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The public health concern of antibiotic residues in animal-origin food has been a long-standing issue. In this work, we present a novel method for antibiotic detection, leveraging optical weak value amplification and harnessing an indirect competitive inhibition assay, which significantly boosts the system's sensitivity in identifying small molecule antibiotics. We chose chloramphenicol as a model compound and mixed it with chloramphenicol-bovine serum albumin conjugates to bind to the chloramphenicol antibody competitively. We achieved a broad linear detection range of up to 3.24 ng/mL and a high concentration resolution of 33.20 pg/mL. To further validate the universality of our proposed detection methodology, we successfully applied it to testing gibberellin and tetracycline. Moreover, we conducted regeneration experiments and real-sample correlation studies. This study introduces a novel strategy for the label-free optical sensing of small molecule antibiotics, greatly expanding the range of applications for sensors utilizing optical weak value amplification.
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Antibacterianos , Cloranfenicol , Cloranfenicol/análisis , Antibacterianos/análisis , Antibacterianos/farmacología , Contaminación de Alimentos/análisis , Animales , Albúmina Sérica Bovina/química , Límite de DetecciónRESUMEN
Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated potential in diagnosing NPC, but the reliance on costly manual annotation hinders development. To address the challenges, this paper proposes a deep learning-based framework for diagnosing NPC without manual annotation. The framework includes a novel unpaired generative network and a prior-driven image classification system. With pathology-fidelity constraints, the generative network achieves accurate digital staining from H&E to EBER images. The classification system leverages staining specificity and pathological prior knowledge to annotate training data automatically and to classify images for NPC diagnosis. This work used 232 cases for study. The experimental results show that the classification system reached a 99.59% accuracy in classifying EBER images, which closely matched the diagnostic results of pathologists. Utilizing PF-GAN as the backbone of the framework, the system attained a specificity of 0.8826 in generating EBER images, markedly outperforming that of other GANs (0.6137, 0.5815). Furthermore, the F1-Score of the framework for patch level diagnosis was 0.9143, exceeding those of fully supervised models (0.9103, 0.8777). To further validate its clinical efficacy, the framework was compared with experienced pathologists at the WSI level, showing comparable NPC diagnosis performance. This low-cost and precise diagnostic framework optimizes the early pathological diagnosis method for NPC and provides an innovative strategic direction for AI-based cancer diagnosis.
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The demand for accurate and efficient immunoassays calls for the development of precise, high-throughput analysis methods. This paper introduces a novel approach utilizing a weak measurement interface sensor for immunoassays, offering a solution for high throughput analysis. Weak measurement is a precise quantum measurement method that amplifies the weak value of a system in the weak interaction through appropriate pre- and post-selection states. To facilitate the simultaneous analysis of multiple samples, we have developed a chip with six flow channels capable of conducting six immunoassays concurrently. We can perform real-time immunoassay to determine the binding characteristics of spike protein and antibody through real-time analysis of the flow channel images and calculating the relative intensity. The proposed method boasts a simple structure, eliminating the need for intricate nano processes. The spike protein concentration and relative intensity curve were fitted using the Log-Log fitting regression equation, and R2 was 0.91. Utilizing a pre-transformation approach to account for slight variations in detection sensitivity across different flow channels, the present method achieves an impressive limit of detection(LOD) of 0.85 ng/mL for the SARS-CoV-2 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, with a system standard deviation of 5.61. Furthermore, this method has been successfully verified for monitoring molecular-specific binding processes and differentiating binding capacities.
