Protein phosphorylation database and prediction tools.

Protein phosphorylation, one of the main protein post-translational modifications, is required for regulating various life activities. Kinases and phosphatases that regulate protein phosphorylation in humans have been targeted to treat various diseases, particularly cancer. High-throughput experimental methods to discover protein phosphosites are laborious and time-consuming. The burgeoning databases and predictors provide essential infrastructure to the research community. To date, >60 publicly available phosphorylation databases and predictors each have been developed. In this review, we have comprehensively summarized the status and applicability of major online phosphorylation databases and predictors, thereby helping researchers rapidly select tools that are most suitable for their projects. Moreover, the organizational strategies and limitations of these databases and predictors have been highlighted, which may facilitate the development of better protein phosphorylation predictors in silico.

iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model.

As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.

BACKGROUND: Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). RESULTS: The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. CONCLUSIONS: VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction.

BACKGROUND: Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly. METHOD: To address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory-based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level. RESULTS: The proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set. CONCLUSIONS: The outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at https://github.com/shafiee 95/shima.shafiee.DP-Site.

msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths.

BACKGROUND: A promoter is a specific sequence in DNA that has transcriptional regulatory functions, playing a role in initiating gene expression. Identifying promoters and their strengths can provide valuable information related to human diseases. In recent years, computational methods have gained prominence as an effective means for identifying promoter, offering a more efficient alternative to labor-intensive biological approaches. RESULTS: In this study, a two-stage integrated predictor called "msBERT-Promoter" is proposed for identifying promoters and predicting their strengths. The model incorporates multi-scale sequence information through a tokenization strategy and fine-tunes the DNABERT model. Soft voting is then used to fuse the multi-scale information, effectively addressing the issue of insufficient DNA sequence information extraction in traditional models. To the best of our knowledge, this is the first time an integrated approach has been used in the DNABERT model for promoter identification and strength prediction. Our model achieves accuracy rates of 96.2% for promoter identification and 79.8% for promoter strength prediction, significantly outperforming existing methods. Furthermore, through attention mechanism analysis, we demonstrate that our model can effectively combine local and global sequence information, enhancing its interpretability. CONCLUSIONS: msBERT-Promoter provides an effective tool that successfully captures sequence-related attributes of DNA promoters and can accurately identify promoters and predict their strengths. This work paves a new path for the application of artificial intelligence in traditional biology.

IgE directly affects eosinophil migration in chronic rhinosinusitis with nasal polyps through CCR3 and predicts the efficacy of omalizumab.

BACKGROUND: Whether IgE affects eosinophil migration in chronic rhinosinusitis with nasal polyps (CRSwNP) remains largely unclear. Moreover, our understanding of local IgE, eosinophils, and omalizumab efficacy in CRSwNP remains limited. OBJECTIVE: We investigated whether IgE acts directly on eosinophils and determined its role in omalizumab therapy. METHODS: Eosinophils and their surface receptors were detected by hematoxylin and eosin staining and flow cytometry. IgE and its receptors, eosinophil peroxidase (EPX), eosinophilic cationic protein, and CCR3 were detected by immunohistochemistry and immunofluorescence. Functional analyses were performed on blood eosinophils and polyp tissues. Logistic regression was performed to screen for risk factors. Receiver operating characteristic curve was generated to evaluate the accuracy. RESULTS: Both FcεRI and CD23 were expressed on eosinophils. The expression of FcεRI and CD23 on eosinophil in nasal polyp tissue was higher than in peripheral blood (both P < .001). IgE and EPX colocalized in CRSwNP. IgE directly promoted eosinophil migration by upregulating CCR3 in CRSwNP but not in healthy controls. Omalizumab and lumiliximab were found to be effective in restraining this migration, indicating CD23 was involved in IgE-induced eosinophil migration. Both IgE+ and EPX+ cells were significantly reduced after omalizumab treatment in those who experienced response (IgE+ cells, P = .001; EPX+ cells, P = .016) but not in those with no response (IgE+ cells, P = .060; EPX+ cells, P = .151). Baseline IgE+ cell levels were higher in those with response compared to those without response (P = .024). The baseline local IgE+ cell count predicted omalizumab efficacy with an accuracy of 0.811. CONCLUSIONS: IgE directly promotes eosinophil migration, and baseline local IgE+ cell counts are predictive of omalizumab efficacy in CRSwNP.

