Preparation and epitope analysis of monoclonal antibodies against African swine fever virus DP96R protein.

BACKGROUND: Many proteins of African swine fever virus (ASFV, such as p72, p54, p30, CD2v, K205R) have been successfully expressed and characterized. However, there are few reports on the DP96R protein of ASFV, which is the virulence protein of ASFV and plays an important role in the process of host infection and invasion of ASFV. RESULTS: Firstly, the prokaryotic expression vector of DP96R gene was constructed, the prokaryotic system was used to induce the expression of DP96R protein, and monoclonal antibody was prepared by immunizing mice. Four monoclonal cells of DP96R protein were obtained by three ELISA screening and two sub-cloning; the titer of ascites antibody was up to 1:500,000, and the monoclonal antibody could specifically recognize DP96R protein. Finally, the subtypes of the four strains of monoclonal antibodies were identified and the minimum epitopes recognized by them were determined. CONCLUSION: Monoclonal antibody against ASFV DP96R protein was successfully prepared and identified, which lays a foundation for further exploration of the structure and function of DP96R protein and ASFV diagnostic technology.

Knowledge-guided learning methods for integrative analysis of multi-omics data.

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Filling the gaps: leveraging large language models for temporal harmonization of clinical text across multiple medical visits for clinical prediction.

Electronic health records offer great promise for early disease detection, treatment evaluation, information discovery, and other important facets of precision health. Clinical notes, in particular, may contain nuanced information about a patient's condition, treatment plans, and history that structured data may not capture. As a result, and with advancements in natural language processing, clinical notes have been increasingly used in supervised prediction models. To predict long-term outcomes such as chronic disease and mortality, it is often advantageous to leverage data occurring at multiple time points in a patient's history. However, these data are often collected at irregular time intervals and varying frequencies, thus posing an analytical challenge. Here, we propose the use of large language models (LLMs) for robust temporal harmonization of clinical notes across multiple visits. We compare multiple state-of-the-art LLMs in their ability to generate useful information during time gaps, and evaluate performance in supervised deep learning models for clinical prediction.

The CNS Relapse in T-Cell Lymphoma Index Predicts CNS Relapse in Patients with T- and NK-Cell Lymphomas.

Little is known about risk factors for central nervous system (CNS) relapse in mature T- and NK-cell neoplasms (MTNKN). We aimed to describe the clinical epidemiology of CNS relapse in patients with MTNKN and developed the CNS relapse In T-cell lymphoma Index (CITI) to predict patients at highest risk of CNS relapse. We reviewed data from 135 patients with MTNKN and CNS relapse from 19 North American institutions. After exclusion of leukemic and most cutaneous forms of MTNKN, patients were pooled with non-CNS relapse control patients from a single institution to create a CNS relapse-enriched training set. Using a complete case analysis (N=182), of whom 91 had CNS relapse, we applied a LASSO Cox regression model to select weighted clinicopathologic variables for the CITI score, which we validated in an external cohort from the Swedish Lymphoma Registry (N=566). CNS relapse was most frequently observed in patients with PTCL, NOS (25%). Median time to CNS relapse and median overall survival after CNS relapse was 8.0 months and 4.7 months, respectively. We calculated unique CITI risk scores for individual training set patients and stratified them into risk terciles. Validation set patients with low-risk (N=158) and high-risk (N=188) CITI scores had a 10-year cumulative risk of CNS relapse of 2.2% and 13.4%, respectively (HR 5.24, 95%CI 1.50-18.26, P=0.018). We developed an open-access web-based CITI calculator (https://redcap.link/citicalc) to provide an easy tool for clinical practice. The CITI score is a validated model to predict patients with MTNKN at highest risk of developing CNS relapse.

Sacubitril/Valsartan inhibits M1 type macrophages polarization in acute myocarditis by targeting C-type natriuretic peptide.

