The mediating role of flow state between recovery and energy levels: An experience sampling method study.

This study uses a resource perspective that combines theories used commonly to explore recovery experiences as a theoretical framework and investigate the effects of recovery at the beginning of the workday on exhaustion and vigour at the end of the workday, with workflow in the morning as a mediator. An experience sampling method was used to collect data from 84 fulltime employees. Participants received three survey links each workday over a 2-week period, resulting in 837 days-level and 2517 data points. Hierarchical linear regression was used to test hypotheses, with results suggesting that greater recovery at the beginning of the workday correlated negatively with exhaustion and positively with vigour at the end of the workday. Recovery at the beginning correlated positively with flow state in the morning, and flow state correlated positively with vigour at the end of the workday. Flow state in the morning mediated the relationship between recovery level at the beginning and vigour at the end of the workday. These findings suggest the importance of recovery and the effects of flow state on employees' vigour.

Interaction of Colorectal Neoplasm Risk Factors and Association with Metabolic Health Status Focusing on Normal Waist-to-Hip Ratio in Adults.

BACKGROUND: We aimed to evaluate the interaction between colorectal adenoma risks among asymptomatic individuals in terms of metabolic health status and obesity, and examine the normal waist-to-hip ratio (WHR) in adults with colorectal adenoma risk. METHODS: A cross-sectional, retrospective study was conducted at MacKay Memorial Hospital involving 16,996 participants who underwent bidirectional gastrointestinal endoscopy between 2013 and 2023. The study recorded important clinicopathological characteristics, including age, body mass index and WHR, Framingham Risk Score (FRS), blood glucose level, and Helicobacter pylori (H. pylori) infection status. RESULTS: Multivariate logistic regression analysis demonstrated that elevated hemoglobin A1C (HbA1c), increased FRS, positive H. pylori infection, and WHR ≥ 0.9 are independent risk factors for colorectal adenoma. In examining the interaction between FRS and WHR using multivariate logistic regression to evaluate adenoma risk, the OR for the interaction term was 0.95, indicating a decline in adenoma risk when considering the interaction between these two factors. Incorporating HbA1c into the analysis, evaluating the interaction between FRS and WHR still demonstrated a statistically significant impact on adenoma risk (OR 0.96, p < 0.001). Participants with WHR < 0.9, elevated FRS, positive H. pylori infection, and increased HbA1c levels were associated with a higher risk of colorectal adenoma formation. Remarkably, the increased risk of adenoma due to rising HbA1c levels was statistically significant only for those with a WHR < 0.9. CONCLUSIONS: An increase in FRS and HbA1c or a positive H. pylori infection still warrants vigilance for colorectal adenoma risk when WHR is 0.9. These factors interacted with each other and were found to have a minimal decline in adenoma risk when considering the interaction between WHR and FRS.

A theory of evolutionary dynamics on any complex population structure reveals stem cell niche architecture as a spatial suppressor of selection.

How the spatial arrangement of a population shapes its evolutionary dynamics has been of long-standing interest in population genetics. Most previous studies assume a small number of demes or symmetrical structures that, most often, act as well-mixed populations. Other studies use network theory to study more heterogeneous spatial structures, however they usually assume small, regular networks, or strong constraints on the strength of selection considered. Here we build network generation algorithms, conduct evolutionary simulations and derive general analytic approximations for probabilities of fixation in populations with complex spatial structure. We build a unifying evolutionary theory across network families and derive the relevant selective parameter, which is a combination of network statistics, predictive of evolutionary dynamics. We also illustrate how to link this theory with novel datasets of spatial organization and use recent imaging data to build the cellular spatial networks of the stem cell niches of the bone marrow. Across a wide variety of parameters, we find these networks to be strong suppressors of selection, delaying mutation accumulation in this tissue. We also find that decreases in stem cell population size also decrease the suppression strength of the tissue spatial structure.

Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes.

Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network-structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially resolved datasets of cellular organization.

Evolutionary graph theory beyond pairwise interactions: Higher-order network motifs shape times to fixation in structured populations.

