Exosomes Derived from Hypertrophic Scar Fibroblasts Suppress Melanogenesis in Normal Human Epidermal Melanocytes.

Post-burn hypertrophic scars often exhibit abnormal pigmentation. Exosomes play important roles in maintaining normal physiological homeostasis and in the pathological development of diseases. This study investigated the effects of the exosomes derived from hypertrophic scar fibroblasts (HTSFs) on melanocytes, which are pigment-producing cells. Normal fibroblasts (NFs) and HTSFs were isolated and cultured from normal skin and hypertrophic scar (HTS) tissue. Both the NF- and HTSF-exosomes were isolated from a cell culture medium and purified using a column-based technique. The normal human epidermal melanocytes were treated with both exosomes at a concentration of 100 µg/mL at different times. The cell proliferation, melanin content in the medium, apoptotic factors, transcription factors, melanin synthesis enzymes, signaling, signal transduction pathways, and activators of transcription factors (STAT) 1, 3, 5, and 6 were investigated. Compared with the Dulbecco's phosphate-buffered saline (DPBS)-treated controls and NF-exosomes, the HTSF-exosomes decreased the melanocyte proliferation and melanin secretion. The molecular patterns of apoptosis, proliferation, melanin synthesis, Smad and non-Smad signaling, and STATs were altered by the treatment with the HTSF-exosomes. No significant differences were observed between the DPBS-treated control and NF-exosome-treated cells. HTSF-derived exosomes may play a role in the pathological epidermal hypopigmentation observed in patients with HTS.

SIRT1 inhibits apoptosis of human lens epithelial cells through suppressing endoplasmic reticulum stress in vitro and in vivo.

AIM: To explore the effect of silent information regulator factor 2-related enzyme 1 (SIRT1) on modulating apoptosis of human lens epithelial cells (HLECs) and alleviating lens opacification of rats through suppressing endoplasmic reticulum (ER) stress. METHODS: HLECs (SRA01/04) were treated with varying concentrations of tunicamycin (TM) for 24h, and the expression of SIRT1 and C/EBP homologous protein (CHOP) was assessed using real-time quantitative polymerase chain reaction (RT-PCR), Western blotting, and immunofluorescence. Cell morphology and proliferation was evaluated using an inverted microscope and cell counting kit-8 (CCK-8) assay, respectively. In the SRA01/04 cell apoptosis model, which underwent siRNA transfection for SIRT1 knockdown and SRT1720 treatment for its activation, the expression levels of SIRT1, CHOP, glucose regulated protein 78 (GRP78), and activating transcription factor 4 (ATF4) were examined. The potential reversal of SIRT1 knockdown effects by 4-phenyl butyric acid (4-PBA; an ER stress inhibitor) was investigated. In vivo, age-related cataract (ARC) rat models were induced by sodium selenite injection, and the protective role of SIRT1, activated by SRT1720 intraperitoneal injections, was evaluated through morphology observation, hematoxylin and eosin (H&E) staining, Western blotting, and RT-PCR. RESULTS: SIRT1 expression was downregulated in TM-induced SRA01/04 cells. Besides, in SRA01/04 cells, both cell apoptosis and CHOP expression increased with the rising doses of TM. ER stress was stimulated by TM, as evidenced by the increased GRP78 and ATF4 in the SRA01/04 cell apoptosis model. Inhibition of SIRT1 by siRNA knockdown increased ER stress activation, whereas SRT1720 treatment had opposite results. 4-PBA partly reverse the adverse effect of SIRT1 knockdown on apoptosis. In vivo, SRT1720 attenuated the lens opacification and weakened the ER stress activation in ARC rat models. CONCLUSION: SIRT1 plays a protective role against TM-induced apoptosis in HLECs and slows the progression of cataract in rats by inhibiting ER stress. These findings suggest a novel strategy for cataract treatment focused on targeting ER stress, highlighting the therapeutic potential of SIRT1 modulation in ARC development.

The nano-artificial periosteum made of PCL/MgO/AS-IV enhances MC3T3-E1 cell osteogenic differentiation and promotes bone defect repair via the EphB4/EphrinB2 signaling pathway.

