Cryo-EM structure of the dopamine transporter with a novel atypical non-competitive inhibitor bound to the orthosteric site.

The regulation of dopamine (DA) removal from the synaptic cleft is a crucial process in neurotransmission and is facilitated by the sodium- and chloride-coupled dopamine transporter DAT. Psychostimulant drugs, cocaine, and amphetamine, both block the uptake of DA, while amphetamine also triggers the release of DA. As a result, they prolong or even amplify neurotransmitter signaling. Atypical inhibitors of DAT lack cocaine-like rewarding effects and offer a promising strategy for the treatment of drug use disorders. Here, we present the 3.2 Å resolution cryo-electron microscopy structure of the Drosophila melanogaster dopamine transporter (dDAT) in complex with the atypical non-competitive inhibitor AC-4-248. The inhibitor partially binds at the central binding site, extending into the extracellular vestibule, and locks the transporter in an outward open conformation. Our findings propose mechanisms for the non-competitive inhibition of DAT and attenuation of cocaine potency by AC-4-248 and provide a basis for the rational design of more efficacious atypical inhibitors.

Adaptive physiological and metabolic alterations in Staphylococcus aureus evolution under vancomycin exposure.

Staphylococcus aureus can develop antibiotic resistance and evade immune responses, causing infections in different body sites. However, the metabolic changes underlying this process are poorly understood. A variant strain, C1V, was derived from the parental strain C1 by exposing it to increasing concentrations of vancomycin in vitro. C1V exhibited a vancomycin-intermediate phenotype and physiological changes compared to C1. It showed higher survival rates than C1 when phagocytosed by Raw264.7 cells. Metabolomics analysis identified significant metabolic differences pre- and post-induction (C1 + SC1 vs. C1V + SC1V: 201 metabolites) as well as pre- and post-phagocytosis (C1 vs. SC1: 50 metabolites; C1V vs. SC1V: 95 metabolites). The variant strain had distinct morphological characteristics, decreased adhesion ability, impaired virulence, and enhanced resistance to phagocytosis compared to the parental strain. Differential metabolites may contribute to S. aureus ' resistance to antibiotics and phagocytosis, offering insights into potential strategies for altering vancomycin nonsusceptibility and enhancing phagocyte killing by manipulating bacterial metabolism.

The structure of the GM-CSF receptor complex reveals a distinct mode of cytokine receptor activation.

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a pleiotropic cytokine that controls the production and function of blood cells, is deregulated in clinical conditions such as rheumatoid arthritis and leukemia, yet offers therapeutic value for other diseases. Its receptors are heterodimers consisting of a ligand-specific alpha subunit and a betac subunit that is shared with the interleukin (IL)-3 and IL-5 receptors. How signaling is initiated remains an enigma. We report here the crystal structure of the human GM-CSF/GM-CSF receptor ternary complex and its assembly into an unexpected dodecamer or higher-order complex. Importantly, mutagenesis of the GM-CSF receptor at the dodecamer interface and functional studies reveal that dodecamer formation is required for receptor activation and signaling. This unusual form of receptor assembly likely applies also to IL-3 and IL-5 receptors, providing a structural basis for understanding their mechanism of activation and for the development of therapeutics.

Association between ABO blood groups and postoperative pain in children after adenotonsillectomy: a prospective cohort study.

BACKGROUND: It has been known that ABO blood groups are linked to the phenotypes of certain diseases; however, and the relationship between ABO blood groups and postoperative pain have not been extensively studied, especially in children. This study was to investigate whether there would be an association between the four major ABO blood groups and postoperative pain, as indicated by the differences in pain scores and rescue fentanyl requirements among blood groups in children after adenotonsillectomy. METHODS: A total of 124 children, aged 3-7 years, ASA I or II, and undergoing elective adenotonsillectomy were enrolled in the study. Postoperative pain was evaluated using the Children's Hospital of Eastern Ontario Pain Scale (CHEOPS) and the rescue fentanyl requirement in post anesthesia care unit (PACU) was analyzed. Pediatric Anesthesia Emergence Delirium (PAED) score and the duration of PACU were recorded. The postoperative nausea and vomiting (PONV) within 24 h were documented. RESULTS: Among four blood type groups, no significant differences were observed regarding surgery time, and the gaps of fentanyl given at the anesthesia induction and the first rescue fentanyl injection in PACU. However, patients from AB and B blood groups had significantly higher pain score at initial CHEOPS assessment and consequently, higher consumption of rescue fentanyl during PACU stay. A significantly higher percentage of patients had received > 1 µg/kg rescue fentanyl. Higher PAED scores were also observed in AB and B blood groups. CONCLUSION: Paediatric patients with AB and B blood type had higher postoperative CHEOPS pain score and required significantly more fentanyl for pain control than those with A and O blood type after T&A. The initial scores of PAED in patients with AB and B blood type were also higher than that in patients with A and O blood type.