To design population topologies that can accelerate rates of solution discovery in directed evolution problems or for evolutionary optimization applications, we must first systematically understand how population structure shapes evolutionary outcome. Using the mathematical formalism of evolutionary graph theory, recent studies have shown how to topologically build networks of population interaction that increase probabilities of fixation of beneficial mutations, at the expense, however, of longer fixation times, which can slow down rates of evolution, under elevated mutation rate. Here we find that moving beyond dyadic interactions in population graphs is fundamental to explain the trade-offs between probabilities and times to fixation of new mutants in the population. We show that higher-order motifs, and in particular three-node structures, allow the tuning of times to fixation, without changes in probabilities of fixation. This gives a near-continuous control over achieving solutions that allow for a wide range of times to fixation. We apply our algorithms and analytic results to two evolutionary optimization problems and show that the rate of solution discovery can be tuned near continuously by adjusting the higher-order topology of the population. We show that the effects of population structure on the rate of evolution critically depend on the optimization landscape and find that decelerators, with longer times to fixation of new mutants, are able to reach the optimal solutions faster than accelerators in complex solution spaces. Our results highlight that no one population topology fits all optimization applications, and we provide analytic and computational tools that allow for the design of networks suitable for each specific task.

Depth-profiling alkyl chain order in unsaturated lipid monolayers on water.

Unsaturated lipids with C=C groups in their alkyl chains are widely present in the cell membrane and food. The C=C groups alter the lipid packing density, membrane stability, and persistence against lipid oxidation. Yet, molecular-level insights into the structure of the unsaturated lipids remain scarce. Here, we probe the molecular structure and organization of monolayers of unsaturated lipids on the water surface using heterodyne-detected sum-frequency generation (HD-SFG) spectroscopy. We vary the location of the C=C in the alkyl chain and find that at high lipid density, the location of the C=C group affects neither the interfacial water organization nor the tail of the alkyl chain. Based on this observation, we use the C=C stretch HD-SFG response to depth-profile the alkyl chain conformation of the unsaturated lipid. We find that the first 1/3 of carbon atoms from the headgroup are relatively rigid, oriented perpendicular to the surface. In contrast, the remaining carbon atoms can be approximated as free rotators, introducing the disordering of the alkyl chains.

Spontaneous Appearance of Triiodide Covering the Topmost Layer of the Iodide Solution Interface Without Photo-Oxidation.

Ions containing iodine atoms at the vapor-aqueous solution interfaces critically affect aerosol growth and atmospheric chemistry due to their complex chemical nature and multivalency. While the surface propensity of iodide ions has been intensely discussed in the context of the Hofmeister series, the stability of various ions containing iodine atoms at the vapor-water interface has been debated. Here, we combine surface-specific sum-frequency generation (SFG) vibrational spectroscopy with ab initio molecular dynamics simulations to examine the extent to which iodide ions cover the aqueous surface. The SFG probe of the free O-D stretch mode of heavy water indicates that the free O-D group density decreases drastically at the interface when the bulk NaI concentration exceeds ∼2 M. The decrease in the free O-D group density is attributed to the spontaneous appearance of triiodide that covers the topmost interface rather than to the surface adsorption of iodide. This finding demonstrates that iodide is not surface-active, yet the highly surface-active triiodide is generated spontaneously at the water-air interface, even under dark and oxygen-free conditions. Our study provides an important first step toward clarifying iodine chemistry and pathways for aerosol formation.

Applying a multi-faceted infection control strategy to improve hospital environmental cleaning quality.

