Comprehensive assessment of emergency departments in county-level public hospitals: a multicenter descriptive cross-sectional study in Henan province, China.

Background: Emergency Departments (EDs) play a crucial role in providing immediate medical care, particularly in densely populated countries like China. While previous research has predominantly focused on well-funded urban hospitals, this study offers a comprehensive evaluation of EDs in county-level public hospitals in Henan province, China, aiming to identify disparities and challenges. Methods: A descriptive cross-sectional survey was conducted in 382 public hospitals across Henan province, China, from July 1, 2023, to August 1, 2023. Data were collected using an electronic questionnaire covering hospital information, human resources, infrastructure, clinical capabilities, and operational capacities. The data collection period for this survey spanned from January 1 to December 31, 2022. Results: With a remarkable 94.0% response rate, our study reveals significant disparities in county-level public hospitals compared to their provincial or municipal counterparts in Henan Province, China. County-level hospitals, which constitute 266 of the total 342 surveyed facilities, exhibit notable differences, including fewer doctors (median: 11 vs. 23, p < 0.0001) and nurses (median: 18 vs. 37, p < 0.0001). Additionally, a higher proportion of junior doctors is observed in these hospitals, while senior medical staff are more prevalent in provincial or municipal hospitals (p < 0.001). County-level hospitals also face resource challenges, with fewer beds in the emergency room (median: 4 vs. 7, p = 0.0003) and limited proficiency in advanced clinical procedures such as POCT, fiberoptic bronchoscopy, CRRT, ECMO, ultrasound equipment operation, and intraosseous infusion, with significant differences noted in most of these capabilities (p < 0.05). Operational capabilities show distinctions as well, with county-level hospitals managing a lower patient volume (median: 14,516 vs. 34,703, p < 0.0001) and handling fewer pre-hospital CPR cases (median: 33 vs. 89, p < 0.0001). In-hospital CPR success rates are also lower in county-level hospitals (median ROSC: 25.0% vs. 42.8%, p = 0.0068). Conclusion: While provincial or municipal hospitals enjoy better resources, county-level hospitals, especially crucial in less urbanized regions, face substantial challenges. Addressing these disparities is imperative, necessitating targeted investments, improved infrastructure, enhanced clinical training, and the adoption of innovations like telemedicine to enhance the quality of emergency care.

Assessment of disaster preparedness and related impact factors among emergency nurses in tertiary hospitals: descriptive cross-sectional study from Henan Province of China.

Background: The aim of this study was to investigate the current state of disaster preparedness and to determine associated factors among emergency nurses from tertiary hospitals in Henan Province of China. Methods: This multicenter descriptive cross-sectional study was conducted with emergency nurses from 48 tertiary hospitals in Henan Province of China between September 7, 2022-September 27, 2022. Data were collected through a self-designeds online questionnaire using the mainland China version of the Disaster Preparedness Evaluation Tool (DPET-MC). Descriptive analysis and multiple linear regression analysis were used to evaluate disaster preparedness and to determine factors affecting disaster preparedness, respectively. Results: A total of 265 emergency nurses in this study displayed a moderate level of disaster preparedness with a mean item score of 4.24 out 6.0 on the DPET-MC questionnaire. Among the five dimensions of the DPET-MC, the mean item score for pre-disaster awareness was highest (5.17 ± 0.77), while that for disaster management (3.68 ± 1.36) was the lowest. Female gender (B = -9.638, p = 0.046) and married status (B = -8.618, p = 0.038) were negatively correlated with the levels of disaster preparedness. Five factors positively correlated with the levels of disaster preparedness included having attended in the theoretical knowledge training of disaster nursing since work (B = 8.937, p = 0.043), having experienced the disaster response (B = 8.280, p = 0.036), having participated in the disaster rescue simulation exercise (B = 8.929, p = 0.039), having participated in the disaster relief training (B = 11.515, p = 0.025), as well as having participated in the training of disaster nursing specialist nurse (B = 16.101, p = 0.002). The explanatory power of these factors was 26.5%. Conclusion: Emergency nurses in Henan Province of China need more education in all areas of disaster preparedness, especially disaster management, which needs to be incorporated into nursing education, including formal and ongoing education. Besides, blended learning approach with simulation-based training and disaster nursing specialist nurse training should be considered as novel ways to improve disaster preparedness for emergency nurses in mainland China.

