Phosphoribosylpyrophosphate synthetase as a metabolic valve advances Methylobacterium/Methylorubrum phyllosphere colonization and plant growth.

The proficiency of phyllosphere microbiomes in efficiently utilizing plant-provided nutrients is pivotal for their successful colonization of plants. The methylotrophic capabilities of Methylobacterium/Methylorubrum play a crucial role in this process. However, the precise mechanisms facilitating efficient colonization remain elusive. In the present study, we investigate the significance of methanol assimilation in shaping the success of mutualistic relationships between methylotrophs and plants. A set of strains originating from Methylorubrum extorquens AM1 are subjected to evolutionary pressures to thrive under low methanol conditions. A mutation in the phosphoribosylpyrophosphate synthetase gene is identified, which converts it into a metabolic valve. This valve redirects limited C1-carbon resources towards the synthesis of biomass by up-regulating a non-essential phosphoketolase pathway. These newly acquired bacterial traits demonstrate superior colonization capabilities, even at low abundance, leading to increased growth of inoculated plants. This function is prevalent in Methylobacterium/Methylorubrum strains. In summary, our findings offer insights that could guide the selection of Methylobacterium/Methylorubrum strains for advantageous agricultural applications.

Spontaneous aggregation-enhanced electrochemiluminescence via galvanic strategy.

Researchers unremittingly strive to develop innovative luminophores to enhance intrinsic electrochemiluminescence (ECL) performance. However, the potential to harness facile strategies, such as manipulating the physical properties of luminophores while retaining functional chemical properties to fabricate cost-effective ECL complexes, remains underexplored. Herein, we reported a novel and efficient one-step galvanic technique to actualize aggregation-enhanced ECL (AEECL) of ruthenium complexes. It marked the first instance of the galvanic process being employed to synthesize aggregate luminophores through electrostatic attraction. The ECL intensity and efficiency of the prepared ruthenium complexes with AEECL properties surpassed traditional ruthenium complexes by 8.9 and 13.6 times, respectively, outperforming most reported luminophores. Remarkably, the target luminophore exhibited high stability across varied scan rates and temperatures. Furthermore, a binder-free and carbon paper-based AEECL analytical device for lidocaine detection was fabricated, achieving a satisfactory detection limit (0.34 nM) and selectivity. The convenient modulation strategy of aggregate structure, along with the transformative leap from insufficient ECL to AEECL, bring forth a new revenue in aggregate science. This research also promises a universally applicable and versatile protocol for future biological analysis and bioimaging applications.

Construction of m7G RNA modification-related prognostic model and prediction of immune therapy response in hepatocellular carcinoma.

Background: RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed. Methods: RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database. Results: We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy. Conclusions: Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score model was shown to have a strong link to OS in patients with HCC.

DCFNet: Infrared and Visible Image Fusion Network Based on Discrete Wavelet Transform and Convolutional Neural Network.

Aiming to address the issues of missing detailed information, the blurring of significant target information, and poor visual effects in current image fusion algorithms, this paper proposes an infrared and visible-light image fusion algorithm based on discrete wavelet transform and convolutional neural networks. Our backbone network is an autoencoder. A DWT layer is embedded in the encoder to optimize frequency-domain feature extraction and prevent information loss, and a bottleneck residual block and a coordinate attention mechanism are introduced to enhance the ability to capture and characterize the low- and high-frequency feature information; an IDWT layer is embedded in the decoder to achieve the feature reconstruction of the fused frequencies; the fusion strategy adopts the l1-norm fusion strategy to integrate the encoder's output frequency mapping features; a weighted loss containing pixel loss, gradient loss, and structural loss is constructed for optimizing network training. DWT decomposes the image into sub-bands at different scales, including low-frequency sub-bands and high-frequency sub-bands. The low-frequency sub-bands contain the structural information of the image, which corresponds to the important target information, while the high-frequency sub-bands contain the detail information, such as edge and texture information. Through IDWT, the low-frequency sub-bands that contain important target information are synthesized with the high-frequency sub-bands that enhance the details, ensuring that the important target information and texture details are clearly visible in the reconstructed image. The whole process is able to reconstruct the information of different frequency sub-bands back into the image non-destructively, so that the fused image appears natural and harmonious visually. Experimental results on public datasets show that the fusion algorithm performs well according to both subjective and objective evaluation criteria and that the fused image is clearer and contains more scene information, which verifies the effectiveness of the algorithm, and the results of the generalization experiments also show that our network has good generalization ability.