Identification of CCL20 as a Prognostic Predictor for Severe Fever With Thrombocytopenia Syndrome Based on Plasma Proteomics.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics. METHODS: Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 nonsurvivors of SFTS. Validation was performed in a cohort of 154 patients with SFTS via enzyme-linked immunosorbent assay. We utilized the Drug-Gene Interaction Database to identify protein-drug interactions. RESULTS: Nonsurvivors exhibited 110 differentially expressed proteins as compared with survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen differentially expressed proteins-including C-C motif chemokine 20 (CCL20), calcitonin gene-related peptide alpha, and pleiotrophin-were associated with multiple-organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P = .0004) and 0.9033 (P < .0001) in the discovery and validation cohorts, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 Food and Drug Administration-approved drugs targeting 37 death-related plasma proteins. CONCLUSIONS: Distinct plasma proteomic profiles characterize SFTS cases with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis.

MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction.

BACKGROUND: Epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all RNA types. Precise recognition of RNA modifications is critical for understanding their functions and regulatory mechanisms. However, wet experimental methods are often costly and time-consuming, limiting their wide range of applications. Therefore, recent research has focused on developing computational methods, particularly deep learning (DL). Bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and the transformer have demonstrated achievements in modification site prediction. However, BiLSTM cannot achieve parallel computation, leading to a long training time, CNN cannot learn the dependencies of the long distance of the sequence, and the Transformer lacks information interaction with sequences at different scales. This insight underscores the necessity for continued research and development in natural language processing (NLP) and DL to devise an enhanced prediction framework that can effectively address the challenges presented. RESULTS: This study presents a multi-scale self- and cross-attention network (MSCAN) to identify the RNA methylation site using an NLP and DL way. Experiment results on twelve RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) reveal that the area under the receiver operating characteristic of MSCAN obtains respectively 98.34%, 85.41%, 97.29%, 96.74%, 99.04%, 79.94%, 76.22%, 65.69%, 92.92%, 92.03%, 95.77%, 89.66%, which is better than the state-of-the-art prediction model. This indicates that the model has strong generalization capabilities. Furthermore, MSCAN reveals a strong association among different types of RNA modifications from an experimental perspective. A user-friendly web server for predicting twelve widely occurring human RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) is available at http://47.242.23.141/MSCAN/index.php . CONCLUSIONS: A predictor framework has been developed through binary classification to predict RNA methylation sites.

Risk factors and disease trajectories of recurrent immune thrombocytopenia in children.

This retrospective study aimed to analyse the course and outcome of recurrent immune thrombocytopenia (ITP) in children and to identify factors associated with recurrence. A total of 497 newly diagnosed ITP children with platelet <30 × 109/L between January 1988 and December 2019 were included. Recurrent ITP was defined as a new event of thrombocytopenia after at least 3 months of remission without treatment. Twenty-nine (5.8%) children experienced 48 recurrent episodes. The median time from diagnosis to recurrence was 22 months. Most recurrences occurred in children aged 1.5-10 years with a recent infection history. Compared to non-recurrent ITP, children with recurrent ITP had delayed remission with lower platelets at 1 month and 3 months postdiagnosis. Multivariate analysis identified aged 1.5-10 years (hazard ratio [HR] 3.65, 95% confidence interval [CI]: 1.35-9.82) and delayed remission at 7-12 months (HR 4.04, 95% CI: 1.37-11.95) as predictors for recurrence. Most recurrent ITP patients had minor or mild symptoms, higher platelet counts, did not require treatment, and achieved remission within 12 months. The similar remission trajectories among the first and recurrent ITP, but different from the courses in the non-recurrent ITP, suggest that recurrent ITP might have a unique biological basis.

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secretion pathways. The non-classical secretion pathway has recently received increasing attention among these secretion pathways, but its exact mechanism remains unclear. Non-classical secreted proteins (NCSPs) are a class of secreted proteins lacking signal peptides and motifs. Several NCSP predictors have been proposed to identify NCSPs and most of them employed the whole amino acid sequence of NCSPs to construct the model. However, the sequence length of different proteins varies greatly. In addition, not all regions of the protein are equally important and some local regions are not relevant to the secretion. The functional regions of the protein, particularly in the N- and C-terminal regions, contain important determinants for secretion. In this study, we propose a new hybrid deep learning-based framework, referred to as ASPIRER, which improves the prediction of NCSPs from amino acid sequences. More specifically, it combines a whole sequence-based XGBoost model and an N-terminal sequence-based convolutional neural network model; 5-fold cross-validation and independent tests demonstrate that ASPIRER achieves superior performance than existing state-of-the-art approaches. The source code and curated datasets of ASPIRER are publicly available at https://github.com/yanwu20/ASPIRER/. ASPIRER is anticipated to be a useful tool for improved prediction of novel putative NCSPs from sequences information and prioritization of candidate proteins for follow-up experimental validation.

