Annual Fatality Research Articles

1.G. Scientific session: Forecasting dietary habits trends, human and planetary health

Abstract Extensive research has elucidated that dietary factors are pivotal in influencing both human health and the ecological stability of the planet. Suboptimal dietary patterns are implicated in over 10 million annual fatalities attributable to non-communicable diseases. Concurrently, the agricultural sector, a major component of food production, contributes significantly to environmental degradation. This sector is responsible for more than a quarter of global greenhouse gas emissions and utilizes approximately 50% of the planet’s habitable land. Moreover, it accounts for over 70% of global freshwater consumption and is a primary contributor to marine ecosystem disruption through nutrient runoff leading to ocean eutrophication. Despite these critical linkages, current global trends related to dietary practices show little alignment with the improvements needed to achieve the Sustainable Development Goals set for 2030. The existing policy framework and interventions lag considerably in addressing these concerns. Historical and ongoing discussions have repeatedly highlighted these issues, yet the allocation of resources necessary to counteract the pervasive influence of industry-driven marketing promoting less healthful dietary choices remains markedly inadequate. There is an urgent need to intensify dialogues and enhance the conceptual framework regarding the role of diet in sustaining global health and environmental integrity among public health professionals. Such measures are crucial for facilitating a shift towards more sustainable and health-promoting dietary habits globally. The EUPHA Food and Nutrition Section, the Chronic Diseases Section, in collaboration with the World Health Organization (WHO) Regional Office for Europe, aim to propose a joint workshop to share innovative research and activities held in the context of diet and environment. The workshop aims to provide new findings and stimulate the debate about the projections for the future of human and planetary health. Key messages • Human health and environmental preservation require an adaptation of dietary habits globally: are we going in the right direction? • Current evidence suggests that there is an wide area of intervention to improve actual dietary habits, reduce the environmental impact, and improve human health.

Robots as guardians: Industrial automation and workplace safety in China

Industrial robots can improve workplace safety by performing hazardous tasks on behalf of workers. This paper examines the impact of industrial robots on workplace safety in China. We find that a one-standard-deviation increase in robot exposure reduces annual workplace accidents and fatalities by 0.100 and 0.0133 cases per thousand population, compared to sample averages of 0.122 accidents and 0.0351 fatalities. These findings are robust to an instrumental variable strategy and various robustness checks. Our analysis of injuries in household surveys and Baidu search activities reinforces these results. Using an accounting framework, we show that the safety improvement is not driven by the mechanical effects of robot-induced employment reduction. Instead, within-occupation improvement in workplace safety plays a more crucial role.

Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems.

Drowning Among Children 1-4Years of Age in California, 2017-2021.

Drowning, the leading cause of unintentional injury death among California children less than five years of age, averaged 49 annual fatalities for the years 2010-2021. The California Pool Safety Act aims to reduce fatalities by requiring safety measures around residential pools. This study was designed to analyze annual fatality rates and drowning incidents in California among children 1-4years of age from 2017-2021. We identified fatalities, injury hospitalizations, and emergency department (ED) visits from California state vital statistics death data and state hospital and ED discharge data using the EpiCenter California Injury Data Online website. Over the five-year study period, 4,166 drowning incidents were identified: 234 were fatalities, 846 were hospitalizations, and 3,086 were ED visits. The observed difference in fatality rates from 2017 to 2021 failed to achieve statistical significance (P = 0.88). Location-based analysis of the 234 fatal drowning incidents revealed that pools were the most common injury site, accounting for 65% of the cases. Drowning remains the leading cause of unintentional, injury-related death among California children 1-4years of age, as the annual rate of fatality over the five-year study period did not decline. While the EpiCenter California Injury Data Online website is excellent for analyzing annual rates of drowning incidents among California residents over time, it is limited in providing insight into modifiable risk factors and event circumstances that can further inform prevention. The development of robust integrated fatal and non-fatal local, state, and national systematic data collection systems could aid in moving the needle in decreasing pool fatalities among young children.

