FT4-to-FT3 ratio is a novel prognostic marker in subacute combined spinal cord degeneration patients.

Objectives: The FT4-to-FT3 ratio (FFR) variations in patients with subacute combined spinal cord degeneration (SCSD) as a potentially useful prognostic indicator are still unknown. This study aimed to investigate the changes of FFR as a potentially valuable prognostic predictor in patients with SCSD. Methods: This study included 144 consecutive SCSD patients who received standard diagnostic and therapeutic procedures between January 2015 and December 2021 and were admitted to the Department of Neurology at the First Affiliated Hospital of Bengbu Medical University. At the time of admission, we gathered data on all patients' demographics, daily routines, previous chronic conditions, medication histories, and other clinical details. For the purpose of measuring FFR, blood samples were specifically taken within 48 h of admission. The degree of neurological impairment of patients was assessed using the functional disability scale at the time of admission. At 6 months following discharge, the Modified Rankin Scale (mRS) was used to evaluate the clinical prognosis. To evaluate the relationship between the FFR and the risks of a poor outcome (mRS > 2), univariate and multivariate logistic regression analysis was utilized. The significance of the FT4/FT3 ratio in predicting the clinical outcomes in SCSD patients 6 months after discharge was assessed using the area under curve-receiver operating characteristic (AUC-ROC). Results: About 90 patients (62.5%) of the 144 patients had poor outcomes, while 54 (37.5%) had favorable outcomes. Higher FFR at admission was independently linked to higher odds of a poor outcome, according to a logistic analysis. With an optimized cutoff value of >2.843, the FFR exhibited the maximum accuracy for predicting a poor outcome, according to the AUC‒ROC curve (AUC 0.731, P < 0.001; sensitivity, 77.8%; specificity, 83.3%). FFR was identified as an independent predictor of poor outcomes by multivariate logistic regression (OR, 2.244; 95% CI, 1.74-2.90; P < 0.001). Conclusions: We discovered that in patients who had a bad result 6 months after discharge, the FFR had dramatically increased at the time of admission, providing a unique prognostic marker in patients with SCSD.

Chemical characterization and comparative analysis of different parts of Cocculus orbiculatus through UHPLC-Q-TOF-MS.

Cocculus orbiculatus (L.) DC. (C. orbiculatus) is a medicinal herb valued for its dried roots with anti-inflammatory, analgesic, diuretic, and other therapeutic properties. Despite its traditional applications, chemical investigations into C. orbiculatus remain limited, focusing predominantly on alkaloids and flavonoids. Furthermore, the therapeutic use of C. orbiculatus predominantly focuses on the roots, leaving the stems, a significant portion of the plant, underutilized. This study employed ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) with in-house and online databases for comprehensive identification of components in various plant parts. Subsequently, untargeted metabolomics was employed to analyze differences in components across different harvest periods and plant sections of C. orbiculatus, aiming to screen for distinct components in different parts of the plant. Finally, metabolomic analysis of the roots and stems, which contribute significantly to the plant's weight, was conducted using chemometrics, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and heatmaps. A total of 113 components, including alkaloids, flavonoids, and organic acids, were annotated across the root, stem, leaf, flower, and fruit, along with numerous previously unreported compounds. Metabolomic analyses revealed substantial differences in components between the root and stem compared to the leaf, flower, and fruit during the same harvest period. PLS-DA and OPLS-DA annotated 10 differentiating components (VIP > 1.5, P < 0.05, FC > 2 or FC < 0.67), with 5 unique to the root and stem, exhibiting lower mass spectrometric responses. This study provided the first characterization of 113 chemical constituents in different parts of C. orbiculatus, laying the groundwork for pharmacological research and advocating for the enhanced utilization of its stem.

MSC microvesicles loaded G-quadruplex-enhanced circular single-stranded DNA-9 inhibits tumor growth by targeting MDSCs.