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Técnicas Biosensibles , COVID-19 , SARS-CoV-2 , Glicoproteína de la Espiga del Coronavirus , Glicoproteína de la Espiga del Coronavirus/análisis , Inmunoensayo/métodos , Humanos , COVID-19/diagnóstico , COVID-19/virología , Límite de Detección , Ensayos Analíticos de Alto RendimientoRESUMEN
Allergy is a prevalent disease, and the potential allergic population is expanding with industrialization and changes in people's living standards. Serum immunoglobulin E (IgE) level is one of the critical indicators for determining allergy. Here, we proposed a simple, real-time monitoring, low chip cost, label-free aptamer biosensing strategy based on weak value amplification (WVA) for the quantitative detection of IgE in serum samples, enabling early and accurate diagnosis of allergic or hypersensitive patients. The aptasensor combined an imaging weak measurement system with the high specificity of the aptamer for the marker IgE. By modifying the amino group at the 3-terminal end, the anti-IgE aptamers can attach to a dopamine-modified prism's surface and selectively recognize IgE in human serum. In the presence of IgE, a specific binding reaction occurred, resulting in a change in the refractive index of the reactive region's surface, manifested as a change in the light intensity of the camera acquired experimental images. As the concentration of IgE increased, the relative light intensity advanced sequentially. The WVA-aptasensing strategy achieved a wide detection range of 0.01 ng/mL to 2 µg/mL in phosphate buffered saline buffer, with the resolution as low as 4.3 pg/mL. IgE testing experiments in human serum have proved the feasibility of our methods in detecting complex samples. In addition, the method specifically recognized IgE without interference from other proteins. We believe that our proposed sensing strategy opens up new possibilities for ultrahigh sensitivity screening of IgE and can be expanded to detecting other biomolecules.
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Aptámeros de Nucleótidos , Técnicas Biosensibles , Inmunoglobulina E , Inmunoglobulina E/sangre , Humanos , Aptámeros de Nucleótidos/química , Técnicas Biosensibles/métodos , Límite de DetecciónRESUMEN
PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficult to obtain a training set with multiple stains because the labeling of pathology images is very time-consuming and tedious. Therefore, in this paper, we proposed an unsupervised stain augmentation-based method for segmentation of glomerular instances. METHODS: In this study, we successfully realized the conversion between different staining methods such as PAS, MT and PASM by contrastive unpaired translation (CUT), thus improving the staining diversity of the training set. Moreover, we replaced the backbone of mask R-CNN with swin transformer to further improve the efficiency of feature extraction and thus achieve better performance in instance segmentation task. RESULTS: To validate the method presented in this paper, we constructed a dataset from 216 WSIs of the three stains in this study. After conducting in-depth experiments, we verified that the instance segmentation method based on stain augmentation outperforms existing methods across all metrics for PAS, PASM, and MT stains. Furthermore, ablation experiments are performed in this paper to further demonstrate the effectiveness of the proposed module. CONCLUSION: This study successfully demonstrated the potential of unsupervised stain augmentation to improve glomerular segmentation in pathology analysis. Future research could extend this approach to other complex segmentation tasks in the pathology image domain to further explore the potential of applying stain augmentation techniques in different domains of pathology image analysis.
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A label-free optical sandwich immunoassay sensor, utilizing weak value amplification and total internal reflection, was devised for real-time, high-sensitivity analysis and detection of low-concentration targets. 3D printed channels and sodium chloride solution were employed to ensure reproducibility, reliability, and stability of the measurements for calibration. The sandwich structure demonstrated enhanced responsiveness in the proposed optical biosensor through a comparative analysis of the direct assay and sandwich assay for detecting alpha-fetoprotein (AFP) at the same concentration. By optimizing the binding sequences of the coating antibody, target, and detection antibody in the sandwich method, a more suitable sandwich sensing approach based on weak value amplification was achieved. With this approach, the limit of detection (LOD) of 6.29 ng/mL (pM level) for AFP in PBS solution was achieved. AFP testing and regeneration experiments in human serum have proved the feasibility of our methods in detecting complex samples and the reusability of sensing chips. Additionally, the method demonstrated excellent selectivity for unpaired antigens. The efficacy of this methodology was evaluated by simultaneously detecting AFP, carcinoembryonic antigen (CEA), and CA15-3 on a singular sensor chip. In conclusion, the label-free sandwich immunoassay sensing scheme holds promise for advancing the proposed optical sensors based on weak value amplification in early diagnosis and prevention applications. Compared to other biomarker detection methods, it will be easier to promote in practical applications.
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Técnicas Biosensibles , Antígeno Carcinoembrionario , Límite de Detección , alfa-Fetoproteínas , Técnicas Biosensibles/métodos , alfa-Fetoproteínas/análisis , Humanos , Antígeno Carcinoembrionario/sangre , Antígeno Carcinoembrionario/análisis , Inmunoensayo/métodos , Mucina-1/sangre , Mucina-1/análisis , Anticuerpos Inmovilizados/inmunología , Anticuerpos Inmovilizados/químicaRESUMEN
BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models. RESULTS: In this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget's performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years. CONCLUSIONS: We propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget . Our results provide new perspectives for microRNA target prediction.
