PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.

The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological function mechanisms. In this document, we have constructed a predictor, named PPSNO, forecasting protein SNO sites using stacked integrated learning. PPSNO integrates multiple machine learning techniques into an ensemble model, enhancing its predictive accuracy. First, we established benchmark datasets by collecting SNO sites from various sources, including literature, databases, and other predictors. Second, various techniques for feature extraction are applied to derive characteristics from protein sequences, which are subsequently amalgamated into the PPSNO predictor for training. Five-fold cross-validation experiments show that PPSNO outperformed existing predictors, such as PSNO, PreSNO, pCysMod, DeepNitro, RecSNO, and Mul-SNO. The PPSNO predictor achieved an impressive accuracy of 92.8%, an area under the curve (AUC) of 96.1%, a Matthews correlation coefficient (MCC) of 81.3%, an F1-score of 85.6%, an SN of 79.3%, an SP of 97.7%, and an average precision (AP) of 92.2%. We also employed ROC curves, PR curves, and radar plots to show the superior performance of PPSNO. Our study shows that fused protein sequence features and two-layer stacked ensemble models can improve the accuracy of predicting SNO sites, which can aid in comprehending cellular processes and disease mechanisms. The codes and data are available at https://github.com/serendipity-wly/PPSNO .

AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information.

Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5-100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%-6.13%), MCC (2.92%-12.86%) and AUC (5.13%-8.56%) and AP (9.20%-21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R2 on a public dataset by 10-fold cross-validation with an improvement of (18.82%-19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.

LncCat: An ORF attention model to identify LncRNA based on ensemble learning strategy and fused sequence information.

Background: Long non-coding RNA (lncRNA) is one of the most essential forms of transcripts, playing crucial regulatory roles in the development of cancers and diseases without protein-coding ability. It was assumed that short ORFs (sORFs) in lncRNA were weak to translate proteins. However, recent research has shown that sORFs can encode peptides, which increases the difficulty to identify lncRNA. Therefore, identifying lncRNAs with sORFs facilitates finding novel regulatory factors. Results: In this paper, we propose LncCat for identifying lncRNA based on category boosting (CatBoost) and ORF-attention features. LncCat combines five types of features to encode transcript sequences and employs CatBoost to build a prediction model. In addition, the visualization comparison reveals that the ORF-attention features between lncRNAs and protein-coding transcripts are significantly distinct. The comparison results show that LncCat outperforms competing methods on several benchmark datasets. For Matthew's Correlation Coefficient (MCC), LncCat achieves 0.9503, 0.9219, 0.8591, 0.8672, and 0.9047 on the human, mouse, zebrafish, wheat, and chicken datasets, with improvements ranging from 1.90% to 7.82%, 1.49-17.63%, 6.11-21.50%, 3.02-51.64% and 5.35-26.90%, respectively. Moreover, LncCat dramatically improves the MCC by at least 11.90%, 12.96% and 42.61% on sORF test datasets of human, mouse, and zebrafish, respectively. Conclusions: Experiments indicate that LncCat performs better both on long ORF and sORF datasets, and ORF-attention features show positive effects on predicting lncRNA. In brief, LncCat is a reliable method for identifying lncRNA. Additionally, a user-friendly web server is developed for academics at http://cczubio.top/lnccat.

Risk Factors and Three Radiological Predictor Models for the Progression of Proximal Junctional Kyphosis in Adult Degenerative Scoliosis Following Posterior Corrective Surgery: 113 Cases With 2-years Minimum Follow-Up.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To identify risk factors and predictive models for proximal junctional kyphosis (PJK) in a long-term follow-up of patients with adult degenerative scoliosis (ADS) following posterior corrective surgeries. MATERIALS AND METHODS: A consecutive 113 ADS patients undergoing posterior corrective surgery between January 2008 and April 2019 with minimum 2-year follow-up were included. All patients underwent preoperative, postoperative, and final follow-up by X-ray imaging. Multivariate logistic analysis was performed on various risk factors and radiological predictor models. RESULTS: PJK was identified radiographically in 46.9% of patients. Potential risk factors for PJK included postoperative thoracic kyphosis (TK) (P < .05), final follow-up Pelvic Tilt (PT) (P < .05), PT changes at final follow-up (P < .05), age over 55 years old at the surgery (P < .05), theoretical thoracic kyphosis-actual thoracic kyphosis mismatch (TK mismatch) (P < .05) and theoretical lumbar lordosis-acutal lumbar lordosis mismatch (LL mismatch) (P < .05). As for the predictive models, PJK was predictive by the following indicators: preoperative global sagittal alignment ≥45° (Model 1), postoperative pelvic incidence-lumbar lordosis mismatch (PI-LL)≤10° and postoperative PI-LL overcorrection (Model 2), and TK+LL≥0° (Model 3) (P < .05). Postoperative TK mismatch (OR = 1.064) was independent as risk factors for PJK, with the cut-off values respectively set at -28.56° to predict occurrence of PJK. CONCLUSION: The risk of radiographic PJK increases with an age over 55 years old and higher postoperative TK. In addition, postoperative TK mismatch is an independent risk factor for developing PJK. All three predictive models could effectively indicate the occurrence of PJK.