Background: Along with existing infection control policies, repeated education and training of environmental service workers (ESWs) improves their compliance and ultimately reduces hospital-associated infection (HAI) rates. However, only limited studies have explored the health behavioral determinants of ESWs regarding their cleaning performance after implementing an educational intervention with multi-faceted infection control strategy. Objective: To determine whether an educational intervention with multi-faceted infection control strategy improves the health behavioral determinants associated with ESWs' cleaning performance. Methods: Twenty-eight ESWs who received an educational intervention with multi-faceted hospital infection control strategy were included. ESWs' knowledge, perceived benefits and barriers, self-efficacy, health literacy, and cleaning performance were evaluated at pre-intervention, post-intervention, and 3-month follow-up. Results: HAI-related adenosine triphosphate (ATP) levels decreased significantly at post-intervention and 3-month follow-up compared with pre-intervention levels (all p < 0.05). All post-intervention ATP levels met the standard criterion after the 2nd environmental cleaning, with a median score of 267 (range, 71-386). High baseline ATP levels (odds ratio [OR] = 4.195, 95%CI 2.500-7.042, p < 0.05) were positively associated with qualified post-intervention ATP levels, while high education (OR = 0.480, 95%CI 0.276-0.833, p < 0.05) and high baseline knowledge scores (OR = 0.481, 95%CI 0.257-0.903, p = 0.023) were negatively associated with qualified post-intervention ATP levels. Conclusion: Educational intervention using a multi-faceted infection control strategy improves health behavioral determinants (baseline education, knowledge scores and ATP levels) associated with ESWs' hospital cleaning performance. Receiving an educational intervention may increase HAI knowledge of environmental cleaning among ESWs with high education or low baseline HAI knowledge.

ProFun: A web server for functional enrichment analysis of parasitic protozoan genes.

BACKGROUND: The initial step to interpreting putative biological functions from comparative multi-omics studies usually starts from a differential expressed gene list followed by functional enrichment analysis (FEA). However, most FEA packages are designed exclusively for humans and model organisms. Although parasitic protozoan is the most important pathogen in the tropics, no FEA package is available for protozoan functional (ProFun) enrichment analysis. To speed up comparative multi-omics research on parasitic protozoans, we constructed ProFun, a web-based, user-friendly platform for the research community. METHODS: ProFun utilizes the Docker container, ShinyProxy, and R Shiny to construct a scalable web service with load-balancing infrastructure. We have integrated a series of visual analytic functions, in-house scripts, and custom-made annotation packages to create three analytical modules for 40 protozoan species: (1) Gene Overlaps; (2) Over-representation Analysis (ORA); (3) Gene Set Enrichment Analysis (GSEA). RESULTS: We have established ProFun, a web server for functional enrichment analysis of differentially expressed genes. FEA becomes as simple as pasting a list of gene IDs into the textbox of our website. Users can customize enrichment parameters and results with just one click. The intuitive web interface and publication-ready charts enable users to reveal meaningful biological events and pinpoint potential targets for further studies. CONCLUSION: ProFun is the first web application that enables gene functional enrichment analysis of parasitic protozoans. In addition to supporting FEA analysis, ProFun also allows the comparison of FEA results across complicated experimental designs. ProFun is freely available at http://dalek.cgu.edu.tw:8080/app/profun.

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer. METHODS: This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance. RESULTS: The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively. CONCLUSION: Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance.

Surface stratification determines the interfacial water structure of simple electrolyte solutions.

The distribution of ions at the air/water interface plays a decisive role in many natural processes. Several studies have reported that larger ions tend to be surface-active, implying ions are located on top of the water surface, thereby inducing electric fields that determine the interfacial water structure. Here we challenge this view by combining surface-specific heterodyne-detected vibrational sum-frequency generation with neural network-assisted ab initio molecular dynamics simulations. Our results show that ions in typical electrolyte solutions are, in fact, located in a subsurface region, leading to a stratification of such interfaces into two distinctive water layers. The outermost surface is ion-depleted, and the subsurface layer is ion-enriched. This surface stratification is a key element in explaining the ion-induced water reorganization at the outermost air/water interface.

