Search Results (536)

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

Currently, there are common reference intervals for creatinine levels in dogs, despite different weight categories. This can result in missing the diagnosis of kidney disease at an early stage in small dogs. The aim of this study was to determine and compare available markers of the GFR (serum cystatin C, creatinine and SDMA) in healthy small and miniature dogs. Each dog underwent the same protocol, including physical examination, blood pressure measurement and ultrasound of the urinary system. The serum filtration markers determined in the study were creatinine, cystatin C and SDMA. Urinalysis was performed, including urine specific gravity (USG) and the urine protein/creatinine ratio (UPC). Forty samples were included. The median values with the IQR (interquartile range) for creatinine, SDMA and cystatin C levels in the examined dogs were 63.8 µmol/L (16.7), 10.63 µg/dL (4.64) and 0.5 mg/L (0.2), respectively. Compared with Yorkshire Terriers and Chihuahuas, Maltese dogs had significantly higher median serum creatinine concentration (p < 0.001). Combining results of GFR markers, USG, UPC, patient’s clinical condition as well as the breed is mandatory when assessing renal function in small dogs. Maltese dogs may have physiologically higher serum creatinine levels. Full article

Heat stress has a major impact on dairy cow health and productivity, especially during early lactation. Conventional heat stress monitoring methods frequently rely on single indicators, such as the temperature–humidity index (THI), which may miss subtle physiological and metabolic responses. This study presents a novel threshold-based classification framework that integrates biologically meaningful combinations of environmental, behavioural, and physiological variables to detect early-stage heat stress responses in dairy cows. Six composite heat stress conditions (C1–C6) were developed using real-time THI, milk temperature, reticulorumen pH, rumination time, milk lactose, and milk fat-to-protein ratio. The study applied and assessed five supervised machine learning models (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF0, Neural Network (NN), and an Ensemble approach) trained on daily datasets gathered from early-lactation dairy cows fitted with intraruminal boluses and monitored through milking parlour sensor systems. The dataset comprised approximately 36,000 matched records from 200 cows monitored over 60 days. The highest classification performance was observed for RF and NN models, particularly under C1 (THI > 73 and milk temperature > 38.6 °C) and C6 (THI > 74 and milk temperature > 38.7 °C), with AUC values exceeding 0.90. SHAP analysis revealed that milk temperature, THI, rumination time, and milk lactose were the most informative features across conditions. This integrative approach enhances precision livestock monitoring by enabling individualised heat stress risk classification well before clinical or production-level consequences emerge. Full article

Polyhedral cages (p-cages) are Euclidean geometric structures corresponding to polyhedra with holes. They are a good example of the geometry of some artificial protein cages. In this paper we identify p-cages made out of two families of equivalent polygonal faces, where the face of one family is attached to three other faces while the faces of the other family are attached to three, four, five or six other faces. To restrict ourselves to p-cages with small holes, we consider p-cages where each hole comprises at most four faces. The construction starts from planar graphs made out of two families of equivalent nodes. One can then construct the dual of the solid corresponding to that graph and tile its faces with regular or nearly regular polygons. An energy function is then defined to quantify the amount of irregularity of the p-cages which is then minimised using a simulated annealing algorithm. We have analysed nearly 100,000 possible configurations, ruling out the p-cages made out of faces with deformations exceeding 10%. We then present graphically some of the most interesting geometries. Full article

Approximately half of the world population is infected with the human pathogen Helicobacter pylori, which causes gastric inflammation, chronic gastritis, or peptide ulceration. A significant factor in the colonization of the upper digestive system is the helical shape of H. pylori. This helical form is maintained by a complex network of peptidoglycan (PG)-modifying enzymes and cytoskeletal proteins. Among these, the D,D-endopeptidase Csd2 plays a central role, working in conjunction with other cell shape-determining (Csd) proteins. Csd1 and Csd2 have been categorized as members of the M23B metallopeptidase family. These enzymes are classified as D,D-endopeptidases, and their function involves the cleavage of the D-Ala4-mDAP3 bond, which is present in the cross-linked di-mer muropeptides. Despite the fact that the structure of the Csd1:Csd2 complex has been examined via biochemical methods, information on the in vivo localization and dynamics of D,D-endopeptidases is still missing. Here, we use an approach that employs sophisticated different microscopy methods to visualize the spatial temporal localization and dynamics of Csd2, involving both structured illumination microscopy and single-molecule tracking. Our findings thus contribute to refining the existing model for this cellular complex by revealing curvature-dependent spatial organization and temporal dynamics underlying peptidoglycan remodeling processes essential for helical cell shape formation and maintenance. Understanding the dynamics provides insight into the mechanisms that maintain bacterial morphology and potential targets for therapeutic intervention. Full article

