ReadCurrent: a VDCNN-based tool for fast and accurate nanopore selective sequencing.

Nanopore selective sequencing allows the targeted sequencing of DNA of interest using computational approaches rather than experimental methods such as targeted multiplex polymerase chain reaction or hybridization capture. Compared to sequence-alignment strategies, deep learning (DL) models for classifying target and nontarget DNA provide large speed advantages. However, the relatively low accuracy of these DL-based tools hinders their application in nanopore selective sequencing. Here, we present a DL-based tool named ReadCurrent for nanopore selective sequencing, which takes electric currents as inputs. ReadCurrent employs a modified very deep convolutional neural network (VDCNN) architecture, enabling significantly lower computational costs for training and quicker inference compared to conventional VDCNN. We evaluated the performance of ReadCurrent across 10 nanopore sequencing datasets spanning human, yeasts, bacteria, and viruses. We observed that ReadCurrent achieved a mean accuracy of 98.57% for classification, outperforming four other DL-based selective sequencing methods. In experimental validation that selectively sequenced microbial DNA from human DNA, ReadCurrent achieved an enrichment ratio of 2.85, which was higher than the 2.7 ratio achieved by MinKNOW using the sequence-alignment strategy. In summary, ReadCurrent can rapidly classify target and nontarget DNA with high accuracy, providing an alternative in the toolbox for nanopore selective sequencing. ReadCurrent is available at https://github.com/Ming-Ni-Group/ReadCurrent.

RGA1 regulates grain size, rice quality and seed germination in the small and round grain mutant srg5.

BACKGROUND: Generating elite rice varieties with high yield and superior quality is the main goal of rice breeding programs. Key agronomic traits, including grain size and seed germination characteristics, affect the final yield and quality of rice. The RGA1 gene, which encodes the α-subunit of rice G-protein, plays an important role in regulating rice architecture, seed size and abiotic stress responses. However, whether RGA1 is involved in the regulation of rice quality and seed germination traits is still unclear. RESULTS: In this study, a rice mutant small and round grain 5 (srg5), was identified in an EMS-induced rice mutant library. Systematic analysis of its major agronomic traits revealed that the srg5 mutant exhibited a semi-dwarf plant height with small and round grain and reduced panicle length. Analysis of the physicochemical properties of rice showed that the difference in rice eating and cooking quality (ECQ) between the srg5 mutant and its wild-type control was small, but the appearance quality was significantly improved. Interestingly, a significant suppression of rice seed germination and shoot growth was observed in the srg5 mutant, which was mainly related to the regulation of ABA metabolism. RGA1 was identified as the candidate gene for the srg5 mutant by BSA analysis. A SNP at the splice site of the first intron disrupted the normal splicing of the RGA1 transcript precursor, resulting in a premature stop codon. Additional linkage analysis confirmed that the target gene causing the srg5 mutant phenotype was RGA1. Finally, the introduction of the RGA1 mutant allele into two indica rice varieties also resulted in small and round rice grains with less chalkiness. CONCLUSIONS: These results indicate that RGA1 is not only involved in the control of rice architecture and grain size, but also in the regulation of rice quality and seed germination. This study sheds new light on the biological functions of RGA1, thereby providing valuable information for future systematic analysis of the G-protein pathway and its potential application in rice breeding programs.

Role of electroanatomical mapping-guided superior vena cava isolation in paroxysmal atrial fibrillation patients without provoked superior vena cava triggers: a randomized controlled study.

