Solvent Effect on Dative and Ionic Bond Strengths: A Unified Theory from the Potential Analysis and Valence Bond (VB) Computation.

Solvent molecules interact with a solute through various intermolecular forces. Here we employed a potential energy surface (PES) analysis to interpret the solvent-induced variations in the strengths of dative (Me3NBH3) and ionic (LiCl) bonds, which possess both ionic and covalent (neutral) characteristics. The change of a bond is driven by the gradient (force) of the solvent-solute interaction energy with respect to the focused bond length. Positive force shortens the bond length and increases the bond force constant, leading to a blue-shift of the bond stretching vibrational frequency upon solvation. Conversely, negative force elongates the bond, resulting in a reduced bond force constant and red-shift of the stretching vibrational frequency. The different responses of Me3NBH3 and LiCl to solvation are studied with valence bond (VB) theory, as Me3NBH3 and LiCl are dominated by the neutral covalent VB structure and the ionic VB structure, respectively. The dipole moment of an ionic VB structure increases along the increasing bond distance, while the dipole moment of a neutral covalent VB structure increases with the decreasing bond distance. The roles of the dominating VB structures are further examined by the geometry optimizations and frequency calculations with the block-localized wavefunction (BLW) method.

Conformational Preference of Lithium Polysulfide Clusters Li2Sx (x = 4-8) in Lithium-Sulfur Batteries.

Structures are of fundamental importance for diverse studies of lithium polysulfide clusters, which govern the performance of lithium-sulfur batteries. The ring-like geometries were regarded as the most stable structures, but their physical origin remains elusive. In this work, we systematically explored the minimal structures of Li2Sx (x = 4-8) clusters to uncover the driving force for their conformational preferences. All low-lying isomers were generated by performing global searches using the ABCluster program, and the ionic nature of the Li···S interactions was evidenced with the energy decomposition analysis based on the block-localized wave function (BLW-ED) approach and further confirmed with the quantum theory of atoms in molecule (QTAIM). By analysis of the contributions of various energy components to the relative stability with the references of the lowest-lying isomers, the controlling factor for isomer preferences was found to be the polarization interaction. Notably, although the electrostatic interaction dominates the binding energies, it contributes favorably to the relative stabilities of most isomers. The Li+···Li+ distance is identified as the key geometrical parameter that correlates with the strength of the polarization of the Sx2- fragment imposed by the Li+ cations. Further BLW-ED analyses reveal that the cooperativity of the Li+ cations primarily determines the relative strength of the polarization.

Electrochemical immuno determination of connective tissue growth factor levels on nitrogen-doped graphene.

Connective tissue growth factor (CTGF) is a disease marker of rheumatoid arthritis (RA), and its rapid and sensitive detection is essential for the diagnosis of RA. In this work, a three-dimensional pore structure of alkali-activated nitrogen-doped graphene (aN-G) was used as an electrode modification material, and a label-free electrochemical immunosensor for the sensitive detection of CTGF was successfully constructed by the formation of an amide bond between amino groups in protein and carboxyl groups on the carbon surface. Under optimized conditions, the sensor achieved accurate detection of CTGF in the wide range of 0.0625 ~ 2000 pg mL-1. It had good accuracy (95.0 ~ 100.1%), repeatability (1.2 ~ 2.2%), stability, selectivity, and a low limit of detection (0.0424 pg mL-1, S/N = 3). The sensor was used in serum samples of patients with RA, and CTGF was also successfully detected. Based on this, the electrochemical sensor is expected to become an effective method for RA diagnosis and treatment effect evaluation.

Successful endotracheal intervention for primary tracheal acinic cell carcinoma: A case report and literature review.