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Aprendizaje Profundo , MicroARNs , MicroARNs/genética , MicroARNs/metabolismo , Redes Neurales de la Computación , Programas Informáticos , ARN Mensajero/genéticaRESUMEN
BACKGROUND: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS: This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION: The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.
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Prácticas Interdisciplinarias , Enfermedades Renales , Humanos , Teorema de Bayes , Enfermedades Renales/diagnóstico por imagen , Glomérulos Renales/diagnóstico por imagen , Redes Neurales de la ComputaciónRESUMEN
De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.
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Análisis de Secuencia de Proteína , Espectrometría de Masas en Tándem , Espectrometría de Masas en Tándem/métodos , Análisis de Secuencia de Proteína/métodos , Péptidos , Secuencia de Aminoácidos , Anticuerpos , AlgoritmosRESUMEN
BACKGROUND: In patients with primary biliary cholangitis (PBC) treated with ursodeoxycholic acid (UDCA), the presence of moderate-to-severe interface hepatitis is associated with a higher risk of liver transplantation and death. This highlights the need for novel treatment approaches. In this study, we aimed to investigate whether combination therapy of UDCA and immunosuppressant (IS) was more effective than UDCA monotherapy. METHODS: We conducted a multicenter study involving PBC patients with moderate-to-severe interface hepatitis who underwent paired liver biopsies. Firstly, we compared the efficacy of the combination therapy with UDCA monotherapy on improving biochemistry, histology, survival rates, and prognosis. Subsequently we investigated the predictors of a beneficial response. RESULTS: This retrospective cohort study with prospectively collected data was conducted in China from January 2009 to April 2023. Of the 198 enrolled patients, 32 underwent UDCA monotherapy, while 166 received combination therapy, consisting of UDCA combined with prednisolone, prednisolone plus mycophenolate mofetil (MMF), or prednisolone plus azathioprine (AZA). The monotherapy group was treated for a median duration of 37.6 months (IQR 27.5-58.1), and the combination therapy group had a median treatment duration of 39.3 months (IQR 34.5-48.8). The combination therapy showed a significantly greater efficacy in reducing fibrosis compared to UDCA monotherapy, with an 8.3-fold increase in the regression rate (from 6.3% to 52.4%, P < 0.001). Other parameters, including biochemistry, survival rates, and prognosis, supported its effectiveness. Baseline IgG >1.3 × ULN and ALP <2.4 × ULN were identified as predictors of regression following the combination therapy. A predictive score named FRS, combining these variables, accurately identified individuals achieving fibrosis regression with a cut-off point of ≥ -0.163. The predictive value was validated internally and externally. CONCLUSION: Combination therapy with IS improves outcomes in PBC patients with moderate-to-severe interface hepatitis compared to UDCA monotherapy. Baseline IgG and ALP are the most significant predictors of fibrosis regression. The new predictive score, FRS, incorporating baseline IgG and ALP, can effectively identify individuals who would benefit from the combination therapy.
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Hepatitis , Cirrosis Hepática Biliar , Humanos , Cirrosis Hepática Biliar/diagnóstico , Cirrosis Hepática Biliar/tratamiento farmacológico , Colagogos y Coleréticos/uso terapéutico , Estudios Retrospectivos , Resultado del Tratamiento , Ácido Ursodesoxicólico/uso terapéutico , Inmunosupresores/uso terapéutico , Prednisolona/uso terapéutico , Terapia de Inmunosupresión , Hepatitis/complicaciones , Inmunoglobulina GRESUMEN
Digitization of pathological slides has promoted the research of computer-aided diagnosis, in which artificial intelligence analysis of pathological images deserves attention. Appropriate deep learning techniques in natural images have been extended to computational pathology. Still, they seldom take into account prior knowledge in pathology, especially the analysis process of lesion morphology by pathologists. Inspired by the diagnosis decision of pathologists, we design a novel deep learning architecture based on tree-like strategies called DeepTree. It imitates pathological diagnosis methods, designed as a binary tree structure, to conditionally learn the correlation between tissue morphology, and optimizes branches to finetune the performance further. To validate and benchmark DeepTree, we build a dataset of frozen lung cancer tissues and design experiments on a public dataset of breast tumor subtypes and our dataset. Results show that the deep learning architecture based on tree-like strategies makes the pathological image classification more accurate, transparent, and convincing. Simultaneously, prior knowledge based on diagnostic strategies yields superior representation ability compared to alternative methods. Our proposed methodology helps improve the trust of pathologists in artificial intelligence analysis and promotes the practical clinical application of pathology-assisted diagnosis.
