Critical Risk Research Articles

The tryptophan-aspartate (WD) repeat domain of bovine Coronin-1A promotes mycobacterial survival by inhibiting calcium signaling-mediated phagosome-lysosome fusion

Bovine tuberculosis is a chronic consumptive zoonosis, causing significant economic losses and critical public health risks. Coronin-1A is a host cytoskeleton-associated protein that is crucial for understanding the inhibition of phagosome-lysosome fusion by Mycobacterium tuberculosis (M.tb) to evade host innate immune clearance. However, the involvement of bovine Coronin-1A (bCoronin-1A) in M.tb infection and whether it can be manipulated so as to enhance host resistance against bovine tuberculosis remains to be seen. Here, we explored the role of bCoronin-1A in phagosome-lysosome fusion in M.tb-infected macrophages. We found that bCoronin-1A was upregulated at both the transcriptional and protein levels following M.tb infection of embryonic bovine lung (EBL) cells. Notably, bCoronin-1A was recruited to M.tb-containing phagosomes where it hindered phagosome-lysosome fusion, leading to increased intracellular mycobacterial survival. Further investigation revealed that mycobacterial lipoamide dehydrogenase C (LpdC) interacted with a single tryptophan-aspartate (WD) unit within the WD repeat domain of bCoronin-1A to sequester it on the phagosomes. The WD repeat domain mediated a decrease in intracellular calcium levels, which reduced levels of calmodulin-dependent kinase II (CaMKII) and its activated forms, thereby inhibiting lysosomal delivery. Overall, our findings revealed that bCoronin-1A had a critical impact on mycobacterial survival in macrophages by inhibiting calcium-mediated phagosome-lysosome fusion. This suggests that targeting bCoronin-1A as a key factor influencing mycobacterial survival may be an effective breeding strategy to develop tuberculosis-resistant dairy cows.

Life Cycle Risk Management for Improving Labor Productivity in Construction Projects in Türkiye

The construction industry, being labor-intensive, prioritizes productivity to boost project performance, yet struggles to achieve expected levels despite increased focus by scholars and practitioners. This lagging causes significant losses in time, cost, and quality performance of construction projects but also broader implications for resource efficiency and environmental impacts. As a remedy to the multifaceted issue, this study aims to identify and evaluate life cycle risks of productivity management in construction projects in Türkiye. A comprehensive literature review identified risk factors affecting labor productivity, followed by a discussion session to finalize the decision framework, including life cycle phases of productivity management and risk factors in each phase. Then, the fuzzy analytical hierarchy (AHP) process revealed the most critical risk factors in each phase, followed by semi-structured interviews to reveal measures for addressing the most significant risks. The findings show that productivity management in construction projects contains nine phases. In addition, the most important factors were chiefly related to collaboration, information sharing, lack of supervision, work interruptions, and changes. Findings from semi-structured interviews emphasize regular employee training and open communication to enhance project outcomes, optimize workflows, and promote sustainability. The study’s key contribution is introducing a life cycle approach to construction productivity management, a previously unexplored perspective. This provides an effective framework that can be implemented in construction projects to manage and improve labor productivity as a whole-life cycle approach.

TRUST IN ALTERNATIVE GOVERNORS: EXPLORING USER CONFIDENCE IN COMPANIES, STATES AND CIVIL SOCIETY IN PLATFORM CONTENT MODERATION

Social media platforms such as Facebook, X and TikTok are the “new governors” or “custodians” of the Internet (Klonick 2018; Gillespie 2018). How they moderate global speech online affects the communication practices of billions of people and it can make or break social movements and political resistance, and generally be a critical risk factor for human rights violations. These platforms are increasingly joined by states, international organizations, civil society, journalists and others in defining and interpreting the limitations of speech online, be it through legislation, guidelines or by helping platforms to distinguish misinformation from legitimate content. In parallel, researchers ponder questions concerning the legitimacy of various approaches of content moderation (Haggart & Keller 2021; Suzor 2019), which must extend to the question of which actors ought to fulfill which function in the moderation of content. A legitimate content moderation constellation (and potentially division of labor) is arguably one that is perceived to be legitimate by the “governed” themselves (for whatever qualities are appraised by them). As of today, however, we have little empirical knowledge about what users actually think about content moderation. The current paper presents novel empirical evidence on how users perceive platform content moderation and how they perceive content moderation roles of different governors of speech. The quantitative analysis is based on a survey of more than 15,000 Facebook and Instagram users in 33 countries in the Global South and Eastern Europe, which was conducted in six languages in late 2022 and early 2023.

