Immune Checkpoint Inhibition Overcomes ADCP-Induced Immunosuppression by Macrophages.

Antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) critically contribute to the efficacy of anti-tumor therapeutic antibodies. We report here an unexpected finding that macrophages after ADCP inhibit NK cell-mediated ADCC and T cell-mediated cytotoxicity in breast cancers and lymphomas. Mechanistically, AIM2 is recruited to the phagosomes by FcγR signaling following ADCP and activated by sensing the phagocytosed tumor DNAs through the disrupted phagosomal membrane, which subsequently upregulates PD-L1 and IDO and causes immunosuppression. Combined treatment with anti-HER2 antibody and inhibitors of PD-L1 and IDO enhances anti-tumor immunity and anti-HER2 therapeutic efficacy in mouse models. Furthermore, neoadjuvant trastuzumab therapy significantly upregulates PD-L1 and IDO in the tumor-associated macrophages (TAMs) of HER2+ breast cancer patients, correlating with poor trastuzumab response. Collectively, our findings unveil a deleterious role of ADCP macrophages in cancer immunosuppression and suggest that therapeutic antibody plus immune checkpoint blockade may provide synergistic effects in cancer treatment.

CD10+GPR77+ Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness.

Carcinoma-associated fibroblasts (CAFs) are abundant and heterogeneous stromal cells in tumor microenvironment that are critically involved in cancer progression. Here, we demonstrate that two cell-surface molecules, CD10 and GPR77, specifically define a CAF subset correlated with chemoresistance and poor survival in multiple cohorts of breast and lung cancer patients. CD10+GPR77+ CAFs promote tumor formation and chemoresistance by providing a survival niche for cancer stem cells (CSCs). Mechanistically, CD10+GPR77+ CAFs are driven by persistent NF-κB activation via p65 phosphorylation and acetylation, which is maintained by complement signaling via GPR77, a C5a receptor. Furthermore, CD10+GPR77+ CAFs promote successful engraftment of patient-derived xenografts (PDXs), and targeting these CAFs with a neutralizing anti-GPR77 antibody abolishes tumor formation and restores tumor chemosensitivity. Our study reveals a functional CAF subset that can be defined and isolated by specific cell-surface markers and suggests that targeting the CD10+GPR77+ CAF subset could be an effective therapeutic strategy against CSC-driven solid tumors.

NKILA lncRNA promotes tumor immune evasion by sensitizing T cells to activation-induced cell death.

Activation-induced cell death (AICD) of T lymphocytes can be exploited by cancers to escape immunological destruction. We demonstrated that tumor-specific cytotoxic T lymphocytes (CTLs) and type 1 helper T (TH1) cells, rather than type 2 helper T cells and regulatory T cells, were sensitive to AICD in breast and lung cancer microenvironments. NKILA, an NF-κB-interacting long noncoding RNA (lncRNA), regulates T cell sensitivity to AICD by inhibiting NF-κB activity. Mechanistically, calcium influx in stimulated T cells via T cell-receptor signaling activates calmodulin, thereby removing deacetylase from the NKILA promoter and enhancing STAT1-mediated transcription. Administering CTLs with NKILA knockdown effectively inhibited growth of breast cancer patient-derived xenografts in mice by increasing CTL infiltration. Clinically, NKILA overexpression in tumor-specific CTLs and TH1 cells correlated with their apoptosis and shorter patient survival. Our findings underscore the importance of lncRNAs in determining tumor-mediated T cell AICD and suggest that engineering lncRNAs in adoptively transferred T cells might provide a novel antitumor immunotherapy.

Sequential neoadjuvant chemotherapy using pegylated liposomal doxorubicin and cyclophosphamide followed by taxanes with complete trastuzumab and pertuzumab treatment for HER2-positive breast cancer: A phase II single-arm study.

