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.

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 non-survivors of SFTS. Validation was performed in a cohort of 154 SFTS patients using enzyme-linked immunosorbent assay. We utilized the Drug Gene Interaction database to identify protein-drug interactions. RESULTS: Non-survivors exhibited 110 differentially expressed proteins (DEPs) compared to survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen DEPs, 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 cohort, 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 FDA-approved drugs targeting 37 death-related plasma proteins. CONCLUSIONS: Distinct plasma proteomic profiles characterize SFTS patients 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.

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.

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.

Effects of right ventricular remodeling in chronic thromboembolic pulmonary hypertension on the outcomes of balloon pulmonary angioplasty: a 2D-speckle tracking echocardiography study.

BACKGROUND: Balloon pulmonary angioplasty (BPA) improves the prognosis of chronic thromboembolic pulmonary hypertension (CTEPH). Right ventricle (RV) is an important predictor of prognosis in CTEPH patients. 2D-speckle tracking echocardiography (2D-STE) can evaluate RV function. This study aimed to evaluate the effectiveness of BPA in CTEPH patients and to assess the value of 2D-STE in predicting outcomes of BPA. METHODS: A total of 76 patients with CTEPH underwent 354 BPA sessions from January 2017 to October 2022. Responders were defined as those with mean pulmonary artery pressure (mPAP) ≤ 30 mmHg or those showing ≥ 30% decrease in pulmonary vascular resistance (PVR) after the last BPA session, compared to baseline. Logistic regression analysis was performed to identify predictors of BPA efficacy. RESULTS: BPA resulted in a significant decrease in mPAP (from 50.8 ± 10.4 mmHg to 35.5 ± 11.9 mmHg, p < 0.001), PVR (from 888.7 ± 363.5 dyn·s·cm-5 to 545.5 ± 383.8 dyn·s·cm-5, p < 0.001), and eccentricity index (from 1.3 to 1.1, p < 0.001), and a significant increase in RV free wall longitudinal strain (RVFWLS: from 15.7% to 21.0%, p < 0.001). Significant improvement was also observed in the 6-min walking distance (from 385.5 m to 454.5 m, p < 0.001). After adjusting for confounders, multivariate analysis showed that RVFWLS was the only independent predictor of BPA efficacy. The optimal RVFWLS cutoff value for predicting BPA responders was 12%. CONCLUSIONS: BPA was found to reduce pulmonary artery pressure, reverse RV remodeling, and improve exercise capacity. RVFWLS obtained by 2D-STE was an independent predictor of BPA outcomes. Our study may provide a meaningful reference for interventional therapy of CTEPH.

Predicting the CD34 content of mobilized peripheral blood leukapheresis products: single institution experience over 20 years.

BACKGROUND AIMS: Since the standardization of CD34 measurement by flow cytometry, predictors of leukapheresis CD34 yield have played a pivotal role in planning donor leukaphereses. We describe here a single institution's experience with a multivariate predictor that was used for 2,929 products without alteration for 20 years. METHODS: The ordinary least squares regression model variables included log peripheral CD34 count, collection duration (3- versus 4-hours), collection number, donor sex, and transplant type. RESULTS: During the study period we changed flow cytometers twice and leukapheresis instruments once. During the Cobe Spectra era the predictor explained 90% of the variability in CD34 collection yield for autologous transplants (r2 = 0.90), and 70% for allogeneic transplants with an overall sensitivity to predict a CD34 yield of ≥ 1 × 106/kg of 97.7%, and specificity of 81.4%. CONCLUSIONS: Implemented prospectively with real-time result reporting, the model allowed us to predict CD34 yield with both 3- and 4-hour collection scenarios. Given this guidance, 3-hour collections were selected by the clinical team 25% of the time, saving patient leukapheresis time and resources. When faced with a prediction of < 1 × 106 CD34/kg, the clinical team chose to defer collection 72% of the time. In instances where leukapheresis was performed despite a poor predicted outcome, 85% of patients collected on the Cobe Spectra, and 92% of patients collected on the Optia, failed to collect at least 1 × 106 CD34/kg. A revised model is tested retrospectively on Optia data, and suggestions for further improvements are discussed.