Studies have shown that Sacubitril/valsartan (Sac/Val) can reduce myocardial inflammation in myocarditis mice, in addition to its the recommended treatment of heart failure. However, the underlying mechanisms of Sac/Val in myocarditis remain unclear. C-type natriuretic peptide (CNP), one of the targeting natriuretic peptides of Sac/Val, was recently reported to exert cardio-protective and anti-inflammatory effects in cardiovascular systems. Here, we focused on circulating levels of CNP in patients with acute myocarditis (AMC) and whether Sac/Val modulates inflammation by targeting CNP in experimental autoimmune myocarditis (EAM) mice as well as LPS-induced RAW 264.7 cells and bone marrow derived macrophages (BMDMs) models. Circulating CNP levels were higher in AMC patients compared to healthy controls, and these levels positively correlated with the elevated inflammatory cytokines IL-6 and monocyte count. In EAM mice, Sac/Val alleviated myocardial inflammation while augmenting circulating CNP levels rather than BNP and ANP, accompanied by reduction in intracardial M1 macrophage infiltration and expression of inflammatory cytokines IL-1ß, TNF-α, and IL-6. Furthermore, Sac/Val inhibited CNP degradation and directly blunted M1 macrophage polarization in LPS-induced RAW 264.7 cells and BMDMs. Mechanistically, the effects might be mediated by the NPR-C/cAMP/JNK/c-Jun signaling pathway apart from NPR-B/cGMP/NF-κB pathway. In conclusion, Sac/Val exerts a protective effect in myocarditis by increasing CNP concentration and inhibiting M1 macrophages polarization.

Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data.

There is a growing body of literature on knowledge-guided statistical learning methods for analysis of structured high-dimensional data (such as genomic and transcriptomic data) that can incorporate knowledge of underlying networks derived from functional genomics and functional proteomics. These methods have been shown to improve variable selection and prediction accuracy and yield more interpretable results. However, these methods typically use graphs extracted from existing databases or rely on subject matter expertise, which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian modeling framework to account for network noise in regression models involving structured high-dimensional predictors. Specifically, we use 2 sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed predictors in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian regression model with structured high-dimensional predictors involving an adaptive structured shrinkage prior. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of a genomics dataset and another proteomics dataset for Alzheimer's disease.

Impact of deep muscle invasion on nodal status and survival in patients with pT2 esophageal squamous cancer.

BACKGROUND: Whether T2 esophageal squamous cell carcinoma should be subclassified remains controversial. We aimed to investigate the impact of the depth of muscularis propria invasion on nodal status and survival outcomes. METHODS: We identified patients with pT2 esophageal squamous cell carcinoma who underwent primary surgery from January 2009 to June 2017. Clinical data were extracted from prospectively maintained databases. Tumor muscularis propria invasion was stratified into superficial or deep. Binary logistic regression was used to determine risk factors for lymph node metastases. The impact of the depth of muscularis propria invasion on survival was investigated using Kaplan‒Meier analysis and a Cox proportional hazard regression model. RESULTS: A total of 750 patients from three institutes were investigated. The depth of muscularis propria invasion (odds ratio [OR]: 3.95, 95% confidence interval [CI]: 2.46-6.35; p < 0.001) was correlated with lymph node metastases using logistic regression. T substage (hazard ratio [HR]: 1.37, 95% CI: 1.05-1.79; p < 0.001) and N status (HR: 1.51, 95% CI: 1.05-2.17; p < 0.001) were independent risk factors in multivariate Cox regression analysis. The deep muscle invasion was associated with worse overall survival (HR: 1.52, 95% CI: 1.19-1.94; p = 0.001) than superficial, specifically in T2N0 patients (HR: 1.38, 95% CI: 1.08-1.94; p = 0.035). CONCLUSIONS: We found that deep muscle invasion was associated with significantly worse outcomes and recommended the substaging of pT2 esophageal squamous cell carcinoma in routine pathological examination.

Hydrolytic endonucleolytic ribozyme (HYER) is programmable for sequence-specific DNA cleavage.

