Partial Thermal Condensation Mediated Synthesis of High-Density Nickel Single Atom Sites on Carbon Nitride for Selective Photooxidation of Methane into Methanol.

Direct selective transformation of greenhouse methane (CH4) to liquid oxygenates (methanol) can substitute energy-intensive two-step (reforming/Fischer-Tropsch) synthesis while creating environmental benefits. The development of inexpensive, selective, and robust catalysts that enable room temperature conversion will decide the future of this technology. Single-atom catalysts (SACs) with isolated active centers embedded in support have displayed significant promises in catalysis to drive challenging reactions. Herein, high-density Ni single atoms are developed and stabilized on carbon nitride (NiCN) via thermal condensation of preorganized Ni-coordinated melem units. The physicochemical characterization of NiCN with various analytical techniques including HAADF-STEM and X-ray absorption fine structure (XAFS) validate the successful formation of Ni single atoms coordinated to the heptazine-constituted CN network. The presence of uniform catalytic sites improved visible absorption and carrier separation in densely populated NiCN SAC resulting in 100% selective photoconversion of (CH4) to methanol using H2O2 as an oxidant. The superior catalytic activity can be attributed to the generation of high oxidation (NiIII═O) sites and selective C─H bond cleavage to generate •CH3 radicals on Ni centers, which can combine with •OH radicals to generate CH3OH.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study.

BACKGROUND: Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions. OBJECTIVE: This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions. METHODS: In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland. RESULTS: The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases. CONCLUSIONS: The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Synthesis and characterization of TiO2 nanoparticles combined with geraniol and their synergistic antibacterial activity.

BACKGROUND: The emergence of antibiotic resistance in pathogenic bacteria has become a global threat, encouraging the adoption of efficient and effective alternatives to conventional antibiotics and promoting their use as replacements. Titanium dioxide nanoparticles (TiO2 NPs) have been reported to exhibit antibacterial properties. In this study, we synthesized and characterized TiO2 NPs in anatase and rutile forms with surface modification by geraniol (GER). RESULTS: The crystallinity and morphology of modified TiO2 NPs were analyzed by UV/Vis spectrophotometry, X-ray powder diffraction (XRD), and scanning electron microscopy (SEM) with elemental mapping (EDS). The antimicrobial activity of TiO2 NPs with geraniol was assessed against Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA), and Escherichia coli. The minimum inhibitory concentration (MIC) values of modified NPs ranged from 0.25 to 1.0 mg/ml against all bacterial strains, and the live dead assay and fractional inhibitory concentration (FIC) supported the antibacterial properties of TiO2 NPs with GER. Moreover, TiO2 NPs with GER also showed a significant decrease in the biofilm thickness of MRSA. CONCLUSIONS: Our results suggest that TiO2 NPs with GER offer a promising alternative to antibiotics, particularly for controlling antibiotic-resistant strains. The surface modification of TiO2 NPs by geraniol resulted in enhanced antibacterial properties against multiple bacterial strains, including antibiotic-resistant MRSA. The potential applications of modified TiO2 NPs in the biomedical and environmental fields warrant further investigation.

Synergistic antibacterial action of the iron complex and ampicillin against Staphylococcus aureus.

OBJECTIVES: Resistance to antibiotics among bacteria of clinical importance, including Staphylococcus aureus, is a serious problem worldwide and the search for alternatives is needed. Some metal complexes have antibacterial properties and when combined with antibiotics, they may increase bacterial sensitivity to antimicrobials. In this study, we synthesized the iron complex and tested it in combination with ampicillin (Fe16 + AMP) against S. aureus. METHODS: An iron complex (Fe16) was synthesized and characterized using spectroscopy methods. Confirmation of the synergistic effect between the iron complex (Fe16) and ampicillin (AMP) was performed using ζ-potential, infrared spectra and FICI index calculated from the minimum inhibitory concentration (MIC) from the checkerboard assay. Cytotoxic properties of combination Fe16 + AMP was evaluated on eukaryotic cell line. Impact of combination Fe16 + AMP on chosen genes of S. aureus were performed by Quantitative Real-Time PCR. RESULTS: The MIC of Fe16 + AMP was significantly lower than that of AMP and Fe16 alone. Furthermore, the infrared spectroscopy revealed the change in the ζ-potential of Fe16 + AMP. We demonstrated the ability of Fe16 + AMP to disrupt the bacterial membrane of S. aureus and that likely allowed for better absorption of AMP. In addition, the change in gene expression of bacterial efflux pumps at the sub-inhibitory concentration of AMP suggests an insufficient import of iron into the bacterial cell. At the same time, Fe16 + AMP did not have any cytotoxic effects on keratinocytes. CONCLUSIONS: Combined Fe16 + AMP therapy demonstrated significant synergistic and antimicrobial effects against S. aureus. This study supports the potential of combination therapy and further research.

