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Exploring the association between patient satisfaction and hospital quality in the United States

Background Patient experience is increasingly used as a public quality metric, but its relationship to hospital safety grades remains incompletely characterized because patient reported experience and technical safety measures capture different dimensions of care. Methods We performed a retrospective cross-sectional analysis of publicly available hospital level data from 2023. Leapfrog Hospital Safety Grades were linked with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) patient experience scores. The linked dataset included 1,442 U.S. hospitals; 1,390 hospitals with letter grades A through F were included in grade stratified and regression analyses. HCAHPS domains were analyzed on a 1–5 scale, with higher scores indicating more favorable patient reported experience. One way ANOVA compared mean HCAHPS scores across Leapfrog grades. Multivariable logistic regression with hospital size adjustment evaluated HCAHPS domains associated with Leapfrog Grade A versus grades B through F. Results Mean HCAHPS domain scores declined across most domains as Leapfrog grades worsened from A to F. In multivariable analysis, nurse communication was associated with higher odds of receiving a Grade A safety rating (odds ratio [OR] 1.54, 95% confidence interval [CI] 1.11 to 2.13; p = 0.01), as was the hospital recommendation score (OR 1.54, 95% CI 1.15 to 2.06; p = 0.004). Doctor communication showed an inverse conditional association (OR 0.75, 95% CI 0.59 to 0.97; p = 0.03). Other HCAHPS domains were not independently associated with Grade A after adjustment. Conclusions Hospitals with higher Leapfrog safety grades generally had more favorable patient reported experience scores, and nurse communication and hospital recommendation were the most clinically interpretable independent predictors of Grade A status. The inverse association for doctor communication should be interpreted cautiously because HCAHPS domains are correlated and the analysis used hospital level cross-sectional data. These findings support the complementary role of patient experience in hospital quality assessment but do not establish causality.

Green HPLC-PDA method for simultaneous determination of linagliptin and cefixime with pharmacokinetic application in rats

Abstract Many individuals with diabetes have compromised immune systems and reduced peripheral sensation, making them susceptible to infections. As a result, combination therapy involving antidiabetic drugs and antibiotics has become essential. A newly introduced combination of the antidiabetic drug linagliptin (LIN) and the third-generation cephalosporin antibiotic cefixime (CEF) has been recommended to combat infections in diabetic patients.To optimize therapeutic efficacy and minimize adverse effects associated with this combination therapy, a reliable analytical method was essential for pharmacokinetic analysis and therapeutic drug monitoring. This study present, for the first time, a green high-performance liquid chromatographic method with photodiode array detection (HPLC-PDA) for the simultaneous determination of LIN and CEF in plasma samples. Chromatographic separation was achieved using a Symmetry C 18 column (250 mm × 4.6 mm, 5 μm particle size) under isocratic elution with a mobile phase consisting of 20 mM sodium phosphate (pH 4.3, adjusted with orthophosphoric acid) and methanol (50:50, v/v). The flow rate was set at 0.8 mLmin − 1 , with a total run time of 12 min. The injection volume was 20 µL and the detection was performed at 230 nm. The method demonstrated linearity over a range of 50–2000 ng mL −1 for both LIN and CEF, with limits of detection (LOD) of 24 and 21 ngmL⁻¹ and limits of quantitation (LOQ) of 43 and 45 ngmL⁻¹ for LIN and CEF, respectively. Validation parameters complied with ICH M10 bioanalytical guidelines. Additionally, the method was successfully applied to a pharmacokinetic study comparing drugs efficacy when administered alone versus concurrently. The results demonstrated that co-administration of LIN and CEF significantly altered their bioavailability: LIN C max increased by 63.2% and AUC increased by 134.2%, while CEF C max decreased by 27.3% and AUC decreased by 23.3%, indicating a bidirectional pharmacokinetic interaction, and underscoring the need for careful monitoring during combination therapy.The greenness of the proposed HPLC-PDA method was evaluated using four metric tools and the findings confirmed the method’s minimal environmental impact. In conclusion, the developed HPLC-PDA method not only provides a reliable tool for therapeutic drug monitoring in clinical practice but also establishes a robust framework for future investigations into drug-drug interactions in human therapeutics.

Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures

Computational protein design using machine learning models has advanced rapidly since the introduction of AlphaFold2. There is now a suite of tools that enable in silico design of proteins with desired structures and properties. Most design workflows require fitting a designed backbone with a sequence that stabilizes it, and many machine learning sequence design models have been proposed. These models are trained to recover the native sequence paired with a known structure, a task known as native sequence recovery (NSR). Here, we demonstrate the limitations of optimizing a sequence design model only for NSR. We show that NSR is often misaligned with more important metrics of model performance: the compatibility of the generated sequence with the desired fold and the ability of the model to predict the energetic effects of mutations. We introduce PottsMPNN, which is trained to generate a Potts energy function consisting of single-residue and residue-pair terms from a protein backbone, and we demonstrate that learning a Potts model reduces NSR but improves sequence generation and energy prediction. We also trained PottsMPNN with noised backbone structures and multiple sequence alignments. In tests on held-out data, NSR decreased, but the quality of the designed sequences and energy predictions improved. By demonstrating the limitations of optimizing for NSR and the effectiveness of strategies that avoid NSR overoptimization, our work advances sequence design and highlights future directions for the broader protein design field.

Expression of Concern: Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review

Motor and cognitive development is associated with anthropometry and severe zinc deficiency in infants, in rural Madagascar

TMED9 drives non-small-cell lung cancer progression via promotion of autophagy by recruiting USP5 to deubiquitinate ATG9A

Non-small-cell lung cancer (NSCLC), the predominant type of lung cancer, is characterized by high invasiveness and significant mortality. Despite its clinical impact, the molecular mechanisms driving its pathogenesis and progression remain poorly understood. This study demonstrates that TMED9 is overexpressed in NSCLC and showed using multiple independent sample sets that its expression level is significantly associated with poor patient prognosis. Gain- and loss-of-function experiments revealed that TMED9 promotes proliferation, invasion, and migration of NSCLC cells in vitro and significantly accelerates tumor growth and metastasis in vivo. Mechanistically, TMED9 interacts with ATG9A and recruits USP5 to facilitate the deubiquitination and stabilization of ATG9A, thereby activating autophagy and driving malignant progression. Notably, genetic depletion of TMED9 enhances the sensitivity of NSCLC cells to osimertinib. Collectively, these findings identify the TMED9–USP5–ATG9A signaling axis as a critical driver of NSCLC malignancy, highlighting TMED9 as a promising therapeutic target.

Appropriate fleet selection using dynamic multi-criteria decision making (case study: Gol-E-Gohar Mine No. 3)

Abstract The growth of large-scale mining operations presents challenging choices when selecting loading and transport equipment, as these decisions significantly impact output, expenditure, and ecological footprint; poor choices can lead to delays, increased fuel consumption, higher pollution, or damaged equipment. Because mining environments are constantly evolving, decisions must be precise, adaptable, and timely; however, past research has often overlooked the inclusion of time factors and lifecycle phases in fleet planning. This work presents a flexible decision-making method using the Analytic Network Process (ANP) at Gol-E-Gohar Iron Mine No. 3. Its main new feature is that it divides the mine’s lifespan into three periods (start-up, middle phase, and final stage) to track how priorities shift over time. For each phase’s key factors, insights were gathered from 25 mining experts (from universities and industry) through side-by-side evaluations. To enhance consistency while mitigating characteristics or peculiarities associated with phase-only reviews, another Total Mine Life (TML) setup is calculated as a reference point, influencing how technical, financial, and ecological factors are evaluated and assessed at each stage. Instead of relying solely on averages, every ANP comparison, supermatrix run, ranking outcome, and sensitivity check was handled via SuperDecisions software, which also delivered the TML and split-phase outputs used for comparison. Findings show that, compared to fixed methods, the shifting method is more effective or performs better, adjusts more easily, and is more logical or coherent, matching choices to the real-world demands of each interval, so cutting costs and easing ecological strain. In the Gol-E-Gohar No. 3 example, the fleet was first reviewed using the TML benchmark, then across three time blocks, revealing how breaking it into phases helps select suitable equipment or appropriate tools actual conditions at every turn.

Effectiveness of minimally invasive pre-measured endotracheal tube suction on physiological indicators in mechanically ventilated infants: a clinical trial study

Tomographic imaging of superconducting order using particle–hole interference

Superconducting phases with exotic symmetries that differ from the underlying crystalline lattice are at the focus of superconductivity research. Yet, despite intense interest, detecting the order parameter symmetry and topology remains a major challenge. Real-space imaging near atomic impurities with scanning tunneling microscopy (STM) has been highly successful in revealing nodes of the superconducting gap, in particular in cuprate superconductors, however the order parameter phase winding has so far remained inaccessible by STM techniques. We demonstrate that STM can access this phase information by exploiting Young-type quasiparticle interference patterns generated by pairs of impurities acting as beam splitters. Superconducting order parameter tomography (SOPT), a technique proposed here, utilizes the response of real-space interference patterns of Bogoliubov quasiparticles to the controlled rotation of impurity configurations, allowing us to reconstruct the momentum space structure of the gap function Δ ( k ) . As a concrete example, we consider Strontium Ruthenate, whose superconducting order remains a subject of ongoing debate, and demonstrate how SOPT can distinguish between competing order parameter candidates. The Young’s interference fringes, nodal directions, and rotating beams, detected by SOPT, encode information about both the nodes and phase winding of the superconducting order parameter. This method provides a broadly applicable route to identifying unconventional and topological superconductivity and establishes particle–hole interference as a new imaging modality for superconducting order.

