LINC00173 silence and estrone supply suppress ER+ breast cancer by estrogen receptor α degradation and LITAF activation.

Persistent activation of estrogen receptor alpha (ERα)-mediated estrogen signaling plays a pivotal role in driving the progression of estrogen receptor positive (ER+) breast cancer (BC). In the current study, LINC00173, a long non-coding RNA, was found to bind both ERα and lipopolysaccharide (LPS)-induced tumor necrosis factor alpha (TNFα) factor (LITAF), then cooperatively to inhibit ERα protein degradation by impeding the nuclear export of ERα. Concurrently, LITAF was found to attenuate TNFα transcription after binding to LINC00173, and this attenuating transcriptional effect was quite significant under lipopolysaccharide stimulation. Distinct functional disparities between estrogen subtypes emerge, with estradiol synergistically promoting ER+ BC cell growth with LINC00173, while estrone (E1) facilitated LITAF-transcriptional activation. In terms of therapeutic significance, silencing LINC00173 alongside moderate addition of E1 heightened TNFα and induced apoptosis, effectively inhibiting ER+ BC progression.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

The association between trajectory of serum cholesterol, statin dosage, and the risk of recurrent biliary stone diseases.

BACKGROUND: Statins may reduce the risk of recurrent gallstone disease by decreasing bile cholesterol saturation and pathogenicity. However, limited studies have investigated this issue. This study aimed to assess whether statin doses and serum cholesterol levels were associated with a decreased risk of recurrent biliary stone diseases after the first event index, with a follow-up time of 15 years. METHODS: Based on the Chang Gung Research Database (CGRD) between January 1, 2001, and December 31, 2020, we enrolled 68,384 patients with the International Classification of Diseases, Ninth and Tenth Revision codes of choledocholithiasis. After exclusions, 32,696 patients were divided into non-statin (<28 cDDD, cumulative defined daily doses) (n = 27,929) and statin (≥28 cDDD) (n = 4767) user groups for analysis. Serum cholesterol trajectories were estimated using group-based trajectory modeling (n = 8410). RESULTS: The statin users had higher Charlson Comorbidity Index (CCI) scores than the non-statin users. Time-dependent Cox regression analysis showed that statin use >365 cDDD was associated with a significantly lower risk of recurrent biliary stones (adjusted hazard ratio [aHR] = 0.28, 95% CI, 0.24-0.34; p < 00.0001), acute pancreatitis (aHR = 0.24, 95% CI, 0.17-0.32, p < 00.0001), and cholangitis (aHR = 0.28, 95% CI, 0.25-0.32, p < 00.0001). Cholecystectomy was also a protective factor for recurrent biliary stones (aHR = 0.41, 95% CI, 0.37-0.46; p < 00.0001). The higher trajectory serum cholesterol group (Group 3) had a lower risk trend for recurrent biliary stones (aHR = 0.79, p = 0.0700) and a lower risk of cholangitis (aHR = 0.79, p = 0.0071). CONCLUSION: This study supports the potential benefits of statin use and the role of cholecystectomy in reducing the risk of recurrent biliary stone diseases.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Protein phosphorylation and kinases: Potential therapeutic targets in necroptosis.

Necroptosis is a pivotal contributor to the pathogenesis of various human diseases, including those affecting the nervous system, cardiovascular system, pulmonary system, and kidneys. Extensive investigations have elucidated the mechanisms and physiological ramifications of necroptosis. Among these, protein phosphorylation emerges as a paramount regulatory process, facilitating the activation or inhibition of specific proteins through the addition of phosphate groups to their corresponding amino acid residues. Currently, the targeting of kinases has gained recognition as a firmly established and efficacious therapeutic approach for diverse diseases, notably cancer. In this comprehensive review, we elucidate the intricate role of phosphorylation in governing key molecular players in the necroptotic pathway. Moreover, we provide an in-depth analysis of recent advancements in the development of kinase inhibitors aimed at modulating necroptosis. Lastly, we deliberate on the prospects and challenges associated with the utilization of kinase inhibitors to modulate necroptotic processes.

The multicenter real-world report of the efficacies of 14-day esomeprazole-based and rabeprazole-based high-dose dual therapy in first-line Helicobacter pylori eradication in Taiwan.

