Cavernous Malformation From Cranial Nerves: A Systematic Review With a Novel Classification and Patient-Level Analysis.

BACKGROUND AND OBJECTIVES: Cavernous malformations (CMs) occurring in the cranial nerve (CN) are extremely rare, and there is currently no comprehensive review on CN CMs, leading to a lack of sufficient understanding of CN CMs. We aimed to systematically review all published CN CM cases; summarize the epidemiology, clinical manifestations, treatment, and prognosis of CN CMs; and identify factors influencing the prognosis of CN CMs. METHODS: This systematic review identified all cases potentially diagnosed with CN CM through a systematic search of PubMed, SCOPUS, Web of Science, and Cochrane databases. This represents the most comprehensive systematic review to date. We classified CN CMs based on their anatomic origins. Patient characteristics, disease manifestations, treatment approaches, and prognosis were summarized descriptively. Further analysis was conducted to identify factors influencing the prognosis of CN CMs. RESULTS: The final analysis included 108 articles (127 individual patient cases). The optic nerve (49/128, 38.3%) is the most commonly affected nerve. Notably, CN CMs can be categorized into 3 types: Intraneural, Perineural, and Extraneural. Preoperative nerve function status and novel classification were associated with the prognosis of CN CMs (P = .001; P < .001). The postoperative neurological deterioration rate for the Intraneural type was 19/37 (51.4%); for the Extraneural type, it was 13/69 (18.8%); and for the Perineural type, it was 1/22 (4.5%) (P < .001). CONCLUSION: We reviewed all the published CN CMs to date, offering a comprehensive description of CN CMs for the first time and identifying prognostic factors. The classification of CN CMs proposed in this study could serve as guidance for the selection of intraoperative treatment regimens. The findings of this systematic review are expected to provide a foundation for clinical decision-making in this crucial rare disease and lay the groundwork for developing relevant clinical guidelines.

Good Cycling Stabilities of Sn/SnO2/C/S Composite Electrodes with Introduced Active Tin Atoms in Li-S Batteries.

Polysulfides are easily dissolved in the electrolyte of Li-S batteries after long cycles. Sn atom modification electrodes are beneficial for improving cycling stabilities of Li-S batteries. However, the influence of Sn atoms on the structure and electrochemical performance of SnO2/C composite materials is not explored. Sn/SnO2/C composite materials are developed as sulfur carriers in Li-S batteries in our work. In addition, the cycling stability mechanism of Sn/SnO2/C/S composite electrodes is also elucidated. Results show that introduced Sn/SnO2/C/S composite electrodes display good cycling stability (420.1 mAh·g-1 at 1C after 1000 cycles) in Li-S batteries. The sulfur load of Sn/SnO2/C/S composite electrodes is 80 wt % (2 mg-1·cm-2). The introduction of Sn into Sn/SnO2/C/S composite electrodes plays three roles. The first role is to enhance the structural stability of SnO2. The second role is to help adsorb active sulfur ions. The last role is to promote the electron transportation ability during the initial discharging/charging process. Sn/SnO2/C/S composite electrodes are beneficial for inhibiting the dissolution of polysulfides in electrolytes after long cycles.

Fusarium pseudograminearum biomass and toxin accumulation in wheat tissues with and without Fusarium crown rot symptoms.

Fusarium crown rot (FCR) is an important and devastating disease of wheat (Triticum aestivum) caused by the fungus Fusarium pseudograminearum and related pathogens. Using two distinct susceptible cultivars, we investigated the isolation frequencies of F. pseudograminearum and quantified its biomass accumulation and the levels of the associated toxins deoxynivalenol (DON) and DON-3-glucoside (D3G) in inoculated field-grown wheat plants. We detected F. pseudograminearum in stem, peduncle, rachis, and husk tissues, but not in grains, whereas DON and D3G accumulated in stem, rachis, husk, and grain tissues. Disease severity was positively correlated with the frequency of pathogen isolation, F. pseudograminearum biomass, and mycotoxin levels. The amount of F. pseudograminearum biomass and mycotoxin contents in asymptomatic tissue of diseased plants were associated with the distance of the tissue from the diseased internode and the disease severity of the plant. Thus, apparently healthy tissue may harbor F. pseudograminearum and contain associated mycotoxins. This research helps clarify the relationship between F. pseudograminearum occurrence, F. pseudograminearum biomass, and mycotoxin accumulation in tissues of susceptible wheat cultivars with or without disease symptoms, providing information that can lead to more effective control measures.

V-set and immunoglobulin domain containing 4 inhibits oxidative stress, mitochondrial dysfunction, and inflammation to attenuate Parkinson's disease progression by activating the JAK2/STAT3 pathway.

