Aim: To investigate approaches to the criminal legal qualification of collaborationism in the field of healthcare, taking into account the norms of international humanitarian law. Materials and Methods: The research methodology involves an analysis of current legislative documents on the qualification of criminal liability for collaborative activities, as well as regulatory legal acts in the healthcare field, and documents of international humanitarian law. The data analysis was conducted using open sources, mainly for the period 2013-2025, with an emphasis on the period of the active phase of the war in Ukraine. The main search keywords were "human rights", "collaborationism", "criminal liability", "crimes against the foundations of national security", "medical immunity in international humanitarian law", and "healthcare". The initial database consisted of 70 sources, of which 34 were included in the final analysis. The materials of the publication form the basis for the systematization of criminal legislation on the problems of protecting the rights of persons accused of collaborative activities in the field of healthcare, the key ones being the Constitution of Ukraine, the Criminal Code of Ukraine, and the European Convention on Human Rights. Conclusions: The study emphasizes the need to improve the approach to the criminal legal qualification of collaborationism in the health sector, which covers both legal and organizational components, as well as mandatory compliance with the norms of international humanitarian law. The social danger of this phenomenon lies not only in the fact that it encroaches on the defense capability and foundations of national security, but also in the fact that it poses a threat to other objects of criminal law protection.
BACKGROUND: Leptospirosis remains a major public health concern in Sri Lanka, a country with a tropical climate conducive to transmission. Despite ongoing surveillance, there is limited evidence on the spatial and climatic determinants driving long-term disease dynamics. This study aimed to investigate the spatiotemporal distribution and climatic sensitivity of leptospirosis from 2009 to 2024 using advanced statistical modelling. METHODS: District-level monthly leptospirosis case data for the period 2009–2024 were obtained from the Epidemiology Unit of the Ministry of Health, Sri Lanka. Corresponding monthly district-level climatic variables, including total rainfall, mean temperature, minimum temperature, maximum temperature, and mean relative humidity, were retrieved from the NASA POWER satellite dataset. Associations between climatic variables and leptospirosis incidence, as well as spatial heterogeneity in case distribution, were assessed using a Generalized Additive Model for Location, Scale, and Shape with a Zero-Adjusted Gamma distribution, while spatial clustering and autocorrelation were examined using Moran’s I. RESULTS: A total of 81,629 confirmed cases of leptospirosis were recorded during the study period. The ZAGA-GAMLSS model identified several climatic, spatial, and temporal predictors that were significantly associated with district-level incidence. Relative humidity and maximum temperature showed immediate negative associations with incidence, while humidity, mean temperature, and rainfall demonstrated positive lag-dependent effects at 1–3 months. In contrast, maximum and minimum temperatures exhibited predominantly negative association at 3-month lag. Spatial heterogeneity was evident in both incidence rates and zero-inflation, and temporal dependence was detected at 1 and 12-month lags. Higher relative humidity three months earlier was linked to more stable leptospirosis incidence rates across districts. Spatial analysis further revealed significant clustering, with hotspots identified in the districts of Ratnapura, Galle, Matara, and Hambantota, while Colombo was identified as a spatial outlier. CONCLUSIONS: Leptospirosis in Sri Lanka exhibits distinct spatiotemporal patterns influenced by climatic variability, with elevated risk following monsoon rains in the south‑western part of the country. Climate‑sensitive modelling, as demonstrated in this study, supports the integration of meteorological surveillance into early warning and response systems to enhance leptospirosis control, particularly in identified hotspot regions.
This dataset compiles attacks on energy infrastructure in Nigeria between 2009 and 2025. It integrates geospatially attributed incident-level data with aggregated estimates of physical and economic losses. The dataset covers deliberate disruptions to oil and gas infrastructure and electricity transmission systems across Nigeria's six geopolitical zones. It consists of two complementary components. The first is a geo-referenced incident data (2011-2025) consisting of 161 recorded attacks, with information on date, location, actor type, targeted asset, and attack method. The second is an annual volume-value dataset (2009-2025) reporting crude oil losses and associated revenue estimates resulting from pipeline vandalism, crude theft, and spill events. Data were compiled from verified open sources, including the Nigeria Security Tracker (Council for Foreign Relations), domestic and international media reports, and industry and regulatory publications. These data were processed using reproducible filtering and classification scripts. Together, these datasets enable quantitative assessment of the physical scale and economic implications of crude oil theft in Nigeria. They also support comparative, temporal, and scenario-based analyses across research, policy, and public-interest applications. All data and supporting files are openly available in a Zenodo repository.