Proteins that span cellular membranes represent around 30% of the proteome and over 50% of drug targets. A variety of synthetic and naturally-occurring small organic molecules interact with membrane proteins and up- and down-regulate protein function. The atomic details of these regulatory molecules offer important information about protein function and aid the discovery, refinement and optimization of new drugs. X-ray crystallography and cryo-electron microscopy (cryo-EM) are not always able to resolve the structures of small molecules in their physiological sites on membrane proteins, particularly if the molecules are unstable or are reactive enzyme substrates. Solid-state nuclear magnetic resonance (SSNMR) is a valuable technique for filling in missing details on the conformations, dynamics and binding environments of small molecules regulators of membrane proteins. SSNMR does not require diffracting crystals possessing long-range order and can be performed on proteins within their native membranes and with freeze-trapping to maintain sample stability. Here, work over the last two decades is described, in which SSNMR methods have been developed to report on interactions of the ATP substrate with the Na,K-ATPase (NKA), an ion-transporting enzyme that maintains cellular potential in all animals. It is shown how a combination of SSNMR measurements on membranous NKA preparations in the frozen and fluid states have provided unique information about the molecular conformation and local environment of ATP in the high-affinity nucleotide site. A combination of chemical shift analysis using density functional theory (DFT) calculations, dipolar coupling measurements using REDOR and measurements of the rates of proton spin diffusion is appraised collectively. The work described herein highlights the methods developed and challenges encountered, which have led to a detailed and unrivalled picture of ATP in its high-affinity binding site. Full article

Cryo-electron microscopy single-particle analysis (cryo-EM SPA) has advanced three-dimensional protein structure determination, yet resolving intrinsically disordered proteins and regions (IDPs/IDRs) remains challenging due to conformational heterogeneity. This research evaluates cryo-EM’s capacity to map dynamic regions, assesses the adaptability of disorder prediction tools, and explores optimization strategies for dynamic structure prediction. Cryo-EM SPA datasets from 2000 to 2024 were categorized into different periods, forming a database integrating sequence data and disorder indices. Established prediction tools—AlphaFold2 (pLDDT), flDPnn, and IUPred—were evaluated for transferability, while a multi-level CLTC hybrid model (combining CNN, LSTM, Transformer, and CRF architectures) was developed to link local conformational fluctuations with global sequence contexts. Analyses revealed consistent advancements in average resolution and model counts over the past decade, although mapping disordered regions remained technically demanding. Both the adapted AlphaFold pLDDT and the CLTC model demonstrated efficacy in predicting structurally variable and poorly resolved regions. A subset of the cryo-EM missing residues exhibited intermediate conformational features, suggesting classification ambiguities potentially influenced by experimental conditions. These findings systematically outline the evolving capabilities of cryo-EM in resolving dynamic regions, benchmark the adaptability of computational tools, and introduce a hybrid model to enhance prediction accuracy. This study provides a framework for addressing conformational heterogeneity, contributing to methodological advancements in structural biology. Full article

Background/Objectives: While complete loss-of-function (LoF) SPINK1 variants in the simple heterozygous state cause chronic pancreatitis, biallelic complete LoF variants result in a rare pediatric disorder termed severe infantile isolated exocrine pancreatic insufficiency (SIIEPI). To date, only two individuals with a null SPINK1 genotype have been reported—one homozygous for a whole-gene deletion and the other for an Alu insertion in the 3′ untranslated region. Here, we report the genetic basis of a third SIIEPI case, presenting in early infancy with severe exocrine pancreatic insufficiency and diffuse pancreatic lipomatosis. Methods: Targeted next-generation sequencing (NGS) was used to analyze the entire coding region and exon–intron boundaries of the SPINK1 gene. Copy number variant (CNV) analysis was performed with SeqNext, based on normalized amplicon coverage. Results: The proband harbored compound heterozygous complete LoF SPINK1 variants. One was the known NM_001379610.1:c.180_181del (p.(Cys61PhefsTer2)), inherited from the father. The second, initially detected as an exon 2 deletion and confirmed by quantitative fluorescent multiplex PCR (QFM-PCR), was further characterized by long-range PCR as a complex rearrangement comprising a 1185 bp deletion removing exon 2, a 118 bp templated insertion followed by a non-templated nucleotide, and an 8 bp deletion. The mutational signature is consistent with serial replication slippage or template switching involving translesion synthesis. This maternally inherited variant has not been previously reported. Conclusions: This study expands the mutational spectrum of SPINK1-related SIIEPI and suggests that this distinct pediatric disorder may be under recognized in clinical practice. Full article