A sound mind in a sound body: a novel concept unravelling heterogeneity of depression.

Depression is a highly prevalent and debilitating condition, yet we still lack both in-depth knowledge concerning its etiopathology and sufficiently efficacious treatment options. With approximately one third of patients resistant to currently available antidepressants there is a pressing need for a better understanding of depression, identifying subgroups within the highly heterogeneous illness category and to understand the divergent underlying biology of such subtypes, to help develop and personalise treatments. The TRAJECTOME project aims to address such challenges by (1) identifying depression-related multimorbidity subgroups and shared molecular pathways based on temporal disease profiles from healthcare systems and biobank data using machine learning approaches, and by (2) characterising these subgroups from multiple aspects including genetic variants, metabolic processes, lifestyle and environmental factors. Following the identification of multimorbidity trajectories, a disease burden score related to depression and adjusted for multimorbidity was established summarising the current state of the patient to weigh the molecular mechanisms associated with depression. In addition, the role of genetic and environmental factors, and also their interactions were identified for all subgroups. The project also attempted to identify potential metabolomic markers for the early diagnostics of these multimorbidity conditions. Finally, we prioritized molecular drug candidates matching the multimorbidity pathways indicated for the individual subgroups which would potentially offer personalised treatment simultaneously for the observable multimorbid conditions yet minimising polypharmacy and related side effects. The present paper overviews the TRAJECTOME project including its aims, tasks, procedures and accomplishments. (Neuropsychopharmacol Hung 2023; 25(4): 183-193)

Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression.

Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: bioinformatics.mit.bme.hu/UKBNetworks.

VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.

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.

Brain galanin system genes interact with life stresses in depression-related phenotypes.

Galanin is a stress-inducible neuropeptide and cotransmitter in serotonin and norepinephrine neurons with a possible role in stress-related disorders. Here we report that variants in genes for galanin (GAL) and its receptors (GALR1, GALR2, GALR3), despite their disparate genomic loci, conferred increased risk of depression and anxiety in people who experienced childhood adversity or recent negative life events in a European white population cohort totaling 2,361 from Manchester, United Kingdom and Budapest, Hungary. Bayesian multivariate analysis revealed a greater relevance of galanin system genes in highly stressed subjects compared with subjects with moderate or low life stress. Using the same method, the effect of the galanin system genes was stronger than the effect of the well-studied 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4). Conventional multivariate analysis using general linear models demonstrated that interaction of galanin system genes with life stressors explained more variance (1.7%, P = 0.005) than the life stress-only model. This effect replicated in independent analysis of the Manchester and Budapest subpopulations, and in males and females. The results suggest that the galanin pathway plays an important role in the pathogenesis of depression in humans by increasing the vulnerability to early and recent psychosocial stress. Correcting abnormal galanin function in depression could prove to be a novel target for drug development. The findings further emphasize the importance of modeling environmental interaction in finding new genes for depression.

Ageing with elegans: a research proposal to map healthspan pathways.

