The coalescence time of bubbles in a liquid depends on the nature of the liquid, which determines both its surface thermodynamics and the molecular interactions between the gas/liquid interfaces, and on the geometry, prescribed by the curvature of the bubbles. Coalescence is well described in pure liquids that have the same composition in bulk and at interfaces and in which the interactions are attractive. In contrast, the mechanisms are poorly understood in more complex liquids in which coalescence times are orders of magnitudes larger than in pure liquids and are unpredictable. To provide insight on these mechanisms, we use model systems: binary mixtures of miscible oils. In these liquids, interfaces have purely attractive molecular interactions and the surface thermodynamics can simply be described using a well-determined Gibbs elastic modulus, which is controlled by the composition of the mixture. We measure the coalescence rate by forming periodic trains of bubbles in millifluidic tubes whose radius varies over 1.5 decade. We report coalescence times spanning more than three decades and, for a given composition, varying according to a power law with curvature, with an exponent larger than that reported in pure liquids and independent of Gibbs elasticity. The experimental behavior is in excellent agreement with a numerical solution of the coupled thermodynamical and hydrodynamical equations, performed in the simple geometry of a suspended liquid film. Our results clearly reveal how geometry and surface thermodynamics modify the coalescence process of bubbles in the limit of small Gibbs elasticity.
Rapid sequence intubation (RSI) with neuromuscular blocking agents (NMBAs) can result in awareness with paralysis (AWP) if sedation is inadequate. This is of particular concern with rocuronium due to its prolonged half-life. This study evaluated sedation practices before and after the implementation of an RSI clinical decision support (CDS) update with sedation guidance linked to rocuronium orders. This retrospective, multicenter, observational, before-after study included adult patients who received rocuronium for RSI. The preimplementation cohort spanned the period July 1, 2022, to January 1, 2023; the postimplementation cohort spanned the period January 1 to July 1, 2024. Coprimary outcomes were sedative selection and initial sedative dose within 1 hour after rocuronium administration. Secondary outcomes included time to sedative initiation, time to adequate sedation, and incidence of hypotension requiring vasopressors. Groups were compared using a t test or Mann-Whitney U test for continuous variables and χ2 or Fisher's exact test for categorical variables. A total of 713 patients were included (308 in the preimplementation and 405 in the postimplementation cohort). After CDS implementation, propofol use increased (56.8% vs 73.1%, P < 0.0001), and initial dosing increased (median, 5 µg/kg/min vs 20 µg/kg/min; P < 0.0001). Midazolam use decreased (from 32.1% to 19.3%, P < 0.0001). The median (interquartile range [IQR]) time to sedation initiation decreased (from 14 [10-22] minutes to 12 [12-15] minutes; P = 0.001), as did median time to adequate sedation (from 32 [15.3-51.4] minutes to 14 [12-25] minutes; P < 0.0001). The incidence of new-onset hypotension was similar between groups (25.0% vs 25.2%, P = 0.95). A CDS update linking sedation guidance to rocuronium orders resulted in faster initiation of higher doses of sedation after paralysis without increasing hemodynamic instability, supporting the use of CDS strategies in the peri-intubation period.
Our aim was to assess the knowledge, awareness, and confidence of US primary care providers (PCPs) in assessing multiple myeloma (MM) and related plasma cell disorders. A survey with quantitative and qualitative questions was conducted from October to December 2024. A total of 560 respondents met the criteria of a representative US PCP cohort, but only 300 respondents were invited to continue the rest of the survey. These respondents had ordered serum protein electrophoresis (SPEP) in the past 12 months and believed that SPEP should be ordered for plasma cell disorder diagnosis. Nearly half of the representative US PCP cohort (46%) do not order tests for plasma cell disorders. Among the 300 who do, there is generally good awareness of symptoms and high-risk characteristics, but only 28% follow guideline-recommended paired testing of SPEP and serum free light chains. The majority (80%) lack confidence in appropriate test selection and interpretation, often relying on peers and hematologists for guidance. Additionally, only 15% of the US PCPs have access to and use myeloma diagnostic ordering sets and panels, while 54% express interest in utilizing such tools. The PCPs identified barriers like insufficient education, collaboration, and specialist support to patient care and outcomes. The study reveals significant gaps in the understanding and practice of MM diagnostic testing in primary care. Results indicate insufficient knowledge and inadequate utilization of testing guidelines, including low confidence in test ordering and interpretation. The survey suggests a lack of comprehensive, guideline-adherent MM diagnostic panels offered by US laboratories.