BACKGROUND: Myeloid-derived suppressor cells (MDSCs) promote tumor growth, metastasis, and lead to immunotherapy resistance. Studies revealed that miRNAs are also expressed in MDSCs and promote the immunosuppressive function of MDSCs. Currently, few studies have been reported on inducible cellular microvesicle delivery of nucleic acid drugs targeting miRNA in MDSCs for the treatment of malignant tumors. RESULTS AND CONCLUSION: In this study, we designed an artificial DNA named G-quadruplex-enhanced circular single-stranded DNA-9 (G4-CSSD9), that specifically adsorbs the miR-9 sequence. Its advanced DNA folding structure, rich in tandem repeat guanine (G-quadruplex), also provides good stability. Mesenchymal stem cells (MSCs) were prepared into nanostructured vesicles by membrane extrusion. The MSC microvesicles-encapsulated G4-CSSD9 (MVs@G4-CSSD9) was delivered into MDSCs, which affected the downstream transcription and translation process, and reduced the immunosuppressive function of MDSCs, so as to achieve the purpose of treating melanoma. In particular, it provides an idea for the malignant tumor treatment.

Image fusion using Y-net-based extractor and global-local discriminator.

Although some deep learning-based image fusion approaches have realized promising results, how to extract information-rich features from different source images while preserving them in the fused image with less distortions remains challenging issue that needs to be addressed. Here, we propose a well worked-out GAN-based scheme with multi-scale feature extractor and global-local discriminator for infrared and visible image fusion. We use Y-Net as the backbone architecture to design the generator network, and introduce the residual dense block (RDblock) to yield more realistic fused images for infrared and visible images by learning discriminative multi-scale representations that are closer to the essence of different modal images. During feature reconstruction, the cross-modality shortcuts with contextual attention (CMSCA) are employed to selectively aggregate features at different scales and different levels to construct information-rich fused images with better visual effect. To ameliorate the information content of the fused image, we not only constrain the structure and contrast information using structural similarity index, but also evaluate the intensity and gradient similarities at both feature and image levels. Two global-local discriminators that combine global GAN with PatchGAN as a unified architecture help to dig for finer differences between the generated image and reference images, which force the generator to learn both the local radiation information and pervasive global details in two source images. It is worth mentioning that image fusion is achieved during confrontation without fusion rules. Lots of assessment tests demonstrate that the reported fusion scheme achieves superior performance against state-of-the-art works in meaningful information preservation.

A general model for predicting enzyme functions based on enzymatic reactions.

Accurate prediction of the enzyme comission (EC) numbers for chemical reactions is essential for the understanding and manipulation of enzyme functions, biocatalytic processes and biosynthetic planning. A number of machine leanring (ML)-based models have been developed to classify enzymatic reactions, showing great advantages over costly and long-winded experimental verifications. However, the prediction accuracy for most available models trained on the records of chemical reactions without specifying the enzymatic catalysts is rather limited. In this study, we introduced BEC-Pred, a BERT-based multiclassification model, for predicting EC numbers associated with reactions. Leveraging transfer learning, our approach achieves precise forecasting across a wide variety of Enzyme Commission (EC) numbers solely through analysis of the SMILES sequences of substrates and products. BEC-Pred model outperformed other sequence and graph-based ML methods, attaining a higher accuracy of 91.6%, surpassing them by 5.5%, and exhibiting superior F1 scores with improvements of 6.6% and 6.0%, respectively. The enhanced performance highlights the potential of BEC-Pred to serve as a reliable foundational tool to accelerate the cutting-edge research in synthetic biology and drug metabolism. Moreover, we discussed a few examples on how BEC-Pred could accurately predict the enzymatic classification for the Novozym 435-induced hydrolysis and lipase efficient catalytic synthesis. We anticipate that BEC-Pred will have a positive impact on the progression of enzymatic research.

Microbial communities of Schisandra sphenanthera Rehd. et Wils. and the correlations between microbial community and the active secondary metabolites.

Background: Schisandra sphenanthera Rehd. et Wils. is a plant used in traditional Chinese medicine (TCM). However, great differences exist in the content of active secondary metabolites in various parts of S. sphenanthera. Do microorganisms critically influence the accumulation of active components in different parts of S. sphenanthera? Methods: In this study, 16S/ITS amplicon sequencing analysis was applied to unravel microbial communities in rhizospheric soil and different parts of wild S. sphenanthera. At the same time, the active secondary metabolites in different parts were detected, and the correlation between the secondary metabolites and microorganisms was analyzed. Results: The major components identified in the essential oils were sesquiterpene and oxygenated sesquiterpenes. The contents of essential oil components in fruit were much higher than that in stem and leaf, and the dominant essential oil components were different in these parts. The dominant components of the three parts were γ-muurolene, δ-cadinol, and trans farnesol (stem); α-cadinol and neoisolongifolene-8-ol (leaf); isosapathulenol, α-santalol, cedrenol, and longiverbenone (fruit). The microbial amplicon sequences were taxonomically grouped into eight (bacteria) and seven (fungi) different phyla. Community diversity and composition analyses showed that different parts of S. sphenanthera had similar and unique microbial communities, and functional prediction analysis showed that the main functions of microorganisms were related to metabolism. Moreover, the accumulation of secondary metabolites in S. sphenanthera was closely related to the microbial community composition, especially bacteria. In endophytic bacteria, Staphylococcus and Hypomicrobium had negative effects on five secondary metabolites, among which γ-muurolene and trans farnesol were the dominant components in the stem. That is, the dominant components in stems were greatly affected by microorganisms. Our results provided a new opportunity to further understand the effects of microorganisms on the active secondary metabolites and provided a basis for further research on the sustainable utilization of S. sphenanthera.