Human longevity continues to increase world-wide, often accompanied by decreasing birth rates. As a larger fraction of the population thus gets older, the number of people suffering from disease or disability increases dramatically, presenting a major societal challenge. Healthy ageing has therefore been selected by EU policy makers as an important priority ( http://www.healthyageing.eu/european-policies-and-initiatives ); it benefits not only the elderly but also their direct environment and broader society, as well as the economy. The theme of healthy ageing figures prominently in the Horizon 2020 programme ( https://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing ), which has launched several research and innovation actions (RIA), like "Understanding health, ageing and disease: determinants, risk factors and pathways" in the work programme on "Personalising healthcare" ( https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/693-phc-01-2014.html ). Here we present our research proposal entitled "ageing with elegans" (AwE) ( http://www.h2020awe.eu/ ), funded by this RIA, which aims for better understanding of the factors causing health and disease in ageing, and to develop evidence-based prevention, diagnostic, therapeutic and other strategies. The aim of this article, authored by the principal investigators of the 17 collaborating teams, is to describe briefly the rationale, aims, strategies and work packages of AwE for the purposes of sharing our ideas and plans with the biogerontological community in order to invite scientific feedback, suggestions, and criticism.

Late First-Row Transition-Metal Complexes Containing a 2-Pyridylmethyl Pendant-Armed 15-Membered Macrocyclic Ligand. Field-Induced Slow Magnetic Relaxation in a Seven-Coordinate Cobalt(II) Compound.

The 2-pyridylmethyl N-pendant-armed heptadentate macrocyclic ligand {3,12-bis(2-methylpyridine)-3,12,18-triaza-6,9-dioxabicyclo[12.3.1]octadeca-1,14,16-triene = L} and [M(L)](ClO4)2 complexes, where M = Mn(II) (1), Fe(II) (2), Co(II) (3), Ni(II) (4), and Cu(II) (5), were prepared and thoroughly characterized, including elucidation of X-ray structures of all the compounds studied. The complexes 1-5 crystallize in non-centrosymmetric Sohncke space groups as racemic compounds. The coordination numbers of 7, 6 + 1, and 5 were found in complexes 1-3, 4, and 5, respectively, with a distorted pentagonal bipyramidal (1-4) or square pyramidal (5) geometry. On the basis of the magnetic susceptibility experiments, a large axial zero-field splitting (ZFS) was found for 2, 3, and 4 (D(Fe) = -7.4(2) cm(-1), D(Co) = 34(1) cm(-1), and D(Ni) = -12.8(1) cm(-1), respectively) together with a rhombic ZFS (E/D = 0.136(3)) for 4. Despite the easy plane anisotropy (D > 0, E/D = 0) in 3, the slow relaxation of the magnetization below 8 K was observed and analyzed either with Orbach relaxation mechanism (the relaxation time τ0 = 9.90 × 10(-10) s and spin reversal barrier Ueff = 24.3 K (16.9 cm(-1))) or with Raman relaxation mechanism (C = 2.12 × 10(-5) and n = 2.84). Therefore, compound 3 enlarges the small family of field-induced single-molecule magnets with pentagonal-bipyramidal chromophore. The cyclic voltammetry in acetonitrile revealed reversible redox processes in 1-3 and 5, except for the Ni(II) complex 4, where a quasi-reversible process was dominantly observed. Presence of the two 2-pyridylmethyl pendant arms in L with a stronger σ-donor/π-acceptor ability had a great impact on the properties of all the complexes (1-5), concretely: (i) strong pyridine-metal bonds provided slight axial compression of the coordination sphere, (ii) substantial changes in magnetic anisotropy, and (iii) stabilization of lower oxidation states.

VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering.

BACKGROUND: The low concordance between different variant calling methods still poses a challenge for the wide-spread application of next-generation sequencing in research and clinical practice. A wide range of variant annotations can be used for filtering call sets in order to improve the precision of the variant calls, but the choice of the appropriate filtering thresholds is not straightforward. Variant quality score recalibration provides an alternative solution to hard filtering, but it requires large-scale, genomic data. RESULTS: We evaluated germline variant calling pipelines based on BWA and Bowtie 2 aligners in combination with GATK UnifiedGenotyper, GATK HaplotypeCaller, FreeBayes and SAMtools variant callers, using simulated and real benchmark sequencing data (NA12878 with Illumina Platinum Genomes). We argue that these pipelines are not merely discordant, but they extract complementary useful information. We introduce VariantMetaCaller to test the hypothesis that the automated fusion of measurement related information allows better performance than the recommended hard-filtering settings or recalibration and the fusion of the individual call sets without using annotations. VariantMetaCaller uses Support Vector Machines to combine multiple information sources generated by variant calling pipelines and estimates probabilities of variants. This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, ranging from a few hundred kilobases to whole exomes. We also demonstrated that VariantMetaCaller supports a quantitative, precision based filtering of variants under wider conditions. Specifically, the computed probabilities of the variants can be used to order the variants, and for a given threshold, probabilities can be used to estimate precision. Precision then can be directly translated to the number of true called variants, or equivalently, to the number of false calls, which allows finding problem-specific balance between sensitivity and precision. CONCLUSIONS: VariantMetaCaller can be applied to small target regions and whole exomes as well, and it can be used in cases of organisms for which highly accurate variant call sets are not yet available, therefore it can be a viable alternative to hard filtering in cases where variant quality score recalibration cannot be used. VariantMetaCaller is freely available at http://bioinformatics.mit.bme.hu/VariantMetaCaller .