AIMS: Data about whether empirical superior vena cava (SVC) isolation (SVCI) improves the success rate of paroxysmal atrial fibrillation (PAF) are conflicting. This study sought to first investigate the characteristics of SVC-triggered atrial fibrillation and secondly investigate the impact of electroanatomical mapping-guided SVCI, in addition to circumferential pulmonary vein isolation (CPVI), on the outcome of PAF ablation in the absence of provoked SVC triggers. METHODS AND RESULTS: A total of 130 patients undergoing PAF ablation underwent electrophysiological studies before ablation. In patients for whom SVC triggers were identified, SVCI was performed in addition to CPVI. Patients without provoked SVC triggers were randomized in a 1:1 ratio to CPVI plus SVCI or CPVI only. The primary endpoint was freedom from any documented atrial tachyarrhythmias lasting over 30 s after a 3-month blanking period without anti-arrhythmic drugs at 12 months after ablation. Superior vena cava triggers were identified in 30 (23.1%) patients with PAF. At 12 months, 93.3% of those with provoked SVC triggers who underwent CPVI plus SVCI were free from atrial tachyarrhythmias. In patients without provoked SVC triggers, SVCI, in addition to CPVI, did not increase freedom from atrial tachyarrhythmias (87.9 vs. 79.6%, log-rank P = 0.28). CONCLUSION: Electroanatomical mapping-guided SVCI, in addition to CPVI, did not increase the success rate of PAF ablation in patients who had no identifiable SVC triggers. REGISTRATION: ChineseClinicalTrials.gov: ChiCTR2000034532.

Clinical and genetic features in autosomal recessive bestrophinopathy in Chinese cohort.

PURPOSE: To provide a genotype and phenotype characterization of the BEST1 mutation in Chinese patients with autosomal recessive bestrophinopathy (ARB) through multimodal imaging and next-generation sequencing (NGS). METHODS: Seventeen patients from 17 unrelated families of Chinese origin with ARB were included in a retrospective cohort study. Phenotypic characteristics, including anterior segment features, were assessed by multimodal imaging. Multigene panel testing, involving 586 ophthalmic disease-associated genes, and Sanger sequencing were performed to identify disease-causing variants. RESULTS: Among 17 ARB patients, the mean follow-up was 15.65 months and average onset age was 30.53 years (range: 9-68). Best corrected visual acuity ranged from light perception to 0.8. EOG recordings showed a typically decreased Arden ratio in 12 patients, and a normal or slightly decreased Arden ratio in two patients. Anterior features included shallow anterior chambers (16/17), ciliary pronation (16/17), iris bombe (13/17), iridoschisis (2/17), iris plateau (1/17), narrow angles (16/17) and reduced axial lengths (16/17). Sixteen patients had multiple bilateral small, round, yellow vitelliform deposits distributed throughout the posterior pole, surrounding the optic disc. Initial diagnoses included angle-closure glaucoma (four patients), Best disease (three patients), and central serous chorioretinopathy secondary to choroidal neovascularization (CNV) (one patient), with the remainder diagnosed with ARB. Fourteen patients underwent preventive laser peripheral iridotomy, four of whom also received combined trabeculectomy and iridotomy in both eyes for uncontrolled intraocular pressure. One patient received intravitreal conbercept for CNV. Overall, 15 distinct disease-causing variants of BEST1 were identified, with 14 (82.35%) patients having missense mutations. Common mutations included p. Arg255-256 and p. Ala195Val (both 23.68%), with the most frequent sites in exons 7 and 5. CONCLUSIONS: This study provides a comprehensive characterization of anterior segment and genetic features in ARB, with a wide array of morphological abnormalities. Findings are relevant for refining clinical practices and genetic counseling and advancing pathogenesis research.

Discovery of LAH-1 as potent c-Met inhibitor for the treatment of non-small cell lung cancer.

ABSTRCTDysregulated HGF/c-Met pathway has been implicated in multiple human cancers and has become an attractive target for cancer intervention. Herein, we report the discovery of N-(3-fluoro-4-((2-(3-hydroxyazetidine-1-carboxamido)pyridin-4-yl)oxy)phenyl)-1-(4-fluorophenyl)-4-methyl-6-oxo-1,6-dihydropyridazine-3-carboxamide (LAH-1), which demonstrated nanomolar MET kinase activity as well as desirable antiproliferative activity, especially against EBC-1 cells. Mechanism studies confirmed the effects of LAH-1 on modulation of HGF/c-Met pathway, induction of cell apoptosis, inhibition on colony formation as well as cell migration and invasion. In addition, LAH-1 also showed desirable in vitro ADME properties as well as acceptable in vivo PK parameters. The design, synthesis, and characterisation of LAH-1 are described herein.

Identification of novel PD-1/PD-L1 small molecule inhibitors: virtual screening, synthesis and in vitro characterisation.