Quantitative characterization of micro-scanning imaging aliasing and optical parameter optimization.

Imaging aliasing is a common problem in the imaging domain. The aliasing of micro-scanning imaging is difficult to characterize accurately, and the matching relationship between the optical system and micro-scanning sampling is unclear. In this paper, a micro-scanning aliasing analysis model is proposed based on the property of sampling squeeze, in which the transfer functions of the optical system, detector, and digital filter are coupled with the micro-scanning sampling process. First, the imaging aliasing under different micro-scanning sampling modes is evaluated based on the constraint relationship of the transfer functions for each part. The stretch factor of the transfer function under micro-scanning sampling is calculated by utilizing the amount of aliasing. Second, the micro-scanning imaging transfer function under different optical parameters is predicted by the stretch factor, and the results indicate the existence of an optimal F-number that maximizes the micro-scanning performance improvement. Furthermore, the optimal micro-scanning imaging F-numbers for different fill factors are given, and the matching relationship between optical parameters, fill factors and micro-scanning mode is analyzed. Finally, a micro-scanning imaging simulation is performed based on the actual imaging transfer and micro-scanning sampling process. The simulation experiment verifies the accuracy of the micro-scanning aliasing model and gives the consistent test results of the optimal F-number. This paper can provide theoretical support for the matching relationship among micro-scanning imaging parameters, which is of great significance for the refined optimal design of micro-scanning imaging systems.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Recent Progress in Droplet Structure Machining for Advanced Optics.

The development of optical and photonic applications using soft-matter droplets holds great scientific and application importance. The machining of droplet structures is expected to drive breakthroughs in advancing frontier applications. This review highlights recent advancements in micro-nanofabrication techniques for soft-matter droplets, encompassing microfluidics, laser injection, and microfluidic 3D printing. The principles, advantages, and weaknesses of these technologies are thoroughly discussed. The review introduces the utilization of a phase separation strategy in microfluidics to assemble complex emulsion droplets and control droplet geometries by adjusting interfacial tension. Additionally, laser injection can take full advantage of the self-assembly properties of soft matter to control the spontaneous organization of internal substructures within droplets, thus providing the possibility of high-precision customized assembly of droplets. Microfluidic 3D printing demonstrates a 3D printing-based method for machining droplet structures. Its programmable nature holds promise for developing device-level applications utilizing droplet arrays. Finally, the review presents novel applications of soft-matter droplets in optics and photonics. The integration of processing concepts from microfluidics, laser micro-nano-machining, and 3D printing into droplet processing, combined with the self-assembly properties of soft materials, may offer novel opportunities for processing and application development.

Identification of mIDH1 R132C/S280F Inhibitors from Natural Products by Integrated Molecular Docking, Pharmacophore Modeling and Molecular Dynamics Simulations.