BACKGROUND: High-dose dual therapy (HDDT) using proton-pump inhibitors (PPI) and amoxicillin attracted attention for its simplicity and lower adverse event profile. Besides, vonoprazan is not available worldwide. This real-world study aims to compare the efficacy of esomeprazole-based and rabeprazole-based HDDT regimens and to identify clinical factors influencing outcomes. METHODS: A retrospective study enrolled 346 Helicobacter pylori-infected naïve patients from January 2016 to August 2023. Patients were assigned to either a 14-day esomeprazole-based HDDT (EA-14; esomeprazole 40 mg t.i.d. and amoxicillin 750 mg q.i.d. for 14 days, n = 173) or a 14-day rabeprazole-based HDDT (RA-14; rabeprazole 20 mg and amoxicillin 750 mg q.i.d. for 14 days, n = 173). RESULTS: Five patients from the EA-14 group and 10 from the RA-14 group were lost to follow-up, resulting in 168 and 163 patients for the per-protocol (PP) analysis, respectively. Eradication rates for the EA-14 and RA-14 groups were 90.2% and 80.9% (P = 0.014) in intention-to-treat (ITT) analysis; and 92.9% and 85.9% (P = 0.039) in PP analysis. Adverse event rates were similar between the two groups (11.9% vs 11.7%, P = 0.944). In multiple logistic regression analysis, age≧60 was associated with eradication failure (P = 0.046) and a trend of significance for smoking (P = 0.060) in the EA-14 group but not in the RA-14 group. A trend of significance was also observed for eradication regimens (EA-14 vs RA-14) (P = 0.071). The antibiotic resistance rates were amoxicillin (2.3%), clarithromycin (14.7%), metronidazole (40.3%), and dual resistance to clarithromycin and metronidazole (7.0%). CONCLUSIONS: Esomeprazole-based HDDT achieved over 90% eradication rates but rabeprazole-based HDDT, which failed.

Caspase-11/GSDMD contributes to the progression of hyperuricemic nephropathy by promoting NETs formation.

Hyperuricemia is an independent risk factor for chronic kidney disease (CKD) and promotes renal fibrosis, but the underlying mechanism remains largely unknown. Unresolved inflammation is strongly associated with renal fibrosis and is a well-known significant contributor to the progression of CKD, including hyperuricemia nephropathy. In the current study, we elucidated the impact of Caspase-11/Gasdermin D (GSDMD)-dependent neutrophil extracellular traps (NETs) on progressive hyperuricemic nephropathy. We found that the Caspase-11/GSDMD signaling were markedly activated in the kidneys of hyperuricemic nephropathy. Deletion of Gsdmd or Caspase-11 protects against the progression of hyperuricemic nephropathy by reducing kidney inflammation, proinflammatory and profibrogenic factors expression, NETs generation, α-smooth muscle actin expression, and fibrosis. Furthermore, specific deletion of Gsdmd or Caspase-11 in hematopoietic cells showed a protective effect on renal fibrosis in hyperuricemic nephropathy. Additionally, in vitro studies unveiled the capability of uric acid in inducing Caspase-11/GSDMD-dependent NETs formation, consequently enhancing α-smooth muscle actin production in macrophages. In summary, this study demonstrated the contributory role of Caspase-11/GSDMD in the progression of hyperuricemic nephropathy by promoting NETs formation, which may shed new light on the therapeutic approach to treating and reversing hyperuricemic nephropathy.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Pre-training molecular representation model with spatial geometry for property prediction.

AI-enhanced bioinformatics and cheminformatics pivots on generating increasingly descriptive and generalized molecular representation. Accurate prediction of molecular properties needs a comprehensive description of molecular geometry. We design a novel Graph Isomorphic Network (GIN) based model integrating a three-level network structure with a dual-level pre-training approach that aligns the characteristics of molecules. In our Spatial Molecular Pre-training (SMPT) Model, the network can learn implicit geometric information in layers from lower to higher according to the dimension. Extensive evaluations against established baseline models validate the enhanced efficacy of SMPT, with notable accomplishments in classification tasks. These results emphasize the importance of spatial geometric information in molecular representation modeling and demonstrate the potential of SMPT as a valuable tool for property prediction.

OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds.

Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.

GSDMD and GSDME synergy in the transition of acute kidney injury to chronic kidney disease.

