Regional Network Research Articles

A novel method to determine bias-dependent source and drain parasitic series resistances in AlGaN/GaN high electron mobility transistors

The AlGaN/GaN high electron mobility transistors (HEMTs) with T-gate that suitable for high frequency applications were fabricated. A novel method to extract the bias-dependent source and drain parasitic series resistances (Rs and Rd) of AlGaN/GaN HEMTs is proposed. By analyzing the distributed capacitance and current generator network in the velocity saturated regions of the AlGaN/GaN HEMTs, a new restriction relationship between small-signal equivalent circuit elements is found. The Rs and Rd can be determined under active bias through wideband S-parameter measurements, which can better reflect the physical mechanism of AlGaN/GaN HEMTs under normal operation. The S-parameters and extrinsic transconductance calculated based the small-signal equivalent circuit element values extracted by the method proposed in this paper are very consistent with the experimental values, which reflects the accuracy of this element extraction method. In this paper, the physical mechanism that causes Rs and Rd to vary with bias voltage is also studied. This study has a deeper insight into the bias-dependence of Rs and Rd, which modifies the understanding for physical mechanisms of AlGaN/GaN HEMTs. The research results provide new ideas for establishing small-signal equivalent circuit models containing more physical effects and is of great significance to GaN-based integrated circuit design.

“I didn’t even wonder why I was on the floor” – mixed methods exploration of stroke awareness and help-seeking behaviour at stroke symptom onset

IntroductionTo better target stroke awareness efforts (pre and post first stroke) and thereby decrease the time window for help-seeking, this study aims to assess quantitatively whether stroke awareness is associated with appropriate help-seeking at symptom onset, and to investigate qualitatively why this may (not) be the case.MethodsThis study conducted in a German regional stroke network comprises a convergent quantitative-dominant, hypothesis-driven mixed methods design including 462 quantitative patient questionnaires combined with qualitative interviews with 28 patients and seven relatives. Quantitative associations were identified using Pearson’s correlation analysis. Open coding was performed on interview transcripts before the quantitative results were used to further focus qualitative analysis. Joint display analysis was conducted to mix data strands. Cooperation with the Patient Council of the Department of Neurology ensured patient involvement in the study.ResultsOur hypothesis that stroke awareness would be associated with appropriate help-seeking behaviour at stroke symptom onset was partially supported by the quantitative data, i.e. showing associations between some dimensions of stroke awareness and appropriate help-seeking, but not others. For example, knowing stroke symptoms is correlated with recognising one’s own symptoms as stroke (r = 0.101; p = 0.030*; N = 459) but not with no hesitation before calling help (r = 0.003; p = 0.941; N = 457). A previous stroke also makes it more likely to recognise one’s own symptoms as stroke (r = 0.114; p = 0.015*; N = 459), but not to be transported by emergency ambulance (r = 0.08; p = 0.872; N = 462) or to arrive at the hospital on time (r = 0.02; p = 0.677; N = 459). Qualitative results showed concordance, discordance or provided potential explanations for quantitative findings. For example, qualitative data showed processes of denial on the part of patients and the important role of relatives in initiating appropriate help-seeking behaviour on patients’ behalf.ConclusionsOur study provides insights into the complexities of the decision-making process at stroke symptom onset. As our findings suggest processes of denial and inabilities to translate abstract disease knowledge into correct actions, we recommend to address relatives as potential saviours of loved ones, increased use of specific situational examples (e.g. lying on the bathroom floor) and the involvement of patient representatives in the preparation of informational resources and campaigns. Future research should include mixed methods research from one sample and more attention to potential reporting inconsistencies.