Mutant isocitrate dehydrogenase 1 (mIDH1) is a common driving factor in acute myeloid leukemia (AML), with the R132 mutation accounting for a high proportion. The U.S. Food and Drug Administration (FDA) approved Ivosidenib, a molecular entity that targets IDH1 with R132 mutations, as a promising therapeutic option for AML with mIDH1 in 2018. It was of concern that the occurrence of disease resistance or recurrence, attributed to the IDH1 R132C/S280F second site mutation, was observed in certain patients treated with Ivosidenib within the same year. Furthermore, it should be noted that most mIDH1 inhibitors demonstrated limited efficacy against mutations at this specific site. Therefore, there is an urgent need to investigate novel inhibitors targeting mIDH1 for combating resistance caused by IDH1 R132C/S280F mutations in AML. This study aimed to identify novel mIDH1 R132C/S280F inhibitors through an integrated strategy of combining virtual screening and dynamics simulations. First, 2000 hits were obtained through structure-based virtual screening of the COCONUT database, and hits with better scores than -10.67 kcal/mol were obtained through molecular docking. A total of 12 potential small molecule inhibitors were identified through pharmacophore modeling screening and Prime MM-GBSA. Dynamics simulations were used to study the binding modes between the positive drug and the first three hits and IDH1 carrying the R132C/S280F mutation. RMSD showed that the four dynamics simulation systems remained stable, and RMSF and Rg showed that the screened molecules have similar local flexibility and tightness to the positive drug. Finally, the lowest energy conformation, hydrogen bond analysis, and free energy decomposition results indicate that in the entire system the key residues LEU120, TRP124, TRP267, and VAL281 mainly contribute van der Waals forces to the interaction, while the key residues VAL276 and CYS379 mainly contribute electrostatic forces.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Trimester-specific effect of maternal co-exposure to organophosphate esters and phthalates on preschooler cognitive development: The moderating role of gestational vitamin D status.

Organophosphate esters (OPEs) and phthalate acid esters (PAEs) are prevalent endocrine-disrupting chemicals (EDCs). Humans are often exposed to OPEs and PAEs simultaneously through multiple routes. Given that fetal stage is a critical period for neurodevelopment, it is necessary to know whether gestational co-exposure to OPEs and PAEs affects fetal neurodevelopment. However, accessible epidemiological studies are limited. The present study included 2, 120 pregnant women from the Ma'anshan Birth Cohort (MABC) study. The concentrations of tris (2-chloroethyl) phosphate (TCEP), 6 OPE metabolites and 7 PAE metabolites were measured in the first, second and third trimester using ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS). Cognitive development of preschooler was assessed based on the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) of the Chinese version. Generalized estimating equations (GEEs), restricted cubic spline (RCS) and generalized additive models (GAMs) were employed to explore the associations between individual OPE exposure and preschooler cognitive development. The quantile-based g-computation (QGC) method was used to estimate the joint effect of PAEs and OPEs exposure on cognitive development. GEEs revealed significant adverse associations between diphenyl phosphate (DPHP) (ß: -0.58, 95% CI: -1.14, -0.01), bis (2-butoxyethyl) phosphate(BBOEP) (ß: -0.44, 95% CI: -0.85, -0.02), bis(1-chloro-2-propyl) phosphate (BCIPP) (ß: -0.81, 95%CI: -1.43, -0.20) and full-scale intelligence quotient (FSIQ) in the first trimester; additionally, TCEP and bis(2-ethylhexyl) phosphate (BEHP) in the second trimester, as well as DPHP in the third trimester, were negatively associated with cognitive development. Through the QGC analyses, mixture exposure in the first trimester was negatively associated with FSIQ scores (ß: -1.70, 95% CI: -3.06, -0.34), mono-butyl phthalate (MBP), BCIPP, and DPHP might be the dominant contributors after controlling for other OPEs and PAEs congeners. Additionally, the effect of OPEs and PAEs mixture on cognitive development might be driven by vitamin D deficiency.

Unveiling the mechanism of high sugar diet induced advanced glycosylation end products damage skin structure via extracellular matrix-receptor interaction pathway.

BACKGROUND: AGEs accumulate in the skin as a result of a high-sugar diet and play an important role in the skin aging process. OBJECTIVES: The aim of this study was to characterize the mechanism underlying the effect of a high-sugar diet on skin aging damage at a holistic level. METHODS: We established a high-sugar diet mouse model to compare and analyze differences in physiological indexes. The effect of a high-sugar diet on skin aging damage was analyzed by means of a transcriptome study and staining of pathological sections. Furthermore, the differences in the protein expression of AGEs and ECM components between the HSD and control groups were further verified by immunohistochemistry. RESULTS: The skin in the HSD group mice tended toward a red, yellow, dark, and deep color. In addition, the epidermis was irregular with anomalous phenomena, the epidermis was thinned, and the dermis lost its normal structure and showed vacuolated changes. Transcriptomics results revealed significant downregulation of the ECM-receptor interaction pathway, significant upregulation of the expression of AGEs and significant downregulation of the expression levels of COLI, FN1, LM5, and TNC, among others ECM proteins and ECM receptors. CONCLUSIONS: High-sugar diets cause skin aging damage by inducing the accumulation of AGEs, disrupting the expression of ECM proteins and their receptors, and downregulating the ECM-receptor interaction pathway, which affects cellular behavioral functions such as cell proliferation, migration, and adhesion, as well as normal skin tissue structure.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

MITF regulates the subcellular location of HIF1α through SUMOylation to promote the invasion and metastasis of daughter cells derived from polyploid giant cancer cells.