AP3B1 facilitates PDIA3/ERP57 function to regulate rabies virus glycoprotein selective degradation and viral entry

ABSTRACT Rabies virus causes an estimated 59,000 annual fatalities worldwide and promising therapeutic treatments are necessary to develop. In this study, affinity tag-purification mass spectrometry was employed to delineate RABV glycoprotein and host protein interactions, and PDIA3/ERP57 was identified as a potential inhibitor of RABV infection. PDIA3 restricted RABV infection with follow mechanisms: PDIA3 mediated the degradation of RABV G protein by targeting lysine 332 via the selective macroautophagy/autophagy pathway; The PDIA3 interactor, AP3B1 (adaptor related protein complex 3 subunit beta 1) was indispensable in PDIA3-triggered selective degradation of the G protein; Furthermore, PDIA3 competitively bound with NCAM1/NCAM (neural cell adhesion molecule 1) to block RABV G, hindering viral entry into host cells. PDIA3 190-199 aa residues bound to the RABV G protein were necessary and sufficient to defend against RABV. These results demonstrated the therapeutic potential of biologics that target PDIA3 or utilize PDIA3 190-199 aa peptide to treat clinical rabies. Abbreviation: aa: amino acids; ANXA2: annexin A2; AP-MS: affinity tag purification-mass spectrometry; AP3B1: adaptor related protein complex 3 subunit beta 1; ATP6V1A: ATPase H+ transporting V1 subunit A; ATP6V1H: ATPase H+ transporting V1 subunit H; BafA1: bafilomycin A1; CHX: cycloheximide; co-IP: co-immunoprecipitation; DDX17: DEAD-box helicase 17; DmERp60: drosophila melanogaster endoplasmic reticulum p60; EBOV: Zaire ebolavirus virus; EV: empty vector; GANAB: glucosidase II alpha subunit; G protein: glycoprotein; GRM2/mGluR2: glutamate metabotropic receptor 2; HsPDIA3: homo sapiens protein disulfide isomerase family A member 3; IAV: influenza virus; ILF2: interleukin enhancer binding factor 2; KO: knockout; MAGT1: magnesium transporter 1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MmPDIA3: mus musculus protein disulfide isomerase associated 3; NCAM1/NCAM: neural cell adhesion molecule 1; NGFR/p75NTR: nerve growth factor receptor; NGLY1: N-glycanase 1; OTUD4: OTU deubiquitinase 4; PDI: protein disulfide isomerase; PPIs: protein-protein interactions; RABV: rabies virus; RUVBL2: RuvB like AAA ATPase 2; SCAMP3: secretory carrier membrane protein 3; ScPdi1: Saccharomyces cerevisiae s288c protein disulfide isomerase 1; SLC25A6: solute carrier family 25 member 6; SQSTM1/p62: sequestosome 1; VSV: vesicular stomatitis virus.

Exploring the Potential of Artificial Intelligence in enhancing Driver Education and Road Safety in the Philippines

Road safety remains a pressing concern in the Philippines, with annual high traffic fatalities and injuries. This study delves into the potential of Artificial Intelligence (AI) to enhance driver education and road safety in the country. The Philippines faces challenges in its transportation system due to driver behavior issues and a need for more discipline. To address this, the study explores the integration of AI into driving simulators and education, aiming to develop more skilled and responsible drivers.

Safety and immunogenicity of the intranasal H3N2 M2-deficient single-replication influenza vaccine alone or coadministered with an inactivated influenza vaccine (Fluzone High-Dose Quadrivalent) in adults aged 65–85 years in the USA: a multicentre, randomised, double-blind, double-dummy, phase 1b trial