Objective: Despite cardiotoxicity overlap, the trastuzumab/pertuzumab and anthracycline combination remains crucial due to significant benefits. Pegylated liposomal doxorubicin (PLD), a less cardiotoxic anthracycline, was evaluated for efficacy and cardiac safety when combined with cyclophosphamide and followed by taxanes with trastuzumab/pertuzumab in human epidermal growth factor receptor-2 (HER2)-positive early breast cancer (BC). Methods: In this multicenter, phase II study, patients with confirmed HER2-positive early BC received four cycles of PLD (30-35 mg/m2) and cyclophosphamide (600 mg/m2), followed by four cycles of taxanes (docetaxel, 90-100 mg/m2 or nab-paclitaxel, 260 mg/m2), concomitant with eight cycles of trastuzumab (8 mg/kg loading dose, then 6 mg/kg) and pertuzumab (840 mg loading dose, then 420 mg) every 3 weeks. The primary endpoint was total pathological complete response (tpCR, ypT0/is ypN0). Secondary endpoints included breast pCR (bpCR), objective response rate (ORR), disease control rate, rate of breast-conserving surgery (BCS), and safety (with a focus on cardiotoxicity). Results: Between May 27, 2020 and May 11, 2022, 78 patients were treated with surgery, 42 (53.8%) of whom had BCS. After neoadjuvant therapy, 47 [60.3%, 95% confidence interval (95% CI), 48.5%-71.2%] patients achieved tpCR, and 49 (62.8%) achieved bpCR. ORRs were 76.9% (95% CI, 66.0%-85.7%) and 93.6% (95% CI, 85.7%-97.9%) after 4-cycle and 8-cycle neoadjuvant therapy, respectively. Nine (11.5%) patients experienced asymptomatic left ventricular ejection fraction (LVEF) reductions of ≥10% from baseline, all with a minimum value of >55%. No treatment-related abnormal cardiac function changes were observed in mean N-terminal pro-BNP (NT-proBNP), troponin I, or high-sensitivity troponin. Conclusions: This dual HER2-blockade with sequential polychemotherapy showed promising activity with rapid tumor regression in HER2-positive BC. Importantly, this regimen showed an acceptable safety profile, especially a low risk of cardiac events, suggesting it as an attractive treatment approach with a favorable risk-benefit balance.

Capturing Protein-Protein Interactions with Acidic Amino Acids Reactive Cross-Linkers.

Acidic residues (Asp and Glu) have a high prevalence on protein surfaces, but cross-linking reactions targeting these residues are limited. Existing methods either require high-concentration coupling reagents or have low structural compatibility. Here a previously reported "plant-and-cast" strategy is extended to develop heterobifunctional cross-linkers. These cross-linkers first react rapidly with Lys sidechains and then react with Asp and Glu sidechains, in a proximity-enhanced fashion. The cross-linking reaction proceeds at neutral pH and room temperature without coupling reagents. The efficiency and robustness of cross-linking using model proteins, ranging from small monomeric proteins to large protein complexes are demonstrated. Importantly, it is shown that this type of cross-linkers are efficient at identifying protein-protein interactions involving acidic domains. The Cross-linking mass spectrometry (XL-MS) study with p53 identified 87 putative binders of the C-terminal domain of p53. Among them, SARNP, ZRAB2, and WBP11 are shown to regulate the expression and alternative splicing of p53 target genes. Thus, these carboxylate-reactive cross-linkers will further expand the power of XL-MS in the analysis of protein structures and protein-protein interactions.

Augmentation of 3ß-hydroxysteroid-Δ24 Reductase (DHCR24) Expression Induced by Bovine Viral Diarrhea Virus Infection Facilitates Viral Replication via Promoting Cholesterol Synthesis.