Effects of community building environment and sports with fitness APP usage on physical exercise habits in teachers in the Yangtze River Delta Region / 中国学校卫生

Objective@#To explore the effects of community building environment and sports with fitness APP usage and their interactions on teachers exercise habits in the Yangtze River Delta Region, so as to provide a scientific basis for the development of a sports and health promotion intervention program for teachers.@*Methods@#A total of 2 530 in service teachers from four provinces and cities in the Yangtze River Delta region, namely, Shanghai, Zhejiang, Jiangsu and Anhui Province, were sampled in May-June 2023 by using convenient cluster random sampling method. Self designed questionnaire was used to collect the basic information of the surveyed teachers, Physical Activity Building Environment Evaluation Questionnaire and the Sports with Fitness APP Usage Questionnaire were used to measure the teachers subjective perception of the community building environment and the usage of sports with fitness APP, respectively. Physical Exercise Habituation Scale was used to assess the level of exercise habits. Logistic regression models were applied to analyze the effects of community building environment and sports with fitness APP usage on physical exercise, and the interaction effects were analyzed by using additive and multiplicative models.@*Results@#Among all the teachers surveyed, 658 of them reported good physical exercise habits (26.0%), and differences in the rate of physical activity habit formation by gender, age, years of teaching, as well as subject of teaching were statistically significant ( χ 2=42.94, 39.73, 35.47, 218.23 , P <0.05). Teachers with physical exercise habits had significantly higher community building environment scores and sports and fitness APP use than teachers without exercise habits ( t =12.17,16.54, P <0.05). Adjusting for the confounders of age, gender, years of teaching experience, and subjects taught, multifactorial unconditional Logistic regression analysis showed that the probability of teachers having good physical exercise habits increased by 22% for every 1-point increase in the community building environment score on average ( OR =1.22, 95% CI =1.11-1.40), and the probability of teachers having good physical exercise habits increased by 16% for every 1-point increase in the sports with fitness APP score on average ( OR = 1.16 , 95% CI =1.03-1.31) ( P <0.05). Interaction analyses showed that there was an additive interaction between the effects of community building environment and sports and fitness APP use on teachers physical exercise habits after adjustment, and the 95% CI for RERI , API and SI were 1.17 -1.65, 0.12-0.46 and 1.78-3.33 ( P <0.05), respectively, and there was no multiplicative interaction ( P >0.05).@*Conclusions@#The community building environment and the usage of sports & fitness APP show impacts in the formation of teachers physical exercise habits in the Yangtze River Delta region, and there is an interaction effect. Enhancing the construction of smart sports centers around the community can provide a high quality external environment for the physical exercise habits formation.

Intestinal microbiota analysis of different Blastocystis subtypes and Blastocystis-negative individuals in Taiwan.

BACKGROUND: Intestinal parasitic infections are the most common infectious diseases among Southeast Asian migrant workers in Taiwan, especially for infections with Blastocystis hominis. However, little is known about the impact of Blastocystis subtypes (STs) on the gut microbiota. MATERIAL AND METHODS: We retrospectively evaluated the prevalence of intestinal parasites in a teaching hospital in Northern Taiwan in the period of 2015 to 2019. Blastocystis-positive stool specimens were collected for ST analysis by polymerase chain reaction in 2020. Intestinal microbiota analyses of different Blastocystis STs and Blastocystis-free individuals were conducted by 16S rRNA sequencing. RESULTS: A total of 13,859 subjects were analyzed, of which 1,802 cases (13%) were diagnosed with intestinal parasitic infections. B. hominis infections were the most prevalent (n = 1546, 85.7%). ST analysis of Blastocystis-positive samples (n=150) indicated that ST1 was the most common type, followed by ST3, ST4, ST2, ST7, and ST5. Different Blastocystis STs (ST1, ST3, and ST4) were associated with distinct richness and diversity of the microbiota. Taxonomic profiles revealed that Akkermansia muciniphila was significantly enriched for all analyzed Blastocystis STs, whereas Holdemanella biformis was more abundant in the Blastocystis-free group. Additionally, Succinivibrio dextrinosolvens and Coprococcus eutactus were specifically more abundant in ST3 carriers than in non-infected individuals. CONCLUSIONS: This study demonstrates that A. muciniphila is positively associated with all Blastocystis STs, while H. biformis was negatively associated with them. Several bacteria were enriched in specific STs, highlighting the need for further microbiota analysis at the ST level to elucidate the pathogenicity of Blastocystis.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML). METHODS: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively. CONCLUSIONS: The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy. ADVANCES IN KNOWLEDGE: The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

A risk stratification model modified from the U.S. guideline could be applied in an Asian population with or without ASCVD.

