➢ Cervical disc arthroplasty (CDA) provides clinical outcomes comparable with those of anterior cervical discectomy and fusion (ACDF) for 1-level and 2-level cervical degenerative disc disease (DDD), with durable long-term motion preservation.➢ Although CDA is associated with lower rates of adjacent-segment pathology compared with ACDF, the absolute risk reduction at longer follow-up is modest and methodological heterogeneity across trials limits definitive conclusions.➢ Expanded indications including multilevel (≥3-level) CDA, hybrid constructs, and other situations that were previously considered contraindicated during U.S. Food and Drug Administration (FDA) Investigational Device Exemption (IDE) trials have shown promising short-term to intermediate-term results in more recent studies.➢ As CDA utilization increases, standardized failure reporting and the development of evidence-based revision algorithms are critical to optimize long-term outcomes.

Cell size is tightly controlled to optimize cell function. In Saccharomyces cerevisiae, multiple regulators of cell size in steady-state conditions have been identified, such as the G1/S activators Cln3 and Bck2 and the inhibitor Whi5. Individual deletions of these regulators alter the mean cell volumes. However, size homeostasis remains largely intact. Here, we show that although the roles of Bck2 and Cln3 for cell size regulation appear largely redundant in steady state, a switch from fermentable to nonfermentable growth media reveals a unique role for Bck2 in cell size adaptation. We use live-cell microscopy and machine learning-assisted image analysis to track single cells and their progeny through the nutrient switch. We find that after the switch, bck2Δ cells experience longer cell cycle arrests and more arrest-associated enlargement than wild-type, whi5Δ, or cln3Δ cells, indicating that Bck2 becomes the critical G1/S activator in changing nutrients. Our work demonstrates that studying size regulation during nutrient shifts can resolve apparent redundancies observed in steady state.

Chitin deacetylases (CDAs) are essential for phytopathogenic fungi to evade plant immune defenses by deacetylating chitin oligomers into chitosan. However, the function of CDAs in Alternaria alternata, the causal agent of pear black spot, remains unclear. In this study, two CDA genes, AaCDA1 and AaCDA2, were cloned and identified. Gene knockout and complementation analyses revealed that ΔAaCDA1 and ΔAaCDA2 mutants showed no significant changes in colony morphology, biomass, or conidiation. However, the ΔAaCDA1 mutant exhibited increased extracellular melanin, linked to the upregulation of the melanin biosynthesis transcription factor AacrmA. The mutants exhibit higher chitin and mannan contents and decreased glucan levels, with corresponding changes in gene expression. Furthermore, they showed reduced appressorium formation and penetration ability, leading to decreased pathogenicity on both pear and tomato fruits. Infected mutants triggered greater reactive oxygen species (ROS) and callose responses, with significant upregulation of host defense genes. Overall, the findings indicate that AaCDA1 and AaCDA2 are crucial for infection structure differentiation and A. alternata pathogenicity by modulating cell wall components and evading host immune responses.