Bovine viral diarrhea virus (BVDV) is the etiologic agent of bovine viral diarrhea-mucosal disease, one of the most important viral diseases of cattle, leading to numerous losses to the cattle rearing industry worldwide. The pathogenicity of BVDV is extremely complex, and many underlying mechanisms involved in BVDV-host interactions are poorly understood, especially how BVDV utilizes host metabolism pathway for efficient viral replication and spread. In our previous study, using an integrative analysis of transcriptomics and proteomics, we found that DHCR24 (3ß-hydroxysteroid-Δ24 reductase), a key enzyme in regulating cholesterol synthesis, was significantly upregulated at both gene and protein levels in the BVDV-infected bovine cells, indicating that cholesterol is important for BVDV replication. In the present study, the effects of DHCR24-mediated cholesterol synthesis on BVDV replication was explored. Our results showed that overexpression of the DHCR24 effectively promoted cholesterol synthesis, as well as BVDV replication, while acute cholesterol depletion in the bovine cells by treating cells with methyl-ß-cyclodextrin (MßCD) obviously inhibited BVDV replication. In addition, knockdown of DHCR24 (gene silencing with siRNA targeting DHCR24, siDHCR24) or chemical inhibition (treating bovine cells with U18666A, an inhibitor of DHCR24 activity and cholesterol synthesis) significantly suppressed BVDV replication, whereas supplementation with exogenous cholesterol to the siDHCR24-transfected or U18666A-treated bovine cells remarkably restored viral replication. We further confirmed that BVDV nonstructural protein NS5A contributed to the augmentation of DHCR24 expression. Conclusively, augmentation of the DHCR24 induced by BVDV infection plays an important role in BVDV replication via promoting cholesterol production. IMPORTANCE Bovine viral diarrhea virus (BVDV), an important pathogen of cattle, is the causative agent of bovine viral diarrhea-mucosal disease, which causes extensive economic losses in both cow- and beef-rearing industry worldwide. The molecular interactions between BVDV and its host are extremely complex. In our previous study, we found that an essential host factor 3ß-hydroxysteroid-δ24 reductase (DHCR24), a key enzyme involved in cholesterol synthesis, was significantly upregulated at both gene and protein levels in BVDV-infected bovine cells. Here, we experimentally explored the function of the DHCR24-mediated cholesterol synthesis in regulating BVDV replication. We elucidated that the augmentation of the DHCR24 induced by BVDV infection played a significant role in viral replication via promoting cholesterol synthesis. Our data provide evidence that BVDV utilizes a host metabolism pathway to facilitate its replication and spread.

Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play important roles in human neurodegenerative disorders such as Alzheimer's disease. In this study, machine learning methods were applied to develop quantitative structure-activity relationship models for the prediction of novel AChE and BChE inhibitors based on data from quantitative high-throughput screening assays. The models were used to virtually screen an in-house collection of ∼360K compounds. The optimal models achieved good performance with area under the receiver operating characteristic curve values ranging from 0.83 ± 0.03 to 0.87 ± 0.01 for the prediction of AChE/BChE inhibition activity and selectivity. Experimental validation showed that the best-performing models increased the assay hit rate by several folds. We identified 88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE) of which showed potent inhibitory effects (IC50 < 5 µM). In addition, structure-activity relationship analysis of the BChE inhibitors revealed scaffolds for chemistry design and optimization. In conclusion, machine learning models were shown to efficiently identify potent and selective inhibitors against AChE and BChE and novel structural series for further design and development of potential therapeutics against neurodegenerative disorders.

Structural analysis of a trimeric assembly of the mitochondrial dynamin-like GTPase Mgm1.

The fusion of inner mitochondrial membranes requires dynamin-like GTPases, Mgm1 in yeast and OPA1 in mammals, but how they mediate membrane fusion is poorly understood. Here, we determined the crystal structure of Saccharomyces cerevisiae short Mgm1 (s-Mgm1) in complex with GDP. It revealed an N-terminal GTPase (G) domain followed by two helix bundles (HB1 and HB2) and a unique C-terminal lipid-interacting stalk (LIS). Dimers can form through antiparallel HB interactions. Head-to-tail trimers are built by intermolecular interactions between the G domain and HB2-LIS. Biochemical and in vivo analyses support the idea that the assembly interfaces observed here are native and critical for Mgm1 function. We also found that s-Mgm1 interacts with negatively charged lipids via both the G domain and LIS. Based on these observations, we propose that membrane targeting via the G domain and LIS facilitates the in cis assembly of Mgm1, potentially generating a highly curved membrane tip to allow inner membrane fusion.