Mental health service needs and mental health of old adults living alone in urban and rural areas in China: The role of coping styles.

This study aimed to explore the level and relationship between the mental health service needs (MHSN), coping styles (CS), and mental health (MH) of old adults living alone in urban and rural, and to explore the moderating effects of coping styles. A cross-sectional study was conducted with 717 older adults living alone. The survey measured MHSN, CS, and MH. Data were analyzed using a structural equation and bootstrapping method. There were significant differences in the MHSN, CS, and MH between urban and rural old adults living alone (P < 0.05). The model of the impact of MHSN on MH in urban and rural old adults living alone showed a good fit. These findings highlighted the importance of high MHSN and positive CS as potential protective factors of mental health in old adults living alone. This provides a theoretical basis for psychological nursing for old adults living alone in the community.

Assessment of the mechanism of drug resistance in Trichophyton mentagrophytes in response to various substances.

BACKGROUND: Trichophyton mentagrophyte (TM), a zoonotic pathogen, has been endangering public health due to emerging drug resistance. Although increased attention is paid to this issue, there is very limited research available on drug resistance in TM. In this study, we studied the gene and proteomic changes, morphological changes, cellular fat localization, fat content changes, and biofilm of TM treated with different substances. RESULTS: The TM growth curve showed a positive correlation with the concentration of Fenarimol (FE), genistein (GE), clotrimazole (KM), and Miconazole nitrate salt (MK). The morphology of TM cells changed in different degrees after treatment with different substances as observed by TEM and SEM. The results showed that under KM and berberine hydrochloride (BB) treatment, a total of 3305 differentially expressed genes were detected, with the highest number in the KM-treated group (578 up-regulated and 615 down-regulated). A total of 847 proteins and 1850 peptides were identified in TM proteomics. Nile red staining showed that the fat content of TM was significantly higher in the BB-, ethidium bromide- (EB), FE-, KM-, Adriamycin hydrochloride- (YA), and MK-treated group compared to the control group. Results of the biofilm thickness showed that it gradually increased under treatment with specific concentrations of KM or BB, which may be related to the up-regulation of ERG25 and CYP related gene proteins. CONCLUSIONS: It is suggested that in order to effectively deal with dermatomycosis caused by TM, it is necessary to inhibit the expression of ERG25 and CYP related genes and fat metabolism, which can result in the inhibition of the production of biofilm by the fungus and solve the problem of fungal drug resistance in clinical settings.

Celastrol alleviates metabolic disturbance in high-fat diet-induced obese mice through increasing energy expenditure by ameliorating metabolic inflammation.

Celastrol, a natural triterpene, has been shown to treat obesity and its related metabolic disorders. In this study, we first assessed the relationship between the antiobesity effects of celastrol and its antiinflammatory activities. Our results showed that celastrol can reduce weight gain, ameliorate glucose intolerance, insulin resistance, and dyslipidemia without affecting food intake in high-fat diet-induced obese mice. A CLAMS was used to clarify the improvement of metabolic profiles was attribute to increased adipose thermogenesis after celastrol treatment. Further studies found that celastrol decreased the infiltration of macrophage as well as its inflammatory products (IL-1ß, IL-18, MCP-1α, and TNF-α) in liver and adipose tissues, which also displayed an obvious inhibition of TLR3/NLRP3 inflammasome molecules. This study demonstrated that celastrol could be a potential drug for treating metabolic disorders, the underlying mechanism is related to ameliorating metabolic inflammation, thus increasing body energy expenditure.

Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities.

OBJECTIVE: Ambient particulate pollution, especially PM2.5, has adverse impacts on health and welfare. To manage and control PM2.5 pollution, it is of great importance to determine the factors that affect PM2.5 levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM2.5 in 109 Chinese cities from January 1, 2015 to December 31, 2015. METHODS: To evaluate potential risk factors associated with the spatial and temporal variations in PM2.5 levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM2.5 levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters. RESULTS: Daily concentrations of PM2.5 peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 µg/m3) in the Beijing-Tianjin-Hebei area. The city-level PM2.5 was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO2), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM2.5 was found. The city-level and daily-level of weather conditions within a city were both associated with PM2.5. CONCLUSION: PM2.5 levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM2.5 pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.