High concentrations of cobalt chloride (CoCl2) can induce the formation of polyploid giant cancer cells (PGCCs) in various tumors, which can produce daughter cells with strong proliferative, migratory and invasive abilities via asymmetric division. To study the role of hypoxia­inducible factor (HIF) 1α in the formation of PGCCs, colon cancer cell lines Hct116 and LoVo were used as experimental subjects. Western blotting, nuclear and cytoplasmic protein extraction and immunocytochemical experiments were used to compare the changes in the expression and subcellular localization of HIF1α, microphthalmia­associated transcription factor (MITF), protein inhibitor of activated STAT protein 4 (PIAS4) and von Hippel­Lindau disease tumor suppressor (VHL) after treatment with CoCl2. The SUMOylation of HIFα was verified by co­immunoprecipitation assay. After inhibiting HIF1α SUMOylation, the changes in proliferation, migration and invasion abilities of Hct116 and LoVo were compared by plate colony formation, wound healing and Transwell migration and invasion. In addition, lysine sites that led to SUMOylation of HIF1α were identified through site mutation experiments. The results showed that CoCl2 can induce the formation of PGCCs with the expression level of HIF1α higher in treated cells than in control cells. HIF1α was primarily located in the cytoplasm of control cell. Following CoCl2 treatment, the subcellular localization of HIF1α was primarily in the nuclei of PGCCs with daughter cells (PDCs). After treatment with SUMOylation inhibitors, the nuclear HIF1α expression in PDCs decreased. Furthermore, their proliferation, migration and invasion abilities also decreased. After inhibiting the expression of MITF, the expression of HIF1α decreased. MITF can regulate HIF1α SUMOylation. Expression and subcellular localization of VHL and HIF1α did not change following PIAS4 knockdown. SUMOylation of HIF1α occurs at the amino acid sites K391 and K477 in PDCs. After mutation of the two sites, nuclear expression of HIF1α in PDCs was reduced, along with a significant reduction in the proliferation, migration and invasion abilities. In conclusion, the post­translation modification regulated the subcellular location of HIF1α and the nuclear expression of HIF1α promoted the proliferation, migration and invasion abilities of PDCs. MITF could regulate the transcription and protein levels of HIF1α and participate in the regulation of HIF1α SUMOylation.

Functionalized sampling swabs array-based portable ATP bioluminescence sensor with on-site enrichment and high specificity for live Salmonella detection.

Live food-borne pathogens, featured with rapid proliferative capacity and high pathogenicity, pose an emerging food safety and public health crisis. The high-sensitivity detection of pathogens is particularly imperative yet remains challenging. This work developed a functionalized nylon swab array with enhanced affinity for Salmonella typhimurium (S.T.) for high-specificity ATP bioluminescence-based S.T. detection. In brief, the nylon swabs (NyS) were turned to N-methylation nylon (NyS-OH) by reacting with formaldehyde, and NyS-OH were further converted to NyS-CA by reacting with carboxylic groups of citric acid (CA) and EDC/NHS solution, for altering the NyS surface energy to favor biomodification. The antibody-immobilized nylon swab (MNyS-Ab) was ready for S.T.-specific adsorption. Three prepared MNyS-Ab were installed on a stirrer to form an MNyS-Ab array, allowing for on-site enrichment of S.T. through absorptive extraction. The enriched S.T. was quantified by measuring the bioluminescence of ATP released from cell lysis utilizing a portable ATP bioluminescence sensor. The bioassay demonstrated a detectable range of 102-107 CFU mL-1 with a detection limit (LOD) of 8 CFU/mL within 35 min. The signal of single MNyS-Ab swabs was 500 times stronger than the direct detection of 106 CFU/mL S.T. The MNyS-Ab array exhibited a 100-fold increase in extraction level compared to a single MNyS. This combination of a portable bioluminescent sensor and modified nylon swab array offers a novel strategy for point-of-care testing of live S.T. strains. It holds promise for high-sensitivity measurements of other pathogens and viruses.

Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning.

Unsupervised graph-structure learning (GSL) which aims to learn an effective graph structure applied to arbitrary downstream tasks by data itself without any labels' guidance, has recently received increasing attention in various real applications. Although several existing unsupervised GSL has achieved superior performance in different graph analytical tasks, how to utilize the popular graph masked autoencoder to sufficiently acquire effective supervision information from the data itself for improving the effectiveness of learned graph structure has been not effectively explored so far. To tackle the above issue, we present a multilevel contrastive graph masked autoencoder (MCGMAE) for unsupervised GSL. Specifically, we first introduce a graph masked autoencoder with the dual feature masking strategy to reconstruct the same input graph-structured data under the original structure generated by the data itself and learned graph-structure scenarios, respectively. And then, the inter-and intra-class contrastive loss is introduced to maximize the mutual information in feature and graph-structure reconstruction levels simultaneously. More importantly, the above inter-and intra-class contrastive loss is also applied to the graph encoder module for further strengthening their agreement at the feature-encoder level. In comparison to the existing unsupervised GSL, our proposed MCGMAE can effectively improve the training robustness of the unsupervised GSL via different-level supervision information from the data itself. Extensive experiments on three graph analytical tasks and eight datasets validate the effectiveness of the proposed MCGMAE.

Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning.

Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.

Explore the pharmacological basis of ShengJiYiSui decoction in the treatment of amyotrophic lateral sclerosis based on network pharmacology and molecular docking technology.

Neurodegenerative illnesses have long been handled clinically by traditional Chinese medicine. This study is the first time to explore the pharmacological basis of application in amyotrophic lateral sclerosis (ALS) through network pharmacology and molecular docking techniques. In the present investigation, the TCMSP database and HIT2 database were examined for 9 TCM constituents of Sheng Ji Yu Sui Decoction (SJYSD), and the desired sites for the components were searched in the Drugbank database. and the Sjysd-target network was constructed. Associated targets for Amyotrophic lateral sclerosis (ALS) were then retrieved and collected in the OMIM, TTD, Genecards and DisGeNET databases. Protein-protein interaction and enrichment analysis were performed for the common targets of drugs and diseases, and molecular anchoring for the chosen core targets and related molecules was carried out. The results showed that SJYSD had 100 active compounds corresponding to 598 targets. ALS has a total of 5,325 genes. SJYSD and ALS share 163 genes, and these targets involve PI3K-AKT signaling, p53 signaling and IL-17 signaling, etc. The core components of luteolin and quercetin were discovered and may be used to treat ALS by regulating PI3K-AKT signaling pathway by HSP90AB1 protein.

Polystyrene-Based Matrix to Enhance the Fluorescence of Aggregation-Induced Emission Luminogen for Fluorescence-Guided Surgery.

Achieving ultrabright fluorogens is a key issue for fluorescence-guided surgery (FGS). Fluorogens with aggregation-induced emission (AIEgens) are potential agents for FGS on the benefit of the bright fluorescence in physiological conditions. Herein, the fluorescence brightness of AIEgen is further improved by preparing the nanoparticle using a polystyrene-based matrix and utilizing it for tumor FGS with a high signal-to-background ratio. After encapsulating AIEgen into polystyrene-poly (ethylene glycol) (PS-PEG), the fluorescence intensity of the prepared AIE@PS-PEG nanoparticles is multiple times that of nanoparticles in 1, 2-distearoyl-sn-glycero-3-phosphoethanolamine-poly (ethylene glycol) (DSPE-PEG), a commonly used polymer matrix for nanoparticle preparation. Molecular dynamics simulations suggest that higher free energy is required for the outer rings of AIEgen to rotate in polystyrene than in the DSPE, indicating that the benzene rings in polystyrene can restrict the intramolecular motions of AIEgen better than the alkyl chain in DSPE-PEG. Fluorescence correlation microscopy detections suggest that the triplet excited state of AIEgens is less in PS-PEG than in DSPE-PEG. The restricted intramolecular motions and suppressed triplet excited state result in ultrabright AIE@PS-PEG nanoparticles, which are more conducive to illuminating tumor tissues in the intestine for FGS. The illumination of metastatic tumors in lungs by AIE@PS-PEG nanoparticles is also tried.