Network regression analysis in transcriptome-wide association studies.

BACKGROUND: Transcriptome-wide association studies (TWASs) have shown great promise in interpreting the findings from genome-wide association studies (GWASs) and exploring the disease mechanisms, by integrating GWAS and eQTL mapping studies. Almost all TWAS methods only focus on one gene at a time, with exception of only two published multiple-gene methods nevertheless failing to account for the inter-dependence as well as the network structure among multiple genes, which may lead to power loss in TWAS analysis as complex disease often owe to multiple genes that interact with each other as a biological network. We therefore developed a Network Regression method in a two-stage TWAS framework (NeRiT) to detect whether a given network is associated with the traits of interest. NeRiT adopts the flexible Bayesian Dirichlet process regression to obtain the gene expression prediction weights in the first stage, uses pointwise mutual information to represent the general between-node correlation in the second stage and can effectively take the network structure among different gene nodes into account. RESULTS: Comprehensive and realistic simulations indicated NeRiT had calibrated type I error control for testing both the node effect and edge effect, and yields higher power than the existed methods, especially in testing the edge effect. The results were consistent regardless of the GWAS sample size, the gene expression prediction model in the first step of TWAS, the network structure as well as the correlation pattern among different gene nodes. Real data applications through analyzing systolic blood pressure and diastolic blood pressure from UK Biobank showed that NeRiT can simultaneously identify the trait-related nodes as well as the trait-related edges. CONCLUSIONS: NeRiT is a powerful and efficient network regression method in TWAS.

Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.

Drug-induced liver injury (DILI) and cardiotoxicity (DICT) are major adverse effects triggered by many clinically important drugs. To provide an alternative to in vivo toxicity testing, the U.S. Tox21 consortium has screened a collection of ∼10K compounds, including drugs in clinical use, against >70 cell-based assays in a quantitative high-throughput screening (qHTS) format. In this study, we compiled reference compound lists for DILI and DICT and compared the potential of Tox21 assay data with chemical structure information in building prediction models for human in vivo hepatotoxicity and cardiotoxicity. Models were built with four different machine learning algorithms (e.g., Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine) and model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Chemical structure-based models showed reasonable predictive power for DILI (best AUC-ROC = 0.75 ± 0.03) and DICT (best AUC-ROC = 0.83 ± 0.03), while Tox21 assay data alone only showed better than random performance. DILI and DICT prediction models built using a combination of assay data and chemical structure information did not have a positive impact on model performance. The suboptimal predictive performance of the assay data is likely due to insufficient coverage of an adequately predictive number of toxicity mechanisms. The Tox21 consortium is currently expanding coverage of biological response space with additional assays that probe toxicologically important targets and under-represented pathways that may improve the prediction of in vivo toxicity such as DILI and DICT.

Retro Drug Design: From Target Properties to Molecular Structures.

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

Akt pathway activation reduces platelet apoptosis and contributes to the increase of platelet counts in solid tumor patients.