Background: Cerebral amyloid angiopathy (CAA) is a common cause of spontaneous intracerebral hemorrhage (ICH) in elderly individuals, and it is characterized by the deposition of amyloid β protein (Aß) in the walls of small-caliber cortical and leptomeningeal vessels. The diagnostic criteria for CAA highlight its association with spontaneous lobar hemorrhage, convexity subarachnoid hemorrhage (SAH), and cortical superficial siderosis but not with subdural hematoma (SDH). This article presents a three-patient case series of CAA who experienced a lobar ICH associated with an SDH, underscoring a potentially under-recognized correlation between an acute ICH and coexistent SDH. Case presentation: We present a case series of three patients in a single university medical center who experienced acute-onset lobar ICH with a concurrent SDH, treated with evacuation. Histopathological examination established the diagnosis of CAA in all three cases. This case series underscores a potentially under-recognized association between an acute ICH and coexistent SDH in the context of CAA. Conclusions: Considering our findings, we emphasize the possibility that SDH may be a more frequent manifestation of CAA than previously recognized. Therefore, patients with CAA who initially present with acute SDH may be underdiagnosed, consequently leading to delayed identification and missed opportunities for proper risk assessment and management. Full article

Background: Lysosomal storage diseases (LSDs) are a genetically and clinically heterogeneous group of inborn errors of metabolism caused by variants in genes encoding lysosomal hydrolases, membrane proteins, activator proteins, or transporters. These disease-causing variants lead to enzymatic deficiencies and the progressive accumulation of undegraded substrates within lysosomes, disrupting cellular function across multiple organ systems. While classical phenotypes typically manifest in infancy or early childhood with severe multisystem involvement, a combination of advances in molecular diagnostics [particularly next-generation sequencing (NGS)] and improved understanding of disease heterogeneity have enabled the identification of attenuated forms characterised by residual enzyme activity and later-onset presentations. These milder phenotypes often evade early recognition due to nonspecific or isolated symptoms, resulting in significant diagnostic delays and missed therapeutic opportunities. Objectives/Methods: This study characterises the clinical, biochemical, and molecular profiles of 10 adult patients diagnosed with LSDs, all representing attenuated forms, and discusses them alongside a narrative review. Results: Enzyme activity, molecular data, and phenotypic assessments are described to explore genotype–phenotype correlations and identify diagnostic challenges. Conclusions: These findings highlight the variable expressivity and organ involvement of attenuated LSDs and reinforce the importance of maintaining clinical suspicion in adults presenting with unexplained cardiovascular, neurological, ophthalmological, or musculoskeletal findings. Enhanced recognition of atypical presentations is critical to facilitate earlier diagnosis, guide management, and enable cascade testing for at-risk family members. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

The ZC4H2 gene is the site of congenital mutations linked to neurodevelopmental and musculoskeletal pathologies collectively termed ZARD (ZC4H2-Associated Rare Disorders). ZC4H2 consists of a coiled coil and a single novel zinc finger with four cysteines and two histidines, from which the protein obtains its name. Alpha Fold 3 confidently predicts a structure for the zinc finger but also for similarly sized random sequences, providing equivocal information on its folding status. We show using synthetic peptide fragments that the zinc finger of ZC4H2 is genuine and folds upon binding a zinc ion with picomolar affinity. NMR pH titration of histidines and UV–Vis of a cobalt complex of the peptide indicate its four cysteines coordinate zinc, while two histidines do not participate in binding. The experimental NMR structure of the zinc finger has a novel structural motif similar to RANBP2 zinc fingers, in which two orthogonal hairpins each contribute two cysteines to coordinate zinc. Most of the nine ZARD mutations that occur in the ZC4H2 zinc finger are likely to perturb this structure. While the ZC4H2 zinc finger shares the folding motif and cysteine-ligand spacing of the RANBP2 family, it is missing key substrate-binding residues. Unlike the NZF branch of the RANBP2 family, the ZC4H2 zinc finger does not bind ubiquitin. Since the ZC4H2 zinc finger occurs in a single copy, it is also unlikely to bind DNA. Based on sequence homology to the VAB-23 protein, the ZC4H2 zinc finger may bind RNA of a currently undetermined sequence or have alternative functions. Full article