The PD-1/PD-L1 pathway is considered as one of the most promising immune checkpoints in tumour immunotherapy. However, researchers are faced with the inherent limitations of antibodies, driving them to pursue PD-L1 small molecule inhibitors. Virtual screening followed by experimental validation is a proven approach to discover active compounds. In this study, we employed multistage virtual screening methods to screen multiple compound databases to predict new PD-1/PD-L1 ligands. 35 compounds were proposed by combined analysis of fitness scores, interaction pattern and MM-GBSA binding affinities. Enzymatic assay confirmed that 10 out of 35 ligands were potential PD-L1 inhibitors, with inhibitory rate higher than 50% at the concentration of 30 µM. Among them, ZDS20 was identified as the most effective inhibitor with low micromolar activity (IC50 = 3.27 µM). Altogether, ZDS20 carrying novel scaffold was identified and could serve as a lead for the development of new classes of PD-L1 inhibitors.

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

Novel Scaffold Agonists of the α2A Adrenergic Receptor Identified via Ensemble-Based Strategy.

The α2A adrenergic receptor (α2A-AR) serves as a critical molecular target for sedatives and analgesics. However, α2A-AR ligands with an imidazole ring also interact with an imidazoline receptor as well as other proteins and lead to undesirable effects, motivating us to develop more novel scaffold α2A-AR ligands. For this purpose, we employed an ensemble-based ligand discovery strategy, integrating long-term molecular dynamics (MD) simulations and virtual screening, to identify new potential α2A-AR agonists with novel scaffold. Our results showed that compounds SY-15 and SY-17 exhibited significant biological effects in the preliminary evaluation of protein kinase A (PKA) redistribution assays. They also reduced levels of intracellular cyclic adenosine monophosphate (cAMP) in a dose-dependent manner. Upon treatment of the cells with 100 µM concentrations of SY-15 and SY-17, there was a respective decrease in the intracellular cAMP levels by 63.43% and 53.83%. Subsequent computational analysis was conducted to elucidate the binding interactions of SY-15 and SY-17 with the α2A-AR. The binding free energies of SY-15 and SY-17 calculated by MD simulations were -45.93 and -71.97 kcal/mol. MD simulations also revealed that both compounds act as bitopic agonists, occupying the orthosteric site and a novel exosite of the receptor simultaneously. Our findings of integrative computational and experimental approaches could offer the potential to enhance ligand affinity and selectivity through dual-site occupancy and provide a novel direction for the rational design of sedatives and analgesics.

Role of MARK4 in methamphetamine-induced acute kidney injury. / MARK4在甲基苯丙胺诱导急性肾损伤中的作用.

OBJECTIVES: Methamphetamine (METH) is an illicit psychoactive substance that can damage various organs, with the urinary system being one of its significant targets. This study aims to explore the role of microtubule affinity-regulating kinase 4 (MARK4) in METH-induced acute kidney injury (AKI). METHODS: A total of 10 healthy adult male C57BL/6 mice were randomly divided into a control group and a METH group, 5 mice in each group. The METH group was administered METH (20 mg/kg, intraperitoneally, once daily for 3 consecutive days), while the control group received an equal volume of physiological saline. The mice were executed 24 hours after the final injection, and the success of the AKI model was detected by blood serum creatinine, blood urea nitrogen, and renal HE staining. Proteins differentially expressed between kidney tissues with METH-induced AKI and normal kidney tissues were screened by proteomics techniques and subjected to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and bioinformatics analysis. The accuracy of proteomic data was validated using Western blotting, and the expression levels of MARK4 and cleaved caspase-3 in mouse kidneys were measured. We further explored the role of MARK4 in METH-induced AKI. Firstly, a METH toxicity model was established in BUMPT cells to screen the appropriate concentration and time of METH treatment; the viability of BUMPT cells after METH treatment and the expression of cleaved caspase-3 were detected by interfering with MARK4 expression through inhibitors. RESULTS: The proteomic analysis of kidney tissues from METH and control groups screened for a total of 17 differentially expressed proteins, of which 11 were up-regulated and 6 were down-regulated (all P<0.05). The expression levels of MARK4 and cleaved caspase-3 were elevated in the kidneys of METH-treated mice (both P<0.05). The activity of BUMPT cells gradually decreased with increasing METH treatment concentration (all P<0.05), where the viability of BUMPT cells decreased to about 60% after METH treatment at 4 mmol/L. Compared with the control group, expression levels of MARK4 and cleaved caspase-3 were increased with higher METH concentrations and longer exposure times in a concentration- and time-dependent manner (all P<0.05). Inhibition of MARK4 expression improved METH-induced decrease in BUMPT cell activity, down-regulated the expression of cleaved caspase-3, and decreased the apoptosis of BUMPT cells induced by METH. CONCLUSIONS: MARK4 is highly expressed in a mouse model of METH-induced AKI, and MARK4 mediates METH-induced AKI by regulating cell apoptosis.

BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network.

BACKGROUND: Automatic and accurate extraction of diverse biomedical relations from literature is a crucial component of bio-medical text mining. Currently, stacking various classification networks on pre-trained language models to perform fine-tuning is a common framework to end-to-end solve the biomedical relation extraction (BioRE) problem. However, the sequence-based pre-trained language models underutilize the graphical topology of language to some extent. In addition, sequence-oriented deep neural networks have limitations in processing graphical features. RESULTS: In this paper, we propose a novel method for sentence-level BioRE task, BioEGRE (BioELECTRA and Graph pointer neural net-work for Relation Extraction), aimed at leveraging the linguistic topological features. First, the biomedical literature is preprocessed to retain sentences involving pre-defined entity pairs. Secondly, SciSpaCy is employed to conduct dependency parsing; sentences are modeled as graphs based on the parsing results; BioELECTRA is utilized to generate token-level representations, which are modeled as attributes of nodes in the sentence graphs; a graph pointer neural network layer is employed to select the most relevant multi-hop neighbors to optimize representations; a fully-connected neural network layer is employed to generate the sentence-level representation. Finally, the Softmax function is employed to calculate the probabilities. Our proposed method is evaluated on three BioRE tasks: a multi-class (CHEMPROT) and two binary tasks (GAD and EU-ADR). The results show that our method achieves F1-scores of 79.97% (CHEMPROT), 83.31% (GAD), and 83.51% (EU-ADR), surpassing the performance of existing state-of-the-art models. CONCLUSION: The experimental results on 3 biomedical benchmark datasets demonstrate the effectiveness and generalization of BioEGRE, which indicates that linguistic topology and a graph pointer neural network layer explicitly improve performance for BioRE tasks.

Improving the Oxygen Evolution Activity of Layered Double-Hydroxide via Erbium-Induced Electronic Engineering.

Layered double-hydroxide (LDH) has been considered an important class of electrocatalysts for the oxygen evolution reaction (OER), but the adsorption-desorption behaviors of oxygen intermediates on its surface still remain unsatisfactory. Apart from transition-metal doping to solve this electrocatalytic problem of LDH, rare-earth (RE) species have sprung up as emerging dopants owing to their unique 4f valence-electronic configurations. Herein, the Er is chosen as a RE model to improve OER activity of LDH via constructing nickel foam supported Er-doped NiFe-LDH catalyst (Er-NiFe-LDH@NF). The optimal Er-NiFe-LDH@NF exhibits a low overpotential (191 mV at 10 mA cm-2 ), high turnover frequency (0.588 s-1 ), and low activation energy (36.03 kJ mol-1 ), which are superior to Er-free sample. Electrochemical in situ Raman spectra reveal the facilitated transition of Ni-OH into Ni-OOH for promoted OER kinetics through the Er doping effect. Theoretical calculations demonstrate that the introduction of Er facilitates the spin crossover of valence electrons by optimizing the d band center of NiFe-LDH, which leads to the GO -GHO closer to the optimal activity of the kinetic OER volcano by balancing the bonding strength of *O and *OH. Moreover, the Er-NiFe-LDH@NF presents high practicability in electrochemical water-splitting devices with a low driving potential of and a well-extended driving period.

Brassinosteroids regulate rice seed germination through the BZR1-RAmy3D transcriptional module.