Older adults (aged ≥65 years) show increased susceptibility to severe disease with influenza virus infection, accounting for 70-85% of annual influenza-related fatalities in the USA. Stimulating mucosal antibodies and T cells might enhance the low vaccine effectiveness seen in older adults for currently licensed inactivated influenza vaccines, which induce mainly serum antibodies. We aimed to evaluate the safety and immunogenicity of the intranasal H3N2 M2-deficient single-replication (M2SR) vaccine, alone or coadministered with a licensed inactivated influenza vaccine (Fluzone High-Dose Quadrivalent; hereafter referred to as Fluzone HD), in older adults. In this multicentre, randomised, double-blind, double-dummy, phase 1b trial, individuals aged 65-85 years who were considered healthy or with stable chronic conditions with no recent (<6 months) influenza vaccinations were recruited from five clinical trial sites in the USA and randomly assigned (3:3:3:1) using a permuted block design to receive the H3N2 M2SR vaccine and Fluzone HD, the H3N2 M2SR vaccine and placebo, Fluzone HD and placebo, or placebo alone. All participants received a single intranasal spray and a single intramuscular injection, whether active or placebo, to maintain masking. The primary outcome was to assess the safety of H3N2 M2SR, administered alone or with Fluzone HD, in the safety analysis set, which included all participants who were randomly assigned and received treatment. Serum and mucosal antibodies were assessed as a secondary endpoint, and cell-mediated immunity as an exploratory endpoint, in participants in the per-protocol population, which included individuals in the safety analysis set without major protocol deviations. This trial is registered with ClinicalTrials.gov, NCT05163847. Between June 14 and Sept 15, 2022, 305 participants were enrolled and randomly assigned to receive the H3N2 M2SR vaccine plus placebo (n=89), H3N2 M2SR vaccine plus Fluzone HD (n=94), Fluzone HD plus placebo (n=92), or placebo alone (n=30). All randomly assigned participants were included in the safety analysis set. The most frequently reported local symptoms up to day 8 in groups that received M2SR were rhinorrhoea (43% [38 of 89] in the H3N2 M2SR plus placebo group and 38% [36 of 94] in the H3N2 M2SR plus Fluzone HD group), nasal congestion (51% [45 of 89] and 35% [33 of 94]), and injection-site pain (8% [seven of 89] and 49% [46 of 94]), and the most frequently reported solicited systemic symptoms were sore throat (28% [25 of 89]) for M2SR and decreased activity (26% [24 of 94]) for the M2SR plus Fluzone HD group. In the Fluzone HD plus placebo group, the most frequently reported local symptom was injection-site pain (48% [44 of 92]) and systemic symptom was muscle aches (22% [20 of 92]). The frequency of participants with any treatment-emergent adverse event related to vaccination was low across all groups (2-5%). One serious adverse event was reported, in a participant in the Fluzone HD plus placebo group. M2SR with Fluzone HD induced seroconversion (≥four-fold increase in haemagglutination inhibition antibodies from baseline to day 29) in 44 (48%) of 91 participants, compared with 28 (31%) of 90 participants who seroconverted in the Fluzone HD plus placebo group (p=0·023). M2SR with Fluzone HD also induced mucosal and cellular immune responses. The H3N2 M2SR vaccine coadministered with Fluzone HD in older adults was well tolerated and provided enhanced immunogenicity compared with Fluzone HD administered alone, suggesting potential for improved efficacy of influenza vaccination in this age group. Additional studies are planned to assess efficacy. US Department of Defense.

Mitigating the Risk of Dengue Fever in Nagpur City, India: Insights from a Five-year Retrospective Study