Ensemble methods of rank-based trees for single sample classification with gene expression profiles.

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of "relative expression reversals". Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble .

A predictor-corrector phase unwrapping algorithm for temporally undersampled gradient-echo MRI.

PURPOSE: To develop a method for unwrapping temporally undersampled and nonlinear gradient recalled echo (GRE) phase. THEORY AND METHODS: Temporal unwrapping is performed as a sequential one step prediction of the echo phase, followed by a correction to the nearest integer wrap-count. A spatio-temporal extension of the 1D predictor corrector unwrapping (PCU) algorithm improves the prediction accuracy, and thereby maintains spatial continuity. The proposed method is evaluated using numerical phantom, physical phantom, and in vivo brain data at both 3 T and 9.4 T. The unwrapping performance is compared with the state-of-the-art temporal and spatial unwrapping algorithms, and the spatio-temporal iterative virtual-echo based Nyquist sampled (iVENyS) algorithm. RESULTS: Simulation results showed significant reduction in unwrapping errors at higher echoes compared with the state-of-the-art algorithms. Similar to the iVENyS algorithm, the PCU algorithm was able to generate spatially smooth phase images for in vivo data acquired at 3 T and 9.4 T, bypassing the use of additional spatial unwrapping step. A key advantage over iVENyS algorithm is the superior performance of PCU algorithm at higher echoes. CONCLUSION: PCU algorithm serves as a robust phase unwrapping method for temporally undersampled and nonlinear GRE phase, particularly in the presence of high field gradients.

Prioritization of genes for translation: a computational approach.

INTRODUCTION: Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED: In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION: Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Nasopharyngeal neutrophilic-retention signatures could predict disease progression in early SARS-CoV-2 infection.

The nasopharynx is the initial site of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and neutrophils play a critical role in preventing viral transmission into the lower airways or lungs during the early phases of infection. However, neutrophil dynamics, functional signatures, and predictive roles in the nasopharynx of coronavirus disease 2019 (COVID-19) patients have not yet been elucidated. In this study, we carried out RNA sequencing of nasopharyngeal swabs from a cohort of COVID-19 patients with mild, moderate, severe outcomes and healthy donors as controls. Over 32.7% of the differentially expressed genes associated with COVID-19 severity were neutrophil-related, including those involved in migration, neutrophil extracellular traps formation, and inflammasome activation. Multicohort single-cell RNA sequencing analysis further confirmed these findings and identified a population of neutrophils expressing Vacuolar-type ATPase (V-ATPase) and the chemokine receptor CXCR4 in the nasopharynx. This population of neutrophils preferentially expressed pro-inflammatory genes relevant to phagosomal maturation as well as local reactive oxygen species and reactive nitrogen species production in the nasopharynx of patients with severe outcomes. A four-gene panel defined as a neutrophil signature associated with COVID-19 progression (NSAP) was identified as an early diagnostic predictor of severe COVID-19, which potentially distinguished severe patients from mild cases with influenza, respiratory syncytial virus, dengue virus, or hepatitis B virus infection. NSAP is mainly expressed on CXCR4high neutrophils and exhibits a significant association with the cell fraction of this neutrophil population. This study highlights novel potential therapeutic targets or diagnostic tools for predicting patients at a higher risk of severe outcomes.

Identifying diagnostic and prognostic factors in cerebral amyloid angiopathy-related inflammation: A systematic analysis of published and seven new cases.

AIMS: Cerebral amyloid angiopathy (CAA)-related inflammation (CAA-RI) is a potentially reversible manifestation of CAA, histopathologically characterised by transmural and/or perivascular inflammatory infiltrates. We aimed to identify clinical, radiological and laboratory variables capable of improving or supporting the diagnosis of or predicting/influencing the prognosis of CAA-RI and to retrospectively evaluate different therapeutic approaches. METHODS: We present clinical and neuroradiological observations in seven unpublished CAA-RI cases, including neuropathological findings in two definite cases. These cases were included in a systematic analysis of probable/definite CAA-RI cases published in the literature up to 31 December 2021. Descriptive and associative analyses were performed, including a set of clinical, radiological and laboratory variables to predict short-term, 6-month and 1-year outcomes and mortality, first on definite and second on an expanded probable/definite CAA-RI cohort. RESULTS: Data on 205 definite and 100 probable cases were analysed. CAA-RI had a younger symptomatic onset than non-inflammatory CAA, without sex preference. Transmural histology was more likely to be associated with the co-localisation of microbleeds with confluent white matter hyperintensities on magnetic resonance imaging (MRI). Incorporating leptomeningeal enhancement and/or sulcal non-nulling on fluid-attenuated inversion recovery (FLAIR) enhanced the sensitivity of the criteria. Cerebrospinal fluid pleocytosis was associated with a decreased probability of clinical improvement and longer term positive outcomes. Future lobar haemorrhage was associated with adverse outcomes, including mortality. Immunosuppression was associated with short-term improvement, with less clear effects on long-term outcomes. The superiority of high-dose over low-dose corticosteroids was not established. CONCLUSIONS: This is the largest retrospective associative analysis of published CAA-RI cases and the first to include an expanded probable/definite cohort to identify diagnostic/prognostic markers. We propose points for further crystallisation of the criteria and directions for future prospective studies.

