Effects of a customized diet education program using a mobile instant messenger for people undergoing peritoneal dialysis: A feasibility test.

PURPOSE: The study was conducted to develop a customized diet education program using mobile instant messenger for people undergoing peritoneal dialysis (PD). Our goal was to examine the program's effects on diet-related self-efficacy, dietary self-care compliance, and physiological indices (hemoglobin, albumin, potassium, and phosphorus). METHODS: This was a quasi-experimental study with a non-equivalent control group pre-post-test design. We applied the Cox interaction model of client health behaviors. Overall, 43 patients (21 in the experimental group and 22 in the control group) attending the renal clinic and undergoing PD at three hospitals were included. The experimental group underwent a customized diet education program using a mobile instant messenger for five weeks. Patients in the control group received routine care. This study was conducted in the following order: pre-test, treatment, post-test 1 (immediately after), and post-test 2 (four weeks after). RESULTS: This study showed significant differences in dietary self-care compliance (F = 15.29, p < .001) and hemoglobin level (F = 7.55, p = .001) in interactions between times and groups. CONCLUSIONS: The diet education program is an effective strategies to preventing complications and helping PD patients perform dietary self-care compliance through systematic and continuous interactions with educational nurse who is an expert in PD management of people undergoing PD.

Swift and precise detection of unlabeled pathogens using a nanogap electrode impedimetric sensor facilitated by electrokinetics.

For the protection of human health and environment, there is a growing demand for high-performance, user-friendly biosensors for the prompt detection of pathogenic bacteria in samples containing various substances. We present a nanogap electrode-based purely electrical impedimetric sensor that utilizes the dielectrophoresis (DEP) mechanism. Our nanogap sensor can directly and sensitively detect pathogens present at concentrations as low as 1-10 cells/assay in buffers and drinking milk without the need for separation, purification, or specific ligand binding. This is achieved by minimizing the electrical double-layer effect and electrode polarization in nanogap impedance sensors, reducing signal loss. In addition, even at low DEP voltages, nanogap sensors can quickly establish strong DEP forces between the nanogap electrodes to control the spatial concentration of pathogens around the electrodes. This activates and stabilizes inter-electrode signal transmission along the nanogap-aligned pathogens, increasing sensitivity and reducing errors during repeated measurements. The DEP-enabled nanogap impedance sensor developed in this study is valuable for a variety of pathogen detection and monitoring systems including point-of-care testing (POCT) as it can detect pathogens in diverse samples containing multiple substances quickly and with high sensitivity, is compatible with complex solutions such as food and beverages, and provides highly reproducible results without the need for separate binding and separation processes.

2023 Korean Society of Echocardiography position paper for diagnosis and management of valvular heart disease, part I: aortic valve disease.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases. This position paper focuses on the clinical management of valvular heart diseases with reference to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee tried to reflect the recently published results on the topic of valvular heart diseases and Korean data by a systematic literature search based on validity and relevance. In part I of this article, we will review and discuss the current position of aortic valve disease in Korea.

Can Older Adult Patients with Hip Fractures Have Their Discharge Destination Predicted by Physical Performance Measures?

Background and Objectives: The majority of patients who undergo hip fracture surgery do not recover their former level of physical function; hence, it is essential to establish a specific rehabilitation strategy for maximal functional recovery of patients after a hip fracture. Knowing which indicators of physical function in hip fracture patients have a significant impact on the decision regarding the place or timing of discharge would make it possible to plan and prepare for discharge as soon as possible. Therefore, this study aimed to investigate the relationship between physical function and discharge destination for older adult patients with hip fracture. Materials and Methods: In this retrospective cohort study, 150 hip fracture patients (mean age 78.9 ± 10.6 years) between January 2019 and June 2021 were enrolled. Patients were categorized into two groups according to their discharge destination, either home or facility. Demographic and disease-related characteristic data were collected from the medical records. All the patients completed performance-based physical function tests including the 10 Meter Walk Test (10MWT), Timed Up and Go test (TUG), Koval's grade, and Berg Balance Scale (BBS) at the start of rehabilitation and at discharge. A backward stepwise binary logistic regression analysis was then performed to determine the independent factors of the discharge destination. Results: The home discharge group had a significantly lower Koval's grade, lower TUG, higher BBS both at baseline and discharge, and younger age. Backward stepwise logistic binary regression analysis showed that TUG, BBS, and 10MWT at baseline and discharge were significant variables affecting the discharge destination after hip fracture. Conclusions: These results demonstrate that balance and gait in older adult patients with hip fractures are highly influential factors in the determining the discharge destination.