Clinical Application of the Roussouly Classification in the Sagittal Balance Reconstruction of 101 Adolescent Idiopathic Scoliosis Patients.

PURPOSE: Although Roussouly classification has been widely used in spinal surgery, it was mainly applied to degenerative scoliosis patients and correlational studies concerning adolescent idiopathic scoliosis (AIS) are still insufficient. This retrospective study explored the clinical application of Roussouly classification in surgeries and prognosis prediction for AIS. METHODS: This clinical research selected 101 AIS patients who received surgeries between August 2005 and November 2019. Whole spine standing radiographs were obtained for each patient preoperatively, postoperatively, and at the last follow-up (>24 months). All patients were classified into "theoretical types" and "current types." Patients were further divided into mismatch or match groups based on the consistency of their current type and theoretical type. The main parameters include: proximal junctional angle (PJA), pelvic incidence (PI), sacral slope (SS), pelvic tilt (PT), fixed thoracic kyphosis (TK), global TK, fixed lumbar lordosis (LL), global LL, thoracic tilt, proximal thoracic alignment (PTA), lumbar tilt, spino-sacral angle (SSA), and spinal tilt (ST). RESULTS: A total of 47.5% of AIS patients were subject to a preoperative mismatch of Roussouly classification. There was a significant difference in PI-LL between the preoperative mismatch and match groups (p = 0.008). There was a significant difference in the rate of PI-LL deformity between the match and mismatch groups with a preoperative mismatch (p = 0.037). A significant difference in thoracic tilt was observed between the postoperative mismatch and match groups (p = 0.019). The preoperative mismatch group has a higher risk of postoperative PI-LL malformation than match group (OR = 2.303, 95% CI: 1.026, 5.165). When mismatch occurred postoperatively, there were significant differences between groups in the rate of pelvic deformity (p = 0.002) and PI-LL deformity (p = 0.025) at the last follow-up. Compared with the postoperative match group, mismatch group had an increased risk of pelvic deformity (OR = 5.029, 95% CI: 1.618, 15.629) and PJK deformity (OR = 3.017, 95% CI: 1.709, 11.375) at the last follow-up. Short Form-36 and Scoliosis Research Society 22 score of the match group was significantly higher than that of the mismatch group at the last follow-up. CONCLUSION: The Roussouly classification mismatch before or after operation leads to increased risks of PI-LL deformity and pelvis deformity postoperatively or at the follow-up, which seriously worsens the clinical symptoms and prognosis of patients. Therefore, recovering to the theoretical type in Roussouly classification may effectively improve patients' prognosis.

A Premature Rise of Luteinizing Hormone Is Associated With a Reduced Cumulative Live Birth Rate in Patients ≥37 Years Old Undergoing GnRH Antagonist In Vitro Fertilization Cycles.

This is a retrospective cohort study included 1021 patients underwent a flexible GnRH antagonist IVF protocol from January 2017 to December 2017 to explore the effect of a premature rise in luteinizing hormone (LH) level on the cumulative live birth rate. All patients included received the first ovarian stimulation and finished a follow-up for 3 years. A premature rise in LH was defined as an LH level >10 IU/L or >50% rise from baseline during ovarian stimulation. The cumulative live birth rate was calculated as the number of women who achieved a live birth divided by the total number of women who had either delivered a baby or had used up all their embryos received from the first stimulated cycle. In the advanced patients (≥37 years), the cumulative live birth rate was reduced in patients with a premature rise of LH (ß: 0.20; 95% CI: 0.05-0.88; p=0.03), compared to patients (≥37 years) without the premature LH rise. The incidence of premature LH rise is associated with decreased rates of cumulative live birth rate in patients of advanced age (≥37 years) and aggravated the reduced potential of embryos produced by the advanced age, not the number of embryos.