Bone regeneration plays a pivotal role in periodontal tissue repair. With advancements in biotechnology materials, the utilization of nanotechnology offers a reliable platform for bone restoration in periodontitis. In this study, we successfully established a long-term bacterial infection model using Porphyromonas gingivalis (P. gingivalis) with MOI = 50. CCK-8 and ROS immunofluorescence results demonstrated that the combined effect of Mg2+ and AS-IV significantly enhanced cell proliferation and effectively suppressed the inflammatory response during bacterial infection. Alkaline phosphatase and alizarin red staining revealed that the synergistic action of Mg2+ and AS-IV notably promoted osteogenic differentiation of MC3T3-E1 cells under P. gingivalis-infected conditions. Considering the properties of these two biomaterials, we fabricated polycaprolactone (PCL) artificial periosteum loaded with MgO and AS-IV using an electrostatic spinning technique. The findings indicated that PCL/MgO/AS-IV artificial periosteum exhibited excellent biocompatibility and hydrophilicity, thereby substantially enhancing cellular adhesion to its surface as well as augmenting cellular value-added rate. Moreover, efficient drug release from the PCL/MgO/AS-IV artificial bone membrane conferred remarkable antimicrobial activity along with in vitro osteogenic potentiality. The in vivo experiments conducted on animals further substantiated the exceptional properties exhibited by PCL/MgO/AS-IV artificial periosteum in bone defect repair. Additionally, it was observed that PCL/MgO/AS-IV artificial periosteum could modulate EphB4-EphrinB2 signaling to enhance osteogenic differentiation under P.gingivalis-infected conditions.This exciting outcome suggests that PCL/MgO/AS-IV artificial periosteum holds great promise as a biomaterial for treating periodontal bone loss.

Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.

DNA nanoassembly based turn-on amplification probe for sensitive colorimetric CRISPR/Cas12a-mediated detection of pathogen DNA.

Clustered regularly interspaced short palindromic repeat (CRISPR) system has been explored as an efficient tool for nucleic acid diagnostics. However, it normally needs instrumentation or produces turn-off signals. Herein, a bulged Y-shape DNA (Y-DNA) nanoassembly was designed and synthesized as a novel turn-on probe. A CRISPR/Cas12a and Y-DNA probe mediated colorimetric assay (named as CYMCOA) strategy was developed for visual detection of pathogen DNA. Upon activating Cas12a with pathogen DNA, the Y-DNA bulge is catalytically trans-cleaved, releasing the G-quadruplex sequence embedded in the Y-DNA nanoassembly as a peroxidase-like DNAzyme. Visible signals with chromogen substrates are thus produced. The CYMCOA strategy was combined with recombinase polymerase amplification (RPA), an isothermal amplification technique, in detecting Helicobacter pylori (Hp) bacteria and SARS-CoV-2 N plasmids as two model pathogens. The bioassay has very excellent detection sensitivity and specificity, owing to the triple cascade amplification reactions and the very low mismatch tolerance. The lower limit of detection values were 0.16 cfu⋅mL-1, 1.5 copies⋅µL-1, and 0.17 copies⋅µL-1 for Hp bacteria, Hp plasmids, and SARS-CoV-2 N plasmids respectively. The detection is fast and accurate. The colorimetric bioassay strategy provides to be a simple, accurate, fast and instrumentation-free platform for nucleic acids detections in various settings, including crude and emergent situations.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

Optimization and mechanisms of proteolytic enzyme immobilization onto large-pore mesoporous silica nanoparticles: Enhanced tumor penetration.