Context matters: task relevance shapes neural responses to emotional facial expressions

Recent research shows that emotional facial expressions impact behavioral responses only when their valence is relevant to the task. Under such conditions, threatening faces delay attentional disengagement, resulting in slower reaction times and increased omission errors compared to happy faces. To investigate the neural underpinnings of this phenomenon, we used functional magnetic resonance imaging to record the brain activity of 23 healthy participants while they completed two versions of the go/no-go task. In the emotion task (ET), participants responded to emotional expressions (fearful or happy faces) and refrained from responding to neutral faces. In the gender task (GT), the same images were displayed, but participants had to respond based on the posers’ gender. Our results confirmed previous behavioral findings and revealed a network of brain regions (including the angular gyrus, the ventral precuneus, the left posterior cingulate cortex, the right anterior superior frontal gyrus, and two face-responsive regions) displaying distinct activation patterns for the same facial emotional expressions in the ET compared to the GT. We propose that this network integrates internal representations of task rules with sensory characteristics of facial expressions to evaluate emotional stimuli and exert top-down control, guiding goal-directed actions according to the context.

Analysing micrologistics initiatives in rural areas – Approaches to typologise and assess complex logistics solutions in regional supply networks

While demand for regional products increases and societal expectations towards a regionalisation of production are on the rise in Germany, little attention is given to the logistical challenges of local producers. At the same time, rural areas are confronted with declining supply options for goods of daily needs. Logistical challenges in rural areas are primarily caused by a scarcity of actors, low or fluctuating quantities of goods and long transport distances. A lack of economies of scale makes supply and logistics very costly. Given the rising interest and new possibilities created by digitisation, numerous private and public initiatives are developing solutions for sustainable regional logistics to secure and improve supply in rural areas - so called micrologistics solution. However, a meta-analysis of such regional logistics solutions is missing that identifies similarities and differences and indicates which characteristics are essential for the development of long-term viable regional logistics solutions.This article therefore aims at providing a general overview and at systematising current micrologistics solutions in rural regions, introducing a typology which is based on a meta-analysis of approximately 80 micrologistics initiatives in Germany and beyond. Referring to two case studies – Smart Village St. Wendel and UCKER Warentakt - we then focus analysis on solutions aiming at providing goods of daily need to the population in rural areas. We share lessons learnt on their characteristics, challenges and factors of success. This is done using a specifically developed morphological box, a viability and impact-oriented assessment framework for micrologistics initiatives as well as results of interviews with initiators and operators of such initiatives. We conclude on the usefulness of the analytical approaches developed for this new research field as well as further research needs.

The Identification of Potential Anti-Depression/Anxiety Drug Targets by Stress-Induced Rat Brain Regional Proteome and Network Analyses.

Depression and anxiety disorders are prevalent stress-related neuropsychiatric disorders and involve multiple molecular changes and dysfunctions across various brain regions. However, the specific and shared pathophysiological mechanisms occurring in these regions remain unclear. Previous research used a rat model of chronic mild stress (CMS) to segregate and identify depression-susceptible, anxiety-susceptible, and insusceptible groups; then the proteomes of six distinct brain regions (the hippocampus, prefrontal cortex, hypothalamus, pituitary, olfactory bulb, and striatum) were separately and quantitatively analyzed. To gain a comprehensive and systematic understanding of the molecular abnormalities, this study aimed to investigate and compare differential proteomics data from the six regions. Differentially expressed proteins (DEPs) were identified in between specific regions and across all regions and subjected to a series of bioinformatics analyses. Regional comparisons showed that stress-induced proteomic changes and corresponding gene ontology and pathway enrichments were largely distinct, attributable to differences in cell populations, protein compositions, and brain functions of these areas. Additionally, a notable degree of overlap in the significantly enriched terms was identified, potentially suggesting strong connections in the enrichment across different regions. Furthermore, intra-regional and inter-regional protein-protein interaction networks and drug-target-DEP networks were constructed. Integrated analysis of the three association networks in the six regions, along with the DisGeNET database, identified ten DEPs as potential targets for anti-depression/anxiety drugs. Collectively, these findings revealed commonalities and differences across different brain regions at the protein level induced by CMS, and identified several novel protein targets for the development of new therapeutics for depression and anxiety.

Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development.