Hydrolases represent a diverse class of drug-metabolizing enzymes that catalyze the hydrolytic cleavage of a broad spectrum of xenobiotics including certain novel drug modalities, thereby modulating pharmacokinetics and potentially influencing pharmacodynamics. However, many hydrolases remain poorly characterized, and their developmental expression and interindividual variability are not well understood. This study aimed to systematically characterize hydrolase expression in the human liver. Human hepatocytes obtained from 8 adult and 50 pediatric donors were subjected to global proteomics analysis to quantify hydrolase abundance and assess variability across demographic factors. Two hundred and thirty-nine hydrolases were studied for the influence of age, sex, and race on their abundance. The key enzymes included arylacetamide deacetylase (AADAC), alpha/beta hydrolase domain-containing protein 10 (ABHD10), alkaline phosphatase, tissue-nonspecific isozyme (ALPL), acyl-peptide hydrolase (APEH), bleomycin hydrolase (BLMH), valacyclovir hydrolase (BPHL), cytidine deaminase (CDA), carboxylesterase 1 (CES1), CES2, carboxymethylenebutenolidase homolog (CMBL), cathepsin A (CTSA), cathepsin B (CTSB), dipeptidyl peptidase 4 (DPP4), epoxide hydrolase 1 (EPHX1), EPHX2, EPHX3, guanine deaminase (GDA), paraoxonase 1 (PON1), PON2, PON3, and deoxynucleoside triphosphate triphosphohydrolase SAMHD1 (SAMHD1). Notably, several enzymes exhibited age-dependent increases in the abundance from neonates to adults (fold), including CDA (>200), GDA (53), CES1 (14), EPHX3 (12), ALPL (10), CES2 (3.5), sialate O-acetylesterase (SIAE) (3), and CMBL (2.6). In contrast, BLMH, CTSA, DPP4, PON2, and SAMHD1 displayed approximately 2-fold to 3-fold lower abundance in adults than in neonates. PON1 was 42% higher in Black individuals compared with White individuals, whereas PON3 abundance was 32% higher in males than in females. This study provides the first systematic quantitative characterization of a broad panel of hydrolases and provides a key foundation for physiologically-based pharmacokinetic and quantitative systems pharmacology modeling of the substrates of hydrolytic enzymes. SIGNIFICANCE STATEMENT: Interindividual variability, including ontogeny, is well characterized for many drug-metabolizing enzymes but remains poorly defined for most hydrolases. The influence of age, sex, and race on 239 hepatic hydrolases was evaluated, revealing important demographic determinants. These data provide a critical foundation for physiologically-based pharmacokinetic modeling. Incorporation of proteomics-informed variability into physiologically-based pharmacokinetic models has the potential to improve the prediction of drug pharmacokinetics, particularly in pediatric populations.

When searching for objects in complex environments, performance often improves with more specific target representations. Yet, sometimes a more general representation may be preferable. For example, when repeatedly searching for unique exemplars from the same object category, a shared category-based representation could help alleviate the cognitive burden of constantly encoding and maintaining new exemplar representations. We therefore examined whether the format of target representations used for search is sensitive to the sustained preservation of object-category membership. Across two experiments, observers searched consecutively for images of real-world objects from the same basic-level category. We recorded contralateral delay activity (CDA), an electrophysiological marker of visual working memory (VWM) maintenance, to assess whether the encoding of object-specific details diminished across category repetitions in favor of more generalized representations. In Experiment 1, we manipulated whether object-category membership alone sufficiently differentiated each unique target from nontargets in the search array. In Experiment 2, we preserved category sufficiency but allowed the targets to additionally repeat at the level of the exemplar to examine how repeatedly searching for the same object differs from repeatedly searching for the same kind of object. We found that while fewer object-specific details were maintained in VWM when category membership was sufficient, the amount of detail only diminished across repetitions of the exact same object. Moreover, observers exhibited sensitivity to the sustained preservation of the target category in the form of perceptual and semantic expectancy violations following category switches, as indexed by N2pc and N400 ERP components. Together, we show that, even though the sustained preservation of a target object category does not continuously reduce the encoding of object-specific details, categorical information is nonetheless represented in parallel. We speculate that object-specific representations principally serve attentional guidance, while categorical representations benefit target-match decisions.