Reducing spatial coherence via dynamic scattering media enables aberration and speckle suppression in optical imaging

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir

HIV-1 cure requires preventing viral rebound after treatment interruption, but quantitative criteria defining the rebound-competent reservoir are lacking. We studied individuals undergoing observational treatment interruption without confounding interventions to identify virologic and immunologic determinants of rebound. In 9 of 13 participants, rebound viruses were genetically identical or similar to proviruses in circulating resting CD4 + T-cells. We found no evidence of recombination among rebound sequences. Instead, resistance to autologous neutralizing antibodies (aNAbs) was a critical determinant of viral rebound. Increased suppression of viral outgrowth by contemporaneous IgG isolated from plasma was correlated with longer time to rebound. Using inhibitory potential ( IP ), the log reduction in single-round infection at physiologic IgG concentrations, we defined quantitative limits governing rebound-competency with respect to contemporaneous aNAbs. Contemporaneous IgG antibodies inhibited different reservoir variants with a wide range of IP values (0.4 to 8.2 logs), whereas rebound viruses were minimally inhibited (0.5 to 2.8 logs), indicating that inhibition by even up to 2.8 logs (631-fold) cannot prevent rebound. Longitudinal analyses revealed that waning aNAb potency over time on antiretroviral therapy (ART) allows previously neutralized variants to gain rebound potential, consistent with the finding that rebound can come from variants deposited in the reservoir at different pre-ART time points. Thus, rebound competency is a dynamic, immune-governed property defined by quantitative immunologic constraints, including those exerted by aNAbs.

Joint burden of obesity and CKM-related metabolic multimorbidity in US adults: development of a survey-based MMBI

Abstract The American Heart Association (AHA) recently defined cardiovascular–kidney–metabolic (CKM) syndrome, highlighting the systemic interplay between metabolic risk factors, kidney disease, and cardiovascular burden. However, biomarker-based CKM staging is difficult to implement in large population surveys where laboratory data are unavailable. We aimed to develop a self-report–based Metabolic Multimorbidity Burden Index (MMBI) for quantifying survey-accessible CKM-related metabolic multimorbidity burden, evaluate its internal anchor-aligned performance, and examine whether combining MMBI with body mass index (BMI) helps characterize self-rated health (SRH) and health-related quality of life (HRQoL) risk heterogeneity among US adults. We used pooled 2017 and 2019 Behavioral Risk Factor Surveillance System (BRFSS) data from 677,784 US adults. MMBI was constructed using an outcome-anchored, survey-weighted logistic model based on five self-reported CKM-related conditions: diabetes, hypertension, hypercholesterolemia, chronic kidney disease, and cardiovascular disease. Poor SRH was used as the anchoring outcome. BMI categories were cross-classified with weighted MMBI tertiles to create a 3 × 3 joint-exposure matrix. The primary outcome was poor SRH; secondary HRQoL outcomes were frequent physical distress and frequent mental distress. In the held-out test set, the anchor-derived MMBI showed stable internal performance for poor SRH, with a survey-weighted area under the receiver operating characteristic curve (AUC) of 0.815 and good calibration. The joint-exposure analysis revealed risk heterogeneity not captured by BMI alone. Compared with adults with normal weight and Low MMBI, adults with normal weight and High MMBI had markedly higher odds of poor SRH (odds ratio [OR], 4.19; 95% confidence interval [CI], 3.89–4.51), exceeding the estimate observed for adults with obesity but Low MMBI (OR, 1.76; 95% CI, 1.63–1.90). Adults with both obesity and High MMBI had the highest odds of poor SRH (OR, 6.06; 95% CI, 5.69–6.47). Statistical interaction between BMI and MMBI was observed. MMBI is a scalable, self-report–based measure of CKM-related metabolic multimorbidity burden for population-surveillance settings where laboratory biomarkers are unavailable. Integrating MMBI with BMI may help characterize HRQoL-oriented risk heterogeneity. Given its reliance on poor SRH anchoring and self-reported diagnosed conditions, MMBI should be interpreted as a survey-based burden measure for population surveillance and hypothesis generation.