BACKGROUND AND HYPOTHESIS: Acute kidney injury (AKI) could progress to chronic kidney disease (CKD) and the AKI-CKD transition has major clinical significance. A growing body of evidence has unveiled the role of pyroptosis in kidney injury. We postulate that GSDMD and GSDME exert cumulative effects on the AKI-CKD transition by modulating different cellular responses. METHODS: We established an AKI-CKD transition model induced by folic acid in wildtype (WT), Gsdmd-/-, Gsdme-/-, and Gsdmd-/-Gsdme-/- mice. Tubular injury, renal fibrosis and inflammatory responses were evaluated. In vitro studies were conducted to investigate the interplay among tubular cells, neutrophils, and macrophages. RESULTS: Double deletion of Gsdmd and Gsdme conferred heightened protection against AKI, mitigating inflammatory responses, including the formation of neutrophil extracellular traps (NETs), macrophage polarization and differentiation, and ultimately renal fibrosis, compared with wildtype mice and mice with single deletion of either Gsdmd or Gsdme. Gsdme, but not Gsdmd deficiency, shielded tubular cells from pyroptosis. GSDME-dependent tubular cell death stimulated NETs formation and prompted macrophage polarization towards a pro-inflammatory phenotype. Gsdmd deficiency suppressed NETs formation and subsequently hindered NETs-induced macrophage-to-myofibroblast transition (MMT). CONCLUSION: GSDMD and GSDME collaborate to contribute to AKI and subsequent renal fibrosis induced by folic acid. Synchronous inhibition of GSDMD and GSDME could be an innovative therapeutic strategy for mitigating the AKI-CKD transition.

The Clinical Presentations of Liver Abscess Development After Endoscopic Retrograde Cholangiopancreatography with Choledocholithiasis: A 17-Year Follow-Up.

Background: Endoscopic Retrograde Cholangiopancreatography (ERCP), used for choledocholithiasis treatment, carries a risk of pyogenic liver abscess (PLA) due to communication between the biliary system and bowel contents. However, limited data exists on this issue. This study aims to identify the risk factors pertaining to liver abscesses following ERCP lithotomy. Methods: We conducted a retrospective case series across multiple centers to evaluate patients who developed PLA after ERCP for choledocholithiasis. Data was obtained from the Chung Gung Research Database (January 2001 to December 2018). Out of 220 enrolled patients, 195 were categorized in the endoscopic sphincterotomy (ES) group, while 25 were in the non-ES group for further analysis. Results: The non-ES group had significantly higher total bilirubin levels compared to the ES group (4.3 ± 5.8 vs 1.9 ± 2.0, p<0.001). Abscess size, location, and distribution (single or multiple) were similar between the two groups. The most common pathogens were Klebsiella pneumoniae and Escherichia coli. Pseudomonas infection was significantly less prevalent in the ES group compared to the non-ES group (3.6% vs 16.7%, p=0.007). Patients with concurrent malignancies (HR: 9.529, 95% CI: 2.667-34.048, p=0.001), elevated total bilirubin levels (HR: 1.246, 95% CI: 1.062-1.461, p=0.007), multiple abscess lesions (HR: 5.146, 95% CI: 1.777-14.903, p=0.003), and growth of enterococcus pathogens (HR: 4.518, 95% CI: 1.290-15.823, p=0.001) faced a significantly higher risk of in-hospital mortality. Conclusion: PLA incidence was higher in the ES group compared to the non-ES group following ERCP for choledocholithiasis. Attention should be given to significant risk factors, including concurrent malignancies, elevated total bilirubin levels, multiple abscess lesions, and growth of enterococcus pathogens, to reduce in-hospital mortality.

The Efficacy and Safety of 14-day Rabeprazole Plus Amoxicillin High Dose Dual Therapy by Comparing to 14-day Rabeprazole-Containing Hybrid Therapy for the Naïve Helicobacter pylori Infection in Taiwan: A Randomized Controlled Trial.