BACKGROUND: This study examines gender representation across the academic anatomy pipeline in Türkiye, from graduate education and thesis authorship to academic faculty ranks, and aims to identify where gender disparities emerge. METHODS: Data were obtained from three national open-access sources: annual reports on the numbers of master’s and doctoral students in anatomy programs (2015–2025), all anatomy theses published between 1969–2024, and the distribution of academic staff in anatomy departments as of March 2025. Gender was classified using publicly available institutional and academic profiles. These datasets were examined comparatively to map longitudinal patterns from training to senior academic ranks. RESULTS: A total of 1,657 theses were included; women accounted for 57.2% of authors, and their proportion increased from 42.9% in early decades to 64.8% in 2020–2024 (p<0.001). Between 2015–2025, women’s representation rose from 58.2% to 69.7% in master’s programs and from 40.1% to 54.7% in doctoral programs. Despite strong representation of women at trainee levels, only 31% of professors in 2025 were women, while their representation was markedly higher among research assistants (74.4%) and lecturers (69.6%). Academic rank and gender were significantly associated (p<0.001), indicating progressive attrition of women along the academic pathway. CONCLUSIONS: Although women enter the anatomy field in Türkiye in strong numbers and successfully complete graduate training, this increasing representation does not translate into senior academic positions. These findings show that women’s strong early-stage presence fails to reach senior ranks, exposing both clear leakage and suggesting marked delayed advancement in the academic pipeline.
Diverse tree communities can bolster urban ecosystem resilience and provide vital ecosystem services. However, existing urban tree species datasets have limited geographic coverage and contain inadequate attributes. To address those gaps, we developed the Global Urban Tree Species (GUTS) dataset by integrating data from literature, biodiversity databases, and other open sources. The new dataset encompasses 159,845 occurrence records of 10,094 tree species in 8,349 cities and 139 countries. Among them, 109,879 records were confirmed from urban areas, representing 11.18% of global tree species diversity. The dataset has been validated using multiple methods. GUTS fills critical data gaps and provides a foundation for future research and management of global urban biodiversity.
Aim: To investigate the negative and positive obligations of the state in ensuring the right of an individual to respect for human dignity in medical and legal relations, taking into account the practice of the European Court of Human Rights, and to reveal the protection of this right among drug addicts. Materials and Methods: The research methodology involves the analysis of national regulatory provisions, international treaties, legislative acts of Ukraine, and legal positions of the ECHR on the problems of implementing the right to respect for human dignity in medical and legal relations, as well as the case law of the European Court of Human Rights. Data analysis was conducted using open sources, mainly for the period 2010-2025. The main search keywords were "ECHR", "protection of rights", "health care", "legal regulation", "the right of an individual to respect for human dignity". The search criteria focused on modern scientific approaches and practical experience in ensuring the right of individuals to respect for human dignity in medical and legal relations. Sources that do not focus on medical-legal relations, that do not take into account the current practice of the ECHR, that do not comply with international human rights standards, legal acts or regulatory documents that contradict the practice of the ECHR were excluded from consideration. Conclusions: The study highlights cases of failure to fulfill negative obligations of a material nature of the studied right of individuals, among which the following are identified: improper performance by officials of their duties due to inaction (failure to provide medical services, which led to serious consequences); improper performance of official duties by officials of state bodies, as well as unlawful actions of law enforcement agencies of a deliberate nature (physical and psychological violence against persons in custody). An approach is proposed in which adherence to the principle of the supremacy of the law under study, taking into account the practice of the European Court of Human Rights, is the basis for respecting human rights and freedoms.
The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide.
