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Aim of the study was to investigate the demographic data and disease course characteristics of patients with Sjögren’s syndrome (SS) and inflammatory joint pain of various origins and to search for factors that might help with the distinction of polyarthritis as an extraglandular manifestation and rheumatoid arthritis as an associated systemic autoimmune disorder. A total of 355 patients were retrospectively analyzed, 128 of whom served as controls (SS-C), while 159 had polyarthritis as an extraglandular symptom of Sjögren’s syndrome (SS-pa) and 68 were diagnosed as having associated rheumatoid arthritis (SS-RA). The patients without any inflammatory joint manifestations were significantly older than the SS-pa patients, while, for the SS-RA group, the difference was not significant. The onset of joint pain appeared significantly earlier in the SS-RA patients. Regarding either extraglandular manifestations or associated autoimmune disorders, there were significant differences between the controls and both SS-pa and SS-RA groups, while no significant difference was found between the SS-pa and SS-RA groups. Thus, laboratory and imaging methods should be used to differentiate between the two conditions, but laboratory biomarkers are even more important for early diagnosis. A ROC curve analysis showed an acceptable diagnostic accuracy in differentiating between SS-pa and SS-RA patients using a binary logistic regression model, where highly positive rheumatoid factor (RF) and anti-cyclic citrullinated peptide (CCP) values, kidney involvement, and anti-Ro/SS-A positivity were shown to significantly raise the odds of having RA, whereas anti-La/SS-B positivity seemed to have a protective role, since it significantly decreased the odds of having it. Further biomarkers are needed to better classify SS patient cohorts with inflammatory joint pain of different origins and, consequently, different management requirements.

Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.

Abstract Background Wolcott-Rallison Syndrome (WRS) is a rare autosomal recessive disease that is the most common cause of neonatal diabetes in consanguineous families. WRS is caused by various genetic alterations of the Eukaryotic Translation Initiation Factor 2-Alpha Kinase 3 (EIF2AK3) gene. Methods Genetic analysis of a consanguineous family where two children were diagnosed with WRS was performed by Sanger sequencing. The altered protein was investigated by in vitro cloning, expression and immunohistochemistry. Results The first cases in Hungary, − two patients in one family, where the parents were fourth-degree cousins - showed the typical clinical features of WRS: early onset diabetes mellitus with hyperglycemia, growth retardation, infection-induced multiple organ failure. The genetic background of the disease was a novel alteration in the EIF2AK3 gene involving the splice site of exon 11– intron 11–12 boundary: g.53051_53062delinsTG. According to cDNA sequencing this created a new splice site and resulted in a frameshift and the development of an early termination codon at amino acid position 633 (p.Pro627AspfsTer7). Based on in vitro cloning and expression studies, the truncated protein was functionally inactive. Immunohistochemistry revealed that the intact protein was absent in the islets of pancreas, furthermore insulin expressing cells were also dramatically diminished. Elevated GRP78 and reduced CHOP protein expression were observed in the liver. Conclusions The novel genetic alteration causing the absence of the EIF2AK3 protein resulted in insufficient handling of severe endoplasmic reticulum stress, leading to liver failure and demise of the patients.

Abstract Introduction Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome‐wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras. Methods We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published. Results We found that the PGS is significantly associated with the attainment of a college degree as well as the number of years in education in a sample of Hungarian study participants (N = 829). PGS effect sizes were not significantly different when compared to an English (N = 976) comparison sample with identical measurement protocols. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance. Discussion Our results provide evidence that polygenic scores for educational attainment have predictive value in culturally diverse European populations.

Abstract Background Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance. Method We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Results VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of “small sample size” regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time. Conclusion In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions.