OBJECTIVE: V-set and immunoglobulin domain containing 4 (VSIG4) inhibits neurological dysfunction, microglial M1 polarization, and inflammation to participate in the progression of neurological disorders, but evidence regarding Parkinson's disease (PD) is scarce. The present study intended to investigate the engagement of VSIG4 in PD progression, and the potential mechanism. METHODS: BV-2 cells were treated with 1-Methyl-4-phenylpyridinium (MPP+) to establish PD model. MPP+ treated BV-2 cells were infected with VSIG4 overexpression adenovirus-associated virus (AAV) (oeVSIG4) and negative control AAV (oeNC), and AZD1480 (JAK2 inhibitor) was added to these cells. RESULTS: MPP+ reduced VSIG4 mRNA (P < 0.05) and protein (P < 0.05) in BV-2 cells. Interestingly, VSIG4 reduced malondialdehyde (P < 0.01), reactive oxygen species (P < 0.01), NOD-like receptor family pyrin domain containing 3 (P < 0.05), cleaved-caspase1 (P < 0.05), tumor necrosis factor-α (P < 0.05), and interleukin-1ß (P < 0.05), but increased glutathione (P < 0.05), mitochondrial membrane potential (P < 0.05), phosphorylation (p)-JAK2 (P < 0.05), and p-STAT3 (P < 0.01) in MPP+ treated BV-2 cells, which indicated that VSIG4 inhibited oxidative stress, mitochondrial dysfunction, and inflammation, as well as activated the JAK2/STAT3 pathway in PD model. Moreover, AZD1480 inhibited the JAK2/STAT3 pathway and aggravated oxidative stress, mitochondrial dysfunction, and inflammation in PD model (all P < 0.05). Importantly, AZD1480 attenuated the influence of VSIG4 on oxidative stress, mitochondrial dysfunction, inflammation, and the JAK2/STAT3 pathway in PD model (all P < 0.05). CONCLUSION: VSIG4 suppresses oxidative stress, mitochondrial dysfunction, and inflammation by activating the JAK2/STAT3 pathway, which may be helpful in attenuating PD progression.

Emerging opportunities of using large language models for translation between drug molecules and indications.

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Metabolomics and proteomics insights into subacute ruminal acidosis etiology and inhibition of proliferation of yak rumen epithelial cells in vitro.

BACKGROUND: Untargeted metabolomics and proteomics were employed to investigate the intracellular response of yak rumen epithelial cells (YRECs) to conditions mimicking subacute rumen acidosis (SARA) etiology, including exposure to short-chain fatty acids (SCFA), low pH5.5 (Acid), and lipopolysaccharide (LPS) exposure for 24 h. RESULTS: These treatments significantly altered the cellular morphology of YRECs. Metabolomic analysis identified significant perturbations with SCFA, Acid and LPS treatment affecting 259, 245 and 196 metabolites (VIP > 1, P < 0.05, and fold change (FC) ≥ 1.5 or FC ≤ 0.667). Proteomic analysis revealed that treatment with SCFA, Acid, and LPS resulted in differential expression of 1251, 1396, and 242 proteins, respectively (FC ≥ 1.2 or ≤ 0.83, P < 0.05, FDR < 1%). Treatment with SCFA induced elevated levels of metabolites involved in purine metabolism, glutathione metabolism, and arginine biosynthesis, and dysregulated proteins associated with actin cytoskeleton organization and ribosome pathways. Furthermore, SCFA reduced the number, morphology, and functionality of mitochondria, leading to oxidative damage and inhibition of cell survival. Gene expression analysis revealed a decrease the genes expression of the cytoskeleton and cell cycle, while the genes expression associated with inflammation and autophagy increased (P < 0.05). Acid exposure altered metabolites related to purine metabolism, and affected proteins associated with complement and coagulation cascades and RNA degradation. Acid also leads to mitochondrial dysfunction, alterations in mitochondrial integrity, and reduced ATP generation. It also causes actin filaments to change from filamentous to punctate, affecting cellular cytoskeletal function, and increases inflammation-related molecules, indicating the promotion of inflammatory responses and cellular damage (P < 0.05). LPS treatment induced differential expression of proteins involved in the TNF signaling pathway and cytokine-cytokine receptor interaction, accompanied by alterations in metabolites associated with arachidonic acid metabolism and MAPK signaling (P < 0.05). The inflammatory response and activation of signaling pathways induced by LPS treatment were also confirmed through protein interaction network analysis. The integrated analysis reveals co-enrichment of proteins and metabolites in cellular signaling and metabolic pathways. CONCLUSIONS: In summary, this study contributes to a comprehensive understanding of the detrimental effects of SARA-associated factors on YRECs, elucidating their molecular mechanisms and providing potential therapeutic targets for mitigating SARA.