Prediction of water–main failures and management of the associated risks using integrated predictive analytics approach

Water distribution networks are vital to delivering potable water. They commonly include interconnected water mains (WMs), pumps and other hydraulic controls. Numerous WM failures have occurred due to ageing and harsh climate. This paper aims to predict the probability of future failures and integrate the predictions into risk-management strategies. The novelty lies in emphasising on the relevance to networks in cold regions like Canada. This study applied clustering and principal component analysis to the WM data from the Canadian City of Kitchener network. Clustering is shown to improve the failure prediction outcomes from the random forest algorithm and risk analysis output. Compared to without implementing clustering, the improvement reached 67–80% for WMs with high-rating risk. This paper successfully produced risk maps for Kitchener’s network, showing that only a small percentage (0.07–1.02%) of the existing WMs needs immediate action (prioritised rehabilitation or replacement). In addition to WM length and diameter, freeze index is shown to be an influence factor for failure predictions. The integrated, proactive approach discussed in this article can be applied to other cold-region WDNs. The results help reduce water losses and develop cost-effective, practical risk-management strategies.

Benefits and functionality of an interorganisational workplace health management network - insights from the companies' perspective.

Workplace health management (WHM) is a worthwhile investment for companies. Nevertheless, the implementation of health-promoting interventions remains limited, especially in small and medium-sized enterprises. Interorganisational networks could be a promising way to raise awareness of the advantages of implementing WHM. Therefore, the aim of this study is to analyse the perceived functionality and benefits of a regional WHM network from companies' perspective and to present initial results on this specific topic. An explorative qualitative case study was conducted analysing ERZgesund, a WHM network in a rural region in Germany. Twenty-two companies that participated in the network were interviewed about their experiences and perceived advantages and disadvantages participating in the WHM network ERZgesund. The findings show that the network has raised awareness about WHM among the companies, provides opportunities for exchange of knowledge and experiences, and generates or strengthens collaboration. The positive effects were enhanced by the network's structure, such as regionality and a direct contact person. Nevertheless, some companies stated that they would welcome a higher level of participation and transparency. Overall, it becomes clear that a WHM network can be a valuable tool to emphasize the relevance of WHM to companies. Therefore, further studies should validate and intensify the research on WHM networks to ensure a long-term benefit from the network.

Upgrading the industrial structure for optimizing water-energy-carbon nexus in regional trade network

To take actions for the SDGs (Goals 6, 7, 9, 12, and 13), the world is undergoing intense industrial transformation and upgrading. However, there has been limited analysis of water-energy-carbon (WEC) nexus within regional trade network to uncover the effects of industrial restructuring. Here, this study explores the trade-related WEC flows in the Yangtze River Delta area (YRD) from 2012 to 2017 by four aspects (spatiotemporal status, sectors, WEC flowing synergies, and environmental-economic trade-off) based on an environmentally extended input-output analysis. The main results show that the resource supply network in the YRD has been optimized, with enhanced synergies of WEC flows. Resource trade in the YRD has shifted from traditional heavy industry (HV Ⅰ) to high-tech manufacturing industry (HV Ⅱ). The share of trade-related WEC in demand of HV Ⅱ among all sectors increased by 10, 5, and 15 percentages, while the share in demand of HV Ⅰ decreased by 15, 10, and 10 percentages during this transition period. This study also discloses an environmental-economic contradiction arising in regional trade: low-carbon and high-value-added economic development in some regions may be premised on relocating environmental resource pressures. Hence, this study could assist policymakers in analyzing sustainable industrial transition models for regional integration clusters and in formulating targeted interregional resource trade policies to alleviate development gaps.

Sensitivity and specificity of the action observation network to kinematics, target object, and gesture meaning.

Hierarchical models have been proposed to explain how the brain encodes actions, whereby different areas represent different features, such as gesture kinematics, target object, action goal, and meaning. The visual processing of action-related information is distributed over a well-known network of brain regions spanning separate anatomical areas, attuned to specific stimulus properties, and referred to as action observation network (AON). To determine the brain organization of these features, we measured representational geometries during the observation of a large set of transitive and intransitive gestures in two independent functional magnetic resonance imaging experiments. We provided evidence for a partial dissociation between kinematics, object characteristics, and action meaning in the occipito-parietal, ventro-temporal, and lateral occipito-temporal cortex, respectively. Importantly, most of the AON showed low specificity to all the explored features, and representational spaces sharing similar information content were spread across the cortex without being anatomically adjacent. Overall, our results support the notion that the AON relies on overlapping and distributed coding and may act as a unique representational space instead of mapping features in a modular and segregated manner.

