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Abstract Background Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. Methods This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. Results A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. Conclusions While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Abstract Background Malaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017–2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data. Methods A Bayesian geostatistical framework incorporating population data and treatment-seeking propensity was developed. The models incorporated static and dynamic factors and accounted for spatial heterogeneity. Results Results showed a significant decline in malaria cases in Laos over the five-year period and a shift in transmission patterns. While the north became malaria-free, the south experienced ongoing transmission with sporadic outbreaks. Conclusion The risk maps provided insights into changing transmission patterns and supported risk stratification. These risk maps are valuable tools for malaria control in Laos, aiding resource allocation, identifying intervention gaps, and raising public awareness. The study enhances understanding of malaria transmission dynamics and facilitates evidence-based decision-making for targeted interventions in high-risk areas.

Lower-grade glioma (LGG), a prevalent malignant tumor in the central nervous system, requires accurate prediction and treatment to prevent aggressive progression. We aimed to explore the role of disulfidptosis-related genes (DRGs) in LGG, a recently discovered form of programmed cell death characterized by abnormal disulfide accumulation. Leveraging public databases, we analyzed 532 LGG tumor tissues (The Cancer Genome Atlas), 1,157 normal samples (Genotype-Tissue Expression), and 21 LGG tumor samples with 8 paired normal samples (GSE16011). Our research uncovered intricate relationships between DRGs and crucial aspects of LGG, including gene expression, immune response, mutation, drug sensitivity, and functional enrichment. Notably, we identified significant heterogeneity among disulfidptosis sub-clusters and elucidated specific differential gene expression in LGG, with myeloid cell leukemia-1 (MCL1) as a key candidate. Machine learning techniques validated the relevance of MCL1, considering its expression patterns, prognostic value, diagnostic potential, and impact on immune infiltration. Our study offers opportunities and challenges to unravel potential mechanisms underlying LGG prognosis, paving the way for personalized cancer care and innovative immunotherapeutic strategies. By shedding light on DRGs, particularly MCL1, we enhance understanding and management of LGG.

Abstract Although Parkinson’s disease (PD) has a heterogeneous disease course, it remains challenging to establish subtypes. We described and clustered the natural course of Parkinson’s disease (PD) with respect to functional disability and mortality. This retrospective cohort study utilized the Korean National Health Insurance Service database, which contains the social support registry database for patients with PD. We extracted patients newly diagnosed with PD in 2009 and followed them up until the end of 2018. Functional disability was assessed based on the long-term care insurance (LTCI) and National Disability Registry data. Further, we measured all-cause mortality during the observation period. We included 2944 eligible patients. The surviving patients were followed up for 113.7 ± 3.3 months. Among the patients who died, patients with and without disability registration were followed up for 61.4 ± 30.1 and 43.2 ± 32.0 months, respectively. The cumulative survival rate was highest in cluster 1 and decreased from Cluster 1 to Cluster 6. In the multivariable Cox regression analysis, the defined clusters were significantly associated with increased long-term mortality (adjusted hazard ratio [aHR], 3.440; 95% confidence interval [CI], 3.233–3.659; p

In lunar science, volatiles are defined as chemical elements and unstable compounds, evaporating, sublimating, or flowing in other ways at low temperature. At moderate temperature, volatiles transfers its flowing elements from solid materials to coexisting gas phases. Such substances include S, Cu, Zn, As, Se, Ag, Cd, In, Te, Hg, Ti, Pb, Bi, and halogens (F, C L, Br, and I). These volatile substances are mainly added by small impactors, so they should be uniformly deposited in lunar regolith. In the permanently shaded regions (PSRs) of the lunar polar region, the extremely low temperature may allow the solar activity record to be kept longer. When the lunar soil is heated, it will release volatile elements in the form of gas, especially the particles injected into the lunar soil by the solar wind. In deeper lunar soil, it means that these particles were implanted in different periods of solar evolution. The study of regolith in polar PSRs may help to solve this problem, because it has not been widely heated and therefore may not be subjected to postimplantation modification. The research on volatiles detection in lunar polar region can help us confirm the content and distribution of water ice in the permanent shadow region of the lunar polar region, and the composition, distribution, source, transport and loss mechanism of other volatiles in this region, as well as the relationship between lunar soil in the polar region and the ancient solar wind environment, and the environment and mineral resources in the region. Chandrayaan-3 provided important assistance to the conceptualization of this project. At the end of the article, we discussed the lunar orbit.

