Identification Of Factors Research Articles

A data-driven approach for analyzing contributions of individual loading factors to GNSS-measured bridge displacements

A bridge may displace due to various loadings (e.g., thermal (Xia et al. in Struct Control Health Monit 28(7):e2738, 2013), winds (Owen et al. in J Wind Eng Ind Aerodyn 206:104389, 2020), and vehicles (Xu et al. in J Struct Eng 133(1):3–11, 2007)) acting upon the bridge. Identifying the contributions of individual loading factors to the measured bridge displacements is important for understanding the structural health conditions of the bridge. There is however no effective method to quantify the contributions when multiple loadings act simultaneously on a bridge. We propose a new data-driven method, termed random forest (RF)-assisted variational mode decomposition (RF-AVMD), for more effective identification of dominant loading factors and for quantifying the contributions of individual loading factors to the measured bridge displacements. The proposed method is applicable to studying the displacements of any bridge structures and allows for the first time to separate the contributions of individual loadings. The effectiveness of the proposed method is validated using data from Tsing Ma Bridge (TMB), a large suspension bridge in Hong Kong recorded during two consecutive strong typhoons. The results reveal that the transverse displacements of TMB mid-span were controlled by the crosswinds, the longitudinal displacements were dominated by the temperature and winds along the bridge centerline, and the vertical displacements were mainly due to the winds along the bridge centerline, temperature, and traffic flows. Displacement time series that responded to each loading factor was derived. The proposed method provides important new insights into the impacts of individual loadings on the displacements of long-span bridges.

Comprehensive mapping of somatotroph pituitary neuroendocrine tumour heterogeneity using spatial and single-cell transcriptomics.

Pituitary neuroendocrine tumours (PitNETs) are common intracranial tumours that are highly heterogeneous with unpredictable growth patterns. The driver genes and mechanisms that are crucial for tumour progression in somatotroph PitNETs are poorly understood. In this study, we performed integrative spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) analysis on somatotroph tumours and normal pituitary samples to comprehensively characterize the differences in cellular characteristics. By analyzing combined copy number variations (CNVs), tumour tissues were divided into two regions, which included the CNVhigh and CNVlow areas. The protumour genes DLK1 and RCN1 were highly expressed in the CNVhigh area, which might be related to tumour progression and could be targeted for precision therapy. We also found that the transforming growth factor beta signalling pathway participated in tumour progression and identified heterogeneity in the expression profiles of key genes. We assessed the intertumoral and intratumoral heterogeneity in somatotroph PitNETs and emphasized the importance of individualized treatment. In summary, we visualized the cellular distribution and transcriptional differences in normal pituitary and somatotroph PitNETs by ST and scRNA-seq for the first time. This study provides a strong theoretical foundation to comprehensively understand the crucial mechanisms involved in tumour progression and develop new strategies to treat somatotroph PitNETs. The first-ever visualization of cellular distributions in normal and tumor pituitary tissues. The inter- and intra-tumoral transcriptomic heterogeneity of somatotroph PitNETs was comprehensively revealed. Identification of potential protumor factors and critical signaling pathways, opening new avenues for therapeutic intervention.

Risk factors associated with post-tuberculosis sequelae: a systematic review and meta-analysis