Background/Objectives: To estimate, against the background of the upcoming German healthcare reform, current access to neurosurgery for patients in Germany, and to derive improvement strategies from geographic information mapping. Methods: We defined access to neurosurgery on a geographical basis as the sum of all points from which one can reach a neurosurgical department within 40 min by car (A2N40). We identified 182 departments of neurosurgery, and we retrieved population numbers and geodetic information from open sources. We processed data and conducted statistical analyses in R. Results: Population density and A2N40 per square kilometer were significantly positively correlated (Spearman's rho = 0.82, p = 0.0001). Population density is significantly lower (Wilcoxon rank sum test, p = 0.009) and A2N40 per square kilometer is significantly worse (Wilcoxon rank sum test, p = 0.005) in the new federal states (without Berlin) as compared to the rest of the country. Geographic information mapping yielded 3 distinct improvement strategies. Conclusions: In Germany, population density and A2N40 per square kilometer are significantly positively correlated, with significantly less A2N40 per square kilometer in the new federal states. Geographic mapping may inform tailored regional improvement policies.
BACKGROUNDPublic Health Intelligence (PHI) aims to detect health threats early for a timely and effective response. The PHI team at the Robert Koch Institute (RKI) uses the Epidemic Intelligence from Open Sources (EIOS) system in combination with other sources for detecting signals of international public health threats relevant to Germany. However, while EIOS is increasingly used for PHI worldwide, it is rarely evaluated.AIMWe designed and conducted an attribute-based evaluation to assess EIOS's performance for international PHI in 2023 and to identify areas for improvement.METHODSWe adapted surveillance system attributes and designed attribute-specific data collection methods. We conducted a mixed-method evaluation combining prospective and retrospective operational data collection with feedback from PHI officers.RESULTSDuring 2 weeks in July 2023, the PHI team reported 20 signals: 16 detected using EIOS and four from other sources. Increasing the number of EIOS sources increased timeliness and sensitivity slightly but caused a 35-fold increase in articles to screen (35,546 vs 1,138). The team found EIOS flexible and simple for signal detection but identified challenges in simplicity of signal documenting and reporting and in completeness of EIOS sources screened by the team.CONCLUSIONThe current use of EIOS proved sensitive and timely. However, PHI must balance sensitivity, timeliness and resource requirements. To maintain this balance, we strongly recommend regular evaluations of the use of EIOS for PHI. Our evaluation offers practical guidance for other PHI teams. We recommend integrating EIOS with an event management system to facilitate signal documentation and reporting.
A scientometric analysis of 3166 publications in the journals of the peer-reviewed Higher Attestation Commission on forensic medicine over a 20-year period (2005-2024) was performed using pubmed resources, mediasphera.ru, open sources. A search, sorting, and grouping of articles has been carried out that reflect topical issues of forensic medical assessment of defects (deficiencies) in the provision of medical care with a detailed analysis of publications regarding the inadequate provision of medical care in obstetrics and gynecology. Выполнен наукометрический анализ 3166 публикаций в журналах, рецензируемых ВАК, по судебной медицине за 20-летний период (2005—2024 гг.) с ресурсов PubMed, mediasphera.ru, открытых источников. Осуществлен поиск, сортировка, группировка статей, в которых отражены актуальные вопросы судебно-медицинской оценки дефектов (недостатков) оказания медицинской помощи, с подробным анализом публикаций в отношении ненадлежащего оказания медицинской помощи в акушерстве и гинекологии.
In the previous study [1], we showed an increased risk of malignant neoplasms in carriers of the minor allele rs1052133*G of the hOGG1 gene who were affected by chronic radiation exposure at a wide range of doses (up to 3507 mGy to the red bone marrow at the Techa River (Southern Urals). The objective of the present study was to assess the contribution of radiation factor to the risk of malignant neoplasm development in persons chronically exposed at the Techa River. For this purpose, we analyzed the background level of genetically determined risk in the general population of unexposed people on the basis of meta-analysis of the world literature data on the search for the association of rs1052133 of the hOGG1 gene with the risk of malignant neoplasm development. At the final stage, the results of the meta-analysis were compared with data on exposed people. The study found that unexposed and exposed carriers of the rs1052133*G allele had a comparable increased risk of developing malignant neoplasms, odds ratio OR = 1.20; 95% confidence interval [1.06-1.35], p = 0.01 and odds ratio OR = 1.38; 95% confidence interval [1.05-1.83], p = 0.023, respectively.
