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Unveiling the drivers of patient satisfaction in the United States hospitals: Assessing quality indicators across regions
Introduction Patient satisfaction in the UnitedStates (U.S.) healthcare varies regionally due to cultural, socioeconomic, and infrastructure differences. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey measures patient satisfaction across several aspects, including staff responsiveness and hospital environment. This survey influences Medicare reimbursements and helps ensure equitable, high-quality care nationwide. Analyzing these results is crucial for enhancing patient-centered care and understanding regional disparities. Methods This study analyzed HCAHPS data from 3,286 U.S. hospitals from July 1, 2021, to June 30, 2022. Hospitals were stratified by region. The categories analyzed included cleanliness, communication, staff responsiveness, medication information, discharge processes, care transition, overall rating, quietness, and hospital recommendations. Kruskal Wallis tests and heat maps were used to compare and visualize regional differences. Results The analysis revealed significant regional differences in hospital performance across the U.S. (p < 0.05). The Midwest consistently scored the highest in hospital performance metrics, while the “Other” region reported the lowest scores with discharge information, 12.91 percentage points (pp) lower than the Midwest. The communication about medicines indicator scored the lowest across all regions, with the Midwest the best at 76.88, 1.67 pp higher than the West and 9.94 pp higher than the “Other” region. State-level heatmaps highlighted disparities, with New York and South Carolina performing poorly, while South Dakota earned 5-star ratings for overall hospital ratings. Conclusions U.S. healthcare service ratings demonstrate regional disparities, with the Midwest scoring highest overall. The study identified specific areas needing improvement in lower-performing states, contrasting with strong performances in others. These findings can guide policymakers in enhancing national healthcare quality by addressing regional challenges and learning from high-performing states. Understanding these disparities is crucial for improving patient-centered care, reducing quality gaps, and ensuring equitable access to high-quality healthcare across the U.S.
Identifying pastoral and plant products in local and imported pottery in Early Bronze Age southeastern Arabia
The origins of ceramic technology in the Oman Peninsula have a unique history in the context of ancient West Asia. Local pottery production in northern Oman and the United Arab Emirates is not documented until the early to mid-third millennium BC during the Early Bronze Age. This period was characterised by increasing sedentism and the expansion of long-distance exchange networks that operated across the Persian Gulf between Arabia, Mesopotamia, Iran and South Asia, including the exchange of ceramic vessels. In order to explore the links between ceramic technology and type, subsistence practices and sedentism as ceramic production was adopted in the region, we analysed the lipid content of Early Bronze Age pottery (n = 179) in southeastern Arabia from inland and coastal sites. The ceramic assemblage examined includes pottery produced locally at the site level as well as vessels that are distributed regionally. The contents of imported pottery from Mesopotamia and the Indus Civilisation from inland and coastal sites were also studied to determine the organic products that may have been transported as part of long-distance exchange. The results reveal the presence of pastoral products, such as meat and dairy products, in some of the earliest vessels produced in southeastern Arabia, as well as imported Mesopotamian vessels. Plant products are detected in a small minority of vessels in locally-produced and imported vessels, such as Fine Red Omani vessels and Black-Slipped Jars from the Indus Civilisation. Such an investigation demonstrates the importance of using biomolecular methods to study dietary practices and vessel use in southeastern Arabia on a larger scale.
Machine Learning Driven Synthesis of Cobalt Oxide Entrapped Heteroatom-Doped Graphitic Carbon Nitride for Enhanced Oxygen Evolution Reaction
Developing highly efficient electrocatalysts for the oxygen evolution reaction is hindered by sluggish multi-electron kinetics, poor charge transfer efficiency, and limited active site accessibility. Transition metal-based electrocatalysts, such as cobalt oxides, have shown promise. However, poor charge transfer efficiency, limited active site accessibility, and suboptimal interaction with support materials have lowered their oxygen evolution reaction performance. Additionally, optimization of materials remains a complex task, often relying on trial-and-error approaches that do not clearly understand the key features that govern oxygen evolution reaction performance. In this study, we have addressed these challenges through machine learning, which enables the systematic design and optimization of electrocatalysts. By leveraging machine learning, we have developed a highly effective cobalt oxide nanocrystal-based electrocatalyst embedded within sulfur and phosphorus-doped carbon nitride. The homogeneous distribution of cobalt oxide nanocrystals on the sulfur and phosphorus-doped carbon nitride substrate further improves the accessibility of active sites during electrochemical reactions, leading to enhanced oxygen evolution reaction performance. The cobalt oxide sulfur and phosphorus-doped carbon nitride catalyst has shown promising oxygen evolution reaction activity, characterized by a low overpotential of 262 mV, a Tafel slope of 66 mV dec ⁻ ¹, and a high electrochemically active surface area of 140.58 cm². These results highlight the synergistic interaction between cobalt oxide and sulfur and phosphorus-doped carbon nitride, which contributes to the catalyst’s superior electrocatalytic performance and provides a promising pathway for the design of advanced oxygen evolution reaction catalysts through machine learning-guided material optimization.