In the context of extreme weather and frequent emergencies, strengthening the integration of resilient cities and smart cities will comprehensively enhance the resilience of cities and promote the modernization, digitalization, standardization, and sustainability of urban management. Smart resilient cities refer to integrating the new-generation information technology and dispatching systems of smart cities in city construction, thus to strengthen cities’ full-cycle resilience capacity to cope with major emergencies. This study elaborates the implication and development values of smart resilient cities, and analyses the challenges faced in smart resilient city construction. Moreover, it proposes a framework, ecology, pathways, and assessment indicators for smart resilient city construction, and clarifies the development elements and modes. Furthermore, we propose the following suggestions: (1) increasing public financial investment to upgrade infrastructure construction, (2) establishing sound emergency laws and regulations to improve social governance systems, (3) focusing on intelligent information technology construction to pursue technology-enabled development, and (4) playing a coordinating role of the government to promote multi-dimensional collaboration and governance, thereby driving the construction of smart resilient cities and supporting high-quality urban development.

Abstract The stroke incidence has increased rapidly in South Korea, calling for a national-wide system for long-term stroke management. We investigated the effects of socioeconomic status (SES) and geographic factors on chronic phase survival after stroke. We retrospectively enrolled 6994 patients who experienced a stroke event in 2009 from the Korean National Health Insurance database. We followed them up from 24 to 120 months after stroke onset. The endpoint was all-cause mortality. We defined SES using a medical-aid group and four groups divided by health insurance premium quartiles. Geographic factors were defined using Model 1 (capital, metropolitan, city, and county) and Model 2 (with or without university hospitals). The higher the insurance premium, the higher the survival rate tended to be (P

The coronavirus disease 2019 (COVID-19) pandemic has caused severe human casualties and economic losses. Strengthening research on the coordination of public health security emergency information and building a more efficient and credible emergency information sharing system have become the core issues to optimize the public health security information management in China. This study analyzes the structure and connotations of public health security emergency information and proposes a new structure for emergency information management of sudden epidemics in China. We create a hierarchical blockchain model for information sharing among emergency response departments based on the blockchain distributed trust mechanism and elaborate the emergency information consortium blockchain, block structure, and implementation of the side chain interaction mechanism. Moreover, the uploading, sharing, downloading, and backtracking processes of emergency information are presented. Using the COVID-19 epidemic control in Nanjing as an example, we verify the validity of this model through scenario simulation and propose policy recommendations for responding to the epidemic. This study is expected to provide a decision-making reference and feasible path for building a new public health information system in China that meets the needs of prevention and control of major epidemics.

Many viral respiratory infections can cause severe acute respiratory symptoms leading to mortality and morbidity. In the spring of 2003, the severe acute respiratory syndrome (SARS) outbreak caused by SARS-CoV spread globally. In the summer of 2012, the Middle East respiratory syndrome (MERS) outbreak caused by MERS-CoV occurred in Saudi Arabia. In the winter of 2019, the coronavirus disease 2019 (COVID-19) outbreak caused by a novel coronavirus SARS-CoV- 2 occurred in China which rapidly spread worldwide causing a global pandemic. Up until 27 May 2020, there are 5.5 million confirmed cases of COVID-19 and 347,587 COVID-19 related deaths worldwide, and there has also been an unprecedented increase in socioeconomic and psychosocial issues related to COVID-19. This overview aims to review the current developments in preventive treatments and therapies for COVID-19. The development of vaccines for SARS-CoV-2 is ongoing and various clinical trials are currently underway around the world. It is hoped that existing antivirals including remdesivir and lopinavir-ritonavir might have roles in the treatment of COVID-19, but results from trials thus far have not been promising. COVID-19 causes a mild respiratory disease in the majority of cases, but in some cases, cytokine activation causes sepsis and acute respiratory distress syndrome, leading to morbidity and mortality. Immunomodulatory treatments and biologics are also being actively explored as therapeutics for COVID-19. On the other hand, the use of steroidal and nonsteroidal anti-inflammatory drugs (NSAIDs) has been discouraged based on concerns about their adverse effects. Over the past two decades, coronaviruses have caused major epidemics and outbreaks worldwide, whilst modern medicine has been playing catch-up all along.

Abstract Background Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. Methods With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise ‘how seasonal’ locations are relative to their surroundings. Results Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March–April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. Conclusions Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

Investigating malaria transmission at three sites in Uganda, the authors identify super-spreaders and show that super-spreading is more prominent at low-intensity transmission, and that seasonality and environmental stochasticity have a greater influence on super-spreading.