Effects of Tirofiban on Neurological Deterioration in Patients With Acute Ischemic Stroke: A Randomized Clinical Trial.

Importance: Evidence supports using antiplatelet therapy in patients with acute ischemic stroke. However, neurological deterioration remains common under the currently recommended antiplatelet regimen, leading to poor clinical outcomes. Objective: To determine whether intravenous tirofiban administered within 24 hours of stroke onset prevents early neurological deterioration in patients with acute noncardioembolic stroke compared with oral aspirin. Design, Setting, and Participants: This investigator-initiated, multicenter, open-label, randomized clinical trial with blinded end-point assessment was conducted at 10 comprehensive stroke centers in China between September 2020 and March 2023. Eligible patients were aged 18 to 80 years with acute noncardioembolic stroke within 24 hours of onset and had a National Institutes of Health Stroke Scale (NIHSS) score of 4 to 20. Intervention: Patients were assigned randomly (1:1) to receive intravenous tirofiban or oral aspirin for 72 hours using a central, web-based, computer-generated randomization schedule; all patients then received oral aspirin. Main Outcome: The primary efficacy outcome was early neurological deterioration (increase in NIHSS score ≥4 points) within 72 hours after randomization. The primary safety outcome was symptomatic intracerebral hemorrhage within 72 hours after randomization. Results: A total of 425 patients were included in the intravenous tirofiban (n = 213) or oral aspirin (n = 212) groups. Median (IQR) age was 64.0 years (56.0-71.0); 124 patients (29.2%) were female, and 301 (70.8%) were male. Early neurological deterioration occurred in 9 patients (4.2%) in the tirofiban group and 28 patients (13.2%) in the aspirin group (adjusted relative risk, 0.32; 95% CI, 0.16-0.65; P = .002). No patients in the tirofiban group experienced intracerebral hemorrhage. At 90-day follow-up, 3 patients (1.3%) in the tirofiban group and 3 (1.5%) in the aspirin group died (adjusted RR, 1.15; 95% CI, 0.27-8.54; P = .63), and the median (IQR) modified Rankin scale scores were 1.0 (0-1.25) and 1.0 (0-2), respectively (adjusted odds ratio, 1.28; 95% CI, 0.90-1.83; P = .17). Conclusions and Relevance: In patients with noncardioembolic stroke who were seen within 24 hours of symptom onset, tirofiban decreased the risk of early neurological deterioration but did not increase the risk of symptomatic intracerebral hemorrhage or systematic bleeding. Trial Registration: ClinicalTrials.gov Identifier: NCT04491695.

PCMRESP: A Method for Polarizable Force Field Parameter Development and Transferability of the Polarizable Gaussian Multipole Models Across Multiple Solvents.

The transferability of force field parameters is a crucial aspect of high-quality force fields. Previous investigations have affirmed the transferability of electrostatic parameters derived from polarizable Gaussian multipole models (pGMs) when applied to water oligomer clusters, polypeptides across various conformations, and different sequences. In this study, we introduce PCMRESP, a novel method for electrostatic parametrization in solution, intended for the development of polarizable force fields. We utilized this method to assess the transferability of three models: a fixed charge model and two variants of pGM models. Our analysis involved testing these models on 377 small molecules and 100 tetra-peptides in five representative dielectric environments: gas, diethyl ether, dichloroethane, acetone, and water. Our findings reveal that the inclusion of atomic polarization significantly enhances transferability and the incorporation of permanent atomic dipoles, in the form of covalent bond dipoles, leads to further improvements. Moreover, our tests on dual-solvent strategies demonstrate consistent transferability for all three models, underscoring the robustness of the dual-solvent approach. In contrast, an evaluation of the traditional HF/6-31G* method indicates poor transferability for the pGM-ind and pGM-perm models, suggesting the limitations of this conventional approach.

Efficient fabrication of anisotropic regenerated cellulose films from bamboo via a facile wet extrusion strategy.