Platelets counts increase in various cancer patients, which is associated with poor prognosis. However, the cause of high platelet counts in cancer patients is still not fully understood. Here we demonstrated that compared with healthy controls, there were significant differences in platelet parameters, mean platelet volume (MPV), platelet distribution width (PDW), platelet larger cell ratio (P-LCR), and platelet crit (PCT), reflecting platelet volume in breast cancer patients by clinical retrospective analysis. The mitochondrial transmembrane potential (ΔΨm) depolarization and phosphatidylserine (PS) externalization declined, accompanied by reduced expression of pro-apoptotic factors Bak, Bax and apoptotic executor caspase-3, and elevated of anti-apoptotic factor Bcl-xl in various cancer patients' platelets. Notably, the phosphorylation level of Akt and its downstream target Bad increased in platelets from cancer patients. MK2206, the inhibitor of Akt, reduced the phosphorylation level of Akt and Bad, and induced apoptosis of platelets. When platelets from healthy controls cocultured with the cultural supernatant of cancer cells, the phosphorylation level of Akt and Bad in the platelets was elevated and the cultural supernatant of cancer cells could rescue the apoptosis of platelet induced by MK2206. Therefore, in our study the apoptosis of platelets in cancer patients was declined, which exerted an influence on the rise of platelet counts in breast cancer patients. The cross-talking between tumor and platelets could affect platelet apoptosis by regulating Akt signaling pathway in platelets.

Identification of environmental chemicals that activate p53 signaling after in vitro metabolic activation.

Currently, approximately 80,000 chemicals are used in commerce. Most have little-to-no toxicity information. The U.S. Toxicology in the 21st Century (Tox21) program has conducted a battery of in vitro assays using a quantitative high-throughput screening (qHTS) platform to gain toxicity information on environmental chemicals. Due to technical challenges, standard methods for providing xenobiotic metabolism could not be applied to qHTS assays. To address this limitation, we screened the Tox21 10,000-compound (10K) library, with concentrations ranging from 2.8 nM to 92 µM, using a p53 beta-lactamase reporter gene assay (p53-bla) alone or with rat liver microsomes (RLM) or human liver microsomes (HLM) supplemented with NADPH, to identify compounds that induce p53 signaling after biotransformation. Two hundred and seventy-eight compounds were identified as active under any of these three conditions. Of these 278 compounds, 73 gave more potent responses in the p53-bla assay with RLM, and 2 were more potent in the p53-bla assay with HLM compared with the responses they generated in the p53-bla assay without microsomes. To confirm the role of metabolism in the differential responses, we re-tested these 75 compounds in the absence of NADPH or with heat-attenuated microsomes. Forty-four compounds treated with RLM, but none with HLM, became less potent under these conditions, confirming the role of RLM in metabolic activation. Further evidence of biotransformation was obtained by measuring the half-life of the parent compounds in the presence of microsomes. Together, the data support the use of RLM in qHTS for identifying chemicals requiring biotransformation to induce biological responses.

Molecular basis of the multifaceted functions of human leucyl-tRNA synthetase in protein synthesis and beyond.

Human cytosolic leucyl-tRNA synthetase (hcLRS) is an essential and multifunctional enzyme. Its canonical function is to catalyze the covalent ligation of leucine to tRNALeu, and it may also hydrolyze mischarged tRNAs through an editing mechanism. Together with eight other aminoacyl-tRNA synthetases (AaRSs) and three auxiliary proteins, it forms a large multi-synthetase complex (MSC). Beyond its role in translation, hcLRS has an important moonlight function as a leucine sensor in the rapamycin complex 1 (mTORC1) pathway. Since this pathway is active in cancer development, hcLRS is a potential target for anti-tumor drug development. Moreover, LRS from pathogenic microbes are proven drug targets for developing antibiotics, which however should not inhibit hcLRS. Here we present the crystal structure of hcLRS at a 2.5 Å resolution, the first complete structure of a eukaryotic LRS, and analyze the binding of various compounds that target different sites of hcLRS. We also deduce the assembly mechanism of hcLRS into the MSC through reconstitution of the entire mega complex in vitro. Overall, our study provides the molecular basis for understanding both the multifaceted functions of hcLRS and for drug development targeting these functions.

Modeling and simulation of microblog-based public health emergency-associated public opinion communication.