The Role of Functional Beverages in Mitigating Cardiovascular Disease Risk Factors: A Focus on Their Antidiabetic and Hypolipidemic Properties

The incidence and mortality rates of cardiovascular diseases (CVDs) are constantly increasing. Among the main risk factors, diabetes mellitus and hyperlipidaemia, which are equally widespread pathological conditions, stand out. Current preventive strategies are based on physical activity and a healthy, balanced diet. Primary therapies, on the other hand, are based on the administration of hypoglycaemic and cholesterol-lowering drugs. Given the increasing consumer demand for food products with healthy properties, functional beverages may represent a breakthrough in this field. Through a careful analysis of studies conducted over the past seven years, it has emerged that herbal teas, fruit and vegetable drinks, as well as milk- and plant-based beverages, can mitigate these two critical CVD risk factors, often linked to the presence of specific polyphenols or fermentation processes. The selection of in vivo, in vitro and clinical trials revealed the ability of such drinks to reduce the enzymatic activity of α-glucosidase and α-amylase, as well as to decrease circulating lipid levels, properties that were surprisingly also exhibited by beverages derived from food waste. Therefore, this review aims to highlight the possibility of employing these drinks as adjuvant therapy in the treatment of diabetes mellitus and hyperlipidaemia in order to reduce two potential CVD risk factors.

Development of deep learning auto-encoder algorithms for predicting alcohol use in Korean adolescents based on cross-sectional data.

Alcohol is a highly addictive substance, presenting significant global public health concerns, particularly among adolescents. Previous studies have been limited by traditional research methods, making it challenging to encompass diverse risk factors and automate screening or prediction of adolescents' alcohol use. This study aimed to develop prediction algorithms for adolescent alcohol use in South Korea using machine learning (ML) and deep learning (DL) models, and to identify important features. The study utilized a combination of DL (i.e., Auto-encoder) and ML (i.e., Logistic regression, Ridge, LASSO, Elasticnet, Decision tree, Random forest, AdaBoost, and XGBoost) algorithms to develop the prediction models. It involves 41,239 Korean adolescents and 46 socio-ecological input variables based on cross-sectional data. The analysis revealed that the prediction algorithms had AUC scores ranging from 0.6325 to 0.7214. The feature importance analysis indicates that variables within the domains of sociodemographic characteristics, physical and mental health, behavioral problems, family factors, school factors, and social factors all play significant roles. The developed algorithms enable automatic and early identification of adolescent alcohol use within public health practice settings. By leveraging a comprehensive array of input variables, these methods surpass the limitations of traditional regression approaches, offering novel insights into the critical risk factors associated with alcohol use among Korean adolescents, thereby facilitating early and targeted prevention efforts.

Development and Validation of a Predictive Model for Fall Risk in Pre-Frail Older Adults.

To develop a predictive model for fall risk in pre-frail older adults, providing a basis for early identification and prevention of falls among this population. This was a multicenter prospective cohort study. A total of 473 pre-frail older adults were included, 335 as the training set and 142 as the test set. Univariate and stepwise binary logistic regression analyses were conducted to identify the relationship between pre-frail and fall risk and establish the frailty risk prediction nomogram. The nomogram was constructed based on the results of logistic regression. The model assessment relied on the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, calibration curves, and decision curve analysis. Fall incidence rate among pre-frail older adults within 6 months was 13.63%. The final fall risk prediction model identified that sex, history of falls in the past year, visual impairment, increased nocturia, and fear of falling are the most critical risk factors for falls in pre-frail older adults. The model exhibited good accuracy in the testing set, with an area under the ROC curve of 0.825 (95% confidence interval [0.736, 0.914]). Pre-frail older adults have a higher incidence of falls. The logistic regression model constructed in this study shows promising predictive capabilities and can be used as a screening tool to identify pre-frail older adults at high risk of falls in clinical practice. We anticipate that this model will assist clinical nurses in enhancing the efficiency of fall prevention efforts and reducing the incidence of falls among pre-frail older adults. [Research in Gerontological Nursing, 18(1), 29-39.].