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Inteligencia Artificial , Patólogos , Humanos , Diagnóstico por Computador/métodosRESUMEN
Polyamines (PAs) are small aliphatic nitrogenous bases with strong biological activity that participate in plant stress response signaling and the alleviation of damage from stress. Herein, the effects of the PA-producing bacterium Bacillus megaterium N3 and PAs on the immobilization of Cd and inhibition of Cd absorption by spinach and the underlying mechanisms were studied. A solution test showed that strain N3 secreted spermine and spermidine in the presence of Cd. Both strain N3 and the PAs (spermine+spermidine) immobilized Cd and increased the pH of the solution. Untargeted metabolomics results showed that strain N3 secreted PAs, N1-acetylspermidine, 3-indolepropionic acid, indole-3-acetaldehyde, cysteinyl-gamma-glutamate, and choline, which correlated with plant growth promotion and Cd immobilization. A pot experiment showed that rhizosphere soil inoculation with strain N3 and PAs improved spinach dry weight and reduced spinach Cd absorption compared with the control. These positive effects were likely due to the increase in rhizosphere soil pH and NH4+-N and PA contents, which can be attributed primarily to Cd immobilization. Moreover, inoculation with strain N3 more effectively inhibited the absorption of Cd by spinach than spraying PAs, mainly because strain N3 enabled a better relative abundance of bacteria (Microvirga, Pedobacter, Bacillus, Brevundimonas, Pseudomonas, Serratia, Devosid, and Aminobacter), that have been reported to have the ability to resist heavy metals and produce PAs. Strain N3 regulated the structure of rhizosphere functional bacterial communities and inhibited Cd uptake by spinach. These results provide a theoretical basis for the prevention of heavy metal absorption by vegetables using PA-producing bacteria.
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Bacillus megaterium , Poliaminas , Espermidina/farmacología , Espermina , Cadmio/toxicidad , Spinacia oleracea , RizosferaRESUMEN
Aromatic maturity parameters were evaluated via closed-system pyrolysis experiments using a Mesozoic lacustrine source rock from the Yingen-Ejinaqi Basin, thereby ensuring a uniform source. Pulverized rock aliquots (200 mg) were reacted with water at temperatures ranging from 250 to 550 °C at 5 °C/min, and the aromatic fractions of expelled oil and extracts of the solid residue were analyzed by GC-MS. The experiments showed that the relative abundance of aromatic hydrocarbons in the oil and extractable organic matter (EOM) of source rock had different evolutionary characteristics. With the increase in the thermal evolution degree, the relative abundance of aromatic hydrocarbons in the EOM showed the characteristics of â³increased early (Ro < 0.80), unchanged middle (Ro = 0.80-2.00%), decreased lately (Ro > 2.00%)â³. While the relative abundance of aromatic hydrocarbons in the expelled oils continuously increased, as the Ro values increased from 0.62 to 2.39%, the relative abundance of aromatic hydrocarbons gradually increased from 8 to 46%. With increased maturity, the relative abundance of 1-3-ring aromatic hydrocarbons continuously decreased, as observed in the phenanthrene homologs. Meanwhile, the relative abundance of 4+-ring aromatic hydrocarbons continuously increased, as seen in chrysene homologs. It was suggested that the effects of maturity on the composition of aromatic hydrocarbons might not be sufficiently obvious. The effective application range of the alkylnaphthalene-related maturity parameters (2-/1-methylnaphthalenes, (2,6- + 2,7-)/1,5-dimethylnaphthalenes, 2,3,6-/(1,4,6- + 1,3,5-) trimethylnaphthalenes, and (2,3,6- + 1,3,7-)/(1,4,6- + 1,3,5- + 1,3,6-) trimethylnaphthalenes) and the alkyldibenzothiophene maturity parameters (4-/1-methyldibenzothiophenes, 4,6-/(1,4- + 1,6-) dimethyldibenzothiophenes, and (2,6- + 3,6-)/(1,4- + 1,6-) dimethyldibenzothiophenes) was 0.84-2.06% Ro. The alkylphenanthrene-related maturity parameters had a wide application range for lacustrine source rocks with an Ro < 2.06%. These parameters included 1.5 × (2- + 3-)/(phenanthrene +1- + 9-) methylphenanthrenes, 3 × 2-/(phenanthrene + 1- + 9-) methylphenanthrenes, (2- + 3-)/(1- + 9-) methylphenanthrenes, 2-/1-methylphenanthrenes, (3- + 2-)/(1- + 2- + 3- + 9-) methylphenanthrenes, 2-/(1- + 2- + 3- + 9-) methylphenanthrenes, and 2,7-/1,8-dimethylphenanthrenes. In addition, the effective applicable range of the methylnaphthalene-related maturity parameter 3-/1-methylchrysenes was an Ro value less than 1.79%. The results clarified the validity scope of some aromatics' maturity parameters and provided a theoretical basis for the scientific application of these parameters.