Emerging and re-emerging infectious diseases continue to pose significant threats to lower-middle-income countries (LMICs), particularly in Africa, where health systems are often resource-constrained and prone to disruptions. Drivers such as climate change, antimicrobial resistance, urbanization, civil war, poverty, globalization of travel, and high population mobility exacerbate outbreak risk and severity. This paper outlines how LMICs can strengthen health system resilience and respond more effectively to such threats by adopting practical tools and frameworks offered by the WHO Hub for Emergency Preparedness and Pandemic and Epidemic Intelligence. Key initiatives, including Epidemic Intelligence from Open Sources (EIOS), the International Pathogen Surveillance Network (IPSN), and Public Health Intelligence (PHI) capacity-building, support early detection, genomic surveillance, and data-driven decision-making. We highlight practical strategies for LMICs, including integration of Hub outputs into national policy, sustainable financing, workforce development, institutional strengthening, regional collaboration, and monitoring and evaluation. While challenges such as infrastructure gaps, data governance, and reliance on external funding remain, the WHO Hub provides a structured pathway for LMICs to enhance outbreak preparedness, reduce response times, and mitigate the impact of future epidemics. Not aplicable.
Background/Objectives: Health system socio-economic inequities in dental care are a long-standing problem in Europe. The issue gained increased relevance during the recent pandemic due to service disruption and socio-economic inequities that become even more pronounced under such circumstances. However, while preventive dental programs are considered key elements of public health, little is known about their role in addressing equity in accessing dental care among different countries and over time between them. This research aims at investigating the relationship between preventive dental policy, socio-economic factors, and the inability to get appropriate dental care within EU member states. Methods: A longitudinal panel dataset at the country level, consisting of data collected during 2020 through 2024, was assembled using open sources of statistics from Europe and other international statistical databases. The dependent variable used in the study was the percentage of the population that had unmet dental care need because of cost. Independent variables were the presence or absence of preventive policies related to dentistry, educational attainment, gross domestic product per capita, unemployment rate, number of dentists, and out-of-pocket expenses. Balanced panel datasets and regressions with robust standard errors in random-effects models were estimated. Interaction terms were created to test the moderating effect of education level on the relationship between policies and access to care. Results: Cross-country variations in terms of the prevention policy environment, socio-economic status, and unmet dental care need were found from descriptive analysis. The higher level of out-of-pocket payment was always related to the higher unmet dental care need, while the lower GDP countries displayed poorer access. Using the balanced panel random-effects model, preventive dental policies and the interaction between preventive policies and educational level were insignificant factors predicting the unmet dental care need. On the other hand, higher out-of-pocket payments, education, and dentists per million population had nearly significant positive relationships. In the sensitivity analysis, GDP per capita showed a negative association, whereas dentists per million population remained positively associated with unmet dental care need. Conclusions: The findings suggest that inequalities in access to dental care during and after the COVID-19 period were shaped primarily by financial and structural determinants rather than by the presence of preventive policies alone. While preventive programs remain an important component of long-term oral health strategies, reducing direct household payment burden and strengthening health system capacity may represent more immediate mechanisms for maintaining equitable access to dental services during periods of system disruption.
While reinforcement learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these causal RL (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide the existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov decision process (MDP), partially observed MDP (POMDP), multiarmed bandits (MABs), imitation learning (IL), and dynamic treatment regime (DTR). Each of them represents a distinct type of causal graphical illustration. Moreover, we summarize the evaluation matrices and open sources, while we discuss emerging applications, along with promising prospects for the future development of CRL.