2023 Korean Society of Echocardiography position paper for the diagnosis and management of valvular heart disease, part II: mitral and tricuspid valve disease.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases. This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to present recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study.

Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm's ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910-0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Precision Diabetes Education and Support Considering Patients' Behavioral and Psychological Phenotype: A Q-Methodology Study.

BACKGROUND: The prevalence of type 2 diabetes is growing, and diabetes burden is increasing. Precision health in diabetes education and support employs different intervention strategies, depending on an individual's viewpoint on diabetes and self-management behaviors, to improve patients' treatment adherence, clinical outcomes, and quality of life. OBJECTIVE: To classify the behavioral and psychological phenotypes of self-management behaviors in adults taking oral glucose-lowering medications to develop a theory-driven, person-centered group intervention applicable to busy clinical settings. METHODS: Q-methodology was used. From January to August 2020, 73 participants (48 male, 25 female) were invited to do Q-sorting with 33 statements. The principal component technique, followed by varimax rotation, was used for factor analysis. The Summary of Diabetes Self-Care Activity questionnaire and HbA1c in the past 6 months were included to obtain comprehensive understanding. RESULTS: Fifty-one sorts (35 male, 16 female) loaded on 1 of 4 factors: factor A (n = 18): Needing emotional support with enhancing problem-solving skills group; factor B (n = 15): Self-help group; factor C (n = 6): Needing personalized coaching group; and factor D (n = 12): Needing basic diabetes education group. CONCLUSIONS: Each factor demonstrated a different need for diabetes education and support. Younger participants (factor D) had the poorest diabetes self-management behaviors and required basic diabetes education. Further research is warranted to develop a screening tool to classify the typologies and adopt the findings in a busy clinical setting.

Social Determinants of Health Among American Indians and Alaska Natives and Tribal Communities: Comparison with Other Major Racial and Ethnic Groups in the United States, 1990-2022.

Background and Objective: Limited research exists on health inequities between American Indians and Alaska Natives (AIANs), tribal communities, and other population groups in the United States. To address this gap in research, we conducted time-trend analyses of social determinants of health and disease outcomes for AIANs as a whole and specific tribal communities and compared them with those from the other major racial/ethnic groups. Methods: We used data from the 1990-2022 National Vital Statistics System, 2015-2022 American Community Survey, and the 2018-2020 Behavioral Risk Factor Surveillance System to examine socioeconomic, health, disability, disease, and mortality patterns for AIANs. Results: In 2021, life expectancy at birth was 70.6 years for AIANs, lower than that for Asian/Pacific Islanders (APIs) (84.1), Hispanics (78.8), and non-Hispanic Whites (76.3). All racial/ethnic groups experienced a decline in life expectancy between the pre-pandemic year of 2019 and the peak pandemic year of 2021. However, the impact of COVID-19 was the greatest for AIANs and Blacks whose life expectancy decreased by 6.3 and 5.8 years, respectively. The infant mortality rate for AIANs was 8.5 per 1,000 live births, 78% higher than the rate for non-Hispanic Whites. One in five AIANs assessed their physical and mental health as poor, at twice the rate of non-Hispanic Whites or the general population. COVID-19 was the leading cause of death among AIANs in 2021. Risks of mortality from alcohol-related problems, drug overdose, unintentional injuries, and homicide were higher among AIANs than the general population. AIANs had the highest overall disability, mental and ambulatory disability, health uninsurance, unemployment, and poverty rates, with differences in these indicators varying markedly across the AIAN tribes. Conclusion and Global Health Implications: AIANs remain a disadvantaged racial/ethnic group in the US in many health and socioeconomic indicators, with poverty rates in many Native American tribal groups and reservations exceeding 40%.

The perception and production of Korean stops in second dialect acquisition.

This study investigated the acoustic cue weighting of the Korean stop contrast in the perception and production of speakers who moved from a nonstandard dialect region to the standard dialect region, Seoul. Through comparing these mobile speakers with data from nonmobile speakers in Seoul and their home region, it was found that the speakers shifted their cue weighting in perception and production to some degree, but also retained some subphonemic features of their home dialect in production. The implications of these results for the role of dialect prestige and awareness in second dialect acquisition are discussed.