Computing molecular thermodynamic properties is instrumental in multiple scientific disciplines, such as statistical physics, N-body simulations, and molecular docking. However, exact thermodynamic calculations are almost always not feasible. In this work, we introduce a versatile algorithm designed to rapidly compute the two-body partition function, its related thermodynamic properties, and the second virial coefficient for anisotropic nanoparticles and proteins under the rigid-body approximation. Our method involves constructing a quasi-regular grid in the 5D angular space between pairs of arbitrary objects and efficiently scanning the radial-angular space between the rigid molecules. Where available, we find excellent agreement with light and X-ray scattering experiments, as well as with Monte Carlo simulations. Our results suggest a correction to current coarse-grained protein force fields, and we further discover a new, counterintuitive effect of temperature on virial coefficients, caused by a population shift in angular space due to the dielectric response of water. Finally, the grid can serve as an interpolation table for N-body simulations, increasing their performance by orders of magnitude.
Self-consistent field theory simulations of rod-coil diblock copolymers in slit confinement present significant numerical challenges due to sharp density gradients near hard walls. To rigorously resolve these systems utilizing the Gaussian and wormlike chain models, a hybrid spectral-compact finite difference scheme is developed on a non-uniform Chebyshev-Gauss-Lobatto grid. Shen's Chebyshev spectral method is employed for the flexible blocks. For the semiflexible blocks, a second-order upwind compact scheme together with an L-stable TR-BDF2 contour-stepping algorithm is adopted. This hybrid framework effectively suppresses spurious numerical oscillations. This unconditionally stable formulation strictly preserves propagator non-negativity and achieves up to a two-orders-of-magnitude speedup over uniform-grid implementations while maintaining linear spatial scaling. Simulations utilizing this advanced framework under neutral wall conditions reveal that the confining walls naturally induce preferential wetting of the semiflexible blocks at the impenetrable boundaries. As the incompressibility penalty increases, the compressible system progressively approaches the incompressible limit. For the selected physical parameters, decreasing the slit width induces a sequence of structural transitions from a smectic-C morphology with three internal periods (SC3) to morphologies with two and one internal periods (SC2 and SC1), and ultimately to a highly compressed smectic-P morphology (SP1). The equilibrium thickness of these confined structures deviates from exact integer multiples of the bulk spatial period. This deviation arises from the volume compensation associated with boundary depletion layers, together with adjustments in the molecular tilt angle and the degree of molecular interdigitation.
Beta-blockers are commonly prescribed for chronic cardiovascular diseases. Despite potential benefits in septic shock, beta-blockers are often held at hospital admission for patients with suspected infection and possible sepsis. We compared the effects of chronic beta-blocker continuation vs. discontinuation on 90-day all-cause mortality among patients admitted from the emergency department with suspected infection. Retrospective cohort study using the target trial emulation framework. We used Cox regression to compare 90-day mortality between treatment groups, with inverse probability of treatment weights to account for baseline differences in sex, race, ethnicity, age, body mass index, presence of a "do not resuscitate" order, comorbidities, and acute illness severity. A single large, academic, tertiary care emergency department in the Midwest United States. Patients 18 years or older on beta-blockers prior to admission hospitalized for suspected infection (defined by orders for blood cultures and broad-spectrum antibiotics). Patients with shock, heart rates less than 40 or greater than 120, or who required an IV beta- or calcium channel blocker at a clinician's discretion were excluded. Continuation of oral beta-blockers within 48 hours of admission vs. no continuation. Of 4635 eligible patients, 1172 (25.3%) received an oral beta-blocker, whereas 3463 (74.7%) did not receive an oral beta-blocker. Beta-blocker continuation was associated with a reduced risk of all-cause mortality within 90 days of hospital admission (hazard ratio 0.77; 95% CI, 0.61-0.98; p = 0.03) and shorter hospital stay (incidence rate ratio 0.39; 95% CI, 0.38-0.41; p < 0.001). There was no significant association between beta-blocker continuation and in-hospital mortality (odds ratio 0.60; 95% CI, 0.30-1.20; p = 0.15). Continuation of chronic beta-blockers in a broad population of patients admitted with suspected infection was associated with improved clinical outcomes. Our findings support the need for controlled experimental studies evaluating the role of chronic beta-blocker continuation among patients hospitalized with possible sepsis.