Objectives: Dengue is a significant global threat, with millions of infections yearly, primarily transmitted by Aedes mosquitoes. Vector control is crucial as no specific treatment is available, and urbanisation has led to increased susceptibility to the disease. We aimed to study dengue fever’s incidence, mortality, and various determinants in Nagpur, India. Material and Methods: This was a retrospective record-based study conducted for the period between January 2018 and December 2022 in Nagpur, India. Results: During 2018–2022, 17,306 blood samples were tested for dengue immunoglobulin M antibodies by enzyme-linked immunosorbent assay method with 2470 positive results indicating an overall test positivity of 14.27%. A median of 27 samples was tested monthly (interquartile range [IQR]: 5–178.5) with seven dengue cases (IQR: 1–15) diagnosed per month, yielding a monthly median dengue test positivity rate of 15.3 % (IQR: 6.41–24.28). Median annual dengue incidence rate (per 1,00,000 population) was 20.12 (IQR: 21.46). The median dengue-attributable death yearly was 2 (IQR: 2.25), remaining stable over the study period. The mean annual case fatality rate (CFR) was 0.43% (standard deviation [SD]: 0.34%). During the same period, India’s mean annual dengue incidence rate (per 1,00,000 population) was 1.74 (SD: 0.81), and the mean annual CFR was 0.134 % (SD: 0.042). During the five years, action was taken on 194,698 containers. The median values of environmental factors and public health interventions, as calculated on a monthly basis, were as follows: Container index: 1.15 % (IQR: 0.7–1.98); action taken on containers: 3108.5 (IQR: 2436–4282); house index: 3.02 % (IQR: 2–4.07); guppy fishes introduced: 1736.5 (1005.5–1794.5) and rainfall (mm): 26.05 (1–69.11). The total number of dengue cases, incidence rate, and CFR showed an overall decline in year-wise evaluation. The important determinants identified were as follows: Higher container index, house index, and total blood samples tested for dengue were significant determinants of the total number of dengue cases, and environmental factors such as Aedes density, container index, and house index were significant determinants of dengue-related mortality. Introducing guppy fishes and taking action on containers successfully arrested the absolute number of dengue deaths. The container index, house index, and rainfall were significant determinants of Aedes’ density. Conclusion: Our study emphasises the effectiveness of public health interventions in reducing the incidence and fatality rate of dengue fever in Nagpur City over the past five years, particularly through introducing guppy fish and container management. Stagnant water around households has been identified as a significant risk factor for dengue transmission. Prevention strategies must prioritise tackling this issue, with enhanced monitoring during the wet season, and encourage community members to take personal responsibility for eliminating stagnant water sources in their households.

Green-Synthesized Characterization, Antioxidant and Antibacterial Applications of CtAC/MNPs-Ag Nanocomposites.

The emergence of antibiotic resistance, caused by the improper use of antibiotics, is a significant challenge in combating infectious diseases, leading to millions of annual fatalities. The occurrence of antimicrobial side effects catalyzes the investigation of novel antimicrobial compounds and sources of drugs. Consequently, the research on biological activity that is conducted on plants, plant extracts, and compounds that are produced from plant components is of utmost significance. In this study, CtAC/MNPs were obtained by the reaction of activated carbon (AC) obtained from the fruits of the Celtis tournefortii (Ct) plant and magnetic nanoparticles (MNPs), and a CtAC/MNPs-Ag nanocomposite was synthesized by the reduction in silver ions added to the reaction. The synthesized CtAC/MNPs and CtAC/MNPs-Ag nanocomposites were analyzed spectroscopically (FTIR, XRD), microscopically (SEM, EDX), optically (DLS), electrochemically (zeta potential) and magnetically (VSM). The antibacterial activities of CtAC/MNPs and CtAC/MNPs-Ag nanocomposites against S. aureus and E. coli were investigated by microdilution method using minimal inhibitory concentration (MIC) and disk diffusion methods. Antioxidant activity study, including total phenolic content and DPPH and cuprac assays, revealed the remarkable effect of the CtAC/MNPs-Ag nanocomposite. This study has the advantages of obtaining CtAC/MNPs and CtAC/MNPs-Ag nanocomposites in a short time without requiring energy, and most importantly, the reaction takes place without using any toxic substances. In addition, according to the data obtained in the study, the CtAC/MNPs-Ag nanocomposite is thought to shed light on biomedical research.

Study of Various Materials for Automobile Bumper: A Review

India leads the world in annual road fatalities (230,000), despite being a top-ten car market. This discrepancy highlights the urgent need for stricter vehicle safety regulations. While concerns regarding an 8-15% increase in car prices due to stricter standards exist, the potential for global car exports and significant lives saved outweighs these costs. This review analyses the critical role car bumpers play in mitigating accident severity. By absorbing impact energy and protecting occupants and vehicles, bumpers directly address a crucial gap in current Indian automotive safety. Implementing stringent safety regulations, including robust bumper testing programs, can significantly reduce road fatalities and injuries, aligning India's car safety standards with global best practices and fostering a safer transportation ecosystem.