AIM: This study aimed to evaluate the performance of a modified US (MUS) model for risk prediction of cardiovascular (CV) events in Asian patients and compare it to European and Japanese models. MATERIAL AND METHODS: The MUS model, based on the US ACC/AHA 2018 lipid treatment guideline, was employed to stratify patients under primary or secondary prevention. Two multi-center prospective observational registry cohorts, T-SPARCLE and T-PPARCLE, were used to validate the scoring system, and the primary outcome was the time to first occurrence/recurrence of major adverse cardiac events (MACEs). The MUS model's performance was compared to other models from Europe and Japan. RESULTS: A total of 10,733 patients with the mean age of 64.2 (SD: 11.9) and 36.5% female were followed up for a median of 5.4 years. The MUS model was validated, with an AUC score of 0.73 (95% CI 0.68-0.78). The European and Japanese models had AUC scores ranging from 0.6 to 0.7. The MUS model categorized patients into four distinct CV risk groups, with hazard ratios (HRs) as follows: very high-vs. high-risk group (HR=1.91, 95% CI 1.53-2.39), high-vs. moderate-risk group (HR=2.08, 95% CI 1.60-2.69), and moderate-vs. low-risk group (HR=3.14, 95% CI 1.63-6.03). After adjusting for the MUS model, a history of ASCVD was not a significant predictor of adverse cardiovascular outcomes within each risk group. CONCLUSION: The MUS model is an effective tool for risk stratification in Asian patients with and without ASCVD, accurately predicting MACEs and performing comparably or better than other established risk models. Our findings suggest that patient management should focus on background risk factors instead of solely on primary or secondary prevention.

Does the Sum-Frequency Generation Signal of Aromatic C-H Vibrations Reflect Molecular Orientation?

Organic molecules with aromatic groups at the aqueous interfaces play a central role in atmospheric chemistry, green chemistry, and on-water synthesis. Insights into the organization of interfacial organic molecules can be obtained using surface-specific vibrational sum-frequency generation (SFG) spectroscopy. However, the origin of the aromatic C-H stretching mode peak is unknown, prohibiting us from connecting the SFG signal to the interfacial molecular structure. Here, we explore the origin of the aromatic C-H stretching response by heterodyne-detected SFG (HD-SFG) at the liquid/vapor interface of benzene derivatives and find that, irrespective of the molecular orientation, the sign of the aromatic C-H stretching signals is negative for all the studied solvents. Together with density functional theory (DFT) calculations, we reveal that the interfacial quadrupole contribution dominates, even for the symmetry-broken benzene derivatives, although the dipole contribution is non-negligible. We propose a simple evaluation of the molecular orientation based on the aromatic C-H peak area.

Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.

Background: Successful weaning from mechanical ventilation is important for patients admitted to intensive care units. However, models for predicting real-time weaning outcomes remain inadequate. Therefore, this study aimed to develop a machine-learning model for predicting successful extubation only using time-series ventilator-derived parameters with good accuracy. Methods: Patients with mechanical ventilation admitted to the Yuanlin Christian Hospital in Taiwan between August 2015 and November 2020 were retrospectively included. A dataset with ventilator-derived parameters was obtained before extubation. Recursive feature elimination was applied to select the most important features. Machine-learning models of logistic regression, random forest (RF), and support vector machine were adopted to predict extubation outcomes. In addition, the synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem. The area under the receiver operating characteristic (AUC), F1 score, and accuracy, along with the 10-fold cross-validation, were used to evaluate prediction performance. Results: In this study, 233 patients were included, of whom 28 (12.0%) failed extubation. The six ventilatory variables per 180 s dataset had optimal feature importance. RF exhibited better performance than the others, with an AUC value of 0.976 (95% confidence interval [CI], 0.975-0.976), accuracy of 94.0% (95% CI, 93.8-94.3%), and an F1 score of 95.8% (95% CI, 95.7-96.0%). The difference in performance between the RF and the original and SMOTE datasets was small. Conclusion: The RF model demonstrated a good performance in predicting successful extubation in mechanically ventilated patients. This algorithm made a precise real-time extubation outcome prediction for patients at different time points.

