MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions.

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug-target interaction predictions.

Integrated Analysis of Microarray Studies to Identify Novel Diagnostic Markers in Bladder Pain Syndrome/Interstitial Cystitis with Hunner Lesion.

Background: The aim of this study was to identify novel genetic features of Hunner's lesion interstitial cystitis (HIC) via comprehensive analysis of the Gene Expression Omnibus (GEO) database. Methods: The GSE11783 and GSE28242 datasets were downloaded from GEO for further analysis. Differentially expressed genes (DEGs) were identified and analyzed for functional annotation. The diagnostic markers for HIC were screened and validated using the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms. Finally, the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was adopted to investigate the correlation between immune cell infiltration and diagnostic markers in HIC. Results: A total of 7837 DEGs were identified in GSE11783 and 1583 DEGs in GSE28242. Venn diagrams were used to obtain 16 overlapping upregulated and 67 overlapping downregulated DEGs separately. The LASSO logistic model and SVM-RFE algorithm were used to identify 6 genes including KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 from DEGs as diagnostic markers for HIC. Their diagnostic potential in HIC and bladder pain syndrome/interstitial cystitis (BPS/IC) were acceptable. PLAC8 exhibited the best diagnostic performance in BPS/IC with an area under the curve of 0.916. The results of immune infiltration involving GSE11783 revealed that the plasma cell ratio (p = 0.017), activated memory CD4+ T cells (p = 0.009), activated dendritic cells (p = 0.01), eosinophils (p = 0.004), and neutrophils (p = 0.03) were significantly higher in HIC than in normal samples, in contrast to resting mast cells (p = 0.022). A positive correlation existed between diagnostic markers and infiltrating immune cells. Conclusion: KRT20, SLFN11, CD86, ITGA4, PLAC8, and BTN3A3 represent novel and potent diagnostic markers for HIC. They also exhibit certain diagnostic potential in BPS/IC. Immune cell infiltration might play a key role in the pathogenesis and progression of BPS/IC.

Robot-assisted retroperitoneal laparoscopic excision of perirenal vascular tumor: A case report.

BACKGROUND: A vascular tumor is a benign tumor with unique clinical and pathological features. Perirenal vascular tumor is extremely rare and has not yet been reported. Clinically, it manifests as soreness and swelling. Color ultrasound and renal angiography illustrated the perirenal mass, which was closely connected with the kidney and the surrounding tissues and organs. Histology showed extensive embedded perirenal fat, and thin-walled vascular tissue displayed a pink stain due to red blood cells. CASE SUMMARY: Herein, a case of robot-assisted retroperitoneal laparoscopic excision of a perirenal vascular tumor is reported. Analysis of the clinical, biological, and histological features of the perirenal vascular tumor can provide an in-depth understanding of the disease, which provides a theoretical and practical basis for better diagnosis and treatment. CONCLUSION: This study contributes to a practical basis for the diagnosis and treatment of perirenal hemangiom.

Evaluation of the Clinical Use of Robot-Assisted Retroperitoneal Laparoscopy and Preoperative RENAL Scoring for Nephron Sparing Surgery in Renal Tumor Patients.

The present study aims to compare the operative outcomes following the use of robot-assisted retroperitoneal partial nephrectomy (RARPN) with radius, exophytic/endophytic, nearness to sinus, anterior/posterior, and location (RENAL) scoring or laparoscopic retroperitoneal partial nephrectomy (LRPN) for the treatment of renal tumors. Eighty-three nephron-sparing surgery (NSS) procedures performed between January 2013 and December 2015 were reviewed. The study set consisted of 26 robot-assisted retroperitoneal laparoscopes, of which 3 were high risk (RENAL score ≥10), 11 were medium risk (RENAL score ≥7 < 9), and 12 were low risk (RENAL score <7) and 57 laparoscopic retroperitoneal partial nephrectomy procedures (7 high, 22 medium, and 28 low risk). All surgeries were successful in the absence of conversion or transfusion. Operative times were 96.0 ± 16.9 and 110.0 ± 19.4 min for RARPN and LRPN, respectively (P < 0.05). Warm ischemia times (WITs) were 17.6 ± 3.1 and 22.8 ± 3.5 min, respectively (P < 0.05). Estimated blood losses (EBLs) were 45 ± 15 and 97 ± 25 mL, respectively (P < 0.05). No statistical significance was found in duration of drainage, intestinal recovery time, hospital stay, serum creatinine, and perioperative complications (P > 0.05). RARPN affords significant advantages in outcomes of WIT, EBL, and recovery time over conventional LRPN owing to an increased accuracy in excision and suturing. Patients bearing high-risk renal tumors (RENAL score ≥10) are suitable candidates for RARPN.