BACKGROUND: To date no age-related reference ranges are available for serum thymidine kinase (TK1) activity. Being a proliferation marker, it may be used as a prognostic marker in malignant diseases, including chronic lymphocytic leukemia (CLL). Our aim was to establish age-specific reference ranges for TK1 and examine its utility as a screening marker in CLL, a disease of the elderly. METHODS: Serum TK1 activity was measured by a competitive chemiluminescent immunoassay in 369 healthy adults and 115 de novo CLL patients. RESULTS: We observed a statistically significant decline in TK1 activity from young (18-35 years) to middle-aged (36-60 years) and further on to elderly (60-86 years) healthy individuals. Age-related reference range was:

In this study we investigated whether polymorphisms in the folate pathway influenced the risk of childhood acute lymphoblastic leukemia (ALL) or the survival rate of the patients. For this we selected and genotyped 67 SNPs in 15 genes in the folate pathway in 543 children with ALL and 529 controls. The results were evaluated by gender adjusted logistic regression and by the Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) methods. Bayesian structure based odds ratios for the relevant variables and interactions were also calculated. Altogether 9 SNPs in 8 genes were associated with altered susceptibility to ALL. After correction for multiple testing, two associations remained significant. The genotype distribution of the MTHFD1 rs1076991 differed significantly between the ALL and control population. Analyzing the subtypes of the disease the GG genotype increased only the risk of B-cell ALL (p = 3.52×10(-4); OR = 2.00). The GG genotype of the rs3776455 SNP in the MTRR gene was associated with a significantly reduced risk to ALL (p = 1.21×10(-3); OR = 0.55), which resulted mainly from the reduced risk to B-cell and hyperdiploid-ALL. The TC genotype of the rs9909104 SNP in the SHMT1 gene was associated with a lower survival rate comparing it to the TT genotype (80.2% vs. 88.8%; p = 0.01). The BN-BMLA confirmed the main findings of the frequentist-based analysis and showed structural interactional maps and the probabilities of the different structural association types of the relevant SNPs especially in the hyperdiploid-ALL, involving additional SNPs in genes like TYMS, DHFR and GGH. We also investigated the statistical interactions and redundancies using structural model properties. These results gave further evidence that polymorphisms in the folate pathway could influence the ALL risk and the effectiveness of the therapy. It was also shown that in gene association studies the BN-BMLA could be a useful supplementary to the traditional frequentist-based statistical method.

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA). This method uses bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated.With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2-1.8); p = 3×10(-4)). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics.In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance.

Due to the favorable features obtained through the incorporation of fluorine atom(s), fluorinated drugs are a group with emerging pharmaceutical importance. As their commercial availability is still very limited, to expand the range of possible candidates, new fluorinated tryptophan analogs were synthesized. Control of enantiopurity during the synthesis procedure requires that highly efficient enantioseparation methods be available. In this work, the enantioseparation of seven fluorinated tryptophans and tryptophan was studied and compared systematically to (i) develop analytical methods for enantioselective separations and (ii) explore the chromatographic features of the fluorotrytophans. For enantioresolution, macrocyclic glycopeptide-based selectors linked to core-shell particles were utilized, applying liquid chromatography-based methods. Application of the polar-ionic mode resulted in asymmetric and broadened peaks, while reversed-phase conditions, together with mobile-phase additives, resulted in baseline separation for all studied fluorinated tryptophans. The marked differences observed between the methanol and acetonitrile-containing eluent systems can be explained by the different solvation abilities of the bulk solvents of the applied mobile phases. Among the studied chiral selectors, teicoplanin and teicoplanin aglycone were found to work effectively. Under optimized conditions, baseline separations were achieved within 6 min. Ionic interactions were semi-quantitatively characterized and found to not influence enantiorecognition. Interestingly, fluorination of the analytes does not lead to marked changes in the chromatographic characteristics of the methanol-containing eluents, while larger differences were noticed when the polar but aprotic acetonitrile was applied. Experiments conducted on the influence of the separation temperature indicated that the separations are enthalpically driven, with only one exception. Enantiomeric elution order was found to be constant on both teicoplanin and teicoplanin aglycone-based chiral stationary phases (L < D) under all applied chromatographic conditions. [ABSTRACT FROM AUTHOR]

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Abstract The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.