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missions, with microgravity-induced changes that threaten astronaut vision and mission outcomes. Current SANS monitoring, limited to pre- and post-flight exams, lacks in-flight diagnostics, highlighting an urgent need for autonomous tools capable of real-time assessment. Grok, an AI platform by xAI, offers promising potential as an advanced diagnostic tool for space-based health monitoring. Originally developed for broader applications, Grok's high-resolution imaging capabilities could be adapted to detect early SANS indicators such as optic nerve edema and shifts in globe morphology, changes linked to fluid redistribution in space. However, realizing this vision requires algorithmic and hardware adjustments to address the unique physiological shifts astronauts experience. By advancing Grok's diagnostic capability, we strongly believe astronauts could manage SANS autonomously, bringing much-needed real-time, high-accuracy diagnostics to isolated, high-stakes environments-essential as humanity embarks on increasingly ambitious missions to Mars and beyond.

Identification of Key Risk Factors for Rupture in Small Intracranial Aneurysms: A Multicenter Study.

Predicting rupture risk in small intracranial aneurysms (IAs) < 5mm is crucial for guiding clinical decisions. This study aims to identify key clinical and morphological risk factors associated with rupture in small IAs, providing better insight for decision-making. A retrospective analysis was performed on patients with small IAs from one center, with external validation data from another center. Logistic regression identified significant risk factors for aneurysm rupture, which were incorporated into a predictive model. The model's performance was evaluated using the area under the receiver operating characteristic curve, calibration plots, and the Hosmer-Lemeshow goodness-of-fit test. Clinical utility was assessed via decision curve analysis. The training cohort consisted of 226 patients (ruptured, n= 181; unruptured, n= 92), while 136 patients (ruptured, n= 100; unruptured, n= 59) were used for external validation. Significant risk factors included hypertension, smoking, anterior communicating artery aneurysms, daughter sacs, aspect ratio, and size ratio. The model demonstrated strong predictive ability with area under the curves of 0.969 and 0.967 in the training and validation cohorts, respectively. Calibration plots indicated a good agreement between predicted and observed rupture risks, while decision curve analysis highlighted the model's clinical relevance. This study identifies and validates critical risk factors associated with small IA rupture and presents a clinically useful, high-accuracy predictive model to aid in individualized patient management.

The Role of Chronic Stress in the Pathogenesis of Ischemic Heart Disease in Women.

Psychological stress has emerged as a critical risk factor for cardiovascular disease, especially in women. While female participation in clinical research has improved, sex-specific data analysis and reporting often remain inadequate, limiting our ability to draw definitive conclusions for women. Conversely, preclinical studies consistently demonstrate adverse effects of stress on female health, yet the molecular mechanisms underlying this association remain elusive. Evidence suggests that female IHD pathogenesis is more complex than in males, involving multiple factors, including inflammation, contractile dysfunction, bioenergetic impairment, and remodeling. However, many of these mechanisms are primarily derived from male studies, and molecular investigations in female models are limited, hindering our understanding of the underlying biological pathways. This is particularly concerning given the increasing prevalence of ischemic heart disease in postmenopausal women. In order to fully elucidate the impact of stress on female cardiac health and develop targeted interventions, further preclinical research on female models is essential.

Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy

BackgroundIntrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the second and third trimesters of pregnancy and is associated with a significant risk of fetal complications, including premature birth and fetal death. In clinical practice, the diagnosis of ICP is predominantly based on the presence of pruritus in pregnant women and elevated serum total bile acid. However, this approach may result in missed or delayed diagnoses. Therefore, it is essential to explore the risk factors associated with ICP and to accurately identify affected individuals to enable timely prophylactic interventions. The existing literature exhibits a paucity of studies employing artificial intelligence to predict ICP. Therefore, developing machine learning-based diagnostic and severity classification models for ICP holds significant importance.MethodsThis study included ICP patients and some healthy pregnant women from Jiaxing Maternity and Child Health Care Hospital in China between July 2020 and October 2023. We collected clinical data during their pregnancies and selected the top 11 critical risk factors through univariable and lasso regression analysis. The dataset was randomly divided into training and testing cohorts. Thirteen machine learning techniques, including Random Forest, Support Vector Machine, and Artificial Neural Network, were employed. Based on their various classification performances on the training set, the top five models were selected for internal validation.ResultsThe dataset included 798 participants (300 normal, 312 mild, and 186 severe cases). Through univariable and lasso regression analysis, total bile acid, gamma-glutamyl transferase, multiple pregnancy, lymphocyte percentage, hematocrit, neutrophil percentage, prothrombin time, Aspartate aminotransferase, red blood cell count, lymphocyte count and platelet count were identified as risk factors of ICP. The AUCs of the selected top five models ranged from 0.9509 to 0.9614. The CatBoost model achieved the best performance, with an AUC of 0.9614 (95% confidence interval, 0.9377–0.9813), an accuracy of 0.9085, a precision of 0.8930, a recall of 0.9059, and a F1-score of 0.8981.ConclusionsWe identified risk factors for ICP and developed machine learning models based on these factors. These models demonstrated good performance and can be used to help predict whether pregnant women have ICP and the degree of ICP (mild or severe).

Solitary Plasmacytoma: Single institution experience and systematic review and meta-analysis of clinical outcomes.

In this study, we first analyzed data from 147 patients with solitary plasmacytomas treated at the Mayo Clinic between 2005 and 2022 and then expanded our investigation through a systematic review and meta-analysis of 62 studies, encompassing 3,487 patients from the years 1960 to 2022. Our findings reveal that patients with up to 10% clonal plasma cells in their bone marrow (BM), denoted as plasmacytoma +, had a significantly reduced median disease-free survival (DFS) of 15.7 months vs. 79 months, p<0.05, observed in patients with true solitary plasmacytomas, with no clonal cells in the BM. Risk factors identified for shorter DFS included the presence of clonal plasma cells in the marrow and a DFLC > 5 mg/dl. The meta-analysis portion of our study highlighted a male predominance among patients, with a median age of 58, and confirmed radiation therapy as the predominant treatment modality. We also found that DFS rates at 3, 5, and 10 years were 66.9%, 55%, and 42.1%, respectively, and noted a significant difference in outcomes between patients with bone and extramedullary plasmacytomas, with the latter group exhibiting better survival rates. This dual-faceted approach provides a thorough overview of survival rates and critical risk factors for plasmacytoma patients, underscoring the vital role of accurate disease staging at diagnosis and the impact of tumor location on patient prognosis.

Cardiometabolic index as a predictor of gallstone incidence in U.S. adults: insights from NHANES 2017–2020

BackgroundGallstone disease (GSD) is associated with obesity. The Cardiometabolic Index (CMI), a metric that accurately assesses central adiposity and visceral fat, has not been extensively studied in relation to GSD risk. This study investigates the link between CMI and GSD incidence in U.S. adults.MethodsThis study utilized data from the National Health and Nutrition Examination Survey(NHANES)(2017–2020) to assess the association between CMI and GSD, adjusting for confounders such as age, sex, race, chronic diseases, and lifestyle factors. Multivariable logistic regression models and subgroup analyses were employed. Generalized Additive Models (GAM) and advanced curve fitting techniques were used to explore potential non-linear relationships, with threshold effects determined via piecewise linear regression if such relationships were identified. Receiver Operating Characteristic (ROC) curves evaluated and compared the predictive performance of CMI, Body Mass Index (BMI), and Waist Circumference (WC), establishing optimal cutoff values along with their sensitivity and specificity.ResultsThis study included 3,706 participants, of whom 10.6% (392) had GSD. Participants with GSD showed significantly higher CMI values (0.57 vs. 0.44, P = 0.0002). The GSD group included more females and older adults, with increased risks for hypertension, diabetes, higher serum cholesterol and creatinine levels, and a higher risk of cancer. Logistic regression analysis revealed that higher CMI was significantly associated with greater GSD incidence (OR = 1.19, 95% CI = 1.02–1.38, P < 0.0001). The ROC curve demonstrated superior predictive performance (AUC = 0.778), outperforming conventional metrics like BMI and WC. GAM analysis indicated a non-linear positive correlation between CMI and GSD, with an optimal threshold of 0.996. Subgroup analysis found the strongest association among females, individuals aged 20–39, non-Hispanic Whites, those without a history of coronary heart disease, and alcohol consumers.ConclusionOur study reveals a nonlinear positive correlation between the CMI and the incidence of GSD among U.S. adults, with a threshold value of 0.996. Despite limitations in sample size that constrained the analysis of a fully adjusted model, after adjusting for confounding factors, the AUC for predicting GSD using CMI reached 0.778, surpassing traditional metrics. These findings underscore the importance of CMI as a critical risk factor and emphasize the necessity of targeted interventions for high-risk populations.