With the advent of the era of "we media," many people's opinions have become easily accessible. Public health emergencies have always been an important aspect of public opinion exchange and emotional communication. In view of this sudden group panic, public opinion cannot be effectively monitored, controlled or guided. This makes it easy to amplify the beliefs and irrationality of social emotions, that threaten social security and stability. Considering the important role of opinion leaders in micro-blogs and users' interest in micro-blog information, a SIR model of public opinion propagation is constructed based on the novel coronavirus pneumonia model and micro-blog's public health emergencies information. The parameters of the model are calculated by combining the actual crawl data from the novel coronavirus pneumonia epidemic period, and the trends in the evolution of public opinion are simulated by MATLAB. The simulation results are consistent with the actual development of public opinion dissemination, which shows the effectiveness of the model. These research findings can help the government understand the principles that guide the propagation of public opinion and advise an appropriate time to control and correctly guide public opinion.

Transcriptome comparative analysis of ovarian follicles reveals the key genes and signaling pathways implicated in hen egg production.

BACKGROUND: Ovarian follicle development plays an important role in determination of poultry egg production. The follicles at the various developmental stages possess their own distinct molecular genetic characteristics and have different biological roles in chicken ovary development and function. In the each stage, several genes of follicle-specific expression and biological pathways are involved in the vary-sized follicular development and physiological events. Identification of the pivotal genes and signaling pathways that control the follicular development is helpful for understanding their exact regulatory functions and molecular mechanisms underlying egg-laying traits of laying hens. RESULTS: The comparative mRNA transcriptomic analysis of ovarian follicles at three key developmental stages including slow growing white follicles (GWF), small yellow follicles (SYF) of recruitment into the hierarchy, and differentiated large yellow follicles (LYF), was accomplished in the layers with lower and higher egg production. Totally, 137, 447, and 229 of up-regulated differentially expressed genes (DEGs), and 99, 97, and 157 of down-regulated DEGs in the GWF, SYF and LYF follicles, including VIPR1, VIPR2, ADRB2, and HSD17B1 were identified, respectively. Moreover, NDUFAB1 and GABRA1 genes, two most promising candidates potentially associated with egg-laying performance were screened out from the 13 co-expressed DEGs in the GWF, SYF and LYF samples. We further investigated the biological effects of NDUFAB1 and GABRA1 on ovarian follicular development and found that NDUFAB1 promotes follicle development by stimulating granulosa cell (GC) proliferation and decreasing cell apoptosis, increases the expression of CCND1 and BCL-2 but attenuates the expression of caspase-3, and facilitates steroidogenesis by enhancing the expression of STAR and CYP11A1. In contrast, GABRA1 inhibits GC proliferation and stimulates cell apoptosis, decreases the expression of CCND1, BCL-2, STAR, and CYP11A1 but elevates the expression of caspase-3. Furthermore, the three crucial signaling pathways such as PPAR signaling pathway, cAMP signaling pathway and neuroactive ligand-receptor interaction were significantly enriched, which may play essential roles in ovarian follicle growth, differentiation, follicle selection, and maturation. CONCLUSIONS: The current study provided new molecular data for insight into the regulatory mechanism underlying ovarian follicle development associated with egg production in chicken.

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors.

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model.