Post-tuberculosis (TB) sequelae present a significant challenge in the management of TB survivors, often leading to persistent health issues even after successful treatment. Identifying risk factors associated with post-TB sequelae is important for improving outcomes and quality of life of TB survivors. This systematic review and meta-analysis aims to identify risk factors associated with long-term physical sequelae among TB survivors. We systematically searched Medline, Embase, PROQUEST, and Scopus for studies on long-term physical sequelae among TB survivors up to December 12, 2023. The primary outcome of interest was to quantify risk factors of long-term physical sequelae (i.e., respiratory, hepatic, hearing, neurological, visual, renal, and musculoskeletal sequelae). We included all forms of TB patients who experienced long-term physical sequelae. We used narrative synthesis for risk factors reported once and random-effect meta-analysis for primary outcomes with two or more studies. Findings were presented with odds ratios (OR) and 95% confidence intervals (CI). Publication bias was assessed using funnel plots and Egger regression, and heterogeneity was examined with a Galbraith radial plot. The protocol was registered on Prospero (CRD42021250909). A total of 73 articles from 28 countries representing 31,553TB-treated patients were included in the narrative synthesis, with 64 of these studies included in the meta-analysis. Risk factors associated with post-TB lung sequelae include older age (OR=1.62, 95% CI: 1.07-2.47), previous TB treatment history (OR=3.43, 95% CI: 2.37-4.97), smoking (OR=1.41, 95% CI: 1.09-1.83), alcohol consumption (OR=1.84, 95% CI: 1.04-3.25), smear-positive pulmonary TB diagnosis (OR=3.11, 95% CI: 1.77-6.44), and the presence of radiographic evidence of pulmonary lesions at the commencement of treatment (OR=2.04, 95% CI: 1.07-3.87). Risk factors associated with post-TB liver injury included pre-existing hepatitis (OR=2.41, 95% CI: 1.16-6.08), previous TB treatment (OR=2.64, 95% CI: 1.22-6.67), hypo-albuminemia (OR=2.10, 95% CI: 1.53-2.88), HIV co-infection (OR=2.72, 95% CI: 1.66-4.46), and CD4 count <200mm3 in HIV-infected individuals (OR=2.03, 95%CI: 1.26-3.27). Risk factors associated with post-TB hearing loss include baseline hearing problems (OR=1.72, 95% CI: 1.30-2.26), and HIV co-infection (OR=3.02, 95% CI: 1.96-4.64). This systematic review and meta-analysis found that long-term physical post-TB sequelae including respiratory, hepatic, and hearing impairment were associated with a range of socio-demographic, behavioral, and clinical factors. Identification of these risk factors will help to identify patients who will benefit from interventions to reduce the burden of suffering from post-TB treatment. Healy Medical Research Raine Foundation, the Australian National Health and Medical Research Council, and Curtin University Higher Degree Research Scholarship fund the study.

Resilience to major life events: Advancing trajectory modeling and resilience factor identification by controlling for background stressor exposure.

Resilience has been defined as the maintenance or quick recovery of mental health during and after stressor exposure. One popular operationalization of this concept is to model prototypical trajectories of mental health in response to an adverse event, where trajectories of undisturbed low or rapidly recovering symptoms both comply with the resilience definition. However, mental health responses are likely also influenced by other stressors occurring before or during the observation time window. These "background" stressors may affect a person's assignment to a trajectory class. When using these classes as dependent variables to identify resilience-predictive factors, this may lead to false estimates. A new method to build exposure-controlled trajectories based on time courses of stressor reactivity (SR), rather than pure mental health scores, is demonstrated on a data set of 707 initially healthy participants living in Germany (67.33% female; Mage = 29.20, SD = 8.27). SR scores express individual deviations from the sample's normative mental health reaction to observed real-life stressors during the observation time window, thus accounting for individual differences in exposure to background stressors. The resulting trajectory models are plausible. In analyses additionally controlling for background stressors occurring before the observation time window (past life events), low SR trajectories are predicted by the well-documented resilience factor sense of coherence, suggesting construct validity. Further, they are associated with lower odds of developing categorical mental health conditions, suggesting predictive validity. Our study provides the first proof of principle for a refined method to identify predictors of resilience to major stressor events. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Novel framework of significant risk factor identification and cardiovascular disease prediction

Cardiovascular disease (CVD) remains a major public health concern, characterized by high mortality rates and complex diagnostic challenges. Risk factor-based prediction models are commonly employed, but existing approaches often treat all factors equally important. This study proposes a novel method for predicting CVD using a minimal set of significant risk factors, without compromising predictive accuracy. The proposed framework consists of two main components: the Heart Disease Assessment and Review Technique (HEART) and the Stacked Meta Neural Network (SMNN). HEART has been introduced to identify the key risk factors of CVD. Different statistical techniques such as McFadden’s pseudo-R-squared, mutual information, Friedman’s and McNemar’s tests, Shapley and Odds ratio analysis were used to signify and prioritize key risk factors over non-key risk factors. SMNN with 5-fold cross-validation uses these significant risk factors to predict CVD. The method is evaluated using multiple benchmark datasets, yielding an average accuracy of 90.5% ± 1.8% (IEEE Data-port), 88.5% ± 3.3% (Government College University, Faisalabad, Pakistan) and 80.3% ± 1.3% (South African Heart Disease dataset) underscoring its robustness. This study offers new insights into CVD risk factor assessment, enhancing diagnostic accuracy and informing more effective clinical interventions to improve patient outcomes.