Transforming LEND leadership training curriculum through the maternal and child health leadership competencies.

The purpose of this article is to describe how the Maternal and Child Health (MCH) Leadership Competencies (v 3.0) were used to examine and improve an MCH Leadership Education in Neurodevelopmental and Related Disabilities (LEND) training curriculum for New Hampshire and Maine. Over 15 % of the nation's children experience neurodevelopmental disabilities or special health care needs and estimates suggest 1 in every 68 children is diagnosed with an autism spectrum disorder. Across the Unites States critical shortages of qualified MCH professionals exist, particularly in poor and rural areas. A continued investment in training interdisciplinary leaders is critical. The MCH Leadership Competencies provide an effective foundation for leadership training through identification of requisite knowledge, skills, and dispositions required of MCH leaders. This paper describes a three-step process, which began in 2010 and included utilizing the MCH Leadership Competencies as a tool to reflect on, develop, and evaluate the NH LEND leadership curriculum. Curriculum development was further supported through participation in a multi-state learning collaborative. Through a series of intentional decisions, the curriculum design of NH LEND utilized the competencies and evidence-based principles of instruction to engage trainees in the development of specific MCH content knowledge and leadership skills. The LEND network specifically, and MCH leadership programs more broadly, may benefit from the intentional use of the MCH competencies to assist in curriculum development and program evaluation, and as a means to support trainees in identifying specific leadership goals and evaluating their leadership skill development.

[From biomarkers to clinical decision support]. / Biomarkerek hálózatától a klinikai döntéstámogatásig.

The rapidly accumulating, heterogeneous, numerous biomedical data and the increasing knowledge represent both the key areas and the major bottleneck in the complete realization of personalized medicine. Traditional clinical aims as well as decision-making processes in drug research need systems-based integration of data and their analyses, which require a wide range of new mathematical approaches and application of information technologies. In the world of biomarkers, all such tasks correspond to three types of biomarkers, namely, endpoint, target and diagnostic biomarkers, which together form a complex network. To decipher the networks, probabilistic graphical models are introduced. Briefly, the authors illustrate their use for exploration and visualization of direct dependencies among biomarkers. Finally, construction of decision networks and their application to structural and quantitative optimization of biomarkers are discussed to provide a novel type of supporting tools for clinical practice.

Lower-limb veins are thicker and vascular reactivity is decreased in a rat PCOS model: concomitant vitamin D3 treatment partially prevents these changes.

Polycystic ovary syndrome (PCOS) causes vascular damage to arteries; however, there are no data for its effect on veins. Our aim was to clarify the effects of dihydrotestosterone (DHT)-induced PCOS both on venous biomechanics and on pharmacological reactivity in a rat model and to test the possible modulatory role of vitamin D3 (vitD). PCOS was induced in female Wistar rats by DHT treatment (83 µg/day, subcutaneous pellet). After 10 wk, the venous biomechanics, norepinephrine (NE)-induced contractility, and acetylcholine-induced relaxation were tested in saphenous veins from control animals and from animals treated with DHT or DHT with vitD using pressure angiography. Additionally, the expression levels of endothelial nitric oxide synthase (eNOS) and cyclooxygenase (COX-2) were measured using immunohistochemistry. Increased diameter, wall thickness, and distensibility as well as decreased vasoconstriction were detected after the DHT treatment. Concomitant vitD treatment lowered the mechanical load on the veins, reduced distensibility, and resulted in vessels that were more relaxed. Although there was no difference in the endothelial dilation tested using acetylcholine (ACh), the blocking effect of N(G)-nitro-l-arginine methyl ester (l-NAME) was lower and was accompanied by lower COX-2 expression in the endothelium after the DHT treatment. Supplementation with vitD prevented these alterations. eNOS expression did not differ among the three groups. We conclude that the hyperandrogenic state resulted in thicker vein walls. These veins showed early remodeling and altered vasorelaxant mechanisms similar to those of varicose veins. Alterations caused by the chronic DHT treatment were prevented partially by concomitant vitD administration.