Orientation-tuned surround suppression exhibits a unique laminar signature in the human primary visual cortex

Spatial context modifies visual perception by enhancing novel and salient features over spatially redundant features in the underlying neural code of the primary visual cortex (V1). Although multiple intracortical pathways contribute to contextual modulation, their specific contributions to different types of contextual modulation are not fully understood. Leveraging the distinct laminar connectivity patterns of feedforward, feedback, and lateral pathways, we used ultra-high-resolution fMRI (7T T 2 *-weighted, 0.6 mm isotropic resolution) to infer their relative contributions to contextual modulation in V1 by analyzing blood-oxygenation-level-dependent (BOLD) signal across cortical depth. Participants viewed sine-wave grating disks embedded in large surround gratings. Segmentation cues were introduced or removed by manipulating the relative phase and orientation of the surround gratings, yielding three contextual conditions and a surround-only condition to measure the effects of context in the absence of feedforward input. Our analysis isolated the effects of orientation-tuned surround suppression (OTSS) from orientation-independent border-induced modulation (BIM). The results show that BOLD laminar profiles differ by modulation type: OTSS was absent from deep layers, whereas BIM was more broadly distributed. We also find that voxels at all depths are driven by spatial context in the absence of feedforward input, which accords with the finding of contextually driven neural responses in mammalian V1. These laminar differences likely reflect different proportional contributions of feedback from higher-order visual areas and long-range lateral connections within V1. Our findings help to explicate the contributions of recurrent processing to visual contextual modulation and its impacts on laminar-dependent BOLD fMRI.

Sub-second a-scan acquisition using marginal spectral-domain quantum optical coherence tomography

Quantifying the repeated evolution of insect raptorial forelegs

Meiotic cohesin Rec8 imposes fitness costs on fission yeast gametes favoring the evolution of parental bias in gene expression

Differences between partner gametes, which evolved repeatedly in eukaryotes, can contribute to the evolution of the sexes, sexual selection, and non-Mendelian inheritance. Yet, the empirical evidence for how functional asymmetries arise between initially equivalent gametes is limited. Here, we combine theoretical and experimental approaches in the fission yeast Schizosaccharomyces pombe to show how selective pressures acting concurrently on gametes and zygotes drive the evolution of gamete differences. We find that despite being morphologically identical, P- and M-type partner gametes invest asymmetrically in zygotic development by contributing different amounts of conserved meiotic cohesins. P-gametes preferentially produce the Rec8 cohesin that increases zygotic fitness but reduces gamete viability, revealing a trade-off between reproductive success and gamete survival. We demonstrate that this asymmetry is mediated by partner-specific communication and model its evolutionary dynamics using empirically determined parameters. Our results support classical theoretical predictions for the evolution of gamete differences and provide a mechanistic understanding of how molecular asymmetries between partners can originate from opposing selection pressures acting in species that lack morphologically distinct gametes.

Structural determinants, economic dynamics, and technological gaps in horticultural entrepreneurship: evidence from the North-Western Himalayas

Abstract Horticulture contributes nearly 9–10% to the Gross State Domestic Product of Jammu and Kashmir and underpins rural livelihoods in the Union Territory, yet limited empirical evidence exists on how entrepreneurial behavioural attributes interact with techno-economic viability and structural constraints to influence hortipreneurship outcomes in the North-Western Himalayan region. The present study examined (i) socio-economic determinants of entrepreneurial behaviour, (ii) behavioural typologies, and (iii) techno-economic viability of major horticultural crops alongside infrastructural and institutional constraints. Primary data were collected from 250 horticultural entrepreneurs across five purposively selected districts of Jammu division using a multi-stage mixed sampling framework. Multiple Linear Regression, K-Means Clustering, Exploratory Factor Analysis (EFA), and One-Way ANOVA were applied using R software. For the Multiple Linear Regression model (R 2 = 0.312, Adjusted R 2 = 0.297, F(5244) = 22.18, p  < 0.001), education (β = 0.206, p  = 0.036) and farming experience (β = 0.112, p  < 0.001) significantly enhanced entrepreneurial behaviour, while landholding size exhibited a significant negative association (β = − 1.172, p  = 0.040), indicating higher entrepreneurial intensity among smallholders. VIF diagnostics confirmed no multicollinearity concerns (all VIF < 2.5). K-Means clustering (K = 3; average silhouette score = 0.42) identified three distinct typologies, with High-Potential Innovators (Cluster 3; N = 82; mean EBI = 59.45) demonstrating the strongest behavioural readiness but experiencing income stagnation due to structural barriers. The EFA solution (KMO = 0.74; Bartlett’s Test: χ 2 = 412.3, p  < 0.001; total variance explained = 68.4%) identified infrastructural deficiencies (cold storage loading = 0.88; processing facilities = 0.85) and institutional gaps (technical knowledge = 0.82; market information asymmetry = 0.78) as dominant constraints. Economic analysis revealed the superior viability of pecan cultivation in Poonch (BCR = 9.21) relative to traditional apple-based systems (BCR = 3.32). One-Way ANOVA confirmed statistically significant inter-district variation in entrepreneurial behaviour (F(4245) = 3.87, p  = 0.005). The findings establish a ‘high-potential–low-realization’ dynamic in Himalayan hortipreneurship and underscore the need for integrated structural and policy interventions targeting cold chain infrastructure, credit access, and demand-driven extension services.