Asthma is a chronic respiratory disorder requiring ongoing medical management. This ecological study investigated the spatial and temporal patterns of notification rates for asthma from clinic visits and hospital discharges and identified demographic, meteorological and environmental factors that drive asthma in Bhutan. Monthly numbers of asthma notifications from 2016 to 2022 were obtained from the Bhutan Ministry of Health. Climatic variables (rainfall, relative humidity, minimum and maximum temperature) were obtained from the National Centre for Hydrology and Meteorology, Bhutan. The Normalised Difference Vegetation Index (NDVI) and surface particulate matter (PM2.5) were extracted from open sources. A multivariable zero-inflated Poisson regression (ZIP) model was developed in a Bayesian framework to quantify the relationship between risk of asthma and sociodemographic and environmental correlates, while also identifying the underlying spatial structure of the data. There were 12 696 asthma notifications, with an annual average prevalence of 244/100 000 population between 2016 and 2022. In ZIP analysis, asthma notifications were 3.4 times (relative risk (RR)=3.39; 95% credible interval (CrI) 3.047 to 3.773) more likely in individuals aged >14 years than those aged ≤14 years, and 43% (RR=1.43; 95% CrI 36.5% to 49.2%) more likely for females than males. Asthma notification increased by 0.8% (RR=1.008, 95% CrI 0.2% to 1.5%) for every 10 cm increase in rainfall, and 1.7% (RR=1.017; 95% CrI 1.2% to 2.3%) for a 1°C increase in maximum temperature. An increase in one unit of NDVI and 10 µg/m3 PM2.5 was associated with 27.3% (RR=1.273; 95% CrI 8.7% to 49.2%), and 2.0% (RR=1.02; 95% CrI 1.0% to 4.0%) increase in asthma notification, respectively. The high-risk spatial clusters were identified in the south and southeastern regions of Bhutan, after accounting for covariates. Environmental risk factors and spatial clusters of asthma notifications were identified. Identification of spatial clusters and environmental risk factors can help develop targeted interventions that maximise impact of limited public health resources for controlling asthma in Bhutan.
Detailed data on hard-to-abate industrial sectors is crucial for developing targeted decarbonization measures in energy system modeling, yet such information is rarely available through open sources. This paper presents a top-down methodology to estimate detailed industrial site-level energy and emissions databases by integrating and expanding publicly available data. The methodology addresses three key challenges: (1) the disaggregation of national energy consumption data to site level, (2) the categorization of process heat by four temperature ranges (<100 °C, 100 °C-500 °C, 500 °C-1000 °C, and >1000 °C) and direct use of electricity, and (3) the integration of process emissions from feedstock use in hard-to-abate industrial sectors. The approach is demonstrated through application to the Italian industrial sector for the year 2022, resulting in a database that documents site-specific consumption across seven energy sources: solid fossil fuels, manufactured gases, oil and petroleum products, natural gas, biofuels, non-renewable wastes, naphtha and electricity. The method can be replicated for other European countries, providing researchers and policymakers with a standardized approach to create detailed industrial energy databases. Results show that the chemical and petrochemical sector dominates the industrial energy landscape of Italy, followed by iron and steel, non-metallic minerals, and paper and pulp. The geographical distribution reveals a concentration of major industrial facilities in northern Italy, with notable exceptions including significant steel production in Taranto (south) and petrochemical complexes in Sicily and Sardinia.
Dengue fever is one of the world's most important re-emerging but neglected infectious diseases. We aimed to develop and evaluate an integrated risk assessment framework to enhance early detection and risk assessment of potential dengue outbreaks in settings with limited routine surveillance and diagnostic capacity. Our risk assessment framework utilizes the combination of various methodological components: We first focused on (I) identifying relevant clinical signals based on a case definition for suspected dengue, (II) refining the signal for potential dengue diagnosis using contextual data, and (III) determining the public health risk associated with a verified dengue signal across various hazard, exposure, and contextual indicators. We then evaluated our framework using (i) historical clinical signals with syndromic and laboratory-confirmed disease information derived from WHO's Epidemic Intelligence from Open Sources (EIOS) technology using decision tree analyses, and (ii) historical dengue outbreak data from Tanzania at the regional level from 2019 (6,795 confirmed cases) using negative binomial regression analyses adjusted for month and region. Finally, we evaluated a test signal across all steps of our integrated framework to demonstrate the implementation of our multi-method approach. The result of the suspected case refinement algorithm for clinically defined syndromic cases was consistent with the laboratory-confirmed diagnosis (dengue yes or no). Regression between confirmed dengue fever cases in 2019 as the dependent variable and a site-specific public health risk score as the independent variable showed strong evidence of an increase in dengue fever cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])). The framework can be used to rapidly determine the public health risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. It further allows for flexibility in its adaptation to target diseases and geographical contexts.