Immobilization of proteolytic enzymes onto nanocarriers is effective to improve drug diffusion in tumors through degrading the dense extracellular matrix (ECM). Herein, immobilization and release behaviors of hyaluronidase, bromelain, and collagenase (Coll) on mesoporous silica nanoparticles (MSNs) were explored. A series of cationic MSNs (CMSNs) with large and adjustable pore sizes were synthesized, and investigated together with two anionic MSNs of different pore sizes. CMSNs4.0 exhibited the highest enzyme loading capacity for hyaluronidase and bromelain, and CMSNs4.5 was the best for Coll. High electrostatic interaction, matched pore size, and large pore volume and surface area favor the immobilization. Changes of the enzyme conformations and surface charges with pH, existence of a space around the immobilized enzymes, and the depth of the pore structures, affect the release ratio and tunability. The optimal CMSNs-enzyme complexes exhibited deep and homogeneous penetration into pancreatic tumors, a tumor model with the densest ECM, with CMSNs4.5-Coll as the best. Upon loading with doxorubicin (DOX), the CMSNs-enzyme complexes induced high anti-tumor efficiencies. Conceivably, the DOX/CMSNs4.5-NH2-Coll nanodrug exhibited the most effective tumor therapy, with a tumor growth inhibition ratio of 86.1 %. The study provides excellent nanocarrier-enzyme complexes, and offers instructive theories for enhanced tumor penetration and therapy.

Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.

Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.

New α-Glucosidase Inhibitors from the Whole Plant of Hypericum beanii Based on Ligand Fishing and Molecular Networking Analysis.

In this work, a new rapid and targeted method for screening α-glucosidase inhibitors from Hypericum beanii was developed and verified. Ten new polycyclic polyprenylated acylphloroglucinols (PPAPs), hyperlagarol A-J (1-10), and nine known PPAPs (11-19) were obtained from H. beanii. Their structures were identified by using comprehensive analyses involving mass spectrometry, ultraviolet spectroscopy, infrared spectroscopy, nuclear magnetic resonance spectroscopy, and electron capture dissociation calculations. 1 and 2 are two new rare 2,3-seco-spirocyclic PPAPs, 3 and 4 are two novel 12,13-seco-spirocyclic PPAPs, 5 and 6 are two novel spirocyclic PPAPs, 7 and 8 are two new unusual spirocyclic PPAPs with complex bridged ring systems, and 9 and 10 are two novel nonspirocyclic PPAPs. α-GC inhibitory activities of all isolated compounds were tested. Most of them displayed inhibitory activities against α-glucosidase, with the IC50 values ranging from 6.85 ± 0.65 to 112.5 ± 9.03 µM. Moreover, the inhibitory type and mechanism of the active compounds were further analyzed using kinetic studies and molecular docking.

Magnetic Anisotropy and Relaxation in Four-Coordinate Cobalt(II) Single-Ion Magnets with a [CoIIO4] Core.

A mononuclear four-coordinate Co(II) complex with a [CoIIO4] core, namely, PPN[Li(MeOH)4][Co(L)2] (1) (PPN = bis(phosphoranediyl)iminium; H2L = perfluoropinacol), has been studied by X-ray crystallography, magnetic characterization, and theoretical calculations. This complex presents a severely distorted coordination geometry. The O-Co-O bite angle is 83.42°/83.65°, and the dihedral twist angle between the O-Co-O chelate planes is 55.6°. The structural distortion results in a large easy-axis magnetic anisotropy with D = -104(1) cm-1 and a transverse component with |E| = +4(2) cm-1. Alternating current (ac) susceptibility measurements demonstrate that 1 exhibits slow relaxation of magnetization at zero static field. However, the frequency-dependent out-of-phase (χ"M) susceptibilities of 1 at 0 Oe do not show a characteristic maximum. Upon the application of a dc field or the dilution with a diamagnetic Zn matrix, the quantum tunneling of magnetization (QTM) process can be successfully suppressed. Notably, after dilution with the Zn matrix, the obtained sample exhibits a structure different from that of the pristine complex. In this altered sample, the asymmetric unit does not contain the Li(MeOH)4+ cation, resulting in an O-Co-O bite angle of 86.05° and a dihedral twist angle of 75.84°, thereby leading to an approximate D2d symmetry. Although such differences are not desirable for magnetic studies, this study still gives some insights. Theoretical calculations reveal that the D parameter is governed by the O-Co-O bite angle, in line with our previous report for other tetrahedral Co(II) complex with a [CoIIN4] core. On the other hand, the rhombic component is found to increase as the dihedral angle deviates from 90°. These findings provide valuable guidelines for fine-tuning the magnetic properties of Co(II) complexes.