Malaria on Bioko Island has been reduced substantially but many acquire malaria while traveling. Here, the authors use survey data, geostatistical and mathematical modeling to investigate malaria prevalence and mobility patterns and find that in some parts of the island a significant fraction of prevalence is attributable to malaria acquired while traveling.

Pancreatic ductal adenocarcinoma (PDAC) accounts for about 3% of all cancers in the United States and about 7% of all cancer deaths. Despite the lower prevalence relative to other solid tumors, it is one of the leading causes of cancer-related death in the US. PDAC is highly resistant to chemotherapy as well as radiation therapy. Current standard-of-care chemotherapeutic regimens provide transient disease control but eventually tumors develop chemoresistance. Tumors that are deficient in DNA damage repair mechanisms such as BRCA mutants respond better to platinum-based chemotherapies. However, these tumor cells can utilize the poly adenosine diphosphate (ADP)-ribose polymerase (PARP) as a salvage DNA repair pathway to prolong survival. Hence, in the presence of BRCA mutations, the inhibition of the PARP pathway can lead to tumor cell death. This provides the rationale for using PARP inhibitors in patients with BRCA mutated PDAC. The phase III POLO trial showed a near doubling of progression-free survival (PFS) compared with placebo in advanced PDAC when a PARP inhibitor, olaparib, was used as maintenance therapy. As a result, the US Food and Drug Administration (FDA) approved olaparib as a maintenance treatment for germline BRCA mutated advanced PDAC that has not progressed on platinum-based chemotherapy. The success of olaparib in treating advanced PDAC opened the new field for utilizing PARP inhibitors in patients with DNA damage repair (DDR) gene defects. Currently, many clinical trials with various PARP inhibitors are ongoing either as monotherapy or in combination with other agents. In addition to germline/somatic BRCA mutations, some trials are enrolling patients with defects in other DDR genes such as ATM , PALB2 , and CHEK2 . With many ongoing PARP inhibitor trials, it is hopeful that the management of PDAC will continuously evolve and eventually lead to improved patient outcomes.

This study aimed to evaluate the behavioral and disease-related characteristics of patients with acute stroke during the Coronavirus disease (COVID-19) pandemic. This retrospective study was conducted using the Korean Stroke Registry database from a single cerebrovascular specialty hospital. We categorized the COVID-19 pandemic (February 2020 to June 2021) into three waves according to the number of COVID-19 cases recorded and the subjective fear index of the general population and matched them with the corresponding pre-COVID-19 (January 2019 to January 2020) periods. The total number of acute stroke hospitalizations during the pre-COVID-19 and COVID-19 periods was 402 and 379, respectively. The number of acute stroke hospitalizations recorded during the regional outbreak of COVID-19 was higher than that recorded during the corresponding pre-COVID-19 period (97 vs. 80). Length of hospital stay was significantly longer during the COVID-19 pandemic than during the pre-COVID-19 period (11.1 and 8.5 days, respectively; p = 0.003). There were no significant differences in the time from onset to hospital arrival, rate of acute intravenous/intra-arterial (IV/IA) treatments, and door-to-IV/IA times between the pre-COVID-19 and COVID-19 periods. This study suggests that specialty hospitals can effectively maintain the quality of healthcare through the management of acute time-dependent diseases, even during pandemics.

Plantaginis Semen (PS) is well recognized in traditional Chinese medicine (TCM) and health products. Crude PS (CPS) and salt-processed CPS (SPS) are the two most commonly used decoction pieces of PS, and are included in the 2020 edition of Chinese Pharmacopoeia. Although they all have multiple effects, the mechanisms for treating diseases are different and remain unclear, the processing mechanism of SPS is also indeterminate, which hinders their clinical application to a certain extent. In order to solve these problems and further develop PS in the clinical application. Here, we used saline-loaded model rats for experiments, and utilized an integrated approach consisting of pharmacological methods and metabolomics, which could assess the diuretic impact of CPS and SPS ethanol extracts on saline-loaded rats and elucidate the underlying mechanism. The results showed that CPS and SPS both produced increased urine volume excretion and urine electrolyte excretion, but the levels of aldosterone (ALD) and aquaporin 2 (AQP2) were decreased. And 30 differential metabolites such as linoleic acid, lysoPC(O-18:0), sphingosine-1-phosphate, lysoPC(18:0) were found, mainly involving three metabolic pathways. In conclusion, CPS and SPS both have a diuretic effect, and that of SPS is better. This work investigated the possible diuretic mechanisms of CPS and SPS which may also be the mechanism of PS for anti-hypertension. In addition, a holistic approach provided novel and helpful insights into the underlying processing mechanisms of TCM.