INTRODUCTION: High-dose dual therapy (HDDT) can attain acceptable eradication rates provided that the optimal doses, timing and treatment duration are applied. The existing evidence still shows inconsistent reports (< 90%) on HDDT therapy except in some Asian countries. We aimed to assess and compare the efficacy of 14-day HDDT by comparing it to 14-day rabeprazole-containing hybrid therapy (HT) and to investigate the host and bacterial factors predicting the treatment outcomes of eradication therapies. METHODS: In this open-label, randomized controlled trial, we recruited 243 naïve Helicobacter pylori-infected patients from September 1, 2018, to November 30, 2021. They were randomly allocated (1:1) to the HDDT group (rabeprazole 20 mg and amoxicillin 750 mg q.i.d for 14 days, n = 122) and the HT group (rabeprazole 20 mg and amoxicillin 1 g b.i.d. for 7 days, followed by rabeprazole 20 mg, amoxicillin 1 g, clarithromycin 500 mg and metronidazole 500 mg b.i.d. for 7 days, n = 121). Twelve patients were absent during follow-up in the HDDT group and 4 in the HT group, resulting in 110 for the HDDT group and 117 for HT group in the per protocol (PP) study. The outcome was determined by urea breath tests 8 weeks later. RESULTS: The eradication rates for the HDDT and HT groups were 77.0% (95% confidence interval [CI]: 68.5% to 84.1%) and 94.2% (95% CI: 88.4% to 97.6%) (P < 0.001) in intention-to-treat analysis; 85.5% (95% CI: 77.5% to 91.5%) and 97.4% [95% CI: 92.6% to 99.5%] (P = 0.001) in per protocol analysis. The adverse event rates were 7.3% in the HDDT group and 14.5% in the HT group (P = 0.081). The habit of coffee drinking was the dependent factor for eradication failure in the HDDT group (88.2% vs. 68.8%, P = 0.040), but had no influence in the HT group (97.9% versus 95.0%, P = 0.449) in the univariate analysis. CONCLUSION: This study demonstrated that 14-day rabeprazole-containing HDDT did not achieve > 90% eradication rates for first-line H. pylori eradication as 14-day rabeprazole-containing HT did. HDDT is a potentially beneficial combination, which involves only two drugs with mild adverse effects; more precise studies are urged to find answers regarding these failures. This clinical trial was registered retrospectively on 28 November, 2021, as ClinicalTrials.gov identifier: NCT05152004.

Molecular dynamics study on the behavior and binding mechanism of target protein Transgelin-2 with its agonist TSG12 for anti-asthma drug discovery.

Transgelin-2 (TG2) is a novel promising therapeutic target for the treatment of asthma as it plays an important role in relaxing airway smooth muscles and reducing pulmonary resistance in asthma. The compound TSG12 is the only reported TG2 agonist with in vivo anti-asthma activity. However, the dynamic behavior and ligand binding sites of TG2 and its binding mechanism with TSG12 remain unclear. In this study, we performed 12.6 µs molecular dynamics (MD) simulations for apo-TG2 and TG2-TSG12 complex, respectively. The results suggested that the apo-TG2 has 4 most populated conformations, and that its binding of the agonist could expand the conformation distribution space of the protein. The simulations revealed 3 potential binding sites in 3 most populated conformations, one of which is induced by the agonist binding. Free energy decomposition uncovered 8 important residues with contributions stronger than -1 kcal/mol. Computational alanine scanning for the important residues by 100 ns conventional MD simulation for each mutated TG2-TSG12 complexes demonstrated that E27, R49 and F52 are essential residues for the agonist binding. These results should be helpful to understand the dynamic behavior of TG2 and its binding mechanism with the agonist TSG12, which could provide some structural insights into the novel mechanism for anti-asthma drug development.

An Open Source Graph-Based Weighted Cycle Closure Method for Relative Binding Free Energy Calculations.

Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.

Free energy perturbation-based large-scale virtual screening for effective drug discovery against COVID-19.

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE-based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Early and Direct Endoscopic Stone Removal in the Moderate Grade of Acute Cholangitis with Choledocholithiasis Was Safe and Effective: A Prospective Study.

BACKGROUND: Evidence supporting the feasibility of single-stage stone removal in patients with a moderate grade of acute cholangitis remains insufficient. The maximal size of a common bile-duct stone suitable for removal during a single-stage ERCP in a moderate grade of acute cholangitis is unknown. METHODS: We prospectively enrolled 196 endoscopic retrograde cholangiopancreatography (ERCP)-naïve patients diagnosed with acute cholangitis and choledocholithiasis. For eligible patients, single-stage treatment involved stone removal at initial ERCP. RESULTS: A total of 123 patients were included in the final analysis. The success rate of complete stone extraction was similar between patients with mild and moderate grades of acute cholangitis (89.2% vs. 95.9%; p = 0. 181). Complication rates were comparable between the two groups. In the moderate grade of the cholangitis group, among patients who underwent early single-stage ERCP, the length of hospitalization declined as short as the patients in the mild grade of cholangitis (10.6 ± 6.2 vs. 10.1 ± 5.1 days; p = 0.408). In the multivariate analysis, early ERCP indicated shorter hospitalization times (≤10 days) (odds ratio (OR), 3.981; p = 0.001). A stone size less than 1.5 cm presented a high success rate (98.0%) for complete stone removal. CONCLUSIONS: Single-stage retrograde endoscopic stone removal in mild and moderate grades of acute cholangitis may be safe and effective, which can obviate the requirement for a second session, thus reducing medical expenses. CLINICALTRIALS: gov: NCT03754491.