Ribozymes are catalytic RNAs with diverse functions including self-splicing and polymerization. This work aims to discover natural ribozymes that behave as hydrolytic and sequence-specific DNA endonucleases, which could be repurposed as DNA manipulation tools. Focused on bacterial group II-C introns, we found that many systems without intron-encoded protein propagate multiple copies in their resident genomes. These introns, named HYdrolytic Endonucleolytic Ribozymes (HYERs), cleaved RNA, single-stranded DNA, bubbled double-stranded DNA (dsDNA), and plasmids in vitro. HYER1 generated dsDNA breaks in the mammalian genome. Cryo-electron microscopy analysis revealed a homodimer structure for HYER1, where each monomer contains a Mg2+-dependent hydrolysis pocket and captures DNA complementary to the target recognition site (TRS). Rational designs including TRS extension, recruiting sequence insertion, and heterodimerization yielded engineered HYERs showing improved specificity and flexibility for DNA manipulation.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions.

Background: Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods: We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion: Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Mechanistic study of transcription factor Sox18 during heart development.

Heart development is a delicate and complex process regulated by coordination of various signaling pathways. In this study, we investigated the role of sox18 in heart development by modulating Wnt/ß-Catenin signaling pathways. Our spatiotemporal expression analysis revealed that sox18 is mainly expressed in the heart, branchial arch, pharyngeal arch, spinal cord, and intersegmental vessels at the tailbud stage of Xenopus tropicalis embryo. Overexpression of sox18 in the X. tropicalis embryos causes heart edema, while loss-of-function of sox18 can change the signal of developmental heart marker gata4 at different stages, suggesting that sox18 plays an essential role in the development of the heart. Knockdown of SOX18 in human umbilical vein endothelial cells suggests a link between Sox18 and ß-CATENIN, a key regulator of the Wnt signaling pathway. Sox18 negatively regulates islet1 and tbx3, the downstream factors of Wnt/ß-Catenin signaling, during the linear heart tube formation and the heart looping stage. Taken together, our findings highlight the crucial role of Sox18 in the development of the heart via inhibiting Wnt/ß-Catenin signaling.

Increased resected lymph node stations improved survival of esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant chemoradiotherapy (nCRT) and surgery have been recommended as the standard treatments for locally advanced esophageal squamous cell carcinoma (ESCC). In addition, nodal metastases decreased in frequency and changed in distribution after neoadjuvant therapy. This study aimed to examine the optimal strategy for lymph node dissection (LND) in patients with ESCC who underwent nCRT. METHODS: The hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS) were calculated using the Cox proportional hazard model. To determine the minimal number of LNDs (n-LNS) or least station of LNDs (e-LNS), the Chow test was used. RESULTS: In total, 333 patients were included. The estimated cut-off values for e-LNS and n-LNS were 9 and 15, respectively. A higher number of e-LNS was significantly associated with improved OS (HR: 0.90; 95% CI 0.84-0.97, P = 0.0075) and DFS (HR: 0.012; 95% CI: 0.84-0.98, P = 0.0074). The e-LNS was a significant prognostic factor in multivariate analyses. The local recurrence rate of 23.1% in high e-LNS is much lower than the results of low e-LNS (13.3%). Comparable morbidity was found in both the e-LNS and n-LND subgroups. CONCLUSION: This cohort study revealed an association between the extent of LND and overall survival, suggesting the therapeutic value of extended lymphadenectomy during esophagectomy. Therefore, more lymph node stations being sampled leads to higher survival rates among patients who receive nCRT, and standard lymphadenectomy of at least 9 stations is strongly recommended.

Impact of Skeletal Muscle Loss and Sarcopenia on Outcomes of Locally Advanced Esophageal Cancer during Neoadjuvant Chemoradiation.