Biomolecular assemblies through weak noncovalent interactions: Higher-order transient structures and their condensate phase

Recent data suggest that many membrane proteins spontaneously organize into spatial patterns through weak noncovalent interactions. These weak interactions are protein type-specific and underlie the formation of higher-order transient structures (HOTS), which can function as 10 to 100 nanometer-sized, transient hubs of membrane signaling. We describe the necessary conditions for HOTS assembly to occur, its thermodynamic relationship to biomolecular condensate formation, and potential roles of HOTS in biology stemming from their unique physical properties. Currently, a quantitative understanding of HOTS is limited to membrane proteins, but many observations suggest that HOTS may also be abundant in three-dimensional cellular compartments.

Integrated application of sugarcane by-product-derived organic fertilizer (SOFA) and mineral nitrogen enhances yield, fruit quality, and soil properties of eggplant (Solanum melongena L.) in sandy soil conditions

Abstract Low soil fertility and restricted nutrient bioavailability are critical limiting factors for sustainable vegetable production, particularly in marginal sandy soils. This study addressed these challenges by investigating the efficacy of a novel sugarcane-derived molasses treated (SOFA) as a soil conditioner and nutrient source for eggplant ( Solanum melongena L.), a crop for which comprehensive studies on such integrated approaches in these soil types are limited. Field experiments were conducted over two consecutive growing seasons to evaluate the impact of SOFA, applied either independently or in combination with mineral nitrogen (N), on soil physicochemical properties and crop performance.The study results demonstrated that integrated fertilization significantly improved soil physicochemical properties and crop performance. The treatment T3 (50% mineral N  + 50% SOFA) achieved the highest total fruit yield (8.84 kg plant −1 ), representing a  111% increase compared to the control (4.18 kg plant −1 ). Similarly, T2 (75% N  + 25% SOFA) significantly enhanced vegetative growth, chlorophyll content, and fruit nutritional quality. Soil organic matter increased by up to 56%, while available nitrogen, phosphorus, and potassium increased by up to 240%, 209%, and 67%, respectively, compared to the control. Moreover, SOFA application contributed to moderating soil alkalinity and improving nutrient availability, despite slight increases in electrical conductivity at higher application rates. Eggplant fruit quality traits were also significantly improved, with total soluble solids (TSS) increasing by  42% and anthocyanin content by  49% under optimized treatments. Economic analysis revealed that T2 and T3 achieved the highest profitability, with investment factors approaching 5.0, indicating superior economic efficiency. These findings highlight the potential of treated molasses-based amendments (SOFA) as a sustainable strategy for restoring marginal sandy soils and optimizing the productivity and nutritional quality of S. melongena.

Analytical modeling for suction cup designs for skin-interfaced wearable devices

Stable mounting is a central requirement for skin-interfaced wearable biomedical devices, because accurate and long-term measurements with clinical utility typically demand intimate contact with the skin, whereas practical use also requires gentle removal to minimize skin irritation and damage. Existing mounting strategies often struggle to satisfy these competing requirements simultaneously, especially under prolonged wear or in the presence of sweat and moisture. Suction-based mounting has recently emerged as a promising alternative because it can provide strong, reversible, and adhesive-free attachment, yet its underlying mechanics remain insufficiently understood. Here, we establish analytical models for the deformation and force of suction cups in a fully explicit form, covering both the cone suction cup and an optimized ring suction cup design. Unlike previous approaches that rely on indirect quantities such as the pressure difference and contact radius, which are not available before experiments and therefore cannot serve as controllable design variables, the present framework yields direct relations between suction performance and geometry parameters, material properties, and loading conditions, including the maximum push down displacement and the subsequent pull up displacement. The resulting formulas agree closely with accurate numerical solutions and lead to compact scaling laws that clearly identify how geometry and material parameters govern suction performance. These results provide a quantitative and physically transparent foundation for the design of suction-based mounting strategies in wearable devices.