Mitral Annular Tissue Velocity Predicts Survival in Patients With Primary Mitral Regurgitation.

BACKGROUND AND OBJECTIVES: Early diastolic mitral annular tissue (e') velocity is a commonly used marker of left ventricular (LV) diastolic function. This study aimed to investigate the prognostic implications of e' velocity in patients with mitral regurgitation (MR). METHODS: This retrospective cohort study included 1,536 consecutive patients aged <65 years with moderate or severe chronic primary MR diagnosed between 2009 and 2018. The primary and secondary outcomes were all-cause and cardiovascular mortality, respectively. According to the current guidelines, the cut-off value of e' velocity was defined as 7 cm/s. RESULTS: A total of 404 individuals were enrolled (median age, 51.0 years; 64.1% male; 47.8% severe MR). During a median 6.0-year follow-up, there were 40 all-cause mortality and 16 cardiovascular deaths. Multivariate analysis revealed a significant association between e' velocity and all-cause death (adjusted hazard ratio [aHR], 0.770; 95% confidence interval [CI], 0.634-0.935; p=0.008) and cardiovascular death (aHR, 0.690; 95% CI, 0.477-0.998; p=0.049). Abnormal e' velocity (≤7 cm/s) independently predicted all-cause death (aHR, 2.467; 95% CI, 1.170-5.200; p=0.018) and cardiovascular death (aHR, 5.021; 95% CI, 1.189-21.211; p=0.028), regardless of symptoms, LV dimension and ejection fraction. Subgroup analysis according to sex, MR severity, mitral valve replacement/repair, and symptoms, showed no significant interactions. Including e' velocity in the 10-year risk score improved reclassification for mortality (net reclassification improvement [NRI], 0.154; 95% CI, 0.308-0.910; p<0.001) and cardiovascular death (NRI, 1.018; 95% CI, 0.680-1.356; p<0.001). CONCLUSIONS: In patients aged <65 years with primary MR, e' velocity served as an independent predictor of all-cause and cardiovascular deaths.

Exercise-induced signaling activation by Chrysanthemum zawadskii and its active compound, linarin, ameliorates age-related sarcopenia through Sestrin 1 regulation.

BACKGROUND: Exercise is an effective strategy to prevent sarcopenia, but high physical inactivity in the elderly requires alternative therapeutic approaches. Exercise mimetics are therapeutic compounds that simulate the beneficial effects of exercise on skeletal muscles. However, the toxicity and adverse effects of exercise mimetics raise serious concerns. PURPOSE: We aimed to search novel plant-based alternatives to activate exercise induced-signaling. METHODS: We used open databases and luciferase assays to identify plant-derived alternatives to activate exercise-induced signaling and compared its efficacy to mild intensity continuous training (MICT) in aged C57BL/6 mice. The nineteen-month-old mice were either fed an experimental diet supplemented with the isolated alternative or subjected to MICT for up to 21 mo of age. RESULTS: Our analysis revealed that Chrysanthemum zawadskii Herbich var latillobum (Maxim.) Kitamura (CZH), a medicinal plant rich in linarin, is a novel activator of peroxisome proliferator-activated receptor δ (PPARδ) and estrogen-related receptor γ (ERRγ), key regulators of exercise-induced positive effects on muscles. CZH supplementation ameliorated the loss of muscle function and mass, and increased PPARδ and ERRγ expression in mouse muscles. CZH also improved mitochondrial functions and proteostasis in aged mice, similar to MICT. Furthermore, CZH and linarin induced the activation of Sestrin 1, a key mediator of exercise benefits, in muscle. Silencing Sestrin 1 negated the increase in myogenesis and mitochondrial respiration by CZH and linarin in primary myoblasts from old mice. CONCLUSION: Our findings suggest the potential of CZH as a novel plant-derived alternative to activate exercise-induced signaling for preventing sarcopenia in sedentary older adults. This could offer a safer therapeutic option for sarcopenia treatment.

Assessing the reliability of point mutation as data augmentation for deep learning with genomic data.