Dorsoventral heterogeneity of synaptic connectivity in hippocampal CA3 pyramidal neurons.

The hippocampal CA3 region plays an important role in learning and memory. CA3 pyramidal neurons (PNs) receive two prominent excitatory inputs - mossy fibers (MFs) from dentate gyrus (DG) and recurrent collaterals (RCs) from CA3 PNs - that play opposing roles in pattern separation and pattern completion, respectively. Although the dorsoventral heterogeneity of the hippocampal anatomy, physiology, and behavior has been well established, nothing is known about the dorsoventral heterogeneity of synaptic connectivity in CA3 PNs. In this study, we performed Timm's sulfide silver staining, dendritic and spine morphological analyses, and ex vivo electrophysiology in mice of both sexes to investigate the heterogeneity of MF and RC pathways along the CA3 dorsoventral axis. Our morphological analyses demonstrate that ventral CA3 (vCA3) PNs possess greater dendritic lengths and more complex dendritic arborization, compared to dorsal CA3 (dCA3) PNs. Moreover, using ChannelRhodopsin2 (ChR2)-assisted patch-clamp recording, we find that the ratio of the RC-to-MF excitatory drive onto CA3 PNs increases substantially from dCA3 to vCA3, with vCA3 PNs receiving significantly weaker MFs, but stronger RCs, excitation than dCA3 PNs. Given the distinct roles of MF versus RC inputs in pattern separation versus completion, our findings of the significant dorsoventral variations of MF and RC excitation in CA3 PNs may have important functional implications for the contribution of CA3 circuit to the dorsoventral difference in hippocampal function.Significance Statement The hippocampal CA3 region is essential for memory formation. CA3 pyramidal neurons receive recurrent collateral (RC) from CA3 and mossy fiber (MF) from dentate gyrus (DG), which have opposite functions in pattern completion (memory generalization) and separation (discrimination), respectively. Although hippocampal dorsoventral heterogeneity is well established, dorsoventral heterogeneity of CA3 connectivity is unknow. Here, we demonstrate that the ratio of RC-to-MF excitation increases substantially from dCA3 to vCA3, with vCA3 receiving significantly weaker MF, but stronger RC, excitation than dCA3. Thus, our study reveals a novel CA3-based synaptic mechanism that may offer the computational advantage for the ventral hippocampus to be more strongly involved in behaviors that require less precision but more generalization than the dorsal hippocampus.

One-pot electrochemical detection of foodborne pathogen based on in situ nucleic acid amplification and wash-free assay.

A signal amplification electrochemical biosensor chip was developed to integrate loop-mediated isothermal amplification (LAMP) based on in situ nucleic acid amplification and methyl blue (MB) serving as the hybridization redox indicator for sensitive and selective foodborne pathogen detection without a washing step. The electrochemical biosensor chip was designed by a screen-printed carbon electrode modified with gold nanoparticles (Au NPs) and covered with polydimethylsiloxane membrane to form a microcell. The primers of the target were immobilized on the Au NPs by covalent attachment for in situ amplification. The electroactive MB was used as the electrochemical signal reporter and embedded into the double-stranded DNA (dsDNA) amplicons generated by LAMP. Differential pulse voltammetry was introduced to survey the dsDNA hybridization with MB, which differentiates the specifically electrode-unbound and -bound labels without a washing step. Pyrene as the back-filling agent can further improve response signaling by reducing non-specific adsorption. This method is operationally simple, specific, and effective. The biosensor showed a detection linear range of 102-107 CFU mL-1 with the limit of detection of 17.7 CFU mL-1 within 40 min. This method showed promise for on-site testing of foodborne pathogens and could be integrated into an all-in-one device.

