TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences.

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.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition.

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.

Immunosuppression induces regression of fibrosis in primary biliary cholangitis with moderate-to-severe interface hepatitis.

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.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

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.

DeepTree: Pathological Image Classification through Imitating Tree-like Strategies of Pathologists.

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.

Polyamine-producing bacteria inhibit the absorption of Cd by spinach and alter the bacterial community composition of rhizosphere soil.

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.

A Study on the Applicability of Aromatic Parameters in the Maturity Evaluation of Lacustrine Source Rocks and Oils Based on Pyrolysis Simulation Experiments.

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 ″increased early (Ro < 0.80), unchanged middle (Ro = 0.80-2.00%), decreased lately (Ro > 2.00%)″. 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.

End-to-end affine registration framework for histopathological images with weak annotations.

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.

Effects of atorvastatin in suppressing pulmonary vascular remodeling in rats with chronic obstructive pulmonary disease.

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.

Differential Responses of Bacterial and Fungal Communities to Siderophore Supplementation in Soil Affected by Tobacco Bacterial Wilt (Ralstonia solanacearum).

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.

Chlamydia psittaci Pneumonia Complicated with Lower Extremity Atherosclerotic Occlusive Disease.

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.

Ultra-precise weak measurement-based interfacial biosensors.

In this study, an interfacial biosensing scheme with ultra-precision is proposed. The scheme uses weak measurement techniques to ensure ultra-high sensitivity of the sensing system while improving the stability of the system through self-referencing and pixel point averaging, thus achieving ultra-high detection accuracy of biological samples. In specific experiments, we have used the biosensor in this study to perform specific binding reaction experiments for protein A and Mouse IgG with a detection line of 2.71 ng/mL for IgG. In addition, the sensor is non-coated, simple in structure, easy to operate, and low in cost of use.

Unique DUOX2+ACE2+ small cholangiocytes are pathogenic targets for primary biliary cholangitis.

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.

Unpaired virtual histological staining using prior-guided generative adversarial networks.

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.

miR-199a-3p promotes gastric cancer progression by promoting its stemness potential via DDR2 mediation.

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.

Improving radish phosphorus utilization efficiency and inhibiting Cd and Pb uptake by using heavy metal-immobilizing and phosphate-solubilizing bacteria.

Phosphate-solubilizing bacteria play a key role in increasing plant growth as potential suppliers of soluble phosphorus and have great potential for the remediation of heavy metal-polluted soils. However, the soil and microbiological mechanisms by which phosphate-solubilizing bacteria prevent heavy metal absorption in radish have not been adequately studied. Here, the mechanisms of phosphorus solubilization, Cd and Pb immobilization, and the inhibition of heavy metal absorption by phosphate-solubilizing bacteria were studied in radish through solution adsorption and pot experiments. Two phosphate-solubilizing bacteria with high Cd and Pb removal rates (46.9-97.12 %), Klebsiella sp. M2 and Kluyvera sp. M8, were isolated. The soluble phosphorus content released by strains M2 and M8 was 265-277 mg L-1, achieved by secreting oxalic acid, ascorbic acid, citric acid, and succinic acid in an inorganic phosphorus medium containing 3 mg L-1 Cd and 5 mg L-1 Pb. Furthermore, these two functional strains induced the formation of Pb2(PO4)2, Cd(PO3)2, Fe2Pb3(PO4)2, CdS, and PbS precipitates that immobilized Cd and Pb in the solution. In general, strains M2 and M8 inhibited the absorption of Cd and Pb by radish by the following mechanisms: i) bacterial cell wall adsorption, ii) induction of Pb2(PO4)2, Cd(PO3)2, Fe2Pb3(PO4)2, CdS, and PbS precipitation in the solution/soil, iii) increases in the Ca2P and FeP contents in the radish rhizosphere, and iv) the promotion of bacterial community enrichment toward phosphorus-solubilizing and plant growth-promoting properties (Ramlibacter, Enterobacter, Bacillus, Gemmatimonas, and Lysinibacillusin) in the radish rhizosphere. These results provide bacterial resources and technical approaches to heavy metal pollution amelioration and efficient phosphorus fertilizer use in farmland.

In Situ Detection of Electrochemical Reaction Surface Area by Optical Weak Measurement.

The surface area is key to electrochemical systems, including those in electrocatalysis and energy storage. Studies have shown that the surface area of the electrocatalyst directly affects the electrochemical activity, adsorption performance, and stability of the electrocatalyst. This paper used an optical weak measurement (WM) method, which has little impact on the analyte, to measure the reaction surface area (RSA) that actually participated in the electrochemical reaction. Then compared the RSA obtained by the WM with the total surface area (TSA) obtained by the standard Brunauer-Emmett-Teller (BET) measurement and the active surface area (ASA) obtained by the electrochemical double-layer capacitance (EDLC) method. Their growth trend was consistent, indicating the reliability of the WM method. Compared with the above two methods, the WM method is an in situ detection and easy to operate experimentally, which can help researchers to consider the effect of surface area on electrocatalyst performance more rationally.

Association of Inhaled Corticosteroids With All-Cause Mortality Risk in Patients With COPD: A Meta-analysis of 60 Randomized Controlled Trials.