Temporal implementation of a regional referral pathway in transthyretin cardiac amyloidosis: Emilia-Romagna experience.

Transthyretin cardiac amyloidosis (ATTR-CA) is a rare and progressive cardiomyopathy caused by amyloid fibril deposition in myocardial tissue. Diagnostic challenges have historically hampered timely detection. Recent advances in noninvasive diagnostic techniques have facilitated ATTR-CA diagnosis. We aimed to examine the development of a regional network for the diagnosis and management of ATTR-CA and describe a cohort of patients with ATTR-CA, investigate diagnostic pathways and assess clinical outcomes according to diagnosis periods. We performed a survey study analyzing answers from 11 cardiology centers and we conducted a retrospective study including patients with ATTR-CA attending a referral center between 1 January 2012 and 31 December 2022, and categorized by the period of diagnosis (2012-2016 and 2017-2022). Over the years, a growing number of patients reached a diagnosis and were treated in the surveyed nonreferral centers of the region. The retrospective study showed a more significant diagnostic delay in the earlier period rather than the later one [13.4 (5-30.2) vs. 10.6 (5.0-17.9) months, P = 0.04]. Patients diagnosed after 2017 showed a greater survival rate than those diagnosed earlier ( P = 0.02). In the multivariate analysis, the year of diagnosis from 2017 remained independently associated with mortality [hazard ratio (HR) 0.46, 95% confidence interval (CI) 0.28-0.79; P = 0.005]. This study emphasized the shift toward noninvasive diagnostic criteria. It revealed a positive impact on patient survival and disease management with the use of disease-modifying therapies and diagnostic developments in more recent years. The findings underscore the importance of disease awareness and networking to reduce diagnostic delays and enhance patient journeys for ATTR-CA.

An optimization approach to evaluating accessibility to community pharmacies

ABSTRACT Accessibility to community pharmacy is key to community health and well-being. An optimal community-pharmacy assignment (OCPA) model is developed to measure the geographical accessibility of community pharmacies in a regional population network. The model minimizes a risk-adjusted distance between census tracts and community pharmacies by optimally matching census tracts to single or multiple pharmacies. Social determinants of health (SDOH), such as vehicle ownership, access to public transit, and insurance, are used as risk factors for adjusting the geographic distance. We present a novel definition of ‘pharmacy desert’ based on the distance between census tracts and their matching community pharmacies. Extensive computational experiments are conducted based on a case study in Louisville, Kentucky. Benchmark studies with leading literature and other public health indicators validate the proposed model. Extensive sensitivity analyses suggest our model for identifying pharmacy desert is effective and can be a valuable tool for pharmacy and public health stakeholders.

Propagation Modeling of Unmanned Aerial Vehicle (UAV) 5G Wireless Networks in Rural Mountainous Regions Using Ray Tracing

Deploying 5G networks in mountainous rural regions can be challenging due to its unique and challenging characteristics. Attaching a transmitter to a UAV to enable connectivity requires a selection of suitable propagation models in such conditions. This research paper comprehensively investigates the signal propagation and performance under multiple frequencies, from mid-band to mmWaves range (3.5, 6, 28, and 60 GHz). The study focuses on rural mountainous regions, which were empirically simulated based on the Skardu, Pakistan, region. A complex 3D ray tracing method carefully figures out the propagation paths using the geometry of a 3D environment and looks at the effects in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The analysis considers critical parameters such as path loss, received power, weather loss, foliage loss, and the impact of varying UAV heights. Based on the analysis and regression modeling techniques, quadratic polynomials were found to accurately model the signal behavior, enabling signal strength predictions as a function of distances between the user and an elevated drone. Results were analyzed and compared with suburban areas with no mountains but more compact buildings surrounding the Universiti Kebangsaan Malaysia (UKM) campus. The findings highlight the need to identify the optimal height for the UAV as a base station, characterize radio channels accurately, and predict coverage to optimize network design and deployment with UAVs as additional sources. The research offers valuable insights for optimizing signal transmission and network planning and resolving spectrum-management difficulties in mountainous areas to enhance wireless communication system performance. The study emphasizes the significance of visualizations, statistical analysis, and outlier detection for understanding signal behavior in diverse environments.