A colorimetric/electrochemical microfluidic biosensor using target-triggered DNA hydrogels for organophosphorus detection.

Organophosphorus compounds are widely distributed and highly toxic to the environment and living organisms. The current detection of organophosphorus compounds is based on a single-mode method, which makes it challenging to achieve good portability, accuracy, and sensitivity simultaneously. This study designed a multifunctional microfluidic chip to develop a dual-mode biosensor employing a DNA hydrogel as a carrier and aptamers as recognition probes for the colorimetric/electrochemical detection of malathion, an organophosphorus compound. The biosensor balanced portability and stability by combining a microfluidic chip and target-triggered DNA hydrogel-sensing technologies. Moreover, the biosensor based on target-triggered DNA hydrogel modified microfluidic developed in this study exhibited a dual-mode response to malathion, providing both colorimetric and electrochemical signals. The colorimetric mode enables rapid visualization and qualitative detection and, when combined with a smartphone, allows on-site quantitative analysis with a detection limit of 56 nM. The electrochemical mode offers a broad linear range (0.01-3000 µM) and high sensitivity (a limit of detection of 5 nM). The two modes could validate each other and improve the accuracy of detection. The colorimetric/electrochemical dual-mode biosensor based on target-triggered DNA hydrogel modified microfluidic chip offers a portable, simple, accurate, and sensitive strategy for detecting harmful environmental and food substances.

Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning.

Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests that plants have the potential to be used as biosensors for monitoring environmental information. However, there are current challenges in linking environmental information with plant electrical signals, especially in collecting and classifying the corresponding electrical signals under soil moisture gradients. This study documented the electrical signals of clivia under different soil moisture gradients and created a dataset for classifying electrical signals. Subsequently, we proposed a lightweight convolutional neural network (CNN) model (PlantNet) for classifying the electrical signal dataset. Compared to traditional CNN models, our model achieved optimal classification performance with the lowest computational resource consumption. The model achieved an accuracy of 99.26%, precision of 99.31%, recall of 92.26%, F1-score of 99.21%, with 0.17M parameters, a size of 7.17MB, and 14.66M FLOPs. Therefore, this research provides scientific evidence for the future development of plants as biosensors for detecting soil moisture, and offers insight into developing plants as biosensors for detecting signals such as ozone, PM2.5, Volatile Organic Compounds(VOCs), and more. These studies are expected to drive the development of environmental monitoring technology and provide new pathways for better understanding the interaction between plants and the environment.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Selenium nanoparticles enhance mucosal immunity against Mycobacterium bovis infection.

Selenium nanoparticles (SeNPs) enhance the immune response as adjuvants, increasing the efficacy of viral vaccines, including those for COVID-19. However, the efficiency of mucosal SeNPs in boosting vaccine-induced protective immunity against tuberculosis remains unclear. Therefore, this study aims to investigate whether the combination of SeNPs with the AH antigen (Ag85A-HspX) can boost respiratory mucosal immunity and thereby enhance the protective effects against tuberculosis. We synthesized SeNPs and assessed their impact on the immune response and protection against Mycobacterium bovis (M. bovis) as a mucosal adjuvant in mice, administered intranasally at a dose of 20 µg. SeNPs outperformed polyinosinic-polycytidylic acid (Poly IC) in stimulating the maturation of bone marrow-derived dendritic cells (BMDCs), which enhanced antigen presentation. SeNPs significantly activated and proliferated tissue-resident memory T cells (TRMs) and effector CD4+ T cells in the lungs. The vaccines elicited specific antibody responses in the respiratory tract and stimulated systemic Th1 and Th17 immune responses. Immunization with AH and SeNPs led to higher levels of mucosal secretory IgA in bronchoalveolar lavage fluid (BALF) and secretory IL-17 in splenocytes. Moreover, SeNPs immunized mice showed reduced M. bovis infection loads and inflammatory lesions in the lungs post-challenge. Notably, immunization with AH and SeNPs significantly reduced bacterial load in the lungs, achieving the lowest levels compared to all other tested groups. This study calls for pre-clinical investigation of AHB-SeNPs as an anti-bovine tuberculosis vaccine and for exploring its human vaccine potential, which is anticipated to aid in the development of innovative vaccines or adjuvants.