Seed dormancy and germination, two physiological processes unique to seed-bearing plants, are critical for plant growth and crop production. The phytohormone brassinosteroid (BR) regulates many aspects of plant growth and development, including seed germination. The molecular mechanisms underlying BR control of rice (Oryza sativa) seed germination are mostly unknown. We investigated the molecular regulatory cascade of BR in promoting rice seed germination and post-germination growth. Physiological assays indicated that blocking BR signaling, including introducing defects into the BR-insensitive 1 (BRI1) receptor or overexpressing the glycogen synthase kinase 2 (GSK2) kinase delayed seed germination and suppressed embryo growth. Our results also indicated that brassinazole-resistant 1 (BZR1) is the key downstream transcription factor that mediates BR regulation of seed germination by binding to the alpha-Amylase 3D (RAmy3D) promoter, which affects α-amylase expression and activity and the degradation of starch in the endosperm. The BZR1-RAmy3D module functions independently from the established Gibberellin MYB-alpha-amylase 1A (RAmy1A) module of the gibberellin (GA) pathway. We demonstrate that the BZR1-RAmy3D module also functions in embryo-related tissues. Moreover, RNA-sequencing (RNA-seq) analysis identified more potential BZR1-responsive genes, including those involved in starch and sucrose metabolism. Our study successfully identified the role of the BZR1-RAmy3D transcriptional module in regulating rice seed germination.

Fluvoxamine alleviates bleomycin-induced lung fibrosis via regulating the cGAS-STING pathway.

Idiopathic pulmonary fibrosis (IPF) is a fatal disease with high mortality and limited effective therapy. Herein, we reported that fluvoxamine, a selective serotonin reuptake inhibitor (SSRI), used in depression and anxiety treatment, also exhibited therapeutic activities in IPF. Fluvoxamine inhibited cyclic GMP-AMP synthase (cGAS) and stimulator of interferon genes (STING), restrained the activation of their downstream targets, including PERK/ eIF2α/ c-Myc/ miR-9-5p/ TBPL1 and TBK1/ YAP/ JNK1/2/ Bnip3/ CaMKII/ cofilin signaling, thus attenuated the activation and migration of fibroblasts upon TGF-ß1 challenge. Fluvoxamine dose-dependently improved pulmonary function, decreased the expression of inflammatory factors, reduced excessive production of extracellular matrix, and thus alleviated bleomycin (BLM)-induced lung fibrosis in mice. Moreover, fluvoxamine at a dose of 10 mg/ kg showed similar efficacy as pirfenidone (PFD) at a dose of 30 mg/kg in a mice model of lung fibrosis. In summary, our results suggest that fluvoxamine is an effective anti-fibrotic agent for IPF.

Eu(III)-Functionalized MOF-Based Dual-Emission Ratiometric Sensor Integrated with Logic Gate Operation for Efficient Detection of Hippuric Acid in Urine and Serum.

With the introduction of Eu3+ ions as the secondary fluorescent signal reporter and sensing active sites, a dual-emission ratiometric sensor of Eu3+@NiMOF (Eu3+ functional NiMOF) for hippuric acid (HA) detection in urine and serum was fabricated via the postsynthetic encapsulating strategy. Based on the two emission signals at 441 nm (turn-on) and 628 nm (turn-off), the produced Eu3+@NiMOF ratiometric sensor provided enhanced sensitivity, higher selectivity, and 9.7 times lower limits of detection (LOD) for the detection of HA (2.38 µM, 0.42 µg·mL-1) than that of the pristine NiMOF. Considering the high sensitivity and visualization results, further exploration of intelligent applications in the HA sensing process was carried out by constructing a tandem combinational logic gate to improve the practicability and convenience with the help of a smartphone. This work provides a promising approach for developing MOF-based ratiometric sensors to detect biomarkers.

Association between serum apolipoprotein A1 and atrial fibrillation in the Chinese population: a case-control study.