BACKGROUND: Deep neural networks (DNNs) have the potential to revolutionize our understanding and treatment of genetic diseases. An inherent limitation of deep neural networks, however, is their high demand for data during training. To overcome this challenge, other fields, such as computer vision, use various data augmentation techniques to artificially increase the available training data for DNNs. Unfortunately, most data augmentation techniques used in other domains do not transfer well to genomic data. RESULTS: Most genomic data possesses peculiar properties and data augmentations may significantly alter the intrinsic properties of the data. In this work, we propose a novel data augmentation technique for genomic data inspired by biology: point mutations. By employing point mutations as substitutes for codons, we demonstrate that our newly proposed data augmentation technique enhances the performance of DNNs across various genomic tasks that involve coding regions, such as translation initiation and splice site detection. CONCLUSION: Silent and missense mutations are found to positively influence effectiveness, while nonsense mutations and random mutations in non-coding regions generally lead to degradation. Overall, point mutation-based augmentations in genomic datasets present valuable opportunities for improving the accuracy and reliability of predictive models for DNA sequences.

Mildly Reduced Renal Function Is Associated With Increased Heart Failure Admissions in Patients With Hypertrophic Cardiomyopathy.

BACKGROUND: The association between renal dysfunction and cardiovascular outcomes has yet to be determined in patients with hypertrophic cardiomyopathy (HCM). We aimed to investigate whether mildly reduced renal function is associated with the prognosis in patients with HCM. METHODS: Patients with HCM were enrolled at two tertiary HCM centers. Patients who were on dialysis, or had a previous history of heart failure (HF) or stroke were excluded. Patients were categorized into 3 groups by estimated glomerular filtration rate (eGFR): stage I (eGFR ≥ 90 mL/min/1.73 m², n = 538), stage II (eGFR 60-89 mL/min/1.73 m², n = 953), and stage III-V (eGFR < 60 mL/min/1.73 m², n = 265). Major adverse cardiovascular events (MACEs) were defined as a composite of cardiovascular death, hospitalization for HF (HHF), or stroke during median 4.0-year follow-up. Multivariable Cox regression model was used to adjust for covariates. RESULTS: Among 1,756 HCM patients (mean 61.0 ± 13.4 years; 68.1% men), patients with stage III-V renal function had a significantly higher risk of MACEs (adjusted hazard ratio [aHR], 2.71; 95% confidence interval [CI], 1.39-5.27; P = 0.003), which was largely driven by increased incidence of cardiovascular death and HHF compared to those with stage I renal function. Even in patients with stage II renal function, the risk of MACE (vs. stage I: aHR, 2.21' 95% CI, 1.23-3.96; P = 0.008) and HHF (vs. stage I: aHR, 2.62; 95% CI, 1.23-5.58; P = 0.012) was significantly increased. CONCLUSION: This real-world observation showed that even mildly reduced renal function (i.e., eGFR 60-89 mL/min/1.73 m²) in patients with HCM was associated with an increased risk of MACEs, especially for HHF.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

High-throughput screening of small-molecules libraries identified antibacterials against clinically relevant multidrug-resistant A. baumannii and K. pneumoniae.

BACKGROUND: The current pipeline for new antibiotics fails to fully address the significant threat posed by drug-resistant Gram-negative bacteria that have been identified by the World Health Organization (WHO) as a global health priority. New antibacterials acting through novel mechanisms of action are urgently needed. We aimed to identify new chemical entities (NCEs) with activity against Klebsiella pneumoniae and Acinetobacter baumannii that could be developed into a new treatment for drug-resistant infections. METHODS: We developed a high-throughput phenotypic screen and selection cascade for generation of hit compounds active against multidrug-resistant (MDR) strains of K. pneumoniae and A. baumannii. We screened compound libraries selected from the proprietary collections of three pharmaceutical companies that had exited antibacterial drug discovery but continued to accumulate new compounds to their collection. Compounds from two out of three libraries were selected using "eNTRy rules" criteria associated with increased likelihood of intracellular accumulation in Escherichia coli. FINDINGS: We identified 72 compounds with confirmed activity against K. pneumoniae and/or drug-resistant A. baumannii. Two new chemical series with activity against XDR A. baumannii were identified meeting our criteria of potency (EC50 ≤50 µM) and absence of cytotoxicity (HepG2 CC50 ≥100 µM and red blood cell lysis HC50 ≥100 µM). The activity of close analogues of the two chemical series was also determined against A. baumannii clinical isolates. INTERPRETATION: This work provides proof of principle for the screening strategy developed to identify NCEs with antibacterial activity against multidrug-resistant critical priority pathogens such as K. pneumoniae and A. baumannii. The screening and hit selection cascade established here provide an excellent foundation for further screening of new compound libraries to identify high quality starting points for new antibacterial lead generation projects. FUNDING: BMBF and GARDP.