Insights into multivalent metal removal-assistant anaerobic fermentation of waste activated sludge: Impact factor identification and mechanism clarification of metal removal

The study investigated the impact factors of metal removal and their role in enhancing anaerobic fermentation of waste activated sludge (WAS), aiming to identify key mechanisms. By optimizing the sludge concentration, it found that a sludge suspension solid range of 10–30 g/L significantly improved the efficiency of anaerobic fermentation, and yielded SCOD of 93.8–177.1 mg/g volatile suspension solid after 8 h, respectively, which was 3.67–4.77 times higher than the control. Correspondingly, the production of short-chain fatty acids (SCFAs) was significantly enhanced. At the optimal sludge suspension solid of 10–30 g/L, the SCFAs were 1.28–1.51 times higher than that of the control. Furthermore, the study evaluated the performance of different metal removal reagents and discovered that microporous COOH reagent outperformed gel SO3H and macroporous SO3H reagent. The macroporous COOH reagent achieved 1.54 and 1.22 times higher SCOD values and significantly enhanced the production of SCFAs, demonstrating its superiority in metal removal and fermentation enhancement. Moreover, the research revealed that sludge concentration and metal removal agent types influenced the activities of key enzymes involved in anaerobic fermentation. Additionally, the macroporous COOH reagent showed the most efficient ability to remove metal ions, contributing to increased nutrient recovery in terms of nitrogen (NH4+-N) and total phosphorus (TP) release during the acidification process. In summary, the enhancing mechanism of metal removal-assistant anaerobic fermentation was discussed from the sludge floc concentration, the skeleton structure and the ion exchange group type. The identified mechanism significantly improves fermentation efficiency and facilitate the organic recovery, representing an innovative approach for wide application in WAS management and resource recovery.

Identification of independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4: the construction of a nomogram.

Hypoalbuminemia is a common complication in patients with chronic kidney disease (CKD) and is associated with various adverse clinical events. Currently, there are few studies focused on identifying risk factors and constructing models for hypoalbuminemia in patients with CKD stages 3 and 4. This study aims to identify independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4 and construct a nomogram for predicting the risk of hypoalbuminemia in these patients. A total of 237 patients with CKD stages 3 and 4 treated at Anning First People's Hospital from January to December 2023 were included. Univariate and bidirectional stepwise multivariate logistic regression analyses were used to identify independent risk factors for hypoalbuminemia in these patients. Based on the results of logistic regression analyses, a nomogram was constructed. The model performance was assessed using area under the receiver operator characteristic curve, calibration curves, and decision curve analysis. Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors for hypoalbuminemia in these patients. The contributions of each independent risk factor to hypoalbuminemia were visualized in a nomogram. The area under the receiver operator characteristic curve of the model was 0.819, indicating good discrimination. The calibration curves showed good agreement between predicted and observed outcomes. The decision curve analysis also verified that the model had the good clinical utility. Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors of hypoalbuminemia in patients with CKD stages 3 and 4. The nomogram exhibits good discrimination, calibration, and clinical utility, offering a reliable tool for the early prediction and identification of hypoalbuminemia in these patients.

Identification of Major Pesticide Drift Factors in Aerial Spraying through Field Trials

Identification of Major Pesticide Drift Factors in Aerial Spraying through Field Trials

Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of “keyword, publication year, and publication language”. The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was “USA”, the journal was “Journal of Medical Internet Research”, the author was “Meng Ji”, and the most cited author was “Weihua Li”. The 55 most cited (≥50) articles focused on themes related to “diagnosis of COVID-19 disease”, “diagnosis of diseases”, “detection and classification of cancerous cells”, “identification of disease risk factors and disease prediction”, “prediction of treatment outcomes”, “prediction of disease course”, “personalized treatment recommendations”, “decision-making processes”, “ethical considerations, risks, and responsibilities”. With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most.