Innate immune cells appear to have an important implication in the resolution and/or the aggravation of the COVID-19 pathogenesis after infection with SARS-CoV-2. To better appreciate the role of these cells during COVID-19, changes in blood eosinophil, the neutrophil and monocyte count, and levels of surface protein markers have been reported. However, analyses at several timepoints of multiple surface markers on granulocytes and monocytes over a period of one month after a SARS-CoV-2 infection are missing. Therefore, in this study, we performed blood eosinophil, neutrophil, and monocyte phenotyping using a list of surface proteins and flow cytometry during a period of 30 days after the hospitalization of patients with severe SARS-CoV-2 infections. Blood cell counts were reported at seven different timepoints over the 30-day period as well as measures of multiple mediators in serum using a targeted multiplex assay approach. Our results indicate a 95% drop in the blood eosinophil count by D1, with eosinophils displaying a phenotype defined as CD69/CD63/CD125^high and CCR3/CD44^low during the early phases of hospitalization. Conversely, by D7 the neutrophil count increased significantly and displayed an immature, activated, and immunosuppressive phenotype (i.e., 3% of CD10/CD16^low and CD10^lowCD177^high, 6.7% of CD11b^highCD62L^low, and 1.6% of CD16^highCD62L^low), corroborated by enhanced serum proteins that are markers of neutrophil activation. Finally, our results suggest a rapid recruitment of non-classical monocytes leaving CD163/CD64^high and CD32^low monocytes in circulation during the very early phase. In conclusion, our study reveals potential very early roles for eosinophils and monocytes in the pathogenesis of COVID-19 with a likely reprogramming of eosinophils in the bone marrow. The exact roles of the pro-inflammatory neutrophils and the functions of the eosinophils and the monocytes, as well as these innate immune cell types, interplays need to be further investigated. Full article

Background/Objectives: The treatment of multiple myeloma (MM) remains a challenge, as almost all patients will eventually relapse. Proteasome inhibitors are a cornerstone in the management of MM. Unfortunately, validated biomarkers predicting drug response are largely missing. Therefore, we aimed to identify genes associated with drug resistance or sensitization to proteasome inhibitors. Methods: We performed genome-wide CRISPR-Cas9 knockout (KO) screens in human KMS-28-BM myeloma cells to identify genetic determinants associated with resistance or sensitization to proteasome inhibitors. Results: We show that KO of KLF13 and PSMC4 induces drug resistance, while NUDCD2, OSER1 and HERC1 KO cause drug sensitization. Subsequently, we focused on top sensitization hit, NUDCD2, which acts as a co-chaperone of Hsp90 to regulate the LIS1/dynein complex. RNA sequencing showed downregulation of genes involved in the ERAD pathway and in ER-associated ubiquitin-dependent protein catabolic processes in both untreated and carfilzomib-treated NUDCD2 KO cells, suggesting that NUDCD2 depletion alters protein degradation. Furthermore, bortezomib-treated NUDCD2 KO cells showed a decreased expression of genes that have a function in oxidative phosphorylation and the mitochondrial membrane, such as Carnitine Palmitoyltransferase 1A (CPT1A). CPT1A catalyzes the uptake of long chain fatty acids into mitochondria. Mitochondrial lipid metabolism has recently been reported as a possible therapeutic target for MM drug sensitivity. Conclusions: These results contribute to the search for therapeutic targets that can sensitize MM patients to proteasome inhibitors. Full article

Polyhedral cages (p-cages) provide a good description of the geometry of some families of artificial protein cages. In this paper we identify p-cages made out of two families of equivalent polygonal faces/protein rings, where each face has at least four neighbours and where the holes are contributed by at most four faces. We start the construction from a planar graph made out of two families of equivalent nodes. We construct the dual of the solid corresponding to that graph, and we tile its faces with regular or nearly regular polygons. We define an energy function describing the amount of irregularity of the p-cages, which we then minimise using a simulated annealing algorithm. We analyse over 600,000 possible geometries but restrict ourselves to p-cages made out of faces with deformations not exceeding 10%. We then present graphically some of the most promising geometries for protein nanocages. Full article