Identification of Peucedanum japonicum Thunb. extract components and their protective effects against dexamethasone-induced muscle atrophy.

BACKGROUND: Peucedanum japonicum Thunb. (PJ) is a vegetable widely consumed in East Asia and is known to have anticancer and anti-inflammatory effects. However, the effect of PJ on muscle atrophy remains elusive. PURPOSE: This study aimed to investigate the effect of PJ and its active compound on dexamethasone (DEX)-induced muscle atrophy. METHODS: We performed qualitative and quantitative analysis of PJ using ultra-performance liquid chromatography-mass spectrometry tandem mass spectrometry (UPLC-MS/MS) and high-performance liquid chromatography (HPLC), respectively. The efficacy of PJ and its main compound 4-caffeoylquinic acid (CQA) on muscle atrophy was evaluated in DEX-induced myotube atrophy and DEX-induced muscle atrophy in mouse myoblasts (C2C12) and C57BL/6 mice, in vitro and in vivo, respectively. RESULTS: The UPLC-MS/MS and HPLC data showed that the concentration of 4-CQA in PJ was 18.845 mg/g. PJ and 4-CQA treatments significantly inhibited DEX-induced myotube atrophy by decreasing protein synthesis and glucocorticoid translocation to the nucleus in C2C12 myotubes. In addition, PJ enhanced myogenesis by upregulating myogenin and myogenic differentiation 1 in C2C12 cells. PJ supplementation effectively increased muscle function and mass, downregulated atrogenes, and decreased proteasome activity in C57BL/6 mice. Additionally, PJ effectively decreased the nuclear translocation of forkhead transcription factor 3 alpha by inhibiting glucocorticoid receptor. CONCLUSION: Overall, PJ and its active compound 4-CQA alleviated skeletal muscle atrophy by inhibiting protein degradation. Hence, our findings present PJ as a potential novel pharmaceutical candidate for the treatment of muscle atrophy.

Widening Disparities in COVID-19 Mortality and Life Expectancy Among 15 Major Racial and Ethnic Groups in the United States, 2020-2021.

Persistent and often widening racial/ethnic and socioeconomic inequalities in health have long existed in the US. Although racial/ethnic disparities in COVID-19 mortality are well documented, COVID-19 mortality risks and resultant reductions in life expectancy during the pandemic for detailed racial and ethnic groups in the US, including Asian and Hispanic subgroups, are not known. We used 2020-2021 US mortality data to estimate age-adjusted COVID-19 mortality rates, life expectancy, and the consequent declines in life expectancy due to COVID-19 overall and for the 15 largest racial/ethnic groups. We used standard life table methodology, cause-elimination life tables, and inequality indices to analyze trends in racial/ethnic disparities. The number of COVID-19 deaths increased from 350,827 in 2020 to 416,890 in 2021. COVID-19 death rates varied 7-fold among the racial/ethnic groups; Japanese and Chinese had the lowest mortality rates and Mexicans and American Indians/Alaska Natives (AIANs) had the highest rates. In 2021, life expectancy ranged from 70.3 years for Blacks and 70.6 years for AIANs to 85.2 years for Japanese and 87.7 years for Chinese. The life-expectancy gap was wide- 22.4 years in 2020 and 23.2 years in 2021. COVID-19 mortality had the greatest impact in reducing the life expectancy of Mexicans (3.53 years in 2020 and 3.78 years in 2021), Central/South Americans (4.86 years in 2020 and 3.50 years in 2021), and AIANs (2.51 years in 2020 and 2.38 years in 2021). Racial/ethnic inequalities in COVID-19 mortality, life expectancy, and resultant reductions in life expectancy during the pandemic widened between 2020 and 2021.

Factors contributing to differences in cervical cancer screening in rural and urban community health centers.