Adherence to dietary guidelines, and the risk of developing rheumatoid arthritis: results from a nested case-control study.

OBJECTIVES: To examine the relationship between adherence to dietary guidelines and the risk of developing RA. METHODS: Participants in the Malmö Diet and Cancer Study (MDCS) cohort diagnosed with RA were identified through register linkage and validated in a structured review. Four controls per case were selected, matched for sex, year of birth, and year of inclusion in the MDCS. Diet was assessed at baseline (1991-1996) using a validated diet history method. A Diet Quality Index (DQI) based on adherence to the Swedish dietary guidelines including intakes of fibre, vegetables and fruits, fish and shellfish, saturated fat, polyunsaturated fat, and sucrose, was used. The associations between the DQI and its components and the risk of RA were assessed using conditional logistic regression analysis, adjusting for total energy intake, smoking, leisure time physical activity and alcohol consumption. RESULTS: We identified 172 validated cases of incident RA in the cohort. Overall adherence to the dietary guidelines was not associated with the risk of RA. Adherence to recommended fibre intake was associated with decreased risk of RA in crude and multivariable-adjusted analyses, with odds ratios (ORs) 0.60 (95% CI 0.39, 0.93) and 0.51 (95% CI 0.29, 0.90), respectively, compared with subjects with non-adherence. CONCLUSIONS: Reaching the recommended intake level of dietary fibre, but not overall diet quality, was independently associated with decreased risk of RA. Further studies are needed to assess the role of different food sources of dietary fibre in relation to risk of RA and the underlying mechanisms.

Predictors of irreversible renal dysfunction in patients with idiopathic retroperitoneal fibrosis.

OBJECTIVES: Idiopathic retroperitoneal fibrosis (iRPF) can lead to irreversible kidney damage. This study aimed to investigate predictors of irreversible renal dysfunction in patients with iRPF. METHODS: Eighty-three patients with newly diagnosed iRPF were enrolled between January 2010 and Sep 2022 at Zhongshan Hospital of Fudan University, including 60 in the training set and 23 in the validation set. They were regularly contacted or followed up via outpatient examinations by specialist doctors, who documented their condition and treatment progress. Predictors of irreversible renal dysfunction were identified using univariate and multivariate regression, logistic model, and receiver operating curve analyses. RESULTS: In the training set, over a median follow-up of 29 months, 16.7% of patients had an estimated glomerular filtration rate (eGFR) of < 60 ml/min/1.73m2 at the last follow-up, and 25% had hydronephrosis or required prolonged double-J stents. A prognostic score was developed by assigning 1, 1, and 2 points for peripheral CD19+ B cells <9.3%, serum creatinine (sCr) ≥120 µmol/l, and no response at 6 months, respectively. A score of ≥ 2 for predicting irreversible renal dysfunction had sensitivity and specificity of 100% and 92%, respectively. In the validation set, 21.7% of patients suffered from irreversible renal dysfunction. The sensitivity and specificity for predicting irreversible renal dysfunction were 100% and 94.4%, respectively. CONCLUSIONS: A prognostic score based on factors including CD19+ B cells <9.3% and sCr ≥120 µmol/l at baseline, and no response at 6 months, is suitable for predicting irreversible renal dysfunction in iRPF.

Changes in Skeletal Muscle Mass in the First 3 Months Following Gastrointestinal Cancer Surgery: A Prospective Study.