Current Status of Poultry Recombinant Virus Vector Vaccine Development.

Inactivated and live attenuated vaccines are the mainstays of preventing viral poultry diseases. However, the development of recombinant DNA technology in recent years has enabled the generation of recombinant virus vector vaccines, which have the advantages of preventing multiple diseases simultaneously and simplifying the vaccination schedule. More importantly, some can induce a protective immune response in the presence of maternal antibodies and offer long-term immune protection. These advantages compensate for the shortcomings of traditional vaccines. This review describes the construction and characterization of primarily poultry vaccine vectors, including fowl poxvirus (FPV), fowl adenovirus (FAdV), Newcastle disease virus (NDV), Marek's disease virus (MDV), and herpesvirus of turkey (HVT). In addition, the pathogens targeted and the immunoprotective effect of different poultry recombinant virus vector vaccines are also presented. Finally, this review discusses the challenges in developing vector vaccines and proposes strategies for improving immune efficacy.

Polymeric microlens array formed on a discontinuous wetting surface using a self-assembly technique.

In this paper, we demonstrate a facile way to prepare polymeric microlens arrays (MLAs) based on a discontinuous wetting surface using a self-assembly technique. A patterned hydrophobic-octadecyltrichlorosilane (OTS) surface was prepared by U V/O 3 irradiation through a shadow mask. The area exposed to U V/O 3 irradiation turned highly hydrophilic, whereas the area protected by the mask remained highly hydrophobic, generating the patterned OTS surface. The surface energy of the OTS/glass surface changed from 23 to 72.8 mN/m after 17 min of U V/O 3 treatment. The scribing of the optical glue-NOA 81 onto the microhole array enabled one to obtain the MLAs due to the generation of the NOA 81 droplet array via the surface tension. After UV light curing, the cured NOA 81 droplet array with uniform dimensions within a large area exhibited excellent MLA characteristics. Moreover, the method developed in this study is simple in operation, low-cost, and requires neither a clean room nor expensive equipment.

Multiple Chirality Switching of a Dye-Grafted Helical Polymer Film Driven by Acid & Base.

A stimuli-responsive multiple chirality switching material, which can regulate opposed chiral absorption characteristics, has great application value in the fields of optical modulation, information storage and encryption, etc. However, due to the rareness of effective functional systems and the complexity of material structures, developing this type of material remains an insurmountable challenge. Herein, a smart polymer film with multiple chirality inversion properties was fabricated efficiently based on a newlydesigned acid & base-sensitive dye-grafted helical polymer. Benefited from the cooperative effects of various weak interactions (hydrogen bonds, electrostatic interaction, etc.) under the aggregated state, this polymer film exhibited a promising acid & base-driven multiple chirality inversion property containing record switchable chiral states (up to five while the solution showed three-state switching) and good reversibility. The creative exploration of such a multiple chirality switching material can not only promote the application progress of current chiroptical regulation technologybut also provide a significant guidance for the design and synthesis of future smart chiroptical switching materials and devices.

Seasonal and Genetic Characteristics of Human Metapneumovirus Circulating - Henan Province, China, 2017-2023.