Enhanced THz Emission and Chirality Control in van der Waals Ferromagnetic FePd<sub>2</sub>Te<sub>2</sub>/Pt Heterostructures
The endophytic fungi Metarhizium, Pochonia, and Trichoderma, improve salt tolerance in hemp (Cannabis sativa L.)
Colonization of plants by fungal endophytes can improve plant growth and can assist in adaptation to biotic and abiotic stresses. The fungal endophytes Metarhizium robertsii and Pochonia chlamydosporia were previously shown to improve hemp growth. Here, the impact of three fungal endophytes, M. robertsii, P. chlamydosporia as well as Trichoderma harzianum on hemp was investigated under treatment with 300 mM NaCl as a salinity stress and reduced watering volume as a drought stress. Plant growth parameters, a lipid oxidation indicator, leaf porphyrins together with the abiotic stress responses genes were assessed in hemp with or without fungal colonization under normal and stressed conditions. Under salinity stress, the growth of hemp was ameliorated by the application of Metarhizium, Pochonia, or Trichoderma in the soil. The increased production of malondialdehyde (MDA) and the reduction in porphyrins in hemp under salinity stress were restored in the presence of fungal endophytes. Under drought stress, the aboveground growth of hemp was recovered by the application of Metarhizium together with the reduced production of porphyrins. The stress related gene CsNAC3 showed decreased expression during fungal application compared with uninoculated hemp under salinity or drought treatment. Colonization of Metarhizium, Pochonia or Trichoderma improved salt stress tolerance in hemp and this was accompanied by a reduction in oxidative stress.
An Ethynyl-Linked sp-Carbon-Conjugated Covalent Organic Framework through Sonogashira Cross-Coupling Reactions
Prioritizing long-acting injectable antiretroviral therapy among key populations: Perceptions of persons living with HIV who inject drugs, ART clinic staff, and policymakers in Vietnam
Objectives We explored perceptions of long-acting injectable (LAI) patient prioritization and decision-making among people who inject drugs (PWID) with HIV, HIV clinic staff, and policymakers in Vietnam. Methods From February to November 2021, we conducted 38 interviews with 19 PWID, 14 HIV clinic staff, and 5 policymakers in Hanoi, Vietnam. Interviews were coded and analyzed using thematic analysis to assess themes across participants. Results PWID highlighted the importance of medical providers in treatment decisions, while clinic staff stressed adequate counseling and patient choice. Non-patient participants viewed adherence to Ministry of Health guidelines as essential, and many clinic staff saw PWID as ideal for LAI due to suboptimal adherence. Policymakers, however, preferred prioritizing LAI based on viral suppression and adherence rather than risk behaviors. Conclusions This study reveals conflicting stakeholder views on LAI prioritization, suggesting a need for a more nuanced implementation approach to balance efficiency and fairness in low-resource settings.