INTRODUCTION: Community health centers (CHCs) provide historically marginalized populations with primary care, including cancer screening. Previous studies have reported that women living in rural areas are less likely to be up to date with cervical cancer screening than women living in urban areas. However, little is known about rural-urban differences in cervical cancer screening in CHCs and the contributing factors, and whether such differences changed during the COVID-19 pandemic. METHODS: Using 8-year pooled Uniform Data System (2014-2021) data and Oaxaca-Blinder decomposition, the extent to which CHC- and catchment area-level characteristics explained rural-urban differences in up-to-date cervical cancer screening was estimated. RESULTS: Up-to-date cervical cancer screening was lower in rural CHCs than urban CHCs (38.2% vs 43.0% during 2014-2019), and this difference increased during the pandemic (43.5% vs 49.0%). The rural-urban difference in cervical cancer screening in 2014-2019 was mostly explained by differences in CHC-level proportions of patients with limited English proficiency (55.9%) or income below the poverty level (12.3%) and females aged 21 to 64 years (9.8%), and catchment area-level's unemployment (3.4%) and primary care physician density (3.2%). However, Medicaid (-48.5%) or no insurance (-19.6%) counterbalanced the differences between rural-urban CHCs. The contribution of these factors to rural-urban differences in cervical cancer screening generally increased in 2020-2021. CONCLUSIONS: Rural-urban differences in cervical cancer screening were mostly explained by multiple CHC-level and catchment area-level characteristics. The findings call for tailored interventions, such as providing resources and language services, to improve cancer screening utilization among uninsured, Medicaid, and patients with limited English proficiency in rural CHCs.

Pericardiocentesis versus window formation in malignant pericardial effusion: trends and outcomes.

OBJECTIVES: Malignant pericardial effusion (MPE) in patients with cancer is associated with poor prognosis. This study aimed to compare clinical outcomes in patients with cancer who underwent pericardiocentesis versus pericardial window formation. METHODS: In the present study, 765 consecutive patients with cancer (mean age 58.4 years, 395 men) who underwent pericardial drainage between 2003 and 2022 were retrospectively analysed. All-cause death and MPE recurrence were compared based on the drainage method (pericardiocentesis vs pericardial window formation) and time period (period 1: 2003-2012; period 2: 2013-2022). RESULTS: Pericardiocentesis was performed in 639 (83.5%) patients and pericardial window formation in 126 (16.5%). There was no difference in age, sex distribution, proportion of metastatic or relapsed cancer, and chemotherapy status between the pericardiocentesis and pericardial window formation groups. Difference was not found in all-cause death between the two groups (log-rank p=0.226) regardless of the period. The pericardial window formation group was associated with lower MPE recurrence than the pericardiocentesis group (6.3% vs 18.0%, log-rank p=0.001). This advantage of pericardial window formation was more significant in period 2 (18.1% vs 1.3%, log-rank p=0.005). In multivariate analysis, pericardial window formation was associated with lower MPE recurrence (HR: 0.31, 95% CI: 0.15 to 0.63, p=0.001); younger age, metastatic or relapsed cancer, and positive malignant cells in pericardial fluid were associated with increased recurrence. CONCLUSION: In patients undergoing pericardial drainage for MPE, pericardial window formation showed mortality outcomes comparable with pericardiocentesis and was associated with lower incidence of MPE recurrence.

Sunlight-Driven Self-Cleaning Ultrafine Particulate Matter Filter with Antibacterial Activity.

Air pollution by particulate matter (PM) and airborne pathogens causes severe health problems in the human body. Presently, popular disposable air filters yield huge waste and have a fatal impact on the environment. Postuse cleaning of air filters also leads to secondary air and water pollution. Here, we report a sunlight-driven self-cleaning PM filter by coupling a full-solar-spectrum-active photocatalyst comprising up-conversion nanoparticles (UCNPs) decorated with semiconductor iron(III) oxide (UCNP@α-Fe2O3) shells stabilized upon graphene functionalized borosilicate fibrous membrane (rGO-BF). While rGO-BF ensures high PM adsorption, UCNP@α-Fe2O3 (NP) enables self-photodegradation of adsorbed PM under abundant sunlight and subsequent membrane regeneration, while preventing secondary air or water pollution. Rational surface chemistry and optimal microstructure enable our filters to remove >99% of PM2.5 under deplorable air-quality conditions. Moreover, our filter shows excellent antibacterial activity toward E. coli and S. aureus, demonstrating its potential for practical utilization in face masks, air filtering devices, and protective medical wear. This work successfully suggests an intriguing design platform for self-sustainable zero-waste air filter membranes.