BACKGROUND: The relationship between serum apolipoprotein A1 (APOA1) and atrial fibrillation (AF) is not known. Therefore, we sought to investigate the associations between APOA1 and AF in the Chinese population. METHODS: This case-control study included 950 patients with AF (29-83 years old, 50.42% male) who were hospitalized consecutively in China between January 2019 and September 2021. Controls with sinus rhythm and without AF were matched (1:1) to cases by sex and age. Pearson correlation analysis was performed to investigate the correlation between APOA1 and blood lipid profiles. Multivariate regression models were used to explore the association between APOA1 and AF. The receiver operator characteristic (ROC) curve was constructed to examine the performance of APOA1. RESULTS: Multivariate regression analysis showed that low serum APOA1 in men and women with AF was significantly associated with AF (OR = 0.261, 95% CI: 0.162-0.422, P < 0.001). Pearson correlation analysis indicated that serum APOA1 was positively correlated with total cholesterol (TC) (r = 0.456, p < 0.001), low-density lipoprotein cholesterol (LDL-C) (r = 0.825, p < 0.001), high-density lipoprotein cholesterol (HDL-C) (r = 0.238, p < 0.001), and apolipoprotein B (APOB) (r = 0.083, p = 0.011). ROC curve analysis showed that APOA1 levels of 1.105 g/L and 1.205 g/L were the optimal cut-off values for predicting AF in males and females, respectively. CONCLUSION: Low APOA1 in male and female patients is significantly associated with AF in the Chinese population of non-statin users. APOA1 may be a potential biomarker for AF and contribute to the pathological progression of AF along with low blood lipid profiles. Potential mechanisms remain to be further explored.

Physical activity, sleep disorders, and type of work in the prevention of cognitive function decline in patients with hypertension.

BACKGROUND: Hypertensive patients are likelier to have cognitive function decline (CFD). This study aimed to explore physical activity level, sleep disorders, and type of work that influenced intervention effects on cognitive function decline in hypertensive patients and to establish a decision tree model to analyze their predictive significance on the incidence of CFD in hypertensive patients. METHODS: This cross-sectional study recruited patients with essential hypertension from several hospitals in Shandong Province from May 2022 to December 2022. Subject exclusion criteria included individuals diagnosed with congestive heart failure, valvular heart disease, cardiac surgery, hepatic and renal dysfunction, and malignancy. Recruitment is through multiple channels such as hospital medical and surgical outpatient clinics, wards, and health examination centers. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Moreover, we obtained information on the patients' type of work through a questionnaire and their level of physical activity through the International Physical Activity Questionnaire (IPAQ). RESULTS: The logistic regression analysis results indicate that sleep disorder is a significant risk factor for CFD in hypertension patients(OR:1.85, 95%CI:[1.16,2.94]), mental workers(OR:0.12, 95%CI: [0.04,0.37]) and those who perform both manual and mental workers(OR: 0.5, 95%CI: [0.29,0.86]) exhibit protective effects against CFD. Compared to low-intensity, moderate physical activity(OR: 0.53, 95%CI: [0.32,0.87]) and high-intensity physical activity(OR: 0.26, 95%CI: [0.12,0.58]) protects against CFD in hypertension patients. The importance of predictors in the decision tree model was ranked as follows: physical activity level (54%), type of work (27%), and sleep disorders (19%). The area under the ROC curves the decision tree model predicted was 0.72 [95% CI: 0.68 to 0.76]. CONCLUSION: Moderate and high-intensity physical activity may reduce the risk of developing CFD in hypertensive patients. Sleep disorders is a risk factor for CFD in hypertensive patients. Hypertensive patients who engage in mental work and high-intensity physical activity effectively mitigate the onset of CFD in hypertensive patients.

Design, Synthesis, and Biological Evaluation of mTOR-Targeting PROTACs Based on MLN0128 and Pomalidomide.

Mechanistic target of rapamycin (mTOR) is an effective anti-tumor drug target. Several mTOR kinase inhibitors have entered clinical research, but there are still challenges of potential toxicity. As a new type of targeted drug, proteolysis targeting chimeras (PROTACs) have features of low dosage and low toxicity. However, this approach has been rarely reported to involve mTOR degradation. In this study, the mTOR kinase inhibitor MLN0128 was used as the ligand to the protein of interest and conjugated with pomalidomide by diverse intermediate linkage chains. Several potential small molecule PROTACs for the degradation of mTOR were designed and synthesized. PROTAC compounds exhibited mTOR inhibitory activity and suppressed MCF-7 cell proliferation. The representative compound P1 could inhibit the expression of mTOR downstream proteins and the growth of cancer cells by inducing autophagy but not affecting the cell cycle and not inducing apoptosis.