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques.

Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features-age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality-and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.

Advanced Vehicle Detection and Tracking on Roads Using Deep Learning

Abstract: The world has been overtaken by artificial intelligence (AI), machine learning, and deep learning, which are affecting every aspect of our lives, including our need for companionship and the growth of smart cities and communities. Though tremendous progress has been made in fields like healthcare and education, transportation. Transportation is a significant barrier that has a huge financial and human cost on a worldwide scale. Traditional methods of assessing traffic on roads, which rely on inductive loops, don't give much information and can't control traffic in real-time. This study describes how the transportation sector could undergo a deep learning revolution. Deep learning techniques, including image and video analysis, have the potential to revolutionize road traffic monitoring by enabling computers to recognize traffic congestion, enhance road safety, and address various other concerns. The integration of deep learning technologies promises a new era of data-driven solutions for transportation-related issues, which could ultimately result in safer, more effective, and economically sustainable societies in the face of over a million annual fatalities and trillions of dollars in damages. In order to address these problems, this paper shows how well YOLOv3 detects automobiles and how well Darknet53 tracks these vehicles while they are on the road.

Effect of Inhalation Exposure to Nano-Imidacloprid on Liver and Kidney Functions In Male Rats

Pesticide poisoning is a serious global public health issue and is responsible for a sizable number of annual fatalities. This study was designed to examine the potentially harmful effects of adult rats being exposed to imidacloprid (IMD) as a nanoparticle by determining the chronic effect of inhalation of (5,10 and 20) mg/kg/b.w. of nano-imidacloprid for a duration of 60 days. The most important biochemical parameters of the serum liver function parameters were aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase ALP, kidney function [blood urea, creatinine, and urea], and oxidative stress parameters (MDA, GSH, and CAT) in all treated groups when compared to the control group. The exposure caused a highly significant decrease (P≤0.05) for (AST) and (ALT). No significant changes were observed, while a highly significant decrease was recorded for (ALP) in all treatments with nano-imidacloprid compared with the control. On the other hand, a highly significant decrease in blood urea, creatinine, and uric acid was recorded in all groups treated with nano-imidacloprid compared with the control. Oxidative stress parameters revealed a highly significant (P≤0.05) increase in malondialdehyde (MDA) after 60 days of exposure and a decline in reduced glutathione (GSH) and catalase activity CAT.

A survey on drowsiness detection system with advanced face tracking

To address the increasing dangers associated with driver and worker fatigue, this project introduces an advanced Drowsiness Detection System featuring state-of-the-art face-tracking capabilities. The pressing need for fatigue detection is evident in the alarming figures of 800 annual fatalities and 50,000 injuries resulting from drowsy driving incidents. This research expands the application of the technology to industrial workplaces, where the consequences of drowsiness are equally severe. Our comprehensive approach involves real-time monitoring of facial features, with a focus on eye movements and eyelid patterns. This system goes beyond traditional boundaries, covering drivers and industrial workers operating heavy machinery. By incorporating facial landmarks and introducing the innovative Eyes Aspect Ratio parameter, our technology offers a precise assessment of weariness in individuals within the current frame. This approach enhances safety measures in smart transportation systems and industrial settings. Integrating facial landmarks allows for a nuanced understanding of fatigue, recognizing subtle changes in facial expressions and movements indicative of drowsiness. The Eyes Aspect Ratio parameter, a novel addition, improves weariness assessment precision by considering factors such as eye closure duration, blink frequency, and gaze direction. These outcomes signify a significant contribution to road safety and broader workplace security. By mitigating inherent risks associated with drowsiness among industrial workers, our technology aims to reduce accidents, prevent injuries, and save lives across various occupational settings. The potential impact extends beyond individuals, influencing organizational safety protocols and contributing to the overall well-being of workers in high-risk environments.