Sestrin2 Protects Human Lens Epithelial Cells (HLECs) Against Apoptosis in Cataracts Formation: Interaction Between Endoplasmic Reticulum (ER) Stress and Oxidative Stress (OS) is Involved.

PURPOSE: To explore the correlation of endoplasmic reticulum stress (ERS) and oxidative stress (OS), and the protective effect of Sestrin2 (SESN2) on human lens epithelial cells (HLECs). METHODS: Tunicamycin (TM) was used to induce ERS in HLECs. 4-Phenylbutyric acid (4-PBA) was used to inhibit ERS. Eupatilin applied to HLECs as SESN2 agonist. SESN2 expression was knocked down via si-RNA in HLECs. The morphological changes of HLECs were observed by microscope. ER-tracker to evaluate ERS, ROS production assay to measure ROS, flow cytometry to calculate cell apoptosis rate. Immunofluorescence to observe Nrf2 translocation, and effects of TM or EUP on SESN2. Western blot and qPCR were used to evaluate the expression of GRP78, PERK, ATF4, CHOP, Nrf2, and SESN2 expression in HLECs with different treatment groups. RESULTS: ERS can elevate the expression of ROS and Nrf2 to induce OS. Upregulation of SESN2 was observed in ERS-mediate OS. Overexpression of SESN2 can reduce the overexpression of ERS-related protein GRP78, PERK, ATF4, proapoptotic protein CHOP, OS-related protein Nrf2, as well as ROS, and alleviate ERS injury at the same time. Whereas knockdown of SESN2 can upregulate the expression of GRP78, PERK, ATF4, CHOP, Nrf2, ROS, and deteriorate ERS damage. CONCLUSIONS: ERS can induce OS, they form a vicious cycle to induce apoptosis in HLECs, which may contribute to cataract formation. SESN2 could protect HLECs against the apoptosis by regulating the vicious cycle between ERS and OS.

A non-canonical visual cortical-entorhinal pathway contributes to spatial navigation.

Visual information is important for accurate spatial coding and memory-guided navigation. As a crucial area for spatial cognition, the medial entorhinal cortex (MEC) harbors diverse spatially tuned cells and functions as the major gateway relaying sensory inputs to the hippocampus containing place cells. However, how visual information enters the MEC has not been fully understood. Here, we identify a pathway originating in the secondary visual cortex (V2) and directly targeting MEC layer 5a (L5a). L5a neurons served as a network hub for visual processing in the MEC by routing visual inputs from multiple V2 areas to other local neurons and hippocampal CA1. Interrupting this pathway severely impaired visual stimulus-evoked neural activity in the MEC and performance of mice in navigation tasks. These observations reveal a visual cortical-entorhinal pathway highlighting the role of MEC L5a in sensory information transmission, a function typically attributed to MEC superficial layers before.

[Effects of Vermicomposting on Compost Quality and Heavy Metals：A Meta-analysis].

In order to comprehensively evaluate the effects of vermicomposting on compost quality and the conversion of heavy metals under different control conditions, 109 studies were reviewed. The effects of earthworm species, pre-compost time, ventilation methods, initial C/N, initial pH, and initial moisture of the raw materials on compost quality and the heavy metal toxicity were quantitatively discussed during the vermicomposting process through Meta-analysis. The results showed that the six subgroups of factors all showed obvious influences on the compost quality and heavy metal toxicity. After vermicomposting, the contents of NO3--N (116.2%), TN (29.1%), TP (31.2%), and TK (15.0%) were significantly increased, whereas NH4+-N (-14.8%) and C/N (-36.3%) were significantly decreased. Meanwhile, the total amount of Cu and Cr of the final compost and their bioavailability were significantly reduced. Considering the influences of grouping factors on compost quality and heavy metals, it is recommended to adjust the initial moisture of pile materials to 70%-80%, C/N to 30-85, and pH to 6-7 and to conduct pre-composting for 0-15 d; additionally, vermicomposting should be naturally placed when the composting is aimed at promoting the compost quality. If the main purpose is to weaken the perniciousness of heavy metals in the raw material, it is recommended to adjust the initial moisture of the material to 50%-60%, C/N to less than 30, and pH to 7-8; to conduct no pre-compost; regularly turn the piles; and use the earthworm Eudrilus eugeniae for vermicomposting.