Perovskite single crystals (PSCs) are highly promising direct X-ray detection materials, whereas the mechanical brittleness and thermal instability easily bring about stress cracking and structural decomposition under a conventional bonding process, hindering integrated applications. In this work, we developed a photopolymerization-induced heterogeneous bonding technology based on 4-acryloylmorpholine (ACMO) for monolithic PSC integration, which enables effective bonding within seconds via a liquid film transfer method. The interfacial coordination effect between ACMO and PSCs simultaneously achieves defect passivation for the buried surface of PSCs and robust mechanical bonding with tensile and shear strengths of up to 1.81 and 1.50 MPa, respectively. Furthermore, polymerized ACMO has a high resistivity of 7.72 × 1012 Ω·cm that effectively suppresses dark current in the integrated devices. Compared with the control crystal, the dark current of the integrated device is reduced by 2 orders of magnitude, while the limit of detection (LOD) for X-ray detection improves 20-fold. The 5.6% relative standard deviation of dark currents among 6 × 6 pixels and 96% performance retention after 30 days of storage confirm the reliable uniformity and stability. This work provides a novel technical solution for the heterogeneous bonding of PSCs, facilitating the further development of high-performance PSC-integrated optoelectronic devices.
The human microbiota plays a pivotal role in health, with widespread alterations implicated in conditions ranging from inflammatory disorders to cancer. While correlation-based network analyses have illuminated ecological interactions within these communities, the host environment uniquely mediates microbial relationships, demanding new methods to capture dynamic, condition-dependent modules of species interactions. Here, we present a statistical framework termed differential co-occurrence analysis, which identifies blocks of taxa whose collective presence is strengthened or weakened under distinct host states. By leveraging recent advances in metagenomics that enable detailed taxonomic profiling and higher-order interaction discovery, our method transcends traditional pairwise correlation constraints. Conceptually akin to associative rule mining, it diverges through the integration of robust statistical modeling, directly extracting interactions that differ significantly between conditions. This approach offers a refined lens to dissect microbiota ecology and could pave the way for new insights into microbiome-associated disease mechanisms. The research on the role of the intestinal microbiota in the onset of cancer and as a modulator of anticancer treatments, including chemotherapeutics and immune checkpoint inhibitors, is helping medicine to identify novel strategies for cancer prevention, for the delivery of more effective treatments, and in reducing treatment side effects and complications. Within this context, it is of crucial importance to approach the analysis of clinical microbiome data with an ecology-oriented perspective and to develop bioinformatics tools able to identify functional interactions in bacterial communities of patients from observational cohort studies. Clinical microbiome datasets are typically high dimensional, comprising numerous taxa measured across relatively few samples. This imbalance increases the risk of statistical overfitting and undermines the robustness of analytical findings. However, recent advances in metagenomic bioinformatics pipelines and reference databases have enabled the comprehensive extraction of genetic information from microbiome samples, facilitating the precise characterization of bacterial species presence and absence. In our manuscript, we describe a statistical computational method that we named differential co-occurrence analysis, which focuses on the analysis of the co-presence of microbiota taxa across samples associated with different host conditions. The proposed method can reveal modules of interacting taxa that are strengthened or weakened when the host condition changes (e.g., when passing from a healthy state to a disease state). The method is general and applicable to a broad range of ecological datasets featuring presence/absence data structures. Furthermore, the method accommodates the analysis of higher-order co-occurrence patterns beyond pairwise co-occurrence, thereby enabling the investigation of higher-order interactions, whose detection and identification are a major challenge in ecological network analysis.