Study on the evolution of multi-level collaborative innovation networks in China’s cloud manufacturing industry

ABSTRACT CMfg is a new service-oriented manufacturing mode integrating new-generation information technologies, and its development promotes the digital transformation of China’s manufacturing industry. Exploring the evolution trend of CMfg's co-innovation networks is essential to the development of manufacturing digitalisation. The research is carried out from three levels: The evolutionary characteristics of the organisational co-innovation network in the CMfg industry are identified through SNA and THA; the evolution of technological co-innovation network is analysed by using FP-Growth and the textual analysis, to recognise the technology convergence trends; the evolution of the factors affecting regional collaborations are discovered by using QAP model to analyse the regional co-innovation network. Following conclusions are achieved: CMfg co-innovation is centred on state-owned enterprises in the power and telecommunication industries, with University–Industry collaboration as the main form; the current hot spots of CMfg technologies are the Resource Perception-and-Access, the Resource Virtualisation-and-Service; the regional collaboration in China's CMfg industry tends to be partnership-led and multi-polar. It suggests that: In the CMfg industry, more enterprises-supporting policy should be made, while the Virtualisation-and-Service technologies need more attention, and the partnerships among regions should be further enhanced.

Multiscale graph based spatio-temporal graph convolutional network for energy consumption prediction of natural gas transmission process

Energy consumption prediction is crucial for planning and managing natural gas networks, enhancing transmission efficiency. However, most of relevant studies fail to consider the topology of natural gas network. Therefore, a multiscale graph based spatio-temporal graph convolutional network model (MG-STGCN) is proposed, which can extract the multi-scale relationship of data. Based on graph convolutional and temporal gated convolution network, data correlation, spatial dependence and time dependence are considered to improve the prediction effect. In order to verify the effect of the model, the model is tested based on two natural gas networks in different region of China. The single step prediction results of MG-STGCN are significantly better than spatio-temporal graph convolutional network (STGCN), long short-term memory (LSTM), gate recurrent unit (GRU) and temporal convolutional network (TCN), which proves the effectiveness and generalization ability of MG-STGCN. The training time of MG-STGCN is reduced by 20.8 % compared with STGCN. In different prediction time step, the average prediction effect of MG-STGCN is better than that of other methods, and the performance improvement rate is not less than 8.6 %. MG-STGCN can well capture the parameter correlation, spatial and temporal dependence of data, and achieve effective prediction of pipeline energy consumption.

Land use change and Ecological Network in rapid urban growth region in Selangor region, Malaysia

Rapid urbanization will cause various land use changes and the vast occupation of green spaces, a critical factor in the deterioration of biodiversity in urbanized areas. Some species of wildlife are endangered due to habitat shrunk and fragmentation. However, Malaysia's current biodiversity protection range is still limited. The Ecological Network (EN) refers to a framework of ecological components, which can be obtained by geographical and technical approaches to support more ecological diversity ranges. Furthermore, little research has been found on EN in Malaysia and the impact of land use change on EN. Therefore, the Selangor region is selected as the study area. This paper quantifies land use change and measures the extent of land use change to obtain the EN’s change. The result has shown that forestland has decreased, explored by people for housing and agriculture from 2000 to 2020. The EN has a trend of fragmentation. Overall, this study's results imply that the land use change led to EN's worsened performance from 2000 to 2020 in the study area. This paper hopes that this research could help supply information on conserving biodiversity in future development and urban sustainable planning in Malaysia.