BACKGROUND: Surgical resection is the primary treatment for gastrointestinal (GI) cancers, but postoperative skeletal muscle loss (SML) is common and linked to poor prognosis. This study aims to identify patterns of muscle change, examine its association with quality of life (QoL), and explore predictors of SML in the first 3 months. PATIENTS AND METHODS: A prospective cohort study was conducted on patients newly diagnosed with GI cancer and undergoing surgery in China between September 2021 and May 2022. Skeletal muscle mass (SMM) and QoL were assessed at admission, 7 days, 1 month, and 3 months post-surgery. Demographic, clinical data, and biomarkers were collected. Missing data were imputed using multiple imputation. Data were analyzed using growth mixture modelling, bivariate analyses, and logistic regression. RESULTS: A total of 483 patients completed baseline assessment. Of the 242 patients with complete muscle assessments, 92% experienced SML. Three distinct patterns of muscle change were identified: 57% had normal preoperative SMM with mild postoperative SML, 16% had low preoperative SMM with moderate SML, and 27% had normal preoperative mass but severe postoperative SML. Moderate/severe SML was associated with more postoperative complications, poorer health, and higher symptom burden. Independent predictors included advanced age, preoperative sarcopenia, advanced cancer stage, and low prognostic nutrition index (PNI ≤ 45). The results did not change when using imputed values. CONCLUSIONS: Although SML is prevalent, patterns of muscle change are heterogeneous among patients. Advanced age, preoperative sarcopenia, advanced cancer stage, and cancer-related inflammation are predictors for moderate/severe SML, highlighting the need for early detection and management.

A Novel Predictive Score Model for Successful Weaning From Mechanical Circulatory Support in Patients With Cardiogenic Shock.

BACKGROUND: Clinical evidence regarding predictors of successful weaning from mechanical circulatory support (MCS) is lacking. This study aimed to create a simple risk score to predict successful weaning from MCS in patients with cardiogenic shock. METHODS AND RESULTS: This retrospective single-center cohort study included 114 consecutive patients with cardiogenic shock treated with venoarterial extracorporeal membrane oxygenation or IMPELLA between January 2013 and June 2023. Patients with out-of-hospital cardiac arrest were excluded. The primary end point was successful weaning from MCS, defined as successful decannulation without the need for MCS reimplantation and survival to discharge. Multivariable logistic regression with a stepwise variable selection was performed to generate the prediction model. We first developed a general weaning score model, and then created a simple version of the score model using the same variables. Fifty-five patients were weaned from MCS successfully. The following variables measured during weaning evaluation were selected as the components of the weaning score model: acute myocardial infarction (AMI), mean blood pressure, left ventricular ejection fraction (LVEF), lactate level, and QRS duration. According to the results, we conducted a novel weaning score model to predict successful weaning from MCS: 1.774 - 2.090 × (AMI) + 0.062 × [mean blood pressure (mm Hg)] + 0.139 × [LVEF (%)] - 0.322 × [Lactate (mg/dL)] - 0.066 × [QRS (ms)]. The following variables were selected as the components of the simple version of the weaning score model: AMI, mean blood pressure of ≥80 mm Hg, lactate of <10 mg/dL, QRS duration of ≤95 ms, and LVEF of >35%. CONCLUSIONS: We developed a simple model to predict successful weaning from MCS in patients with cardiogenic shock.

Circulating palmitoyl sphingomyelin levels predict the 10-year increased risk of cardiovascular disease death in Chinese adults: findings from the Da Qing Diabetes Study.

BACKGROUND: Higher levels of palmitoyl sphingomyelin (PSM, synonymous with sphingomyelin 16:0) are associated with an increased risk of cardiovascular disease (CVD) in people with diabetes. Whether circulating PSM levels can practically predict the long-term risk of CVD and all-cause death remains unclear. This study aimed to investigate whether circulating PSM is a real predictor of CVD death in Chinese adults with or without diabetes. METHODS: A total of 286 and 219 individuals with and without diabetes, respectively, from the original Da Qing Diabetes Study were enrolled. Blood samples collected in 2009 were used as a baseline to assess circulating PSM levels. The outcomes of CVD and all-cause death were followed up from 2009 to 2020, and 178 participants died, including 87 deaths due to CVD. Cox proportional hazards regression was used to estimate HRs and their 95% CIs for the outcomes. RESULTS: Fractional polynomial regression analysis showed a linear association between baseline circulating PSM concentration (log-2 transformed) and the risk of all-cause and CVD death (p < 0.001), but not non-CVD death (p > 0.05), in all participants after adjustment for confounders. When the participants were stratified by PSM-tertile, the highest tertile, regardless of diabetes, had a higher incidence of CVD death (41.5 vs. 14.7 and 22.2 vs. 2.9 per 1000 person-years in patients with and without diabetes, respectively, all log-rank p < 0.01). Individuals with diabetes in the highest tertile group had a higher risk of CVD death than those in the lowest tertile (HR = 2.73; 95%CI, 1.20-6.22). CONCLUSIONS: Elevated PSM levels are significantly associated with a higher 10-year risk of CVD death, but not non-CVD death, in Chinese adults with diabetes. These findings suggest that PSM is a potentially useful long-term predictor of CVD death in individuals with diabetes.