Evaluation of the efficacy, prognosis and safety of dexamethasone in the treatment of different types of non-puerperal mastitis: A retrospective study
Objective To analyze the efficacy and safety of dexamethasone in the treatment of non-puerperal mastitis (NPM), providing a new idea for the treatment of NPM. Methods From August 1, 2017 to August 30, 2024, case data were collected from 552 patients with NPM. After grouping according to different treatment options, the SPSS statistical software was used for retrospective analysis of the collected data. Results The number of days of drug treatment before operation in group B was less than other groups (p < 0.001). The group B had the most significant relief of pain symptoms and shorter time to complete relief of pain than other groups (p < 0.001). There were statistically significant differences between the 5 groups in the time required for pain to disappear and the time required for the volume to be reduced by half after treatment (p < 0.05).The overall efficacy evaluation had the highest effective rate in Group B (100%) and the lowest in Group D (2.10%), and the difference was statistically significant (p < 0.001).No side effects such as abnormalities in liver and kidney functions, water-electrolyte disorders, or peptic ulcers were observed in the five groups during drug treatment. There was no statistically significant difference in the occurrence of side effects such as rash, diarrhoea and hyperglycaemia among patients in the five groups (p > 0.05).The side effects of nausea (vomiting) and skin pigmentation in group E were higher than other groups(p < 0.001). In terms of weight gain (full moon face), nervous excitability (insomnia) and menstrual disorders, group B was lower than other groups (except group D without hormone therapy)(p < 0.001). In terms of postoperative recurrence after ipsilateral breast surgery, the recurrence rates of patients in group B were lower than those of the other four groups, and group D had the highest recurrence rate (8.30%), with a statistically significant difference (p < 0.001). Satisfaction survey found that group B had the highest satisfaction rate other groups (p < 0.05). The number of days required for the volume to be reduced by half after treatment was the most influential factor in the satisfaction survey. At the same time, we found that the obvious effect and recovery rate of GLM group was higher than that of PCM group, and the difference was statistically significant (p < 0.05). Conclusion Dexamethasone combined with levofloxacin/Metronidazole in the treatment of NPM has many advantages: first of all, it can significantly relieve the pain symptoms caused by the disease and effectively reduce the size of the lesion. Meanwhile, for the patients who plan to undergo surgery, the number of days of preoperative drug treatment can be reduced, and the overall effective rate is the highest. Secondly, the short-term application of drugs to treat side effects less, high safety; in the meantime, the risk of recurrence of the ipsilateral breast was less and the satisfaction of the patients was higher. The overall significant efficiency and recovery rate of GLM patients were higher than those of PCM patients.
Uncovering the influence of social media marketing activities on Generation Z’s purchase intentions and eWOM for organic cosmetics
The organic cosmetics market in Vietnam is rapidly growing, especially among Generation Z consumers who prioritize sustainability and eco-friendly products. Despite this expansion, the key factors driving purchasing decisions for organic cosmetics have not been adequately researched. This study addresses this gap by examining the influence of Social Media Marketing Activities (SMMAs) on Generation Z’s purchase intentions and eWOM, with perceived quality and perceived value as mediating factors. Using a quantitative approach, data were collected from 315 Generation Z participants in Vietnam through a structured questionnaire. The study explores various dimensions of SMMAs—interaction, customization, trendiness, and entertainment—and their effects on perceived quality, perceived value, eWOM, and purchase intention. Results show that SMMAs have a stronger impact on perceived quality (β = 0.726) than on perceived value (β = 0.503), suggesting that social media marketing strategies are particularly effective in shaping how Generation Z evaluates product quality. Additionally, perceived quality significantly influences perceived value (β = 0.312), eWOM (β = 0.346), and purchase intention (β = 0.279). Similarly, perceived value positively impacts eWOM (β = 0.395) and purchase intention (β = 0.402), while eWOM itself plays a direct role in driving purchase intention (β = 0.167). These findings highlight the interconnected role of social media marketing in influencing consumer behavior through both direct and mediated effects. This research provides strategic direction for brands in Vietnam’s organic cosmetics sector targeting Generation Z. By leveraging these findings, brands develop targeted social media campaigns that emphasize quality perceptions through interactive content and customized experiences, thereby effectively driving both purchase decisions and positive eWOM among Generation Z consumers.
Electrical Detection of Magnetic Perturbations through the Magnetoelectric Effect of a Molecular Spin Triangle
Feasibility trial of Darwin OncoTreat and OncoTarget precision medicine testing to improve outcomes for patients with limited metastatic disease that failed first-line systemic therapy
Despite significant progress in solid tumor oncology, including widespread genomic testing, metastatic cancer remains largely incurable and results in approximately 90% of cancer deaths. In the context of systems biology, RNA transcriptome-based (RNA-seq) testing is utilized to identify master regulator proteins that are putative drivers of tumor progression. After extensive preclinical testing and validation, Darwin OncoTarget and OncoTreat has been developed as a commercially available next generation precision oncology test with preliminary evidence of efficacy in treatment-refractory advanced cancers. We designed this pilot trial to assess the feasibility of integrating Darwin OncoTarget and OncoTreat testing in patients with oligometastases receiving comprehensive involved site radiotherapy. Eligible patients are adults with solid tumor oligometastases with up to 10 discrete tumors amenable to radiation therapy following prior first-line systemic therapy. Tumor biopsy is required to allow for precision medicine testing to supplement standard clinical management. Formalin fixed paraffin embedded tissue with >50% tumor will be sent to the Laboratory of Personalized Genomic Medicine at Columbia University Medical Center for Darwin OncoTarget and OncoTreat testing. Patients will either continue standard of care systemic therapy or proceed with an alternative FDA approved treatment informed by Darwin testing. This trial evaluates the feasibility and utility of integrating novel precision oncology testing in a community hospital setting. This study will utilize precision oncology testing in the population of induced, recurrent or persistent oligometastases that currently have limited or largely ineffective systemic treatment options. This trial represents an early attempt at integrating next generation precision medicine testing and systems biology in the context of radiation therapy.