Antimicrobial resistance (AMR) is a critical public health issue, with overuse of antibiotics being a key driver. This study aimed to examine the determinants of antibiotic prescription in primary care in France, using nationwide panel data from 2022. Data were obtained from several open sources. Antibiotic consumption was measured by the number of prescriptions of all systemic antibiotics per 1000 inhabitants, and patient, physician, healthcare system and seasonal viral outbreak (influenza and COVID-19) were considered as potential related factors. We then performed a linear multivariate regression model. The main findings were that patients <15 years (β = 7.36, P < 0.001), females (β = 9.54, P = 0.01), those with chronic diseases (β = 16.29, P < 0.001), white-collar workers (β = 3.40, P < 0.001) and European Deprivation Index score (β = 4.19, P < 0.001) had higher antibiotic prescription rates. Older physicians (age > 50 years: β = 1.35, P < 0.001) and those practising in areas with higher healthcare accessibility (Local Potential Accessibility score: β = 40.93, P < 0.001) were also associated with higher prescription volumes. In contrast, female physicians were linked to lower prescription rates (β = -0.62, P = 0.002). The study emphasizes the complexity of antibiotic prescription behaviours, showing that both clinical and non-clinical factors contribute to prescription patterns. It also highlights social and accessibility factors as significant drivers of antibiotic use. In order to be effective, strategies for the correct use of antibiotics must account for these different aspects.
In 2023, Republic of Korea's Korea Disease Control and Prevention Agency (KDCA) enhanced its event-based surveillance practices by using the World Health Organization's (WHO) Epidemic Intelligence from Open Sources (EIOS) to actively screen and share information about potential public health threats to the country. This report describes the preliminary assessment of the results of implementing these enhanced event-based surveillance activities from June to October 2023. During this period, 425 (0.4%) events were detected globally by the KDCA from 99 945 media articles, with the highest frequency reported in Asia (185, 43.5%) and North America (81, 19.1%). The most frequently reported diseases or conditions were dengue fever (111, 26.1%) and mpox (32, 7.5%). Eight events were detected early by the KDCA using EIOS before being officially listed on WHO's Event Information Site (EIS) or in Disease Outbreak News (DON), with an average interval of 20 days (range: 5-41) between the detection date and posting on EIS or DON. Thus, EIOS is efficient in aiding early detection of potential public health threats at the national level. This finding highlights the importance of sustaining international cooperation and support to enhance surveillance capabilities in resource-limited settings and expanding the scope of EIOS, including by incorporating additional sources and sources in additional languages, reducing noise. However, as the current report is based on a descriptive analysis, in the future a systematic evaluation of event-based surveillance using EIOS to identify relevant attributes will need to be conducted.
BackgroundT-cell exhaustion (TEX) in the tumor microenvironment causes immunotherapy resistance and poor prognosis.ObjectiveWe used bioinformatics to identify crucial TEX genes associated with the molecular classification and risk stratification of lung adenocarcinoma (LUAD).MethodsBulk RNA sequencing data of patients with LUAD were acquired from open sources. LUAD samples exhibited abnormal TEX gene expression, compared with normal samples. TEX gene-based prognostic signature was established and validated in both TCGA and GSE50081 datasets. Immune correlation and risk group-related functional analyses were also performed.ResultsEight optimized TEX genes were identified using the LASSO algorithm: ERG, BTK, IKZF3, DCC, EML4, MET, LATS2, and LOX. Several crucial Kyoto encyclopedia of genes and genomes (KEGG) pathways were identified, such as T-cell receptor signaling, toll-like receptor signaling, leukocytes trans-endothelial migration, Fcγ R-mediated phagocytosis, and GnRH signaling. Eight TEX gene-based risk score models were established and validated. Patients with high-risk scores had worse prognosis (P < 0.001). A nomogram model comprising three independent clinical factors showed good predictive efficacy for survival rate in patients with LUAD. Correlation analysis revealed that the TEX signature significantly correlated with immune cell infiltration, tumor purity, stromal cells, estimate, and immunophenotype score.ConclusionTEX-derived risk score is a promising and effective prognostic factor that is closely correlated with the immune microenvironment and estimated score. TEX signature may be a useful clinical diagnostic tool for evaluating pre-immune efficacy in patients with LUAD.