Peroxido complexes of vanadium(V) as ligands. Crystal structures of [Cd(NH3)6][{VO(O2)2(OH)}2{µ-Cd(NH3)4}] and [{VO(O2)2(Im)}2{µ-Cu(Im)4}] (Im = Imidazole).

Two novel heterometallic complexes [Cd(NH3)6][{VO(O2)2(OH)}2{µ-Cd(NH3)4}] (2) and [{VO(O2)2(Im)}2{µ-Cu(Im)4}] (3) (Im = imidazole) containing peroxidovanadium complexes as metalloligands were prepared and characterized by spectral methods. X-ray single-crystal analysis revealed the presence of unique trinuclear complexes in the crystal structures of 2 and 3. The structure of 2 contains an anionic complex, whose two {VO(O2)2(OH)}(2-) ions are interconnected by a {µ-Cd(NH3)4}(2+) group. Compound 3 is a trinuclear neutral complex comprising two {VO(O2)2(Im)}(-) ions and a single bridging {µ-Cu(Im)4}(2+) group. The bonding via an equatorial OH(-) ligand in 2 and via a doubly bonded apical oxygen atom in 3 represents coordination modes previously unobserved for diperoxidovanadium complexes. Compared with complex 2, density functional theory studies reported decreased Cu-µ-O bond orders and increased µ-O-V bond orders in 3, in accordance with the expected Jahn-Teller distortion of the latter complex.

Complex analysis of multiple single nucleotide polymorphisms as putative risk factors of tooth agenesis in the Hungarian population.

OBJECTIVES: The role was studied of multiple single nucleotide polymorphisms in tooth agenesis in the Hungarian population using a complex approach. METHODS: Eight SNPs, PAX9 -912 C/T, PAX9 -1031 A/G, MSX1 3755 A/G, FGFR1 T/C rs881301, IRF6 T/C rs764093, AXIN2-8150 A/G, AXIN2-8434 A/G and AXIN2-30224 C/T, were studied in 192 hypodontia and 17 oligodontia cases and in 260 healthy volunteers. Case-control analysis was performed to test both allelic and genotypic associations as well as associations at the level of haplotypes. Multivariate exploratory Bayesian network-based multi-level analysis of relevance (BN-BMLA) as well as logistic regression analysis were performed. RESULTS: Conventional statistics showed that PAX9 SNP -912 C/T and the MSX1 SNP changed the incidence of hypodontia, although after Bonferroni correction for multiple hypothesis testing, the effects were only borderline tendencies. Using a statistical analysis better suited for handling multiple hypotheses, the BN-BMLA, PAX9 SNPs clearly showed a synergistic effect. This was confirmed by other multivariate analyses and it remained significant after corrections for multiple hypothesis testing (p < 0.0025). The PAX9-1031-A-PAX9-912-T haplotype was the most relevant combination causing hypodontia. Interaction was weaker between PAX9 and MSX1, while other SNPs had no joint effect on hypodontia. CONCLUSION: This complex analysis shows the important role of PAX9 and MSX1 SNPs and of their interactions in tooth agenesis, while IRF6, FGFR1 and AXIN2 SNPs had no detectable role in the Hungarian population. These results also reveal that risk factors in hypodontia need to be identified in various populations, since there is considerable variability among them.

Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space.

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.

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters.

BACKGROUND: Major depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters. METHODS: We analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression. RESULTS: At SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction. LIMITATIONS: CTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. CONCLUSIONS: The first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine.

Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males (n = 149,879) and females (n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.