The impact of the duration of the integrated disease management program on COPD-related outcomes.

BACKGROUND: The aim of this study is to assess the impact of the duration of the integrated disease management (IDM) program on COPD-related outcomes in real-world setting. METHODS: A retrospective cohort study among 3771 patients with COPD who had regularly completed 4 visits of IDM program within 1 year between April 1, 2017 and December 31, 2018. CAT score as the primary outcome used to investigate the association between IDM intervention duration and improvement in CAT score. Change in CAT score from baseline to each follow-up visit determined by using least-squares means (LSMeans) approach. The cut-off value of IDM duration for improving the CAT score was determined by the Youden index. Logistic regression was used to analyze the relationship between IDM intervention duration and MCID (the minimal clinically important difference) improvement in CAT score and the factor associated CAT improvement. Risks of COPD exacerbation events (COPD-related ED visit and COPD-related hospitalization) were estimated by using the cumulative incidence curve and Cox proportional hazards models. RESULT: Among 3771 enrolled COPD patients, the majority of the study cohort were males (91.51%) and 42.7% of patients had CAT score of ≥ 10 at baseline. The mean of age was 71.47 years and the mean CAT at baseline were 10.49. The mean change from baseline in CAT score was - 0.87, - 1.19, - 1.23 and - 1.40 at 3-, 6-, 9- and 12 month follow-up (p < 0.0001 for all visits), respectively. Statistically significantly lower likelihood of achieving MCID improvement in CAT were observed at 3- and 6 month compared to 9 month (at 3 month: OR: 0.720, 95% CI 0.655-0.791; at 6 month: OR: 0.905, 95% CI 0.825-0.922). And only a modest increase likelihood of achieving MCID improvement in CAT at 12 month (OR: 1.097, 95% CI 1.001-1.201) compared with 9-month follow-up. In logistic regression on the entire cohort, CAT MCID improvement was most associated with baseline CAT scores ≥ 10, followed by frequent exacerbation in previous year (> 2 episodes/year), wheezing, and GOLD B or D at baseline. In baseline CAT ≥ 10 group, patients were more likely to achieve CAT MCID improvement and had greater decreases from baseline in CAT score observed at 3-, 6-, 9-, and 12 month compared with baseline CAT score < 10 group (all p < 0.0001). Moreover, in CAT ≥ 10 groups, patients who achieved CAT MCID improvement had lower risk of subsequent COPD exacerbation events (COPD-related ED visit: aHR: 1.196, 95% CI 0.985-1.453, p = 0.0713; COPD-related hospitalization: aHR: 1.529, 95% CI 1.215-1.924, p = 0.0003) when compared to those without. CONCLUSION: This is the first real-world study indicating the association between COPD IDM intervention duration and COPD-related outcomes. From 3 to 12 month follow-up results showed that continued improvement over time in COPD-specific health status, particularly in patients with baseline CAT score of ≥ 10. Furthermore, a reduction of the risk of subsequent COPD exacerbations were observed in patients with CAT MCID improvement.

Surface Potential at Electrolyte/Air Interfaces: A Quantitative Analysis via Sum-Frequency Vibrational Spectroscopy.

We conduct a quantitative phase-sensitive sum-frequency vibrational spectroscopic investigation on the air/water interface with various atmospherically relevant ions in water in submolar concentrations. At electrolyte concentrations below 0.1 M, the spectral changes of the OH-stretching resonance induced by ions exhibit no ion specificity and resemble the lineshape of the third-order nonlinear optical susceptibility of bulk water. These findings, along with the result of invariant free OH resonance, indicate that the primary effect of the electric double layer of ions on the interfacial structure arises from the mean-field-induced molecular alignment in a subsurface bulklike hydrogen-bonding network. Analysis of the spectra allows us to determine quantitatively the surface potentials for six electrolyte solutions (MgCl2, CaCl2, NH4Cl, Na2SO4, NaNO3, and NaSCN). Our results agree well with the predictions of Levin's continuum theory, implying fairly small electrostatic correlations for the studied divalent ions.