Evaluating human exposure to antibiotic resistance genes

Antibiotic resistance is an escalating global concern, leading to millions of annual fatalities. Antibiotic resistance genes (ARGs) present in bacteria equip them to withstand the effects of antibiotics. Intra- and interspecific ARGs transmission through horizontal gene transfer further exacerbates resistance dissemination. The presence of ARGs in the environment heightens the probability of human exposure via direct inhalation, ingestion, or contact with polluted air, food, or water, posing substantial biosafety and health hazards. Consequently, ARGs represent a critical focal point in public health and environmental safety and are classified as emerging contaminants. This perspective underscores the necessity to assess ARG exposure within the One Health framework and to accord greater attention to the mitigation strategies and tactics associated with ARGs.

Allosteric inhibition of tRNA synthetase Gln4 by N-pyrimidinyl-β-thiophenylacrylamides exerts highly selective antifungal activity

Candida species are among the most prevalent causes of systemic fungal infections, which account for ∼1.5 million annual fatalities. Here, we build on a compound screen that identified the molecule N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), which strongly inhibits Candida albicans growth. NP-BTA was hypothesized to target C.albicans glutaminyl-tRNA synthetase, Gln4. Here, we confirmed through invitro amino-acylation assays NP-BTA is a potent inhibitor of Gln4, and we defined how NP-BTA arrests Gln4's transferase activity using co-crystallography. This analysis also uncovered Met496 as a critical residue for the compound's species-selective target engagement and potency. Structure-activity relationship (SAR) studies demonstrated the NP-BTA scaffold is subject to oxidative and non-oxidative metabolism, making it unsuitable for systemic administration. In a mouse dermatomycosis model, however, topical application of the compound provided significant therapeutic benefit. This work expands the repertoire of antifungal protein synthesis target mechanisms and provides a path to develop Gln4 inhibitors.

A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on “Early Heart Disease Prediction using Boosting Techniques”, this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost’s superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction.

Abstract C079: Targeting cancer stem cell OXPHOS with tailored ruthenium complexes as a new approach to treat pancreatic cancer

Abstract Background and aims: Annual world-wide cancer-related fatalities remain high for pancreatic ductal adenocarcinoma (PDAC), largely due to the presence of “stem-like” tumor cells known as cancer stem cells (CSCs). Previous studies by our group have shown that oxidative phosphorylation (OXPHOS) is the main pathway by which pancreatic CSCs meet their energetic requirements; therefore, OXPHOS represents an Achille’s heel of these highly tumorigenic cells. Methods: The anti-CSC activity of a ruthenium complex featuring bipyridine and terpyridine ligands and one coordination labile position (Ru1) was evaluated across primary PDAC cultures and 8 patient-derived xenografts (PDXs). RNAseq analysis followed by mitochondria-specific molecular assays were used to determine the mechanism of action. Results: We show that Ru1 is capable of inhibiting CSC OXPHOS function in vitro, and more importantly, it presents excellent anti-cancer activity, with low toxicity, across a large panel of PDXs. Mechanistic studies suggest that this activity stems from Ru1 binding to the D-loop region of the mitochondrial DNA of CSCs, inhibiting OXPHOS complex-associated transcription, leading to reduced mitochondrial oxygen consumption, membrane potential, and ATP production, all of which are necessary for PaCSCs that heavily depend on mitochondrial respiration. Conclusion Overall, the coordination complex Ru1 represents not only an exciting new anti-cancer agent, but also a molecular tool to dissect the role of OXPHOS in CSCs. Results indicating that the compound is non-toxic and highly effective in vivo are extremely exciting, and have allowed us to uncover an unprecedented approach to fight PDAC based on targeting CSC OXPHOS. Citation Format: Bruno Jr Sainz, Sonia Alcalá, Lara Villarino, Laura Ruíz-Cañas, José Couceiro, Miguel Martínez-Calvo, Adrián Palencia-Campos, Diego Navarro, Pablo Cabezas-Sainz, Alfonso Cordero-Barreal, Juan A. Rubiolo, Cristina González-Páramos, Alfredo Carrato, Patrick C. Hermann, Laura Sánchez, Susana Torrente, Miguel Ángel Fernández-Moreno, José L. Mascareñas. Targeting cancer stem cell OXPHOS with tailored ruthenium complexes as a new approach to treat pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C079.