BACKGROUND: The impact of changes in skeletal muscle and sarcopenia on outcomes during neoadjuvant chemoradiotherapy (NACR) for patients with esophageal cancer remains controversial. PATIENTS AND METHODS: We retrospectively analyzed the data of patients with locally advanced esophageal squamous cell cancer who received NACR followed by esophagectomy between June 2013 and December 2021. The images at third lumbar vertebra were analyzed to measure the cross-sectional area and calculate skeletal muscle index (SMI) before and after NACR. SMI less than 52.4 cm2/m2 for men and less than 38.5 cm2/m2 for women were defined as sarcopenia. The nonlinearity of the effect of percent changes in SMI (ΔSMI%) to survival outcomes was assessed by restricted cubic splines. RESULTS: Overall, data of 367 patients were analyzed. The survival outcomes between sarcopenia and non-sarcopenia groups had no significant differences before NACR. However, patients in post-NACR sarcopenia group showed poor overall survival (OS) benefit (P = 0.016) and poor disease-free survival (DFS) (P = 0.043). Severe postoperative complication rates were 11.9% in post-NACR sarcopenia group and 5.0% in post-NACR non-sarcopenia group (P = 0.019). There was a significant non-linear relationship between ΔSMI% and survival outcomes (P < 0.05 for non-linear). On the multivariable analysis of OS, ΔSMI% > 12% was the independent prognostic factor (HR 1.76, 95% CI 1.03-2.99, P = 0.039) and significant difference was also found on DFS analysis (P = 0.025). CONCLUSIONS: Patients with post-neoadjuvant chemoradiotherapy sarcopenia have worse survival and adverse short-term outcomes. Moreover, greater loss in SMI is associated with increased risks of death and disease progression during neoadjuvant chemoradiotherapy, with maximum impact noted with SMI loss greater than 12%.

Vitamin D receptor induces oxidative stress to promote esophageal squamous cell carcinoma proliferation via the p53 signaling pathway.

Background: Esophageal squamous cell carcinoma (ESCC) is a common pathological esophageal cancer with poor prognosis. Vitamin D deficiency reportedly occurs in ESCC patients, and this is related to single nucleotide polymorphism of vitamin D receptor (VDR). Objective: We investigated the effect of VDR on ESCC proliferation, invasion, and metastasis and its potential mechanism. Methods: ESCC and normal tissues were collected from 20 ESCC patients. The ESCC tissue microarray contained 116 pairs of ESCC and normal tissues and 73 single ESCC tissues. VDR expression and its clinicopathological role were determined by real-time quantitative polymerase chain reaction, Western blot, and immunohistochemistry staining. sh-VDR and VDR overexpression were used to validate the effect of VDR on ESCC cell phenotype, and tandem mass tag-based quantitative proteomics and bioinformatics methods identified differential VDR-related proteins. The downstream pathway and regulatory effect were analyzed using ingenuity pathway analysis (IPA). Differentially expressed proteins were verified through parallel reaction monitoring and Western blot. In vivo imaging visualized subcutaneous tumor growth following tail vein injection of VDR-deficient ESCC cells. Results: High VDR expression was observed in ESCC tissues and cells. Gender, T stage, and TNM stage were related to VDR expression, which was the independent prognostic factor related to ESCC. VDR downregulation repressed ESCC cell proliferation, invasion, and migration in vitro and subcutaneous tumor growth and lung metastases in vivo. The cell phenotype changes were reversed upon VDR upregulation, and differential proteins were mainly enriched in the p53 signaling pathway. TP53 cooperated with ABCG2, APOE, FTH1, GCLM, GPX1, HMOX1, JUN, PRDX5, and SOD2 and may activate apoptosis and inhibit oxidative stress, cell metastasis, and proliferation. TP53 was upregulated after VDR knockdown, and TP53 downregulation reversed VDR knockdown-induced cell phenotype changes. Conclusions: VDR may inhibit p53 signaling pathway activation and induce ESCC proliferation, invasion, and metastasis by activating oxidative stress.

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

ZSWIM4 regulates embryonic patterning and BMP signaling by promoting nuclear Smad1 degradation.