Rational design of transition metal complexes with desired optical properties is a major challenge due to high computational costs of quantum-chemical methods that can deliver quantitatively reliable results. We present a machine learning framework for predicting absorption and emission maxima in both transition metal coordination compounds and organic chromophores using joint training on a combined experimental data set. Our featurization strategy integrates ligand environment fingerprints (Morgan), metal center features (Coulomb matrices), and topological descriptors from persistent homology analysis. The combined training data set comprises 19,733 absorption and 2675 emission measurements for 17,359 metal complexes (with focus on Ir, Rh, Pt, and Ru systems) and 17,294 absorption and 18,141 emission measurements for 7065 organic molecules across 365 solvents. Among several architectures evaluated, multilayer perceptrons provide the best absorption predictions (RMSE = 33.5 nm, R2 = 0.83, Pearson r = 0.92 for metal-organic compounds), while gated recurrent units are optimal for emission (RMSE = 41.7 nm, R2 = 0.83, Pearson r = 0.90). Models trained jointly on both data sets show good universal applicability with moderate accuracy trade-offs: RMSE increases by approximately 7-19 nm for organic compounds compared to specialized models, and for metal-organic compounds, RMSE increases by 1-2 nm. In contrast, models trained on organic data alone fail catastrophically when applied to metal complexes (R2 = 0.01). For a test set of 35 metal complexes including metal centers beyond the main training distribution (V, W, Cu, and Os in addition to Ir, Rh, Pt, and Ru), our best models achieve an RMSE of ∼28 nm for absorption maxima, comparable to TDDFT-O3LYP predictions but at substantially lower computational costs. SHAP analysis reveals that Coulomb matrix descriptors are most important for metal complex predictions, while Morgan fingerprints prevail for purely organic compounds. The presented approach enables efficient screening of candidate compounds for various photophysical applications orders of magnitude faster than TDDFT calculations.
The biomechanical performance of bileaflet transcatheter mitral valves (TMVs) depends on complex interactions between leaflet material behavior and stent design. However, the contributions of leaflet materials and constitutive models, stent materials, and stent geometry to valve function and durability remain poorly understood. A parametric finite element study was conducted using a CAD model of a bileaflet TMV subjected to physiological pressure loading. Five leaflet material models were evaluated: 3 glutaraldehyde-fixed tissues-bovine pericardium (BP; FBP1, FBP2) and porcine pericardium (PP; FPP)-and 2 unfixed tissues-bovine (UBP) and porcine pericardium (UPP). BP was modeled as a linear elastic (FBP1) and Ogden (FBP2). Each was paired with 2 stent materials, cobalt chromium (CoCr) and nitinol, and 3 stent cell densities (low, medium, high), yielding 30 configurations. Von Mises stresses and relative leaflet opening were quantified. UPP achieved the largest opening (30-32%) with the lowest leaflet 99th percentile stresses (0.048-0.050 MPa), while FBP1 produced the smallest opening (8-9%) and highest leaflet stresses (0.077-0.078 MPa). Leaflet 99th percentile stress was strongly inversely correlated with valve opening (Spearman r=-0.9, p<0.01). Stent material had a negligible effect on valve opening but affected stent stress notably, with nitinol exhibiting about 2-3 orders of magnitude lower stresses compared to CoCr. Increasing stent cell density improved stress distribution with only a modest reduction in opening. Leaflet material model was the primary determinant of bileaflet TMV biomechanical performance, whereas stent material and cell density exerted secondary but meaningful effects. These findings offer insights that may guide the design of more optimized TMV replacement devices.
Fungal and mycobacterial smear and culture are often inappropriately ordered, straining clinical laboratory resources and risking patient harm from false-positive results. To identify opportunities for optimization, we assessed utilization and yield of fungal and mycobacterial smear and culture over five years at a large academic medical center in an area of low tuberculosis prevalence. Positive rates were generally lower for non-respiratory specimens compared to respiratory specimens, prompting us to focus on collection method (swab vs non-swab) and location (operating room [OR] vs non-OR) for non-respiratory specimens. For non-respiratory fungal smear and culture, swabs demonstrated poor recovery of mold in culture, and positive smear results from swabs rarely affected clinical management (6/3,611 cases [0.17%]). Positive rates were significantly lower among OR specimens. For mycobacterial smear and culture, positive rates were exceedingly low for non-respiratory specimens (smear: 70/23,661 [0.3%]; culture: 278/23,918 [1.2%]). Swabs were inferior for mycobacterial culture, and positive smear results from swabs rarely impacted clinical management (3/7,398 cases [0.04%]). Among patients with at least one non-respiratory mycobacterial smear or culture over a two-year period, the smear or culture was diagnostic in only 32/4,572 (0.70%), 31 (96.9%) of whom had risk factors for mycobacterial infection. The smear result changed clinical management in only 5/4,497 cases (0.11%). Our results reveal considerable overutilization of fungal and mycobacterial smear and culture for non-respiratory specimens and confirm the low yield of non-respiratory swabs for mold and mycobacterial detection. These data inform strategies to optimize fungal and mycobacterial diagnostic testing in clinical laboratories. Fungal and mycobacterial infections are associated with specific risk factors. However, diagnostic studies for fungal and mycobacterial infections, including fungal and mycobacterial smear and culture, are often inappropriately ordered in clinical scenarios where the probability of infection is low. Due to the manual nature of these tests, high volumes of unnecessary testing strain limited clinical laboratory resources. In addition, patients can be harmed by false-positive results. In this study, we analyzed the results of fungal and mycobacterial smear and culture performed over five years at a large academic medical center in a location with low tuberculosis prevalence to identify strategies to reduce unnecessary testing. Implementation of these strategies could allow clinical laboratories to optimize fungal and mycobacterial diagnostic testing and more efficiently use limited clinical laboratory resources without compromising patient care.