A risk factor attention-based model for cardiovascular disease prediction.

BACKGROUND: Cardiovascular disease (CVD) is a serious disease that endangers human health and is one of the main causes of death. Therefore, using the patient's electronic medical record (EMR) to predict CVD automatically has important application value in intelligent assisted diagnosis and treatment, and is a hot issue in intelligent medical research. However, existing methods based on natural language processing can only predict CVD according to the whole or part of the context information of EMR. RESULTS: Given the deficiencies of the existing research on CVD prediction based on EMRs, this paper proposes a risk factor attention-based model (RFAB) to predict CVD by utilizing CVD risk factors and general EMRs text, which adopts the attention mechanism of a deep neural network to fuse the character sequence and CVD risk factors contained in EMRs text. The experimental results show that the proposed method can significantly improve the prediction performance of CVD, and the F-score reaches 0.9586, which outperforms the existing related methods. CONCLUSIONS: RFAB focuses on the key information in EMR that leads to CVD, that is, 12 risk factors. In the stage of risk factor identification and extraction, risk factors are labeled with category information and time attribute information by BiLSTM-CRF model. In the stage of CVD prediction, the information contained in risk factors and their labels is fused with the information of character sequence in EMR to predict CVD. RFAB makes well use of the fine-grained information contained in EMR, and also provides a reliable idea for predicting CVD.

Exploring the Binding Affinity and Mechanism between ACE2 and the Trimers of Delta and Omicron Spike Proteins by Molecular Dynamics Simulation and Bioassay.

Five major variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged and posed challenges in controlling the pandemic. Among them, the current dominant variant, viz., Omicron, has raised serious concerns about its infectiousness and antibody neutralization. However, few studies pay attention to the effect of the mutations on the dynamic interaction network of Omicron S protein trimers binding to the host angiotensin-converting enzyme 2 (ACE2). In this study, we conducted molecular dynamics (MD) simulations and enzyme linked immunosorbent assay (ELISA) to explore the binding strength and mechanism of wild type (WT), Delta, and Omicron S protein trimers to ACE2. The results showed that the binding capacities of both the two variants' S protein trimers to ACE2 are enhanced in varying degrees, indicating possibly higher cell infectiousness. Energy decomposition and protein-protein interaction network analysis suggested that both the mutational and conserved sites make effects on the increase in the overall affinity through a variety of interactions. The experimentally determined KD values by biolayer interferometry (BLI) and the predicted binding free energies of the RBDs of Delta and Omicron to mAb HLX70 revealed that the two variants may have the high risk of immune evasion from the mAb. These results are not only helpful in understanding the binding strength and mechanism of S protein trimer-ACE2 but also beneficial for drug, especially for antibody development.

MICER: a pre-trained encoder-decoder architecture for molecular image captioning.

MOTIVATION: Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches that rely on expert knowledge and fail to consider all the stylistic variations of molecular images usually suffer from cumbersome recognition processes and low generalization ability. Deep learning-based methods that integrate different image styles and automatically learn valuable features are flexible, but currently under-researched and have limitations, and are therefore not fully exploited. RESULTS: MICER, an encoder-decoder-based, reconstructed architecture for molecular image captioning, combines transfer learning, attention mechanisms and several strategies to strengthen effectiveness and plasticity in different datasets. The effects of stereochemical information, molecular complexity, data volume and pre-trained encoders on MICER performance were evaluated. Experimental results show that the intrinsic features of the molecular images and the sub-model match have a significant impact on the performance of this task. These findings inspire us to design the training dataset and the encoder for the final validation model, and the experimental results suggest that the MICER model consistently outperforms the state-of-the-art methods on four datasets. MICER was more reliable and scalable due to its interpretability and transfer capacity and provides a practical framework for developing comprehensive and accurate automated molecular structure identification tools to explore unknown chemical space. AVAILABILITY AND IMPLEMENTATION: https://github.com/Jiacai-Yi/MICER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.