Bamboo, featuring fast growth rate and high cellulose content, is considered to be one of the most attractive feedstocks for degradable bio-materials as a substitute for plastics. However, those was limited to the fields of bamboo structural materials mainly by physical processes. Herein, we report a facile continuous wet extrusion strategy for scalable manufacturing of anisotropic regenerated cellulose films in alkali/urea aqueous solution for the first time. The bamboo cellulose solution was regenerated in H2SO4/Na2SO4/ZnSO4 aqueous solution to facilitate the construction of dense fibrils networks. Moreover, under the synergistic effect of shear orientations and stretching processes in wet extrusion molding, the cellulose networks promoted further orientated assembly into aligned fibrils. Therefore, these anisotropic cellulose hydrogels exhibited good mechanical properties, and the tensile strength was increased from 1.67 MPa of anisotropic cellulose hydrogel with 1.0 of stretching ration (ACH-1.0) to 2.13 MPa of ACH-1.4 with increasing stretching ratio from 1.0 to 1.4, which was about 1.34 times higher than that of the isotropic hydrogel fabricated by tape-casting. Moreover, ACH-1.4 exhibited commendable thermal stability and air barrier properties. This work demonstrated a simple and continuous bottom-up approach for fabrication of anisotropic bamboo-based cellulose hydrogels and films with excellent mechanical properties.

In Situ Formation of Fibronectin-Enriched Protein Corona on Epigenetic Nanocarrier for Enhanced Synthetic Lethal Therapy.

PARP inhibitors (PARPi)-based synthetic lethal therapy demonstrates limited efficacy for most cancer types that are homologous recombination (HR) proficient. To potentiate the PARPi application, a nanocarrier based on 5-azacytidine (AZA)-conjugated polymer (PAZA) for the codelivery of AZA and a PARP inhibitor, BMN673 (BMN) is developed. AZA conjugation significantly decreased the nanoparticle (NP) size and increased BMN loading. Molecular dynamics simulation and experimental validations shed mechanistic insights into the self-assembly of effective NPs. The small PAZA NPs demonstrated higher efficiency of tumor targeting and penetration than larger NPs, which is mediated by a new mechanism of active targeting that involves the recruitment of fibronectin from serum proteins following systemic administration of PAZA NPs. Furthermore, it is found that PAZA carrier sensitize the HR-proficient nonsmall cell lung cancer (NSCLC) to BMN, a combination therapy that is more effective at a lower AZA/BMN dosage. To investigate the underlying mechanism, the tumor immune microenvironment and various gene expressions by RNAseq are explored. Moreover, the BMN/PAZA combination increased the immunogenicity and synergized with PD-1 antibody in improving the overall therapeutic effect in an orthotopic model of lung cancer (LLC).

Lower-grade gliomas surgery guided by GRPR-targeting PET/NIR dual-modality image probe: a prospective and single-arm clinical trial.

Purpose: Lower-grade gliomas (LGGs) are a group of infiltrative growing glial brain tumors characterized by intricate intratumoral heterogeneity and subtle visual appearance differences from non-tumor tissue, which can lead to errors in pathologic tissue sampling. Although 5-ALA fluorescence has been an essential method for visualizing gliomas during surgery, its effectiveness is limited in the case of LGGs due to low sensitivity. Therefore, we developed a novel PET/NIR dual-modality image probe targeting gastrin-releasing peptide receptor (GRPR) in glioma cells to enhance tumor visualization and improve the accuracy of sampling. Methods: A prospective, non-randomized, single-center feasibility clinical trial (NCT03407781) was conducted in the referral center from October 21, 2016, to August 17, 2018. Consecutive enrollment included patients suspected of having LGGs and considered suitable candidates for surgical removal. Group 1 comprised ten patients who underwent preoperative 68Ga-IRDye800CW-BBN PET/MRI assessment followed by intraoperative fluorescence-guided surgery. Group 2 included 42 patients who underwent IRDye800CW-BBN fluorescence-guided surgery. The primary endpoints were the predictive value of preoperative PET imaging for intraoperative fluorescence and the sensitivity and specificity of fluorescence-guided sampling. Results: Thirty-nine patients were included in the in-depth analysis of endpoints, with 25 (64.1%) exhibiting visible fluorescence, while 14 (35.9%) did not. The preoperative positive PET uptake exhibited a greater accuracy in predicting intraoperative fluorescence compared to MRI enhancement (100% [10/10] vs. 87.2% [34/39]). A total of 125 samples were harvested during surgery. Compared with pathology, subjective fluorescence intensity showed a sensitivity of 88.6% and a specificity of 88.2% in identifying WHO grade III samples. For WHO grade II samples, the sensitivity and specificity of fluorescence were 54.7% and 88.2%, respectively. Conclusion: This study has demonstrated the feasibility of the novel dual-modality imaging technique for integrated pre- and intraoperative targeted imaging via the same molecular receptor in surgeries for LGGs. The PET/NIR dual-modality probe exhibits promise for preoperative surgical planning in fluorescence-guided surgery and provides greater accuracy in guiding tumor sampling compared to 5-ALA in patients with LGGs.