Identifying novel drug-disease associations (DDAs) is critical for advancing drug discovery. While AI-driven approaches have shown promise, most still struggle to maintain semantic consistency between learned high-order representations and the original feature space. Additionally, they often neglect the varying importance of different views and fail to model the semantic gaps between heterogeneous drug and disease features, hindering cross-modal alignment. To overcome these challenges, we propose a novel diffusion-enhanced fine-grained cross-semantic fusion framework for DDA prediction, namely DFCDDA. First, a conditional diffusion-based decoder is leveraged to ensure semantic consistency between the high-order features learned by the model and the original features. Second, an attention-guided fine-grained graph convolutional network dynamically generates soft adjacency matrices from multi-view structural data, enabling precise feature aggregation. Third, a bidirectional cross-attention module aligns heterogeneous drug and disease features and captures their complementary interactions. These three modules work collaboratively to improve the learning of robust drug and disease embeddings. Experiments on three real-world datasets show that DFCDDA consistently outperforms existing approaches in DDA prediction. Case studies further demonstrate its effectiveness in uncovering plausible drug-disease associations.
This study investigates the impact of a metacognitive intervention on student content performance and confidence across three key biology content modules: protein, respiration, and cell division. The intervention, conducted in Fall 2021 across six large classrooms at three institutions, included two treatment groups and one control group, alternating by module. A two-way analysis of variance (ANOVA) and post hoc analyses were used to identify group differences. Results show that metacognitive strategies significantly enhanced student performance in the protein module, the first to receive the intervention, with notable score gains in the treatment group. Students with metacognitive training performed significantly better on higher-order questions (HOQs) than the control, suggesting a greater benefit for tackling complex tasks in some contexts. Confidence was also significantly boosted, especially among low-confidence students, but the impact varied by module. The effects in the respiration and cell division modules were less pronounced. Overall, the study highlights the effectiveness of metacognitive strategies in promoting both content understanding and confidence, especially for students with low initial confidence, but their effectiveness appears to depend on contextual factors. The results underscore the importance of tailoring these strategies to diverse topics or content, and individual learning needs to maximize their effectiveness.
Liquid crystal (LC) phases formed by anisotropic particles have long attracted interest due to their unique combination of fluidity and directional order, as well as their prevalence in natural systems. Among them, chiral entities such as helices exhibit exotic LC phases, like the cholesteric and screw nematic, in addition to isotropic, smectic, and crystal phases that are observed in systems of achiral particles. Chiral particles are ubiquitous in nature across length scales. Helices, being the simplest examples of chiral particles, are important components in a variety of biological systems in the form of DNA, protein fragments, and others. In this work, we employ molecular dynamics to investigate the thermodynamics and structural characteristics of LC phases formed by a system of chiral particles compared to those formed by a system of achiral particles of the same aspect ratio. We consider a system of soft chiral rods modeled as made of fused beads and a system of soft repulsive spherocylinders (SRS) for this comparison. We evaluate the role of chirality in phase behavior with special emphasis on cholesteric and screw nematic phases. Extending the two-phase thermodynamic (2PT) model, we compute entropy, free energy, and fluidicity parameters to understand the thermodynamic stability of the similar phases exhibited by both systems. Our results reveal significant differences in phase stability and structure between the two systems, with crystals of achiral particles having a higher packing fraction than crystals of chiral particles. The study also demonstrates that translational and rotational fluidicity parameters can serve as effective phase identifiers, offering a simplified approach to characterizing complex LC phases. These findings deepen our understanding of chirality-induced phase behavior and also pave the way for applying fluidicity-based analysis to other anisotropic systems.