Lipid-Lowering Meroterpenoids Penihemeroterpenoids A-F from Penicillium herquei GZU-31-6 via Targeting the AMPK/ACC/SREBP-1c Signaling Pathway.

Penihemeroterpenoids A-C, the first meroterpenoids with an unprecedented 6/5/6/5/5/6/5 heptacyclic ring system, together with precursors penihemeroterpenoids D-F, were co-isolated from the fungus Penicillium herquei GZU-31-6. Among them, penihemeroterpenoids C-F exhibited lipid-lowering effects comparable to those of the positive control simvastatin by the activation of the AMPK/ACC/SREBP-1c signaling pathway, downregulated the mRNA levels of lipid synthesis genes FAS and PNPLA3, and increased the level of mRNA expression of the lipid export gene MTTP.

Associations of Sedentary Behavior with Risks of Cardiovascular Disease Events among Chinese Adults.

AIMS: Evidence regarding the modification effects of age, sex, ethnicity, socioeconomic status, or weight status on the associations of sedentary behavior (SB) with cardiovascular diseases (CVDs) is limited. Moreover, the mechanisms for the associations also remain unclear. We aimed to investigate the possible influence of these factors on the associations of SB with CVD events and whether the associations are mediated by metabolic phenotypes. METHODS: This study included 42,619 participants aged 20-74 years, recruited from the Shanghai Suburban Adult Cohort and Biobank study. SB was assessed at baseline and integrated with health information systems to predict future CVD events. Cox proportional hazards models, interaction analyses, restricted cubic splines and causal mediation analyses were used for assessments. RESULTS: Compared to those with ＜3 h/d sedentary time, participants having SB ≥ 5 h/d had significantly higher risks of CVD (HR[95%CI]: 1.27[1.12-1.44]), coronary heart disease (CHD, 1.35[1.14-1.60]), and ischemic stroke (IS, 1.30[1.06-1.60]). The association of CHD was more pronounced in the retired individuals than their counterparts (1.45[1.20-1.76] versus 1.06[0.74-1.52], pinteraction=0.046). When SB was expressed as a continuous variable, a 1 h/d increment in SB was positively associated with risks of CVD (1.03[1.01-1.05]), CHD (1.04[1.01-1.07]), and IS (1.05[1.01-1.08]). High-density lipoprotein cholesterol (HDL-C, proportion mediated: 12.54%, 12.23%, and 11.36%, all p＜0.001), followed by triglyceride (TG, 5.28%, 4.77%, and 4.86%, all p＜0.01) and serum uric acid (SUA, 3.64%, 4.24%, and 2.29%, all p＜0.05) were major mediators through metabolic phenotypes. CONCLUSIONS: Higher SB was associated with elevated risks of CVD events. The detrimental effect of SB on CHD risk was more pronounced among retired individuals. Moreover, HDL-C, TG and SUA partially mediated the relationships between SB and CVD events. Our findings may have implications for preventing and controlling CVD associated with SB.

The A subunit of vacuolar H+-ATPase gene (CmVHA-A) plays opposite roles in plant growth and drought tolerance of chrysanthemum under different growing conditions.

As the most prominent proton pumps in plants, vacuolar H+-ATPases (VHAs) comprise multiple subunits that are important for physiological processes and stress tolerance in plants. However, few studies on the roles of subunit genes of VHAs in chrysanthemum have been reported to date. In this study, the gene of A subunit of V-ATPase in chrysanthemum (CmVHA-A) was cloned and identified. CmVHA-A was conserved with VHA-A proteins from other plants. Expression analysis showed that CmVHA-A was highly expressed in most tissues of chrysanthemum except for the flower bud, and was readily induced by polyethylene glycol (PEG) treatment. Functional analysis demonstrated that CmVHA-A exerted a negative influence on the growth and development of shoot and root of chrysanthemum under normal conditions. RNA-sequencing (RNA-seq) analysis revealed the possible explanations for phenotypic differences between transgenic and wild-type (WT) plants. Under drought conditions, CmVHA-A positively affected the drought tolerance of chrysanthemum by enhancing antioxidase activity and alleviating photosynthetic disruption. Overall, CmVHA-A plays opposite roles in plant growth and drought tolerance of chrysanthemums under different growing conditions.