Adaptive kinetic Monte Carlo simulation of methanol decomposition on Cu(100).

The adaptive kinetic Monte Carlo method was used to calculate the decomposition dynamics of a methanol molecule on Cu(100) at room temperature over a time scale of minutes. Mechanisms of reaction were found using minimum mode following saddle point searches based on forces and energies from density functional theory. Rates of reaction were calculated with harmonic transition state theory. The dynamics followed a pathway from CH(3)OH, CH(3)O, CH(2)O, HCO, and finally to CO. Our calculations confirm that methanol decomposition starts with breaking the O-H bond followed by breaking C-H bonds in the dehydrogenated intermediates until CO is produced. The bridge site on the Cu(100) surface is the active site for scissoring chemical bonds. Reaction intermediates are mobile on the surface which allows them to find this active reaction site. This study illustrates how the adaptive kinetic Monte Carlo method can model the dynamics of surface chemistry from first principles.

Adaptive kinetic Monte Carlo for first-principles accelerated dynamics.

The adaptive kinetic Monte Carlo method uses minimum-mode following saddle point searches and harmonic transition state theory to model rare-event, state-to-state dynamics in chemical and material systems. The dynamical events can be complex, involve many atoms, and are not constrained to a grid-relaxing many of the limitations of regular kinetic Monte Carlo. By focusing on low energy processes and asserting a minimum probability of finding any saddle, a confidence level is used to describe the completeness of the calculated event table for each state visited. This confidence level provides a dynamic criterion to decide when sufficient saddle point searches have been completed. The method has been made efficient enough to work with forces and energies from density functional theory calculations. Finding saddle points in parallel reduces the simulation time when many computers are available. Even more important is the recycling of calculated reaction mechanisms from previous states along the dynamics. For systems with localized reactions, the work required to update the event table from state to state does not increase with system size. When the reaction barriers are high with respect to the thermal energy, first-principles simulations over long time scales are possible.

California regional registered nurse workforce forecast.

OBJECTIVE: To forecast the shortage of registered nurses (RNs) of the 24 Primary Metropolitan Statistical Areas (PMSA) and Metropolitan Statistical Areas (MSA) in California. BACKGROUND: A nursing shortage prevails nationally and is most serious in the state of California. Successful interventions in the alleviation of the RN shortage will require effective resource allocation and academic program development in various regions throughout the state. While various published studies have focused on nursing workforce development at the state and even regional levels, there are no studies focused on identifying RN shortages at the PMSA or MSA (P/MSA) level. In this report, a forecasting model is developed to systematically analyze the future supply and demand of the RN workforce within each California P/MSA. METHODS: Using accessible public databases, forecasting models were constructed to project the demand and supply of RN jobs in California P/MSAs. In the demand model, population age and size were used as determinants of regionally required RN jobs. In the RN jobs (supply) model, a region's supply of RNs was the net sum of factors increasing and decreasing the regional presence of RN jobs, including RN graduations, migration, and aging of the RN workforce. The combination of these supply and demand models was used to produce regional RN shortage forecasts for future years. RESULTS: Almost all regions exhibited growing shortages by 2020 at rates ranging from 3% to 600%. Using a modified version of the grading rubric of the California Regional Registered Nurse Workforce Report Card (Lin, Lee, Juraschek, & Jones, 2006), only two regions will receive a grade above "C" in 2020. The number of "F" grades will grow to nine. CONCLUSIONS: California has the lowest RN ratio in the United States (Fletcher, Guzley, Barnhill, & Philhour, 2004; Health Resources and Services Administration, 2004a) and this RN workforce forecasting model shows that over the next 15 years, the majority of P/MSAs in California will have increasing RN shortages. This analysis has significant policy implications including the need to create specific plans to mitigate the effect of the California shortage.