The dorsoventral gradient of BMP signaling plays an essential role in embryonic patterning. Zinc Finger SWIM-Type Containing 4 (zswim4) is expressed in the Spemann-Mangold organizer at the onset of Xenopus gastrulation and is then enriched in the developing neuroectoderm at the mid-gastrula stages. Knockdown or knockout of zswim4 causes ventralization. Overexpression of zswim4 decreases, whereas knockdown of zswim4 increases the expression levels of ventrolateral mesoderm marker genes. Mechanistically, ZSWIM4 attenuates the BMP signal by reducing the protein stability of SMAD1 in the nucleus. Stable isotope labeling by amino acids in cell culture (SILAC) identifies Elongin B (ELOB) and Elongin C (ELOC) as the interaction partners of ZSWIM4. Accordingly, ZSWIM4 forms a complex with the Cul2-RING ubiquitin ligase and ELOB and ELOC, promoting the ubiquitination and degradation of SMAD1 in the nucleus. Our study identifies a novel mechanism that restricts BMP signaling in the nucleus.

Mutational signature and prognosis in adenocarcinoma of the bladder.

Adenocarcinoma of the bladder is a rare urinary bladder carcinoma with limited therapy options due to lack of molecular characterization. Here, we aimed to reveal the mutational and transcriptomic landscapes of adenocarcinoma of the bladder and assess any relationship with prognosis. Between February 2015 and June 2021, a total of 23 patients with adenocarcinoma of the bladder were enrolled. These included 16 patients with primary bladder adenocarcinomas and seven patients with urachal adenocarcinoma. Whole exome sequencing (16 patients), whole genome sequencing (16 patients), bulk RNA sequencing (RNA-seq) (19 patients), and single-cell RNA-seq (5 patients) were conducted for the specimens. Correlation analysis, survival analysis, and t-tests were also performed. Prevalent T>A substitutions were observed among somatic mutations, and major trinucleotide contexts included 5'-CTC-3' and 5'-CTG-3'. This pattern was mainly contributed by COSMIC signature 22 related to chemical carcinogen exposure (probably aristolochic acid), which has not been reported in bladder adenocarcinoma. Moreover, genes with copy number changes were also enriched in the KEGG term 'chemical carcinogenesis'. Transcriptomic analysis suggested high immune cell infiltration and luminal-like features in the majority of samples. Interestingly, a small fraction of samples with an APOBEC-derived mutational signature exhibited a higher risk of disease progression compared with samples with only a chemical carcinogen-related signature, confirming the molecular and prognostic heterogeneity of bladder adenocarcinoma. This study presents mutational and transcriptomic landscapes of bladder adenocarcinoma, and indicates that a chemical carcinogen-related mutational signature may be related to a better prognosis compared with an APOBEC signature in adenocarcinoma of the bladder. © 2024 The Pathological Society of Great Britain and Ireland.

Blunt pancreatic injury in children: Lessons from 11-year experience in a pediatric center.

OBJECTIVE: To study the diagnosis, management strategies, and outcomes of pediatric patients with blunt pancreatic injury. METHODS: The clinical data of patients with blunt pancreatic injury at Children's Hospital, Zhejiang University School of Medicine from January 2011 to September 2022 were collected and analyzed retrospectively. RESULTS: A total of 51 patients were enrolled. According to the American Association for the Surgery of Trauma (AAST) classification of pancreatic injury, 41 patients were categorized in the low-grade and nine in the high-grade groups. On admission, serum amylase and lipase levels in the high-grade group were significantly higher than in the low-grade group (P = 0.025 and P = 0.021, respectively). The peak levels of serum amylase and lipase in the high-grade group were considerably higher than in the low-grade group (P = 0.017 and P = 0.038, respectively). Two patients received surgical treatment, but none experienced pancreatectomy. The remaining 49 patients were successfully treated with conservative methods. The incidence of pancreatic pseudocysts in patients was 47.1% (24/51), and half required external drainage. CONCLUSION: Conservative management is safe for most children with blunt pancreatic injury, surgical intervention should be adopted in patients with hemodynamic instability or multiple organ failure.