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BACKGROUND AND OBJECTIVE: Histopathological image registration is an essential component in digital pathology and biomedical image analysis. Deep-learning-based algorithms have been proposed to achieve fast and accurate affine registration. Some previous studies assume that the pairs are free from sizeable initial position misalignment and large rotation angles before performing the affine transformation. However, large-rotation angles are often introduced into image pairs during the production process in real-world pathology images. Reliable initial alignment is important for registration performance. The existing deep-learning-based approaches often use a two-step affine registration pipeline because convolutional neural networks (CNNs) cannot correct large-angle rotations. METHODS: In this manuscript, a general framework ARoNet is developed to achieve end-to-end affine registration for histopathological images. We use CNNs to extract global features of images and fuse them to construct correspondent information for affine transformation. In ARoNet, a rotation recognition network is implemented to eliminate great rotation misalignment. In addition, a self-supervised learning task is proposed to assist the learning of image representations in an unsupervised manner. RESULTS: We applied our model to four datasets, and the results indicate that ARoNet surpasses existing affine registration algorithms in alignment accuracy when large angular misalignments (e.g., 180 rotation) are present, providing accurate affine initialization for subsequent non-rigid alignments. Besides, ARoNet shows advantages in execution time (0.05 per pair), registration accuracy, and robustness. CONCLUSION: We believe that the proposed general framework promises to simplify and speed up the registration process and has the potential for clinical applications.
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Algoritmos , Procesamiento de Imagen Asistido por Computador , Redes Neurales de la ComputaciónRESUMEN
OBJECTIVE: To investigate the effects of atorvastatin calcium on pulmonary vascular remodeling, the authors explored the regulatory mechanism of Histone Deacetylation Enzyme-2 (HDAC2) in rats with Chronic Obstructive Pulmonary Disease (COPD), and provided a new direction for drug treatment in the progression of vascular remodeling. METHODS: Eighteen female SD rats were randomly divided into control (Group S1), COPD (Group S2), and atorvastatin calcium + COPD (Group S3) groups. A COPD rat model was established by passive smoking and intratracheal injection of Lipopolysaccharide (LPS). Haematoxylin and eosin staining and Victoria Blue + Van Gibson staining were used to observe pathological changes in the lung tissue. The pulmonary vascular inflammation score was calculated, and the degree of pulmonary vascular remodeling was evaluated. The ratio of Muscular Arteries in lung tissue (MA%), the ratio of the vessel Wall Area to the vessel total area (WA%), and the ratio of the vessel Wall Thickness to the vascular outer diameter (WT%) were measured using imaging software. The expression of HDAC2 was measured using western blotting, ELISA (Enzyme-Linked Immunosorbent Assay), and qPCR (Real-time PCR). RESULTS: Compared with the control group, the degree of pulmonary vascular inflammation and pulmonary vascular remodeling increased in rats with COPD. The WT%, WA%, and lung inflammation scores increased significantly; the expression of HDAC2 and HDAC2mRNA in the serum and lung tissue decreased, and the level of Vascular Endothelial Growth Factor (VEGF) in the lung tissues increased (p < 0.05). Compared with the COPD group, the lung tissues from rats in the atorvastatin group had fewer inflammatory cells, and the vascular pathological changes were significantly relieved. The WT%, WA%, and lung inflammation scores decreased significantly; the expression of HDAC2 and HDAC2mRNA in the serum and lung tissues increased, and the level of VEGF in the lung tissues decreased (p < 0.05). CONCLUSION: The present study revealed that atorvastatin calcium could regulate the contents and expression of HDAC2 in serum and lung tissues and inhibit the production of VEGF, thereby regulating pulmonary vascular remodeling in a rat model with COPD.