Introduction: This study examines the seasonal and genetic characteristics of human metapneumovirus (HMPV) in Henan from 2017 to 2023. Methods: Samples from patients with acute respiratory infection (ARI) testing positive for HMPV were subjected to real-time reverse transcription polymerase chain reaction The G gene was amplified and sequenced from these samples for epidemiological and phylogenetic analysis. Results: We enrolled 2,707 ARI patients from October 2017 to March 2023, finding an HMPV positivity rate of 6.17% (167/2,707). Children under five exhibited the highest infection rate at 7.78% (138/1,774). The 2018 and 2019 HMPV outbreaks predominantly occurred in spring (March to May), with peak positivity rates of 31.11% in May 2018 and 19.57% in May 2019. A notable increase occurred in November 2020, when positivity reached a historic high of 42.11%, continuing until January 2021. From February 2021 through March 2023, no significant seasonal peaks were observed, with rates ranging from 0% to 8.70%. Out of 81 G gene sequences analyzed, 46.91% (38/81) were identified as subtype A (A2c: 45.67%, 37/81; A2b: 1.23%, 1/81) and 53.09% (43/81) as subtype B (B1: 9.88%, 8/81; B2: 43.21%, 35/81). Notably, an AAABBA switch pattern was observed in HMPV subtypes. The dominant strains were A2c111nt-dup in subtype A and B2 in subtype B. Conclusions: Six years of surveillance in Henan Province has detailed the seasonal and genetic dynamics of HMPV, contributing valuable insights for the control and prevention of HMPV infections in China. These findings support the development of targeted HMPV vaccines and immunization strategies.

The Glucose-Succinate Pathway: A Crucial Anaerobic Metabolic Pathway in the Scallop Chlamys farreri Experiencing Heat Stress.

Recently, the increase in marine temperatures has become an important global marine environmental issue. The ability of energy supply in marine animals plays a crucial role in avoiding the stress of elevated temperatures. The investigation into anaerobic metabolism, an essential mechanism for regulating energy provision under heat stress, is limited in mollusks. In this study, key enzymes of four anaerobic metabolic pathways were identified in the genome of scallop Chlamys farreri, respectively including five opine dehydrogenases (CfOpDHs), two aspartate aminotransferases (CfASTs) divided into cytoplasmic (CfAST1) and mitochondrial subtype (CfAST2), and two phosphoenolpyruvate carboxykinases (CfPEPCKs) divided into a primitive type (CfPEPCK2) and a cytoplasmic subtype (CfPEPCK1). It was surprising that lactate dehydrogenase (LDH), a key enzyme in the anaerobic metabolism of the glucose-lactate pathway in vertebrates, was absent in the genome of scallops. Phylogenetic analysis verified that CfOpDHs clustered according to the phylogenetic relationships of the organisms rather than substrate specificity. Furthermore, CfOpDHs, CfASTs, and CfPEPCKs displayed distinct expression patterns throughout the developmental process and showed a prominent expression in muscle, foot, kidney, male gonad, and ganglia tissues. Notably, CfASTs displayed the highest level of expression among these genes during the developmental process and in adult tissues. Under heat stress, the expression of CfASTs exhibited a general downregulation trend in the six tissues examined. The expression of CfOpDHs also displayed a downregulation trend in most tissues, except CfOpDH1/3 in striated muscle showing significant up-regulation at some time points. Remarkably, CfPEPCK1 was significantly upregulated in all six tested tissues at almost all time points. Therefore, we speculated that the glucose-succinate pathway, catalyzed by CfPEPCK1, serves as the primary anaerobic metabolic pathway in mollusks experiencing heat stress, with CfOpDH3 catalyzing the glucose-opine pathway in striated muscle as supplementary. Additionally, the high and stable expression level of CfASTs is crucial for the maintenance of the essential functions of aspartate aminotransferase (AST). This study provides a comprehensive and systematic analysis of the key enzymes involved in anaerobic metabolism pathways, which holds significant importance in understanding the mechanism of energy supply in mollusks.