FinTech ecosystems are growing at a rapid pace, creating large-scale, heterogeneous, and highly interconnected data environments that pose challenges to traditional frameworks for innovation management and decision support. Even while artificial intelligence (AI) is being used more and more to make use of this data, the majority of current methods are still opaque, reactive, and not well-suited to the needs of human-centered decision-making. In order to facilitate enterprise innovation in intricate FinTech ecosystems, this study suggests an explainable agentic AI-driven big data decision framework. The platform combines explainable big data analytics and visual analytics pipelines with autonomous AI agents that are capable of goal-directed reasoning, adaptive collaboration, and continuous learning. The suggested method permits transparent investigation of extensive financial, transactional, and behavioral data by fusing network-aware data modeling, agent-based decision orchestration, and interpretable machine learning processes. By converting agent recommendations into clear, traceable insights for strategic innovation planning, visual analytics interfaces further support human-AI co-decision-making. When compared with black-box AI models, the framework's capacity to improve decision accuracy, adaptability, and trust is demonstrated through a case-driven evaluation inside real FinTech scenarios. The findings show that by coordinating AI with organizational, ethical, and legal restrictions, explainable agentic AI can greatly enhance company innovation outcomes. By providing a scalable and comprehensible decision framework for next-generation FinTech innovation ecosystems, this work advances the developing field of agentic AI for explainable large data exploration and visual analytics.
The development of novel organic semiconductors with enhanced conducting properties is often hindered by the challenge of accurately describing and modeling charge transport within the (pseudo-)amorphous films typically found in optoelectronic devices. In this study, we present a multiscale computational protocol to predict hole mobilities of non-crystalline hole-transporting materials with order-of-magnitude accuracy. Our approach, which integrates density functional theory, molecular dynamics, docking, and kinetic Monte Carlo simulations, reveals the impact of modeling different film morphologies-amorphous and pseudo-amorphous films as well as docking aggregates-on the charge transport properties of these materials. In particular, we demonstrate that experimentally observed mobility trends across a family of ten hole-transporting molecules, including the enhancement associated with increased aromatic core planarity and extended π-conjugation, can only be reproduced when both amorphous disorder and locally ordered molecular aggregates are explicitly considered. This work establishes a robust, morphology-aware framework for the rational, in silico design and optimization of next-generation organic semiconductors.
With increasing petroleum transport through coastal waters of the northeastern Pacific, understanding the ecological hazards of these mixtures is essential, yet the sublethal impacts on benthic filter feeders remain poorly characterized. The sub-chronic (7-d exposure) effects of crude oil (CO), marine diesel (MD), and diluted bitumen (DB) water-accommodated fractions (WAFs) on the scope for growth (oxygen consumption, food absorption efficiency, and clearance rate), as well as gonadal and digestive gland histopathology in Pacific oysters (Crassostrea gigas) were examined. Initial total polycyclic aromatic compound (TPAC) concentrations in WAFs were ranked CO > MD > DB, which also accumulated within oyster tissues in the same order. Sub-chronic exposures to different WAF dilutions of three petroleum products did not alter any measured endpoints in Pacific oysters. PACs accumulated in oyster tissues had a rapid depuration after the exposure period due to the potential biotransformation of parent hydrocarbons, which could elucidate the lack of significant adverse effects. These results indicate that adult C. gigas maintained physiological and tissue-level integrity under transient, declining petroleum exposure conditions, providing context for interpreting potential impacts of short-term nearshore spill scenarios.
In this paper, we address the characterization of the structure of condensed materials, periodic and non-periodic. Carrying out an extensive study of over 7000 different ground-state structures of a 2D lattice model of binary packing, we find a predominance of non-periodic structures (over 96%) that extend across the entire range of possible diversities. These non-periodic structures are resolved by establishing whether a structure will accommodate or reject additional local structures. This property, structural selectivity, is treated as a signature of an underlying ordering principle. The major result of this paper is the determination that roughly 35% of the non-periodic structures are selective and, hence, ordered in some way. This selectivity extends up to a diversity of ∼9, well beyond the upper threshold for diversity in periodically ordered states.