BACKGROUND: Inhaled corticosteroids (ICSs) have been used widely in the maintenance therapy of COPD. However, whether inhaled therapy containing ICSs can reduce the all-cause mortality risk and the possible benefited patient subgroups is unclear. RESEARCH QUESTION: Does inhaled therapy containing ICSs reduce the all-cause mortality risk in patients with COPD compared with other inhaled therapies not containing ICSs? STUDY DESIGN AND METHODS: We searched PubMed, Cochrane Library, Embase, and ClinicalTrials.gov for relevant randomized clinical trials (RCTs). Pooled results were calculated using Peto ORs with corresponding 95% CIs. RESULTS: Sixty RCTs enrolling 103,034 patients were analyzed. Inhaled therapy containing ICSs (Peto OR, 0.90; 95% CI, 0.84-0.97), especially triple therapy (Peto OR, 0.73; 95% CI, 0.59-0.91), was associated with a reduction in the all-cause mortality risk among patients with COPD when compared with inhaled therapy without ICSs. Subgroup analyses revealed that treatment duration of > 6 months (Peto OR, 0.90; 95% CI, 0.83-0.97), medium-dose ICSs (Peto OR, 0.71; 95% CI, 0.56-0.91), low-dose ICSs (Peto OR, 0.88; 95% CI, 0.79-0.97), and budesonide (Peto OR, 0.75; 95% CI, 0.59-0.94) were involved in this association. The predictors of this association included eosinophil counts of ≥ 200/µL or percentage of ≥ 2%, documented history of ≥ 2 moderate and severe exacerbations in the previous year, Global Initiative for Chronic Obstructive Lung Disease stages III or IV, age younger than 65 years, and BMI of ≥ 25 kg/m2, among which eosinophil counts of ≥ 200/µL (Peto OR, 0.58; 95% CI, 0.36-0.95) were the strongest predictor. INTERPRETATION: Inhaled therapy containing ICSs, especially triple therapy, of longer than 6 months was associated with a reduction in the all-cause mortality risk in patients with COPD. The predictors of this association included medication factors and patient characteristics, among which eosinophil counts of ≥ 200/µL were the strongest predictor. TRIAL REGISTRY: PROSPERO; No.: CRD42022304725; URL: https://www.crd.york.ac.uk/prospero/.

Expression site agnostic histopathology image segmentation framework by self supervised domain adaption.

MOTIVATION: With the sites of antigen expression different, the segmentation of immunohistochemical (IHC) histopathology images is challenging, due to the visual variances. With H&E images highlighting the tissue structure and cell distribution more broadly, transferring more salient features from H&E images can achieve considerable performance on expression site agnostic IHC images segmentation. METHODS: To the best of our knowledge, this is the first work that focuses on domain adaptive segmentation for different expression sites. We propose an expression site agnostic domain adaptive histopathology image semantic segmentation framework (ESASeg). In ESASeg, multi-level feature alignment encodes expression site invariance by learning generic representations of global and multi-scale local features. Moreover, self-supervision enhances domain adaptation to perceive high-level semantics by predicting pseudo-labels. RESULTS: We construct a dataset with three IHCs (Her2 with membrane stained, Ki67 with nucleus stained, GPC3 with cytoplasm stained) with different expression sites from two diseases (breast and liver cancer). Intensive experiments on tumor region segmentation illustrate that ESASeg performs best across all metrics, and the implementation of each module proves to achieve impressive improvements. CONCLUSION: The performance of ESASeg on the tumor region segmentation demonstrates the efficiency of the proposed framework, which provides a novel solution on expression site agnostic IHC related tasks. Moreover, the proposed domain adaption and self-supervision module can improve feature domain adaption and extraction without labels. In addition, ESASeg lays the foundation to perform joint analysis and information interaction for IHCs with different expression sites.

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

BACKGROUND: Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy. METHODS: We designed a regional multiple-instance learning algorithm to predict the OBMC based on hematoxylin-eosin (H&E) staining slides alone. We collected 1041 cases from eight different hospitals and labeled 26,431 regions of interest to train the model. The performance of the model was assessed by ten-fold cross validation and external validation. Under the guidance of top3 predictions, we conducted an IHC test on 175 cases of unknown origins to compare the consistency of the results predicted by the model and indicated by the IHC markers. We also applied the model to identify whether there was tumor or not in a region, as well as distinguishing squamous cell carcinoma, adenocarcinoma, and neuroendocrine tumor. FINDINGS: In the within-cohort, our model achieved a top1-accuracy of 91.35% and a top3-accuracy of 97.75%. In the external cohort, our model displayed a good generalizability with a top3-accuracy of 97.44%. The top1 consistency between the results of the model and the immunohistochemistry markers was 83.90% and the top3 consistency was 94.33%. The model obtained an accuracy of 98.98% to identify whether there was tumor or not and an accuracy of 93.85% to differentiate three types of cancers. INTERPRETATION: Our model demonstrated good performance to predict the OBMC from routine histology and had great potential for assisting pathologists with determining the OBMC accurately. FUNDING: National Science Foundation of China (61875102 and 61975089), Natural Science Foundation of Guangdong province (2021A15-15012379 and 2022A1515 012550), Science and Technology Research Program of Shenzhen City (JCYJ20200109110606054 and WDZC20200821141349001), and Tsinghua University Spring Breeze Fund (2020Z99CFZ023).