Activating Macrophage Continual Efferocytosis via Microenvironment Biomimetic Short Fibers for Reversing Inflammation in Bone Repair.

Efferocytosis-mediated inflammatory reversal plays a crucial role in bone repairing process. However, in refractory bone defects, the macrophage continual efferocytosis may be suppressed due to the disrupted microenvironment homeostasis, particularly the loss of apoptotic signals and overactivation of intracellular oxidative stress. In this study, a polydopamine-coated short fiber matrix containing biomimetic "apoptotic signals" to reconstruct the microenvironment and reactivate macrophage continual efferocytosis for inflammatory reversal and bone defect repair is presented. The "apoptotic signals" (AM/CeO2) are prepared using CeO2 nanoenzymes with apoptotic neutrophil membrane coating for macrophage recognition and oxidative stress regulation. Additionally, a short fiber "biomimetic matrix" is utilized for loading AM/CeO2 signals via abundant adhesion sites involving π-π stacking and hydrogen bonding interactions. Ultimately, the implantable apoptosis-mimetic nanoenzyme/short-fiber matrixes (PFS@AM/CeO2), integrating apoptotic signals and biomimetic matrixes, are constructed to facilitate inflammatory reversal and reestablish the pro-efferocytosis microenvironment. In vitro and in vivo data indicate that the microenvironment biomimetic short fibers can activate macrophage continual efferocytosis, leading to the suppression of overactivated inflammation. The enhanced repair of rat femoral defect further demonstrates the osteogenic potential of the pro-efferocytosis strategy. It is believed that the regulation of macrophage efferocytosis through microenvironment biomimetic materials can provide a new perspective for tissue repair.

Efficient photocatalytic hydrogen peroxide production over S-scheme In2S3/molten salt modified C3N5 heterojunction.

Graphitic phase carbon nitride (g-C3N5), as a novel n-type metal-free material, is employed as a visible light-receptive catalyst because of its narrow band gap and abundant nitrogen. To overcome the low carrier mobility efficiency of g-C3N5, its modification by K ions was adopted. In addition, In2S3 was selected to couple with modified g-C3N5 to overcome the recombination of photogenerated e-/h+. As a novel photocatalytic material, it was proven to possess a high visible light absorption capacity and a strong H2O2 production ability (up to 3.89 mmol⋅L-1 in 2 h). Moreover, a S-scheme heterojunction structure was successfully constructed between the two materials, which was tested and confirmed to be successful in raising the photogenerated e-/h+ separation efficiency. Ultimately, the primary processes of photocatalytic H2O2 production were summarized by superoxide radical and rotating disc electron measurements. This research provides a fresh perspective for the synthesis of C3N5-based S-scheme heterojunction photocatalysts for producing H2O2.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities.

Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .

Circ_0003945: an emerging biomarker and therapeutic target for human diseases.

Due to the rapid development of RNA sequencing techniques, a circular non-coding RNA (ncRNA) known as circular RNAs (circRNAs) has gradually come into focus. As a distinguished member of the circRNA family, circ_0003945 has garnered attention for its aberrant expression and biochemical functions in human diseases. Subsequent studies have revealed that circ_0003945 could regulate tumor cells proliferation, migration, invasion, apoptosis, autophagy, angiogenesis, drug resistance, and radio resistance through the molecular mechanism of competitive endogenous RNA (ceRNA) during tumorigenesis. The expression of circ_0003945 is frequently associated with some clinical parameters and implies a poorer prognosis in the majority of cancers. In non-malignant conditions, circ_0003945 also holds considerable importance in diseases pathogenesis. This review aims to recapitulate molecular mechanism of circ_0003945 and elucidates its potential as a diagnostic and therapeutic target in neoplasms and other diseases.