Spatiotemporal Distribution of Human Rabies and Identification of Predominant Risk Factors in China from 2004 to 2020.

Human rabies is a prevalent issue in China, posing a significant public health concern in the country. This study fitted the Bayesian model of separable in spatial and temporal variation and inseparable spatiotemporal variation in disease risk respectively based on Integrated Nested Laplace Approximation (INLA) to investigate the spatiotemporal characteristics of human rabies across 31 provinces in China from 2004 to 2020. It also investigated the influence of natural and socio-environmental factors on the incidence of the disease. Within the study period, a total of 26,807 cases of human rabies were reported, with the highest risk of incidence occurring in 2007, followed by a steady annual decline to the lowest risk in 2020. Guangxi Province exhibited the highest risk, while Jilin Province had the lowest, with the southern, central, and eastern regions reporting higher risks than the northern and western areas. By 2020, most provinces such as Guangxi and Guizhou had significantly reduced their relative risk (RR) of human rabies from historical highs. However, some provinces like Hunan, Henan, and Jiangsu experienced an increase in RR compared to previous years. As the annual average temperature increases, the risk of human rabies incidence in China correspondingly rises. Conversely, with increases in the annual average daily sunshine duration, per capita disposable income of urban residents, and local government healthcare expenditures, the risk of human rabies incidence declines. We conclude that the risk of human rabies in China initially increased and then decreased annually from 2004 to 2020. Future efforts should continuously increase financial investments in rabies prevention and control, focusing particularly on Hunan, Henan, Jiangsu, and provinces characterized by higher temperatures, shorter sunshine durations, and lower economic levels.

Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis.

Pervasive technologies are used to investigate various phenomena outside the laboratory setting, providing valuable insights into real-world human behavior and interaction with the environment. However, conducting longitudinal field trials in natural settings remains challenging due to factors such as low recruitment success and high dropout rates due to participation burden or data quality issues with wireless sensing in changing environments. This study gathers insights and lessons from 3 real-world longitudinal field studies assessing human behavior and derives factors that impacted their research success. We aim to categorize challenges, observe how they were managed, and offer recommendations for designing and conducting studies involving human participants and pervasive technology in natural settings. We developed a qualitative coding framework to categorize and address the unique challenges encountered in real-life studies related to influential factor identification, stakeholder management, data harvesting and management, and analysis and interpretation. We applied inductive reasoning to identify issues and related mitigation actions in 3 separate field studies carried out between 2018 and 2022. These 3 field studies relied on gathering annotated sensor data. The topics involved stress and environmental assessment in an office and a school, collecting self-reports and wrist device and environmental sensor data from 27 participants for 3.5 to 7 months; work activity recognition at a construction site, collecting observations and wearable sensor data from 15 participants for 3 months; and stress recognition in location-independent knowledge work, collecting self-reports and computer use data from 57 participants for 2 to 5 months. Our key extension for the coding framework used a stakeholder identification method to identify the type and role of the involved stakeholder groups, evaluating the nature and degree of their involvement and influence on the field trial success. Our analysis identifies 17 key lessons related to planning, implementing, and managing a longitudinal, sensor-based field study on human behavior. The findings highlight the importance of recognizing different stakeholder groups, including those not directly involved but whose areas of responsibility are impacted by the study and therefore have the power to influence it. In general, customizing communication strategies to engage stakeholders on their terms and addressing their concerns and expectations is essential, while planning for dropouts, offering incentives for participants, conducting field tests to identify problems, and using tools for quality assurance are relevant for successful outcomes. Our findings suggest that field trial implementation should include additional effort to clarify the expectations of stakeholders and to communicate with them throughout the process. Our framework provides a structured approach that can be adopted by other researchers in the field, facilitating robust and comparable studies across different contexts. Constantly managing the possible challenges will lead to better success in longitudinal field trials and developing future technology-based solutions.

Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard.