Heterometallic ZnHoMOF as a Dual-Responsive Luminescence Sensor for Efficient Detection of Hippuric Acid Biomarker and Nitrofuran Antibiotics.

Developing efficient and sensitive MOF-based luminescence sensors for bioactive molecule detection is of great significance and remains a challenge. Benefiting from favorable chemical and thermal stability, as well as excellent luminescence performance, a porous Zn(II)Ho(III) heterometallic-organic framework (ZnHoMOF) was selected here as a bifunctional luminescence sensor for the early diagnosis of a toluene exposure biomarker of hippuric acid (HA) through "turn-on" luminescence enhancing response and the daily monitoring of NFT/NFZ antibiotics through "turn-off" quenching effects in aqueous media with high sensitivity, acceptable selectivity, good anti-interference, exceptional recyclability performance, and low detection limits (LODs) of 0.7 ppm for HA, 0.04 ppm for NFT, and 0.05 ppm for NFZ. Moreover, the developed sensor was employed to quantify HA in diluted urine samples and NFT/NFZ in natural river water with satisfactory results. In addition, the sensing mechanisms of ZnHoMOF as a dual-response chemosensor in efficient detection of HA and NFT/NFZ antibiotics were conducted from the view of photo-induced electron transfer (PET), as well as inner filter effects (IFEs), with the help of time-dependent density functional theory (TD-DFT) and spectral overlap experiments.

BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework.

BACKGROUND: Automatic and accurate recognition of various biomedical named entities from literature is an important task of biomedical text mining, which is the foundation of extracting biomedical knowledge from unstructured texts into structured formats. Using the sequence labeling framework and deep neural networks to implement biomedical named entity recognition (BioNER) is a common method at present. However, the above method often underutilizes syntactic features such as dependencies and topology of sentences. Therefore, it is an urgent problem to be solved to integrate semantic and syntactic features into the BioNER model. RESULTS: In this paper, we propose a novel biomedical named entity recognition model, named BioByGANS (BioBERT/SpaCy-Graph Attention Network-Softmax), which uses a graph to model the dependencies and topology of a sentence and formulate the BioNER task as a node classification problem. This formulation can introduce more topological features of language and no longer be only concerned about the distance between words in the sequence. First, we use periods to segment sentences and spaces and symbols to segment words. Second, contextual features are encoded by BioBERT, and syntactic features such as part of speeches, dependencies and topology are preprocessed by SpaCy respectively. A graph attention network is then used to generate a fusing representation considering both the contextual features and syntactic features. Last, a softmax function is used to calculate the probabilities and get the results. We conduct experiments on 8 benchmark datasets, and our proposed model outperforms existing BioNER state-of-the-art methods on the BC2GM, JNLPBA, BC4CHEMD, BC5CDR-chem, BC5CDR-disease, NCBI-disease, Species-800, and LINNAEUS datasets, and achieves F1-scores of 85.15%, 78.16%, 92.97%, 94.74%, 87.74%, 91.57%, 75.01%, 90.99%, respectively. CONCLUSION: The experimental results on 8 biomedical benchmark datasets demonstrate the effectiveness of our model, and indicate that formulating the BioNER task into a node classification problem and combining syntactic features into the graph attention networks can significantly improve model performance.

Lysine biofortification of crops to promote sustained human health in the 21st century.

Crop biofortification is pivotal in preventing malnutrition, with lysine considered the main limiting essential amino acid (EAA) required to maintain human health. Lysine deficiency is predominant in developing countries where cereal crops are the staple food, highlighting the need for efforts aimed at enriching the staple diet through lysine biofortification. Successful modification of aspartate kinase (AK) and dihydrodipicolinate synthase (DHDPS) feedback inhibition has been used to enrich lysine in transgenic rice plants without yield penalty, while increases in the lysine content of quality protein maize have been achieved via marker-assisted selection. Here, we reviewed the lysine metabolic pathway and proposed the use of metabolic engineering targets as the preferred option for fortification of lysine in crops. Use of gene editing technologies to translate the findings and engineer lysine catabolism is thus a pioneering step forward.