INTRODUCTION: Primary tracheal acinic cell carcinoma (ACC) is an exceptionally rare malignancy, posing challenges in understanding its clinical behavior and optimal management. Surgical resection has traditionally been the primary treatment modality, but we present a compelling case of tracheal ACC managed with endotracheal intervention, challenging conventional approaches. PATIENT CONCERNS: A 53-year-old woman presented with shortness of breath, cough, and hemoptysis. Enhanced computed tomography revealed an obstructive tracheal lesion, leading to her referral for further assessment. DIAGNOSIS: Microscopic evaluation, immunohistochemistry, and clinical assessments confirmed primary tracheal ACC, an exceedingly rare condition with limited clinical insights. INTERVENTIONS: We utilized rigid bronchoscopy to perform endotracheal intervention, successfully resecting the tumor and restoring tracheal patency. Postoperatively, the patient received no radiotherapy or chemotherapy. OUTCOMES: The patient achieved complete recovery, with 24-month follow-up examinations indicating no recurrence or metastatic disease. Only minimal scar tissue remained at the resection site. CONCLUSION: This case demonstrates the potential of endotracheal intervention as a curative approach for primary tracheal ACC, minimizing invasiveness and preserving tracheal function. Collaborative research efforts and extensive case reporting are crucial for advancing our understanding of this rare malignancy and optimizing treatment strategies for improved patient outcomes.

MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.

Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in accelerating antimalarial drug discovery, the performance of deep learning models based on molecular graph and other co-representation approaches warrants further exploration. Current research has overlooked mutant strains of the malaria parasite with varying degrees of sensitivity or resistance, and has not covered the prediction of inhibitory activities across the three major life cycle stages (liver, asexual blood, and gametocyte) within the human host, which is crucial for both treatment and transmission blocking. In this study, we manually curated a benchmark antimalarial activity dataset comprising 407,404 unique compounds and 410,654 bioactivity data points across ten Plasmodium phenotypes and three stages. The performance was systematically compared among two fingerprint-based ML models (RF::Morgan and XGBoost:Morgan), four graph-based DL models (GCN, GAT, MPNN, and Attentive FP), and three co-representations DL models (FP-GNN, HiGNN, and FG-BERT), which reveal that: 1) The FP-GNN model achieved the best predictive performance, outperforming the other methods in distinguishing active and inactive compounds across balanced, more positive, and more negative datasets, with an overall AUROC of 0.900; 2) Fingerprint-based ML models outperformed graph-based DL models on large datasets (>1000 compounds), but the three co-representations DL models were able to incorporate domain-specific chemical knowledge to bridge this gap, achieving better predictive performance. These findings provide valuable guidance for selecting appropriate ML and DL methods for antimalarial activity prediction tasks. The interpretability analysis of the FP-GNN model revealed its ability to accurately capture the key structural features responsible for the liver- and blood-stage activities of the known antimalarial drug atovaquone. Finally, we developed a web server, MalariaFlow, incorporating these high-quality models for antimalarial activity prediction, virtual screening, and similarity search, successfully predicting novel triple-stage antimalarial hits validated through experimental testing, demonstrating its effectiveness and value in discovering potential multistage antimalarial drug candidates.

Molecularly imprinted flexible sensor based on nitrogen-doped graphene for selective determination of formononetin.

Formononetin is an isoflavone compound found in many traditional Chinese medicines that has a wide range of pharmacological activities. It is critical to develop a sensitive, accurate, and rapid determination method for in-depth formononetin research. A molecularly imprinted electrochemical sensor was constructed in this study. After the flexible electrode (ITO-PET) was modified with nitrogen-doped graphene (NG), the molecularly imprinted polymer (MIP) was electropolymerized on the surface using o-phenylenediamine (OPD) as the functional monomer and formononetin as the template. Under optimized experimental conditions, the MIP sensor detected formononetin selectively in 0.1 M phosphate buffer solution (PBS) with the linear range of 3 ~ 120 µM, a detection limit as low as 1.14 µM (S/N = 3), and good anti-interference ability and reproducibility. The analytical performance of the proposed MIP/NG/ITO-PET was evaluated for the detection of formononetin in real samples such as methanol extract of Radix Astragali and mouse plasma with good accuracy and precision.