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Enfermedad Pulmonar Obstructiva Crónica , Factor A de Crecimiento Endotelial Vascular , Ratas , Femenino , Animales , Atorvastatina/farmacología , Atorvastatina/uso terapéutico , Atorvastatina/metabolismo , Remodelación Vascular , Ratas Sprague-Dawley , Enfermedad Pulmonar Obstructiva Crónica/tratamiento farmacológico , Pulmón , Inflamación/tratamiento farmacológicoRESUMEN
Siderophores secreted by microorganisms can promote ecological efficiency and could be used to regulate the unbalanced microbial community structure. The influence of the siderophore activity of Trichoderma yunnanense strain 2-14F2 and Beauveria pseudobassiana strain (2-8F2) on the physiological/biochemical functions and community structure of soil microbes affected by tobacco bacterial wilt (TBW) was studied. DNS Colorimetry and Biolog-eco plates were used to quantify the impacts of strain siderophores on soil enzyme activities and microbial metabolism. Based on Illumina MiSeq high-throughput sequencing, the soil 16S rDNA and ITS sequences were amplified to dissect the response characteristics of alpha/beta diversity and the structure/composition of a soil microbial community toward siderophores. The KEGG database was used to perform the PICRUSt functional prediction of the microbial community. We found that siderophores of 2-14F2 and 2-8F2, at certain concentrations, significantly increased the activities of sucrase (S-SC) and urease (S-UE) in the TBW soil and enhanced the average well color development (AWCD, carbon source utilization capacity) of the microbial community. The metabolic capacity of the diseased soil to amino acids, carbohydrates, polymers, aromatics, and carboxylic acids also increased significantly. The response of the bacterial community to siderophore active metabolites was more significant in alpha diversity, while the beta diversity of the fungal community responded more positively to siderophores. The relative abundance of Actinobacteria, Chloroflexi, and Acidobacteria increased and was accompanied by reductions in Proteobacteria and Firmicutes. LEfSe analysis showed that Pseudonocardiaceae, Gemmatimonas, Castellaniella, Chloridiumand and Acrophialophora altered the most under different concentrations of siderophore active metabolites. The PICRUSt functional prediction results showed that siderophore increased the abundance of the redox-related enzymes of the microbial community in TBW soil. The BugBase phenotypic prediction results showed that the siderophore activity could decrease the abundance of pathogenic bacteria. The study concludes that siderophore activity could decrease the abundance of pathogenic bacteria and regulate the composition of the microbial community in TBW soil. The activities of sucrase (S-SC) and urease (S-UE) in TBW soil were significantly increased. Overall, the siderophore regulation of community structures is a sustainable management strategy for soil ecosystems.
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Chlamydia is a zoonotic pathogen that mainly infects poultry and pet birds. This Gram-negative obligate intracellular parasite also causes human psittacosis, the severity of which varies from mild flu-like symptoms to life-threatening severe pneumonia, including sepsis, acute respiratory distress syndrome, and multiple organ failure. Inhalation of aerosols from contaminated bird excreta through the respiratory tract is the main route of transmission to humans. Here, we present a case of Chlamydia psittaci pneumonia accompanied by lower extremity atherosclerotic occlusive disease. A 48-year-old man was admitted to the emergency department with a four-day history of cough and dyspnea. A detailed history revealed his contact with domestic pigeons. The results of metagenomic next-generation sequencing of bronchoalveolar lavage fluid suggested C. psittaci infection. Antibacterial agents were switched to targeted doxycycline, but in the next week, skin examination revealed acrocyanosis of both lower extremities, and the remarkable palpable purpura progressively worsened. Re-examination of the lower extremity vascular ultrasound suggested left dorsalis pedis artery occlusion and right peroneal vein thrombosis, which resulted in the amputation of both legs. This case is the first report of C. psittaci pneumonia combined with arterioocclusive sclerosis of both lower extremities.