Outcomes of pediatrics cardiac patients in the PICU: an analysis of clinical and echocardiographic risk factors

Background: Cardiovascular diseases in pediatric patients present significant challenges in Pediatric Intensive Care Units (PICUs), with adverse outcomes influenced by factors such as ventricular dysfunction, sepsis and delayed intervention. Echocardiography plays a vital role in assessing cardiac function, identifying risk factors and guiding treatment. This study aims to analyze the clinical and echocardiographic profiles of pediatric cardiac patients admitted to the PICU and identify critical mortality risk factors to improve care and outcomes. Methods: A prospective observational study was conducted over 18 months from November 2022 to April 2024 in a tertiary care hospital in Srinagar. The study included 103 pediatric patients aged 1 month to 18 years with congenital or acquired heart diseases. Data collected included demographics, clinical presentations, echocardiographic findings, laboratory results and outcomes. Statistical analysis was performed using SPSS 25, with p&lt;0.05 considered significant. Results: The cohort comprised 48 males (46.6%) and 55 females (53.4%), predominantly aged 1 month to 5 years. Ventricular Septal Defect (VSD) was the most common cardiac anomaly (18%), followed by Tetralogy of Fallot (4.8%). Respiratory distress (65.5%) and bronchopneumonia (30%) were prevalent comorbidities. Significant mortality risk factors included infant age, cyanosis (50% mortality), congestive heart failure (52.3%), deranged kidney function tests, altered pH and prolonged mechanical ventilation. Conclusions: Early diagnosis and timely interventions targeting high-risk groups are critical to improving survival. Identifying key echocardiographic profiles and clinical risk factors provides insights for optimizing PICU care for pediatric cardiac patients.

Professional consensus on orthodontic risks: what orthodontists should tell their patients. A cross-sectional study at Saidu college of dentistry, Swat

The importance of transparent communication regarding orthodontic risks during the informed consent process in paramount in terms of ensuring a strong orthodontist-patient relationship. Conducted on a national scale in Pakistan, the current study was planned to establish a consensus among orthodontic professionals regarding critical risks involved in such a discussion. The cross-sectional survey involving 132 orthodontists across Pakistan led to the emergence of 10 key risks, including relapse, treatment duration and gingivitis. However, the study's sole focus on orthodontists' perspectives limited its insights, overlooking invaluable input from patients' experiences. Nevertheless, the consensus underscored the significance of addressing these risks to ensure informed consent and patient autonomy. Going forward, it is imperative to customise this evidence-based information according to individual patient needs, and maintain an ongoing dialogue regarding potential risks, fostering a collaborative approach to orthodontic care and decision-making. Key Words: Orthodontic risks communication, Patient education and safety, Medico-legal complications, Informed consent.

Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm.

