The regular use of calcium channel blockers before flexible URS appears to facilitate primary UAS insertion: a retrospective study in a single center.

PURPOSE: To evaluate the effect of regular use of CCB before flexible URS for successful primary UAS insertion. MATERIALS AND METHODS: We retrospectively analyzed 209 patients who underwent flexible ureteroscopy (URS) for upper urinary tract calculi between Jan 2021 and Dec 2021. Patients were divided into two groups based on whether calcium channel blockers (CCB) were used (n = 72) or not (n = 137). The following parameters were collected: age, sex, height and weight, BMI, stone location, stone burden, number of stones, operation time, hospital stay, hospital readmission, post-operative fever, post-operative SIRS rate, Clavien-Dindo grade, hospitalization costs, successful primary UAS insertion. We compared the two groups using Student's t test, Mann-Whitney U test and χ2 test for quantitative and categorical variables, respectively. A logistic regression model was used to identify predictive factors of UAS successful primary insertion. RESULTS: Compared with the non-CCB group, the CCB group had a higher successful primary UAS insertion rate (97.2% vs.85.4%, p = 0.008), and a lower hospital readmission rate (2.8% vs.12.4%, p = 0.021). In multivariate analyses, the regular use of CCB was the only predictive factor of successful primary UAS insertion rate (OR 6.32, 95% CI 1.41-28.29, p = 0.016). CONCLUSION: The regular use of calcium channel blockers (CCB) before flexible URS appears to facilitate ureteral access sheaths (UAS) primary insertion.

Targeting peptide-mediated interactions in omics.

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships.

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .

Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys.

Protein-peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity-of-opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date. In this study, we classified PpI specificity into three types: (i) class-I specificity: peptide selectivity for its cognate wild-type protein receptor over the noncognate mutant decoys of this receptor, (ii) class-II specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are homologous with this receptor, and (iii) class-III specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are the cognate receptors of other peptides. We performed affinity and selectivity analysis for the three types of PpI specificity and revealed that the PpIs generally exhibit a moderate or modest specificity; peptide selectivity increases in the order: class-I < class-II < class-III. All the three types of PpI specificity were observed to have no statistically significant correlation with peptide length and hydrophobicity, but the class-I and class-II specificities can be influenced considerably by peptide secondary structures; the high specificity is preferentially associated with ordered structure types as compared to undefined structure types. In addition, the mutation distribution (for class-I specificity), sequence conservation (for class-II specificity), and structural similarity (for class-III specificity) seem also to address effects on peptide selectivity.

Comparison of pre-indwelling double-J stents versus ureteral catheters for artificial hydronephrosis in percutaneous nephrolithotomy: A retrospective cohort study.

PURPOSE: To compare the clinical efficacy and safety of pre-indwelling double-J stents versus ureteral catheters for artificial hydronephrosis in percutaneous nephrolithotomy (PCNL). MATERIALS AND METHODS: We retrospectively analyzed the data of 1,258 patients who underwent PCNL for kidney stones from August 2017 to July 2020 in our hospital. Among them, 682 patients had double-J stents inserted (DJ group) and 576 patients had ureteral catheters (UC group). We analyzed baseline patient characteristics, perioperative outcomes, and complications in both groups. RESULTS: The puncture success rate was 97.9% and 97.4% in the DJ and UC groups, respectively (p>0.05). The operation time was 74.5±37.8 minutes in the DJ group compared with 80.8±38.5 minutes in the UC group (p=0.004). The total stone-free rate in the DJ and UC groups was 80.5% and 78.7%, respectively (p>0.05). The incidence of perioperative complications was relatively low in both groups and showed no obvious differences. In the subgroup analysis, the operation time for patients with no obvious or mild hydronephrosis preoperatively was significantly shorter in the DJ group than in the UC group (p<0.05). However, there were no significant differences among patients who had moderate or severe hydronephrosis preoperatively. CONCLUSIONS: It is feasible, safe, and effective to create artificial hydronephrosis by insertion of pre-indwelling double-J stents in PCNL surgery. Furthermore, the operation time was significantly shorter in the DJ group than in the group with pre-indwelling ureteral catheters, especially in patients who had no obvious or mild hydronephrosis preoperatively.

Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level.

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.