CircDCLRE1C Regulated Lipopolysaccharide-Induced Inflammatory Response and Apoptosis by Regulating miR-214b-3p/STAT3 Pathway in Macrophages.

The immune cell inflammation response is closely related to the occurrence of disease, and much evidence has shown that circular RNAs (circRNAs) play vital roles in the occurrence of disease. However, the biological function and regulatory mechanisms of circRNAs in the immune cell inflammation response remain poorly understood. In this study, we constructed an inflammatory model using lipopolysaccharide (LPS)-stimulated chicken macrophage lines (also known as HD11) to verify the function and mechanism of the novel circDCLRE1C (ID: gga_circ_0001674), which was significantly upregulated in spleen tissues infected by coccidia and the macrophage cells exposed to LPS. The results showed that circDCLRE1C aggravated LPS-induced inflammation and apoptosis in HD11 cells. Systemically, circDCLRE1C acted as a sponge for miR-214b-3p binding sites thereby regulating the expression of STAT3. The overexpression of miR-214b-3p rescued the pro-inflammatory effect of circDCLRE1C in HD11 cells stimulated with LPS, and rescued the high expression of STAT3. In conclusion, our study showed that circDCLRE1C could aggravate LPS-induced inflammation and apoptosis through competitive adsorption of miR-214b-3p, thereby increasing the expression of STAT3.

Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation.

BACKGROUND: Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them. METHODS: To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation. RESULTS: Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results. CONCLUSIONS: We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.

Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.

BACKGROUND: Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. METHODS: An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. RESULTS: Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. CONCLUSIONS: A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes.

We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.

Aptamer-assisted superparamagnetic iron oxide nanoparticles as multifunctional drug delivery platform for chemo-photodynamic combination therapy.

Superparamagnetic iron oxide nanoparticles (SPION) were widely employed as targeted drug delivery platform due to their unique magnetic property and effortless surface modification. However, the lack of targeting accuracy has been a big obstacle for SPION used in precise medicine. Herein, the tumor-targeting of SPION was enhanced by the conjugation of an aptamer-hybridized nucleic acid structure. The aptamer modified on the surface of SPION was composed of a double-stranded DNA (dsDNA) and a G-quadruplex DNA (AS1411) structure, which carried a chemical anticancer drug, daunomycin (DNM) and a photosensitizer molecule, namely 5, 10, 15, 20-tetra (phenyl-4-N-methyl-4-pyridyl) porphyrin (TMPyP), respectively. The aptamer-dsDNA conjugated SPION nanocarriers (named Apt-S8@SPION) exhibited good stability in serum and nuclease DNase I. The drug-loaded nanocarriers (TMPyP&DNM&Apt-S8@SPION) have high cellular cytotoxicity to A549 and C26 cells which are represently nucleolin-overexpressing cancer cells. The nucleolin-blocking experiments unambiguously evidenced that the formed nanomedicine could target to the cell surface via the specific AS1411-nucleolin interaction, which increased the efficiency of cell uptake. Meanwhile, the TMPyP&DNM&Apt-S8@SPION nanospheres could produce cytotoxic reactive oxygen species efficiently by irradiation of visible light for establishing a new type of PDT to cancer cells. Therefore, the designed TMPyP&DNM&Apt-S8@SPION nanoparticles have magnetic-aptamer dual targeting and combined chemo-photodynamic therapy, and thus were supposed to be ideal drug delivery vehicles with great potential in the era of precision medicine.

Broadening of the sum-frequency phase-matching bandwidth by temperature gradient in MgO:PPLN.

We demonstrate bandwidth broadening in cascaded MgO-doped periodically poled lithium niobate (MgO:PPLN) crystals (Λ=10.3 µm) using the temperature-gradient technique. Up to 2.8 nm bandwidth at 600 nm spectral region is achieved using two 50 mm long cascaded MgO:PPLN crystals via sum frequency generation. This technique combines the merits of high conversion efficiency attributed to cascaded nonlinearity and the reconfigurability of temperature-gradient-induced chirp for broadening of the input wavelength acceptance range.