Recent research highlights the critical involvement of circRNAs (circular RNAs) in the pathogenesis of various human diseases. Consequently, the efficient identification of circRNA-disease associations (CDA) holds significant potential for advancing disease diagnostics and therapeutic strategies. However, traditional experimental validation methods are often costly and time-consuming, leading to the development of computational approaches as promising alternatives. To address the persistent challenges of data sparsity and the effective capture of intricate underlying associations from complex circRNA-disease interaction networks, this paper introduces a ‌Knowledge Graph Attention-Aware Algorithm for CircRNA-Disease Association (KGAACDA)‌ prediction. The algorithm first constructs initial feature embeddings for nodes based on multi-dimensional similarity metrics for circRNAs and diseases. It also introduces an optimized negative sampling strategy informed by these similarity measures to enhance model training. Subsequently, a comprehensive ‌knowledge graph (KG)‌ is constructed by integrating diverse biological databases, thereby leveraging rich heterogeneous information to mitigate data sparsity issues. The KG is then processed using a multi-head attention mechanism that dynamically weights different types of biological relationships, thereby providing greater biological interpretability for the associations learned between heterogeneous nodes. Higher-order features extracted from the KG and the initial node embeddings are then fused through a Multi-Layer Perceptron (MLP), with the model's parameters being optimized using a composite loss function. Our extensive experimental evaluations demonstrate that ‌KGAACDA outperforms current state-of-the-art baseline algorithms‌ in terms of AUC (Area Under the Receiver Operating Characteristic Curve) and AUPR (Area Under the Precision-Recall Curve) on two independent datasets, establishing it as a robust and effective computational tool for predicting CDA.

Multimorbidity is associated with an increased risk of renal function decline in older individuals. In this study, we aimed to evaluate whether multimorbidity patterns differently contributed to this risk in three different hospital settings. We conducted a comparative cohort study of patients aged ≥ 65 years admitted to acute-care hospitals in Italy. Data were drawn from the retrospective SIN-SIGG study (geriatric units, n = 1600; nephrology units, n = 560) and the prospective REPOSI registry (internal medicine units, n = 4595). Quantitative multimorbidity was defined as the number of chronic conditions with prevalence ≥ 5% per cohort; qualitative multimorbidity was derived through latent class analysis of disease combinations. The primary outcome was change in estimated glomerular filtration rate (eGFR) from admission to discharge, calculated using the Berlin Initiative Study 1 (BIS1) equation. A relative decline was defined as ≥ 20% reduction from baseline. Multivariable models were adjusted for age, sex, baseline eGFR, acute diagnoses, and number of medications. Among 6,755 patients (mean age 80.6 years; 47% women), multimorbidity prevalence ranged from 87 to 93%. Quantitative multimorbidity showed modest and inconsistent associations with renal decline. In contrast, qualitative multimorbidity consistently predicted eGFR deterioration. Across all cohorts, the cardiorespiratory and renal cluster was associated with higher odds of in-hospital eGFR decline compared with neurogeriatric and mixed patterns (geriatrics OR 2.24 [95% CI 1.16-4.70]; internal medicine OR 1.72 [95% CI 1.22-2.45]; nephrology OR 2.11 [95% CI 1.01-4.56]). Disease-pattern-based multimorbidity better stratifies short-term renal risk than disease counts, identifying high-risk patients for targeted nephroprotective care during hospitalization.

Recovery in elite professional sport is now widely understood as a multidimensional process spanning physiological, psychological, cognitive, and social domains, yet recovery models applied in professional clubs and sporting organisations have been slow to reflect this. Most remain reactive and athlete-centred, leaving the full complexity of competitive fatigue inadequately addressed and the equivalent recovery demands on coaching and performance staff entirely unrecognised. No structural framework currently places initiative-taking, calendar-embedded multidimensional recovery as a deliberate component of seasonal planning. Main body. We introduce micro-recovery dosing (MRD): proactively planned, pre-scheduled 36-hour multidimensional recovery periods embedded within the competitive season as a proposed starting point at five to eight instances per year, extending to all members of the high-performance organisation. Drawing on neuromuscular recovery kinetics, autonomic science, sleep architecture research, occupational recovery psychology, and complex systems theory, we construct a convergent theoretical rationale for the proposed parameters and distinguish MRD from related constructs. We present a role-differentiated implementation framework with supporting evidence and a discussion of emerging AI-driven monitoring tools. Conclusions. Taken together, the four-domain convergent rationale and the complex systems perspective provide a theoretically coherent basis for MRD's proposed structure. We argue for a specific organisational transformation: the shift from recovery as an incidental gap between performance demands to an intentionally designed recovery architecture that encompasses athletes and staff alike. The article identifies the active ingredient problem as the central scientific challenge and sets out a structured empirical agenda.