Hypertension is a critical risk factor and cause of mortality in cardiovascular diseases, and it remains a global public health issue. Therefore, understanding its mechanisms is essential for treating and preventing hypertension. Gene expression data is an important source for obtaining hypertension biomarkers. However, this data has a small sample size and high feature dimensionality, posing challenges to biomarker identification. We propose a novel deep graph clustering feature selection (DeepGCFS) algorithm to identify hypertension gene biomarkers with more biological significance. This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. The algorithm then uses hybrid clustering methods for gene module detection. Finally, it combines integrated feature selection methods to determine the gene biomarkers. The experiment revealed that all the ten identified hypertension biomarkers were significantly differentiated, and it was found that the classification performance of AUC can reach 97.50%, which is better than other literature methods. Six genes (PTGS2, TBXA2R, ZNF101, KCNJ2, MSRA, and CMTM5) have been reported to be associated with hypertension. By using GSE113439 as the validation dataset, the AUC value of classification performance was to be 95.45%, and seven of the genes (LYSMD3, TBXA2R, KLC3, GPR171, PTGS2, MSRA, and CMTM5) were to be significantly different. In addition, this algorithm's performance of gene feature vector clustering was better than other comparative methods. Therefore, the proposed algorithm has significant advantages in selecting potential hypertension biomarkers.

TOPSIS prefabricated building construction evaluation based on interval-valued Pythagorean fuzzy numbers based on prospect theory

Prefabricated buildings have a series of advantages such as high efficiency, energy savings, and environmental protection, and are being strongly promoted by the Chinese government. However, due to the late start of prefabricated buildings in China, the installation process of prefabricated components is relatively complex, leading to difficulties in quality and safety control. A novel evaluation methodology integrating the technique for order preference by similarity to ideal solution (TOPSIS) with prospect theory and interval-valued Pythagorean fuzzy numbers (IVPFNs) is proposed. Fully considering the subjective value perceptions of decision-makers when faced with losses and gains, a risk evaluation model is established based on different types of decision-makers. This model is then compared with the weight correction coefficient model from a balanced perspective to prevent bias caused by over-reliance on decision-makers’ subjective preferences. This study establishes an assessment index system for assembly construction risk, considering personnel, materials, machinery, management, and technology factors. By merging prospect theory with the TOPSIS method, the application of Pythagorean fuzzy numbers is refined, enhancing the group decision-making process for evaluating risks in prefabricated construction projects. On the other hand, the prospect theory approach can focus on the actual psychological state of managers during decision-making, addressing the research gap concerning the impact of managers’ psychological states on the evaluation process. The study identifies critical risk factors and suggests specific risk management strategies to improve the risk mitigation capabilities of construction entities, offering a structured approach to enhancing the efficiency and safety of prefabricated construction projects.

Influence of the external N and P inputs on nutrients in the coastal area of Xiamen, China

Environmental context External nutrients giving rise to critical ecological issues of the coastal seawater. We investigated the nearshore nutrient concentrations and their sources in Xiamen Bay during 2013–18. Our results could provide theoretical support for controlling nearshore nutrient pollution. Rationale External nutrients greatly increase the critical ecological risks of the coastal seawater. Therefore, it is important to understand the sources and variation characteristics of nitrogen (N) and phosphorus (P) in the coastal area. Methodology The dissolved inorganic nitrogen (DIN) and phosphate (PO4–P) in coastal Xiamen were monitored from 2013 to 2018. The input of nitrogen from runoff in the coastal Xiamen was calculated from the runoff flow amount collected by Xiamen Marine and Fisheries Bureau. Atmospheric dry deposition of DIN was simulated using Williams model to analyse the potential sources of nutrients in the coastal Xiamen. Results High DIN and PO4–P levels occurred in the inner bay and decreased dramatically outside Xiamen Bay. The lowest values of DIN and PO4–P were observed in summer, which is consistent with the temporal variation in nutrient inputs from the surface runoff and atmospheric deposition. The nutrient input of Jiulong River is the main source of eutrophication for Xiamen Bay, providing more than 4 × 104 tonnes (Mg) of N input per year. The atmospheric N dry deposition provided more than 2.2 × 103 Mg of DIN input per year, which accounts for ~3.4–6.3% of the Jiulong River DIN input in the coastal Xiamen. Discussion The ratio of atmospheric nitrogen deposition to nitrogen enrichment from Jiulong River varied from 0.21 to 0.40, indicating that atmospheric nitrogen deposition was an important contributor to the coastal nutrient in Xiamen. This study provides an insight into the major sources of N and P and highlights the importance of atmospheric nitrogen deposition to the DIN in the coastal city Xiamen.