Trends in fatalities from road traffic injuries and associated factors of patients admitted to the emergency department of St. Luke Hospital, South West Shewa Zone, Ethiopia

IntroductionRoad traffic injuries are a major global public health problem. They are fatal or non-fatal injuries resulting from a road traffic accident involving at least one moving vehicle on a path or road open to public traffic. Therefore, this study aimed to determine the trends in fatalities from road traffic injuries and the associated factors of patients admitted to the emergency department of St. Luke Hospital.MethodsAn institution-based cross-sectional study was conducted from July 10 to 30, 2019. The study on the trends in road traffic injuries was conducted monthly bases from January 1 to December 31, 2018. A total of 364 road traffic–injured victim records were included. A simple random sampling technique was used for the record selection. A structured data abstraction format was used to gather the required data. Binary logistic regression and multivariate logistic regression were used to determine the association between the outcome and explanatory variables. Adjusted odds ratios (AORs) at 95% confidence intervals were used to report independent variables.Results and conclusionsThe average annual fatalities from road traffic injuries was 8.34% deaths/month. The magnitude of road traffic injury fatalities was 8.79%. Of all victims who visited the hospital, 75.5% were male. The median age of the victims was 26 (51.6%), with a range from 6 to 70. The identified risk factors for road traffic injury fatalities were a lack of ambulance for transportation to the hospital after the accident (AOR = 7.093, 95% CI [1.950, 25.793], p = 0.003) and head injury (AOR = 2.515, 95% CI [1.075, 5.885], p = 0.034). Road traffic injury fatalities are an important public health problem that needs special attention. A shortage of ambulance service for transportation and injury sites related to head injury were associated factors. So it is important to establish and strengthen advanced prehospital care and an effective ambulance system for transporting victims with road traffic injuries.

Exploring the Utility of Auto-machine Learning in Predicting Traffic Accident Severity in Jordan

The World Health Organization (WHO) estimates that approximately 1.35 million annual fatalities are caused by road traffic accidents, representing a significant global challenge. This study focuses on creating a machine-learning model to forecast traffic accident severity in Jordan. The objective of this study is to help reduce fatalities and economic losses caused by accidents, which can achieve using various machinelearning classifiers, like Decision Trees, Random Forests, Light Gradient Boosting Machines, Extra Trees, Bagging Classifiers, and Gradient Boosting. These models were assessed before and after down-sampling to deal with imbalance in accuracy, balanced accuracy, recall, precision, and F1-score metrics with and without hyper parameter tuning. The study emphasized the importance of advanced analytics in improving road safety measures and reducing accident severity. The researchers analyzed a dataset of 115,148 accidents. Factors considered included traffic volume, environmental conditions, and road geometry features. The data was segmented into urban and rural categories for customized modeling. Down-sampling improved the models' ability to detect injuries and deaths in both urban and rural areas. Hyper parameter tuning offered an additional improvement in balanced recall and F1-score, particularly after down-sampling. In urban areas, the LGBM and Gradient Boosting classifiers showed the most significant gains in recall for minority classes, while in rural areas the Random Forest, Bagging, and Extra Trees classifiers maintained a better balance between precision and recall. All classifiers achieved high accuracy (above 0.97) in both urban and rural areas, so that these models can accurately forecast the severity of accidents, including property damage, fatalities, and injuries. The authors have also recommended incorporating variables of temporal patterns, driver behavior, demographic data, and geographic location into the models of accident prediction to make them more efficient and stronger. Keywords: Accident severity prediction, Auto-machine learning, Accident classifiers, Python, Jordan.