PURPOSE: The minimal clinically important difference (MCID) of a patient-reported outcome (PRO) represents the threshold value of the change in the score for that PRO. It is deemed to have an important implication in clinical management. This study was performed to evaluate the clinical significance of chronic disease self-management (CDSM) for patients with chronic heart failure based on the MCID of the chronic heart failure-PRO measure (CHF-PROM). METHODS: A multicenter, prospective cohort study of 555 patients with heart failure were enrolled from July 2018. Advice of CDSM was provided in written form at discharge to all patients. Information regarding CHF-PROM and CDSM were collected during follow-up. Multilevel models were applied to dynamically evaluate the effects of CDSM for CHF-PROM scores, as well as its physical and psychological domains. MCID changes of the PRO were introduced and compared with ß values of CDSM obtained from the multi-level models to further evaluate the clinical significance. The STROBE checklist is shown in Additional file 1. RESULTS: Scores for CHF-PROM improved significantly after discharge. The multilevel models showed that a regular schedule, avoidance of over-eating, a low-sodium diet and exercise increased scores on CHF-PROM. Compared with the MCID, avoidance of over-eating (12.39 vs. 9.75) and maintenance of a regular schedule often (10.98 vs. 9.75), and exercise almost every day (11.36 vs. 9.75) reached clinical significance for the overall summary. Avoidance of over-eating (5.88 vs. 4.79) and a regular schedule almost every day (4.96 vs. 4.79) reached clinical significance for the physical scores. Avoidance of over-eating half of the time (5.26 vs. 4.87) and a regular schedule almost every day (5.84 vs. 4.87) demonstrated clinical significance for the psychological scores. CONCLUSIONS: This study observed an association of avoidance of over-eating and maintenance of a regular schedule with the improvement of CHF-PROM. It provides further evidence for management of heart failure. TRIAL REGISTRATION: Current Prospective Trials NCT02878811; registered August 25, 2016; https://clinicaltrials.gov/ct2/show/NCT02878811?term=NCT02878811&draw=2&rank=1 .

Quantitative Assessment of the Pupil: An Underrecognized Yet Important Factor Related to Orbital Blowout Fracture Repair.

PURPOSE: To investigate dynamic pupil changes after orbital blowout fracture repair. To compare postoperative changes in under photopic and mesopic pupil size and center position after orbital blowout fracture repair surgery. METHODS: The study evaluated 19 eyes. Pupils were imaged for pupil size and center position before and 3 months after orbital blowout fracture repair surgery. Pupil size changes were measured, and the correlation between preoperative and postoperative pupil centroid shift was evaluated. RESULTS: After repair, operative eyes exhibited a growth of 9.3% ±â€Š8.6% in pupil size, and contralateral eyes showed a growth of 8.6% ±â€Š8.2% (P = 0.011, P = 0.007). Similar findings were noted in mesopic conditions. Under mesopic conditions, the pupil of operative eyes in medial orbital wall fracture deviated 0.030 ±â€Š0.019 mm towards the nasal side along the X-axis (P = 0.031). The postoperative orbital floor fracture group demonstrated statistical significance at a spatial frequency of 5 (P = 0.041). CONCLUSIONS: Orbital blowout fracture repair surgery affects pupil size and center position.

Evaluation of the Combination of Azithromycin and Naphthoquine in Animal Malaria Models.

Combination therapy using drugs with different mechanisms of action is the current state of the art in antimalarial treatment. However, except for artemisinin-based combination therapies, only a few other combinations are now available. Increasing concern regarding the emergence and spread of artemisinin resistance in Plasmodium falciparum has led to a need for the development of new antimalarials. Moreover, the efficacy of current available chemoprophylaxis is compromised by drug resistance and noncompliance due to intolerable adverse effects or complicated dosing regimens. Therefore, new antimalarials that are more effective, safer, and more convenient are also urgently needed for malaria chemoprophylaxis. In this study, we assessed the combination of azithromycin and naphthoquine in animal malaria models. A dose-dependent interaction was observed in Peters' 4-day suppressive test on P. berghei K173-infected mice. Moreover, at inhibition levels of ≥90%, synergistic effects were found for combinations at various ratios. At an optimal dose ratio of 1:1, the combination of azithromycin and naphthoquine acted synergistically even by 4 weeks after the first dose and provided a more effective and sustained prophylaxis than did naphthoquine alone in blood-stage P. berghei K173 and P. cynomolgibastianelli L challenge models. The ability of the combination to delay and slow down resistance development in P. berghei K173 was also shown. These results showed clear evidence for the benefit of the combination therapy with azithromycin and naphthoquine in animal malaria models, providing some insight for further development of this therapy for malaria treatment and prophylaxis.