Enlighten the Environmental Factors Associated with the Risk of Leukemia: Evidence from Case-Control Study

Background Leukemia stands as a complex disorder characterized by multifactorial influences, encompassing both polygenic and diverse environmental factors. Aim Consequently, this “case-control observational study” was conceived to elucidate the specific environmental variables contributing to an elevated risk of leukemia among individuals in northern India. Method Samples were collected for data curation using a convenient non-probability sampling method. Furthermore, blood samples were obtained for antigenic expression analysis, conducted with the assistance of a flow cytometer. Result A total of 272 individuals participated in the research study, comprising 111 patients and 161 controls. This diverse sample allowed for an in-depth exploration of the risk associations with various environmental factors. The findings revealed that lower levels of physical activity (odds ratio [OR]: 1.87), increased caffeine intake (OR: 2.06), reliance on a non-vegetarian diet (OR: 3.46), smoking (OR: 3.85), and alcohol consumption (OR: 4.81) demonstrated significant associations with leukemia. Also, antigenic expression using immunophenotyping revealed higher expression of different markers such as HLA-DR, CD45, CD19, CD, etc. Conclusion The present study showed that multiple factors such as low physical activity, caffeine intake, smoking, and alcohol consumption are critical risk attributes of leukemia susceptibility. These findings underscore the nuanced interplay between environmental elements and leukemia risk, shedding light on critical factors warranting attention in public health discourse.

Reaction-advection-diffusion model of highly pathogenic avian influenza with behavior of migratory wild birds.

Wild birds are one of the main natural reservoirs for avian influenza viruses, and their migratory behavior significantly influences the transmission of avian influenza. To better describe the migratory behavior of wild birds, a system of reaction-advection-diffusion equations is developed to characterize the interactions among wild birds, poultry, and humans. By the next-generation operator, the basic reproduction number of the model is formulated. Then the threshold dynamic of the model is explored by some techniques including the theory of uniform persistence, internally chain transitive sets, and so on. Subsequently, the sensitivity analysis of parameters associated with the basic reproduction number is implemented. According to the temporal and spatial overlapping relationship between wild blue-winged ducks and poultry in North America, the effect of this relationship on the characteristic of spatial-temporal distribution of the viruses is well studied. Additionally, the risk of virus transmission from wild birds to poultry and humans is evaluated. The main results highlight that the basic reproduction number is more significantly affected by the parameters related to wild birds. Interestingly, the model output regarding the spatial distribution of poultry infections is consistent with the actual findings. Moreover, the risk of virus spillover from wild birds into poultry and humans varies with wild bird behavior and has a more substantial impact on poultry. Throughout this study, the critical risk points in the transmission process are identified, providing a theoretical basis for the prevention and control of avian influenza.

Design and Implementation of a Virtual Hoisting System: A Digital Twin Approach for Early Warning and Fault Detection

Mine hoists are vital for efficiently transporting materials and personnel between surface and underground mining operations, making their safety and reliability paramount. However, conventional monitoring approaches, which rely on distributed sensors or video surveillance, are limited in their ability to capture the global dynamics of the system, leading to suboptimal decision-making and potential safety risks. This research introduces the integration of digital twin technology into the monitoring and management of mine hoists to address these challenges. This study uses Unity-based modeling to create a virtual system capable of real-time synchronization with its physical counterpart. The digital twin simulates dynamic load changes, predicts potential faults, and optimizes operational parameters through intelligent algorithms. A robust monitoring framework was developed, consisting of a multi-layered architecture that integrates physical systems, real-time data collection, and virtual simulations. Field tests on a multi-rope friction hoist verified the system’s performance, with results demonstrating improved stability, accuracy, and predictive capabilities. A health evaluation model further enhances safety by categorizing the hoist’s operational state into health levels such as ‘healthy,’ ‘sub-healthy,’ ‘warning,’ and ‘fault.’ This model identifies critical risks, such as wire rope tension anomalies, and provides early warnings, ensuring timely interventions.