Cytotoxic indole diterpenoids and rare pyridoxatin atropisomers from the endophytic fungus Tolypocladium sp. SHJJ1.

Phytochemical investigation on the extract of endophytic fungus Tolypocladium sp. SHJJ1 resulted in the identification of a pair of previously undescribed pyridoxatin atropisomers [1 (M/P)] and three new indole diterpenoids (3-5), together with a pair of known pyridoxatin atropisomers [2 (M/P)] and ten known indole diterpenoids (6-15). Their structures, including their absolute configurations were elucidated by extensive spectroscopic analysis, quantum chemical calculations, and X-ray diffraction. Among the undescribed natural products, [1 (M/P)] that two rapidly interconverting atropisomers are the third example to report in the pyridoxatin atropisomers. Except for compounds 1 (M/P) and 2 (M/P), all other compounds were tested for their cytotoxicity using HepG2, A549, and MCF-7 human cell lines. Compound 9 displayed moderate cytotoxicity against the HepG2, A549, and MCF-7 cell lines with IC50 values of 32.39 ± 1.48 µM, 26.06 ± 1.14 µM, and 31.44 ± 1.94 µM, respectively, which was similar to the positive drug cisplatin (with IC50 values of 32.55 ± 1.76 µM, 18.40 ± 1.43 µM, and 27.31 ± 1.22 µM, respectively).

Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis using Histopathological Images.

Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diagnosis. The intricate representation of features in histopathological images encompasses abundant information crucial for disease diagnosis, regarding cell appearance, tumor microenvironment, and geometric characteristics. However, recent deep learning methods have not adequately exploited geometric features for pathological image classification due to the absence of effective descriptors that can capture both cell distribution and gathering patterns, which often serve as potent indicators. In this paper, inspired by clinical practice, a Hierarchical Graph Pyramid Transformer (HGPT) is proposed to guide pathological image classification by effectively exploiting a geometric representation of tissue distribution which was ignored by existing state-of-the-art methods. First, a graph representation is constructed according to morphological feature of input pathological image and learn geometric representation through the proposed multi-head graph aggregator. Then, the image and its graph representation are feed into the transformer encoder layer to model long-range dependency. Finally, a locality feature enhancement block is designed to enhance the 2D local representation of feature embedding, which is not well explored in the existing vision transformers. An extensive experimental study is conducted on Kather-5K, MHIST, NCT-CRC-HE, and GasHisSDB for binary or multi-category classification of multiple cancer types. Results demonstrated that our method is capable of consistently reaching superior classification outcomes for histopathological images, which provide an effective diagnostic tool for malignant tumors in clinical practice.

Biological Interactions Mediate Soil Functions by Altering Rare Microbial Communities.

Soil microbes, the main driving force of terrestrial biogeochemical cycles, facilitate soil organic matter turnover. However, the influence of the soil fauna on microbial communities remains poorly understood. We investigated soil microbiota dynamics by introducing competition and predation among fauna into two soil ecosystems with different fertilization histories. The interactions significantly affected rare microbial communities including bacteria and fungi. Predation enhanced the abundance of C/N cycle-related genes. Rare microbial communities are important drivers of soil functional gene enrichment. Key rare microbial taxa, including SM1A02, Gammaproteobacteria, and HSB_OF53-F07, were identified. Metabolomics analysis suggested that increased functional gene abundance may be due to specific microbial metabolic activity mediated by soil fauna interactions. Predation had a stronger effect on rare microbes, functional genes, and microbial metabolism compared to competition. Long-term organic fertilizer application increased the soil resistance to animal interactions. These findings provide a comprehensive understanding of microbial community dynamics under soil biological interactions, emphasizing the roles of competition and predation among soil fauna in terrestrial ecosystems.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.