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Fibrosis is an inevitable stage in the development of chronic liver disease and has an irreplaceable role in characterizing the degree of progression of chronic liver disease. Histopathological diagnosis is the gold standard for the interpretation of fibrosis parameters. Conventional hematoxylin-eosin (H&E) staining can only reflect the gross structure of the tissue and the distribution of hepatocytes, while Masson trichrome can highlight specific types of collagen fiber structure, thus providing the necessary structural information for fibrosis scoring. However, the expensive costs of time, economy, and patient specimens as well as the non-uniform preparation and staining process make the conversion of existing H&E staining into virtual Masson trichrome staining a solution for fibrosis evaluation. Existing translation approaches fail to extract fiber features accurately enough, and the decoder of staining is unable to converge due to the inconsistent color of physical staining. In this work, we propose a prior-guided generative adversarial network, based on unpaired data for effective Masson trichrome stained image generation from the corresponding H&E stained image. Conducted on a small training set, our method takes full advantage of prior knowledge to set up better constraints on both the encoder and the decoder. Experiments indicate the superior performance of our method that surpasses the previous approaches. For various liver diseases, our results demonstrate a high correlation between the staging of real and virtual stains (ρ=0.82; 95% CI: 0.73-0.89). In addition, our finetuning strategy is able to standardize the staining color and release the memory and computational burden, which can be employed in clinical assessment.
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Colorantes , Humanos , Coloración y Etiquetado , Eosina Amarillenta-(YS) , FibrosisRESUMEN
Cholangiocytes play a crucial role in bile formation. Cholangiocyte injury causes cholestasis, including primary biliary cholangitis (PBC). However, the etiology of PBC remains unclear despite being characterized as an autoimmune disease. Using single-cell RNA sequencing (scRNA-seq), fluorescence-activated-cell-sorting, multiplex immunofluorescence (IF) and RNAscope analyses, we identified unique DUOX2+ACE2+ small cholangiocytes in human and mouse livers. Their selective decrease in PBC patients was associated with the severity of disease. Moreover, proteomics, scRNA-seq, and qPCR analyses indicated that polymeric immunoglobulin receptor (pIgR) was highly expressed in DUOX2+ACE2+ cholangiocytes. Serum anti-pIgR autoantibody levels were significantly increased in PBC patients, regardless of positive and negative AMA-M2. Spatial transcriptomics and multiplex IF revealed that CD27+ memory B and plasma cells accumulated in the hepatic portal tracts of PBC patients. Collectively, DUOX2+ACE2+ small cholangiocytes are pathogenic targets in PBC, and preservation of DUOX2+ACE2+ cholangiocytes and targeting anti-pIgR autoantibodies may be valuable strategies for therapeutic interventions in PBC.
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Cirrosis Hepática Biliar , Animales , Ratones , Humanos , Cirrosis Hepática Biliar/genética , Enzima Convertidora de Angiotensina 2 , Oxidasas Duales/genética , Células EpitelialesRESUMEN
BACKGROUND: Peritoneal metastasis (PM) is an independent prognostic factor in gastric cancer (GC), however, the underlying mechanisms of PM occurrence remain unclear. METHOD: The roles of DDR2 were investigated in GC and its potential relationship to PM, and orthotopic implants into nude mice were performed to assess the biological effects of DDR2 on PM. RESULTS: Herein, DDR2 level is more significantly observed to elevate in PM lesion than the primary lesion. GC with DDR2-high expression evokes a worse overall survival (OS) in TCGA, similar results of the gloomy OS with high DDR2 levels are clarified via the stratifying stage of TNM. The conspicuously increased expression of DDR2 was found in GC cell lines, luciferase reporter assays verified that miR-199a-3p directly targeted DDR2 gene, which was correlated to tumor progression. We ulteriorly observed DDR2 participated in GC stemness maintenance via mediating pluripotency factor SOX2 expression and implicated in autophagy and DNA damage of cancer stem cells (CSCs). In particular, DDR2 dominated EMT programming through recruiting NFATc1-SOX2 complex to Snai1 in governing cell progression, controlling by DDR2-mTOR-SOX2 axis in SGC-7901 CSCs. Furthermore, DDR2 promoted the tumor peritoneal dissemination in gastric xenograft mouse model. CONCLUSION: Phenotype screens and disseminated verifications incriminating in GC exposit the miR-199a-3p-DDR2-mTOR-SOX2 axis as a clinically actionable target for tumor PM progression. The herein-reported DDR2-based underlying axis in GC represents novel and potent tools for studying the mechanisms of PM.