Chemotherapy-induced peripheral neuropathy (CIPN) affects approximately 50% of patients who receive chemotherapy. CIPN often results in dose reductions, therapy discontinuation, and long-term neurological impairment. Despite existing studies, identifying high-risk populations remains challenging, particularly in patients with diabetes, diabetic neuropathy, and those undergoing corticosteroid therapy. We sought to evaluate the key risk factors associated with CIPN by analyzing patient demographics, comorbidities, and chemotherapy regimens, with a specific focus on diabetes-related variables in order to inform early identification and prevention strategies. Retrospective, single-center, observational cohort study. Academic tertiary care cancer center. Adult patients who received chemotherapy between January 2016 and December 2023 were identified through electronic medical records. Patients with CIPN were defined by the International Classification of Disease, Tenth Revision G62.0 diagnosis code (drug-induced polyneuropathy) and an associated diagnosis of "painful peripheral neuropathy." Extracted data included demographics (age, body mass index [kg/m2], race/ethnicity), clinical variables (alcohol use, corticosteroid use, diabetes-related factors), and chemotherapy regimen details. Descriptive statistics, Wilcoxon rank sum, c2/Fisher's exact tests, and multivariable logistic regression were performed. Institutional review board (IRB) approval was obtained (IRB exemption #2024-0139). Among 36,949 patients, significant CIPN risk factors included older age (40-80 years, P < 0.0001), women (odds ratio [OR] 1.89; P < 0.0001), non-Hispanic/non-Latino ethnicity (OR 1.29; P < 0.0001), and corticosteroid use (OR 2.01; P < 0.0001). African American patients had lower odds of CIPN than White patients (OR 0.78; P < 0.0001). Diabetic neuropathy was strongly associated with increased CIPN risk (OR 5.35; P < 0.0001). Alcohol use was inversely associated with CIPN (OR 0.75; P < 0.0001). Several chemotherapy agents also showed significant associations. Our study is limited by its retrospective design, potential misclassification bias in CIPN diagnosis, and reliance on electronic medical records. Alcohol use data were frequently missing or unspecified, limiting interpretation. Key CIPN risk factors include age, race/ethnicity, corticosteroid use, and diabetic neuropathy. Alcohol use appeared inversely associated with CIPN, though causality remains unclear. Individualized patient assessments and proactive management strategies may help reduce CIPN incidence and improve outcomes in patients with cancer who are receiving chemotherapy.
Long non-coding RNAs play an important role in stress response in all forms of life; however, a tight regulation of lncRNAs is required for normal function. Abnormal expression of lncRNA is associated with uncontrolled cell growth in many forms of cancer. Recent studies have highlighted the role of lncRNAs in Aspergillus fumigatus in azole stress response and virulence. Here, using a transcriptome data set of A. fumigatus response to stress, we identified afu-254 as an 854 bp lncRNA that plays a role in modulating oxidative stress, fungal sub-MIC azole response to posaconazole and itraconazole, cell wall stress, macrophage phagocytosis and killing ex vivo, and virulence in an invertebrate model of Aspergillus infection. Importantly, afu-254 does not produce cross-azole susceptible responses and plays a role in fungal azole responses against posaconazole and itraconazole but not voriconazole. Furthermore, we showed that stoichiometric levels of afu-254 are important for its function, and ectopic overexpression of afu-254 in the WT strain leads to an antimorph. This phenotype may stem from a higher-order structure that is denatured with heat, indicating the presence of a non-functional isoform. In conclusion, we characterized a novel lncRNA, afu-254, that is important for stress response and virulence in the pathogenic fungus A. fumigatus.IMPORTANCEFailure of azole treatment for invasive Aspergillus infection by both drug-resistant and drug-sensitive isolates is an area of concern and global importance. Fungal stress response is multifaceted, and long non-coding RNAs have emerged as important players in mediating it, including regulating responses to azole antifungals. Here, we have identified a long non-coding RNA, afu-254, that plays a role in modulating fungal response to oxidative stress, cell wall stress, azole stress, immune cell stress, and virulence in an invertebrate model of invasive Aspergillus infection.