Concentration-Driven Ring Expansion Metathesis Polymerization via Tunable Ring Transfer Processes
The effects of luck perception on consumer variety-seeking
Companies often use luck, which is thought to be a psychological force, as a marketing tool to perform marketing activities. This paper designed three experiments to verify the effects of luck perception on variety-seeking. Experiment 1 found that luck perception allows consumers to have more variety-seeking. Experiment 2 examined the mediating role of novelty-seeking motivation and found that luck perception improves the novelty-seeking motivation and makes consumers prefer variety-seeking. Experiment 3 examined the moderating role of need for cognitive closure. For consumers who have low need for cognitive closure, whether prime luck perception or not, their variety-seeking are not significantly different, while for consumers with high need for cognitive closure who are after luck perception primed, they have more variety-seeking. This study provides theoretical support for understanding luck scientifically and enlightens and guides companies to expand product lines, present new brands, and implement precision marketing.
Formally Stereoretentive S<sub>N</sub>1 Reactions of Homoallylic Tertiary Alcohols Via Nonclassical Carbocation
An ensemble-based 3D residual network for the classification of Alzheimer’s disease
Alzheimer’s disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtle imaging differences. Furthermore, differentiating early MCI (EMCI) from late MCI (LMCI) is also important for interventions. This study proposes a deep learning-based approach using a weighted probability-based ensemble method to integrate results from three-dimensional residual networks (3D ResNet). (1) This study employs 3D ResNet-18, 3D ResNet-34, and 3D ResNet-50 architectures with the Convolutional Block Attention Module (CBAM). The attention mechanism enhances performance by helping the model focus on pertinent information. Data augmentation techniques are applied to address limited data and improve accuracy. (2) To overcome the limitation of the individual convolutional neural network (CNN), an ensemble learning method is adopted. The method assigns weights to each 3D CNN model based on prediction accuracy and integrates them to obtain the final result. Our method achieves accuracy of 94.87%, 92.31%, 95.49%, and 95.97% for MCI vs. NC, MCI vs. AD, EMCI vs. LMCI, and NC vs. EMCI vs. LMCI vs. AD, respectively. The results demonstrate the effectiveness of our method for AD diagnosis.
Simultaneous optimized orthogonal matching pursuit with application to ECG compression
A greedy pursuit strategy which finds a common basis for approximating a set of similar signals is proposed. The strategy extends the Optimized Orthogonal Matching Pursuit approach to selecting the subspace containing the approximation of all the signals in the set. The method, called Simultaneous Optimized Orthogonal Matching Pursuit, is stepwise optimal in the sense of minimizing at each iteration the mean square error norm of the signals in the set. When applied to compression of electrocardiograms, significant gains over other transformation based compression techniques are demonstrated on the MIT-BIH Arrhythmia dataset.
Bayesian optimization and machine learning for vaccine formulation development
Developing vaccines with a better stability is an area of improvement to meet the global health needs of preventing infectious diseases. With the advancement of data science and artificial intelligence, innovative approaches have emerged. This manuscript highlights the applications of machine learning through two cases in which Bayesian optimization was used to develop viral vaccine formulations. The two case studies monitored the critical quality attributes of virus A in liquid form by infectious titer loss and virus B in freeze-dried form by glass transition temperature. Stepwise analysis and model optimization demonstrated progressive improvements of model quality and prediction accuracy. The cross-validation matrices of the models’ predictions showed high R² and low root mean square errors, indicating their reliability. The prediction accuracy of models was further validated by using test datasets. Model analysis using prediction error plot, Shapeley Additive exPlanations, permutation importance, etc. can provide additional insights into relations between model and experimental design, the influence of features of interest, and non-linear responses. Overall, Bayesian optimization is a useful complementary tool in formulation development that can help scientists make effective data-driven decisions.