Unveiling Treatment Response Predictors in Predominant Subtypes of Chronic Inducible Urticaria.

INTRODUCTION: Chronic inducible urticaria (CIndU) is a subtype of chronic urticaria (CU), which requires specific triggers to occur. Despite their common occurrence, treatment response rates and predictors of treatment responses are largely lacking in the literature. This study evaluates antihistamine (AH) and omalizumab response rates in the most common CIndU subtypes and examines whether certain features can predict treatment responses. METHODS: We retrospectively analyzed CU patients with at least one CIndU subtype and performed comparisons between subgroups, in a total of 423 patients (70% CIndU, 30% chronic spontaneous urticaria [CSU] plus CIndU). RESULTS: The treatment response rates in CIndU were 51.6%, 51.5%, and 86.5% with standard-dose second-generation H1-antihistamines (sgAHs), updosed/combined sgAH, and omalizumab, respectively. Overall AH response was higher in CIndU than CSU plus CIndU (78.3% vs. 62%, p = 0.002) and in symptomatic dermographism (SD) and cold urticaria (ColdU) than cholinergic urticaria (ChoU) (83.2% vs. 78.3 vs. 60.9%, p = 0.04). AH-refractory patients had a longer disease duration (45.2 ± 56.7 months vs. 37 ± 51.9 months, p = 0.04), more angioedema, accompanying CSU, mixed CIndU subtypes (37.5% vs. 21.1%, p = 0.003; 45.1% vs. 27.1%, p = 0.002; 8.8% vs. 2.4%, p = 0.014), and lower baseline urticaria control test scores (5.86 ± 3.3 vs. 8.6 ± 3.6, p < 0.001) than AH-responsive patients. CONCLUSION: CIndU exhibits a good response to both AHs and omalizumab. Notably, the response to AHs is more pronounced in SD and ColdU compared to ChoU. Disease duration, angioedema, accompanying CSU, mixed CIndU, and lower baseline UCT scores may be used to predict AH treatment outcome in CIndU.

MmisAT and MmisP: an efficient and accurate suite of variant analysis toolkit for primary mitochondrial diseases.

Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields.

Incidence and predictors of posttraumatic epilepsy and cognitive impairment in patients with traumatic brain injury: A retrospective cohort study in Malaysia.

OBJECTIVE: Posttraumatic epilepsy (PTE) significantly impacts morbidity and mortality, yet local PTE data remain scarce. In addition, there is a lack of evidence on cognitive comorbidity in individuals with PTE in the literature. We sought to identify potential PTE predictors and evaluate cognitive comorbidity in patients with PTE. METHODS: A 2-year retrospective cohort study was employed, in which adults with a history of admission for traumatic brain injury (TBI) in 2019 and 2020 were contacted. Three hundred one individuals agreed to participate, with a median follow-up time of 30.75 months. The development of epilepsy was ascertained using a validated tool and confirmed by our neurologists during visits. Clinical psychologists assessed the patients' cognitive performance. RESULTS: The 2-year cumulative incidence of PTE was 9.3% (95% confidence interval [CI] 5.9-12.7). The significant predictors of PTE were identified as a previous history of brain injury [hazard ratio [HR] 4.025, p = .021], and intraparenchymal hemorrhage (HR: 2.291, p = .036), after adjusting for other confounders. TBI patients with PTE performed significantly worse on the total ACE-III cognitive test (73.5 vs 87.0, p = .018), CTMT (27.5 vs 33.0, p = .044), and PSI (74.0 vs 86.0, p = .006) than TBI patients without PTE. A significantly higher percentage of individuals in the PTE group had cognitive impairment, compared to the non-PTE group based on ACE-III (53.6% vs 46.4%, p = .001) and PSI (70% vs 31.7%, p = .005) scores at 2 years post-TBI follow-up. SIGNIFICANCE: This study emphasizes the link between TBI and PTE and the chance of developing cognitive impairment in the future. Clinicians can target interventions to prevent PTE by identifying specific predictors, which helps them make care decisions and develop therapies to improve patients' quality of life.