (PS) microspheres, which acted as probes for capturing CTCs and allowed the reaction to proceed with further facilitation of electron transfer. Aptamer, CTCs, and PS microspheres with HRP and anti-epithelial cell adhesion molecule (anti-EpCAM) antibody were assembled on the MWCNTs-CS/GCE to allow for the modulation of enzyme distance at the micrometer level, and thus ultra-long-range signal transmission was made possible. An ultrasensitive response to CTCs was obtained via this proposed sensing strategy, with a linear range from 10 cell mL-1 to 6 × 106 cell mL-1 and a detection limit of 3 cell mL-1. Moreover, this electrochemical sensor possessed the capability to detect CTCs in serum samples with satisfactory accuracy, which indicated great potential for early diagnosis and clinical analysis of cancer.
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Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality. Previously, many supervised or unsupervised deep learning-based LDCT denoising algorithms have been proposed. Unsupervised LDCT denoising algorithms are more practical than supervised ones since they do not need paired samples. However, unsupervised LDCT denoising algorithms are rarely used clinically due to their unsatisfactory denoising ability. In unsupervised LDCT denoising, the lack of paired samples makes the direction of gradient descent full of uncertainty. On the contrary, paired samples used in supervised denoising allow the parameters of networks to have a clear direction of gradient descent. To bridge the gap in performance between unsupervised and supervised LDCT denoising, we propose dual-scale similarity-guided cycle generative adversarial network (DSC-GAN). DSC-GAN uses similarity-based pseudo-pairing to better accomplish unsupervised LDCT denoising. We design a Vision Transformer-based global similarity descriptor and a residual neural network-based local similarity descriptor for DSC-GAN to effectively describe the similarity between two samples. During training, pseudo-pairs, i.e., similar LDCT samples and normal-dose CT (NDCT) samples, dominate parameter updates. Thus, the training can achieve equivalent effect as training with paired samples. Experiments on two datasets demonstrate that DSC-GAN beats the state-of-the-art unsupervised algorithms and reaches a level close to supervised LDCT denoising algorithms.
Subject(s)
Image Processing, Computer-Assisted , Tomography, X-Ray Computed , Neural Networks, Computer , Algorithms , Signal-To-Noise RatioABSTRACT
Circulating tumor cells (CTCs) are the important biomarker for cancer diagnosis and individualized treatment. However, due to the extreme rarity of CTCs (only 1-10 CTCs are found in every milliliter of peripheral blood) high sensitivity and selectivity are urgently needed for CTC detection. Here, a sandwich PEC cytosensor for the ultrasensitive detection of CTCs was developed using the photoactive material Au NP/-Fe2O3 and core-shell CdSe@CdS QD sensitizer. In the proposed protocol, the CdSe@CdS QD/Au NP/α-Fe2O3-sensitized structure with cascade band-edge levels could evidently promote the photoelectric conversion efficiency due to suitable light absorption and efficient electron-hole pair recombination inhibition. Additionally, a dendritic aptamer-DNA concatemer was constructed for highly efficient capture of MCF-7 cells carrying CdSe@CdS QDs, a sensitive material. The linear range of this proposed signal-on PEC sensing method was 300 cell mL-1 to 6 × 105 cell mL-1 with a detection limit of 3 cell mL-1, and it demonstrated an ultrasensitive response to CTCs. Furthermore, this PEC sensor enabled accurate detection of CTCs in serum samples. Hence, a promising strategy for CTC detection in clinical diagnosis was developed based on CdSe@CdS QD-sensitized Au NP/α-Fe2O3-based PEC cytosensor with dendritic aptamer-DNA concatemer.
Subject(s)
Biosensing Techniques , Cadmium Compounds , Neoplastic Cells, Circulating , Quantum Dots , Selenium Compounds , Humans , Electrochemical Techniques/methods , Cadmium Compounds/chemistry , Limit of Detection , Quantum Dots/chemistry , Biosensing Techniques/methods , Selenium Compounds/chemistry , DNA , OligonucleotidesABSTRACT
Japanese apricot (Prunus mume Sieb. et Zucc.) is a traditional fruit tree with a long history. Multiple pistils (MP) lead to the formation of multiple fruits, decreasing fruit quality and yield. In this study, the morphology of flowers was observed at 4 stages of pistil development: undifferentiated stage (S1), predifferentiation stage (S2), differentiation stage (S3), and late differentiation stage (S4). In S2 and S3, the expression of PmWUSCHEL (PmWUS) in the MP cultivar was significantly higher than that in the single-pistil (SP) cultivar, and the gene expression of its inhibitor, PmAGAMOUS (PmAG), also showed the same trend, indicating that other regulators participate in the regulation of PmWUS during this period. Chromatin immunoprecipitation-qPCR (ChIP-qPCR) showed that PmAG could bind to the promoter and the locus of PmWUS, and H3K27me3 repressive marks were also detected at these sites. The SP cultivar exhibited an elevated level of DNA methylation in the promoter region of PmWUS, which partially overlapped with the region of histone methylation. This suggests that the regulation of PmWUS involves both transcription factors and epigenetic modifications. Also, the gene expression of Japanese apricot LIKE HETEROCHROMATIN PROTEIN (PmLHP1), an epigenetic regulator, in MP was significantly lower than that in SP in S2 to 3, contrary to the trend in expression of PmWUS. Our results showed that PmAG recruited sufficient PmLHP1 to maintain the level of H3K27me3 on PmWUS during the S2 of pistil development. This recruitment of PmLHP1 by PmAG inhibits the expression of PmWUS at the precise time, leading to the formation of 1 normal pistil primordium.