Background: Spinal cord infarction (SCI) is a rare disease and its early diagnosis is challenging. Here, we described the clinical features and imaging findings of SCI, and assessed the results of evoked potential (EP) studies to elucidate their diagnostic role in the early stage of SCI.Methods: We retrospectively analyzed 14 patients who had spontaneous SCI. The demographic, neurological, and temporal profiles of the SCI patients were identified. We reviewed the imaging findings and assessed the changes in them over time. To review EP, central motor conduction time (CMCT) and somatosensory evoked potential (SEP) values were obtained. We also enrolled 15 patients with transverse myelitis (TM), and compared the clinical, radiological and electrophysiological features between SCI and TM patients.Results: The ages of the SCI patients ranged from 54 to 73 years. Nine patients (64.3%) showed nadir deficits within 6 h. The most common type of clinical visit was via the emergency center. Nine patients (64.3%) presented with peri-onset focal pain. The median initial modified Rankin scale score was 3. For 9 patients (64.3%), initial T2 imaging findings were negative, but subsequent diffusion weighed imaging (DWI) showed diffusion restriction. Vertebral body infarction was observed in 5 patients (35.7%). EP data were available for 10 SCI patients. All 8 patients who had their CMCT measured showed abnormalities. Among them, motor evoked potentials were not evoked in 6 patients at all. SEP was measured in 10 patients, and 9 of them showed abnormalities; one of them showed no SEP response. For 5 patients, the EP studies were done prior to DWI, and all the patients showed definite abnormalities. The abnormalities in the EP findings of the SCI patients were more profound than those of the TM patients, even though the duration from the onset to the start of the study was much shorter for SCI patients.Conclusion: SCI can be diagnosed based on typical clinical manifestations and appropriate imaging studies. Our study also indicates that immediate sensory and motor EP study can have an adjuvant diagnostic role in the hyperacute stage of SCI, and can improve the accuracy of diagnosis.

Fair trade represents one of the most influential social movement that encourages sustainability and ethical consumerism in the past 20 years. While fair trade movement of apparel products is rapidly expanding, there is a dearth of understanding about the characteristics of consumers who regularly purchase fair trade apparel products. The purpose of the study was, therefore, to explore whether demographics, ethical traits, and individual characteristics such as clothing involvement helped predict fair trade purchase behaviors. Demographic variables such as gender, generational cohorts, education, and income were effective in predicting fair trade purchases. Ethical traits such as altruism, ethical concerns, and ethical obligation along with socially responsible attitudes were also able to differentiate fair trade purchase behaviors from non-purchaser behaviors. The final set of variables in a hierarchical regression model were price sensitivity, materialism, and clothing involvement. Among the individual characteristics, only the extent to which consumers were involved in clothing was associated with fair trade purchases. Investigating several sets of variables closely related to ethical consumption contributed to the literature in the context of fair trade consumer behavior.

Abstract Background Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country’s routine case reporting system. Methods A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence. Spatio-temporal trends were quantified across the country. Results Despite a relatively small decrease between 2013 and 2016, the prevalence of malaria infection has increased substantially in all areas of Madagascar since 2011. In 2011, almost half (42.3%) of the country’s population lived in areas of very low malaria risk (20%) increased from only 2.2% in 2011 to 9.2% in 2016. A comparison of the model-based estimates with the raw MIS results indicates there was an underestimation of the situation in 2016, since the raw figures likely associated with survey timings were delayed until after the peak transmission season. Conclusions Malaria remains an important health problem in Madagascar. The monthly and annual prevalence maps developed here provide a way to evaluate the magnitude of change over time, taking into account variability in survey input data. These methods can contribute to monitoring sub-national trends of malaria prevalence in Madagascar as the country aims for geographically progressive elimination.

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Mosquito biting or exposure is a risk factor for malaria transmission. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in vector abundance hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots of vector abundance, and spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at abundance hotspots which may increase malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda. Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown reduction of household biting propensity. Improvements in house quality should be recommended as a supplementary measure for malaria control reducing risk of infection.