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Enhancing primary healthcare nurses’ preparedness for climate-induced extreme weather events

BackgroundClimate Change is causing frequent and sever extreme weather events globally, impacting human health and well-being. Primary healthcare (PHC) nurses' are at the forefront of addressing these challenges and must be prepared. PurposeThis scoping revieww explored literature on the preparedness of the PHC nursing workforce for extreme weather events and identify gaps in knowledge and practice. MethodsUsing Arksey and O’Malley's framework, a comprehensive search was conducted across PubMed, Scopus, CINHAL, Web of Sciences, and ProQuest, on studies from 2014-2024, addressing PHC nurses’ preparedness. DiscussionNine studies were identified and highlighted a need for preparedness training and facility-based preparedness plans. Key themes included prioritizing regional networks, clinical leadership, service delivery, health information, health workforce, medical products and technologies, and financing. ConclusionStrengthening PHC nurses' resilience against extreme weather requires targeted professional development, mental health support, comprehensive planning, and collaborative efforts. Future strategies should enhance PHC nurses' capacity through training, support, and policy development.

Clinical predictors and outcomes of ST-elevation myocardial infarction related cardiogenic shock in the Asian population

BackgroundCardiogenic shock (CS) complicating myocardial infarction is associated with poor outcomes. Data among Asian populations are scarce. We aimed to investigate the long-term outcomes, prognostic factors, and predictors of CS among Asian ST elevation myocardial infarction (STEMI) patients. MethodsThis was a retrospective cohort study of consecutive patients undergoing primary percutaneous coronary intervention (PPCI) for STEMI within our regional STEMI network between 2015 and 2019. The long-term outcomes of those with and without CS were compared. Clinical predictors of outcomes and development of CS were investigated. ResultsA total of 1791 patients who underwent PPCI were included. Patients completed at least 2 years’ follow-up with a median follow-up period of 2.6 years (IQR 1.0, 3,9). Overall, 208/1791 (11.6 %) STEMI patients developed CS. These patients were older (61.1 ± 12.5 vs 57.8 ± 12.2, P < 0.001) and mostly men (87.0 %). All-cause mortality (59.9 % vs 4.7 % P < 0.001), cardiac mortality (43.8 % vs 2.2 %, P < 0.001) and major adverse cardiovascular events (MACE) was significantly higher in the CS group (59.1 % vs 14.0 %, P < 0.001). Independent predictors of survival were higher index LVEF (adjusted hazards ratio [aHR] 0.967, 95 %CI 0.951–0.984, p < 0.001) and higher arterial pH at onset of shock (aHR 0.750, 0.626–0.897, p = 0.002). Increased serum lactate concentration independently predicts poor prognosis (aHR 1.084, 95 % CI 1.046–1.124, p < 0.001). ConclusionIn Asian STEMI patients who underwent PPCI, CS was associated with poor outcomes. Higher LVEF on index admission was associated with better outcomes; while lactic acidosis independently predicted mortality.

Ecuadorian Provinces with High Morbidity and Mortality Rates Due to Asthma among the Working-Age Population: An Ecological Study to Promote Respiratory Health.

Asthma is a significant public health concern. This study identified the provinces with the highest morbidity and mortality rates due to asthma among the working-age population (15-69 years) in the Republic of Ecuador. The secondary objective was to explain the possible differences attributable to occupational exposure. This nationwide ecological study was conducted in 24 provinces between 2016 and 2019. Government databases were used as sources of information. Age-standardized rates were calculated for codes J45 and J46. The hospitalization morbidity rate for asthma decreased from 6.51 to 5.76 cases per 100,000 working-age population, and the mortality rate has consistently been low and stable from 0.14 to 0.15 deaths per 100,000 working-age population. Geographic differences between the provinces were evident. The risk of hospitalization and death due to asthma was higher in the Pacific coast (Manabí with 7.26 and 0.38, Esmeraldas with 6.24 and 0.43, Los Ríos with 4.16 and 0.40, El Oro with 7.98 and 0.21, Guayas with 4.42 and 0.17 and the Andean region (Azuay with 6.33 and 0.45, Cotopaxi (5.84 and 0.48)). The high rates observed in provinces with greater agricultural and industrial development could be national heterogeneity's main determinants and act as occupational risk factors. The contribution of occupational hazards in each province should be examined in depth through ad hoc studies. The findings presented here provide valuable information that should prompt further detailed studies, which will assist in designing public policies aimed at promoting and safeguarding the respiratory health of the population, particularly that of workers. We believe that this study will inspire the creation of regional networks for the research and surveillance of occupational health.