Systemic and cerebrospinal fluid biomarkers for tuberculous meningitis identification and treatment monitoring.

IMPORTANCE: Tuberculous meningitis is a life-threatening infection with high mortality and disability rates. Current diagnostic methods using cerebrospinal fluid (CSF) samples have limited sensitivity and lack predictive biomarkers for evaluating prognosis. This study's findings reveal excessive activation of the immune response during tuberculous meningitis (TBM) infection. Notably, a strong negative correlation was observed between CSF levels of monokine induced by interferon-γ (MIG) and the CSF/blood glucose ratio in TBM patients. MIG also exhibited the highest area under the curve with high sensitivity and specificity. This study suggests that MIG may serve as a novel biomarker for differentiating TBM infection in CSF or serum, potentially leading to improved diagnostic accuracy and better patient outcomes.

Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the "AUCell" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using "CellChat." The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation.

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

Predicting anti-SARS-CoV-2 activities of chemical compounds using machine learning models.

To accelerate the discovery of novel drug candidates for Coronavirus Disease 2019 (COVID-19) therapeutics, we reported a series of machine learning (ML)-based models to accurately predict the anti-SARS-CoV-2 activities of screening compounds. We explored 6 popular ML algorithms in combination with 15 molecular descriptors for molecular structures from 9 screening assays in the COVID-19 OpenData Portal hosted by NCATS. As a result, the models constructed by k-nearest neighbors (KNN) using the molecular descriptor GAFF+RDKit achieved the best overall performance with the highest average accuracy of 0.68 and relatively high average area under the receiver operating characteristic curve of 0.74, better than other ML algorithms. Meanwhile, The KNN model for all assays using GAFF+RDKit descriptor outperformed using other descriptors. The overall performance of our developed models was better than REDIAL-2020 (R). A web server (https://clickff.org/amberweb/covid-19-cp) was developed to enable users to predict anti-SARS-CoV-2 activities of arbitrary compounds using the COVID-19-CP (P) models. Besides the descriptor-based machine learning models, we also developed graph-based Attentive FP (A) models for the 9 assays. We found that the Attentive FP models achieved a comparable performance to that of COVID-19-CP and outperformed the REDIAL-2020 models. The consensus prediction utilizing both COVID-19-CP and Attentive FP can significantly boost the prediction accuracy as assessed by comparing its performance with other three individual models (R, P, A) utilizing the Wilcoxon signed-rank test, thus can ultimately improve the success rate of COVID-19 drug discovery.

Sodium-dependent glucose transporter 2 inhibitor alleviates renal lipid deposition and improves renal oxygenation levels in newly diagnosed type 2 diabetes mellitus patients: a randomized controlled trial.

BACKGROUND: Sodium-dependent glucose transporter 2 inhibitor (SGLT2i) has the advantages of effectively lowering blood glucose levels and improving renal outcomes in diabetic patients. This study evaluated the effect of canagliflozin on intrarenal lipid content and oxygenation in newly diagnosed type 2 diabetes mellitus (T2DM) patients. METHODS: A total of 64 newly diagnosed T2DM patients with normal renal function were randomly divided into canagliflozin (n = 33) and glimepiride control (n = 31) groups. All patients underwent functional magnetic resonance imaging (fMRI) scanning to assay patients' intrarenal lipid content and oxygenation level before and after 24 weeks of treatment. Furthermore, the relationship between body mass index and intrarenal lipid content in T2DM patients was analyzed and the correlation between changes in intrarenal lipid content and improvements in renal hypoxia was further assessed. RESULTS: The canagliflozin group had a greater decrease in body weight and blood uric acid level than the glimepiride group (all P < 0.05). The intrarenal lipid content could be significantly reduced after canagliflozin treatment for 24 weeks. The R2* values, a parameter for quantifying the oxygen content in tissues and is inversely related to the oxygen content, of the renal cortex and medulla in the canagliflozin group decreased from the baseline by 6.40% (P < 0.01) and 12.09% (P = 0.000007), respectively. In addition, the degree of reduction of fat fraction (ΔFF) in the kidneys of the canagliflozin group was correlated with the degree of improvement of oxygenation level (ΔR2*) in the renal cortex (r = 0.422, P = 0.014). CONCLUSIONS: The early renal protective effect of SGLT2i in newly diagnosed T2DM patients may be partly attributed to the amelioration of renal hypoxia via the alleviation of ectopic lipid deposition in the kidneys. TRIAL REGISTRATION: Chu Hsien-I Memorial Hospital of Tianjin Medical University (ChiCTR2000037951).

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges.

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling.

The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.