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer's full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression. We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology's impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice. Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations. Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

ОЦЕНКА И ПРОГНОЗИРОВАНИЕ РИСКА ПАЦИЕНТСКОГО НОН-КОМПЛАЕНСА В СИСТЕМЕ СПЕЦИАЛИЗИРОВАННОЙ ФАРМАЦЕВТИЧЕСКОЙ ПОМОЩИ НАСЕЛЕНИЮ С ОНКОЛОГИЧЕСКИМИ ЗАБОЛЕВАНИЯМИ

Cardiovascular, oncological and other diseases occupy a significant place in the structure of morbidity and causes of death of the population, the growth of which largely depends on lifestyle and risk factors. Identification of risk factors contributing to the development of cancer is one of the main achievements of epidemiology of the 21st century. Subsequent research helped scientists to build multifactorial algorithms for predicting the risks of cancer and mortality from them, which greatly facilitated the work of practitioners in the field of risk stratification. The above indicates that the research results of many foreign and domestic authors confirm that significant predictors of cancer are risk factors such as old age, pre-existing cancer in blood relatives, smoking, obesity, alcohol, certain types of viral infections, certain chemicals, ionizing radiation, including UV rays of the sun and others. It is known that reducing the influence of risk factors leads to a decrease in morbidity and mortality from cancer. An important condition in the treatment of oncological diseases is the rational and effective use of medicines. Pharmaceutical specialists play an active role in this process. In the treatment of long-term or lifelong diseases, such as cancer, great importance is attached to the process of daily pharmacotherapy, compliance with the rules of adherence to treatment or compliance. Thus, problems with patients performing medical appointments are based on a complex of factors, including psychological and behavioral ones, including patients' adherence to treatment.

Genome-wide identification, in-silico characterisation and expression analysis of multiprotein bridging factor 1 gene family members in rice

Genome-wide identification, in-silico characterisation and expression analysis of multiprotein bridging factor 1 gene family members in rice

A Nomogram for Predicting Late-Onset Neurological Deficits in the Natural Course of Kyphosis or Kyphoscoliosis.

Retrospective single-center comparative analysis. To develop a nomogram model for predicting late-onset neurological deficits (LONDs) in patients with kyphosis or kyphoscoliosis. Patients with kyphosis or kyphoscoliosis might suffer from LONDs, and surgical correction may improve neurological function. Nevertheless, there exists a significant gap in the identification of predictive factors for LONDs in these patients. A consecutive series of 244 patients with kyphosis or kyphoscoliosis who underwent corrective surgery between April 2010 and June 2024 were included in our study. Relevant measurements, including the Cobb angle, deformity angular ratio (DAR), and level of the apex were assessed and calculated using X-ray imaging. Spinal cord morphology at the apex of the major curve was evaluated using preoperative axial T2-weighted magnetic resonance imaging (MRI) to categorize patients into three types based on the spinal cord shape classification system (SCSCS). To identify independent risk factors associated with LONDs, we employed univariate analysis followed by backward stepwise multivariate logistic regression analysis. A nomogram was established based on the identified independent risk factors to predict the likelihood of LONDs in patients with kyphosis or kyphoscoliosis. The mean age of the 244 patients was 46.4±17.8 years, with an observed incidence of LONDs at 57.8%. The backward stepwise multivariate logistic regression analysis indicated that age, etiological diagnosis and SCSCS were independent predictors of LONDs. Utilizing these independent risk factors, we constructed a nomogram model to estimate the probability of LONDs. The concordance index (C-index) of the model was 0.912 (95% CI, 0.876-0.947), indicating a satisfactory level of accuracy in predicting the likelihood of LONDs. The predictive factors for LONDs include age, etiological diagnosis and SCSCS. We developed a nomogram model to predict LONDs, which could be useful for patient counseling and facilitating treatment-related decision-making.

Is There a Correlation Between Platelet Count, Mesenteric Lymph Node Involvement, and Hematogenous Metastases in Advanced Stage Ovarian Cancer?

Ovarian cancer remains a major cause of death in women worldwide, mainly due to late diagnosis and the lack of a reliable screening test for early detection of the disease. In this context, attention has been focused on the identification of other prognostic factors that might allow a better identification of cases with worse long-term outcome. Data of patients who underwent cytoreductive surgery between 2014-2019 were retrospectively reviewed and 57 patients were considered eligible for this study. These cases were further classified according to preoperative platelet count, with a cut-off value of 335,000/μl as a positive predictive value for long-term survival. According to this value, there were 27 cases with a preoperative platelet count lower than 335,000/μl and 30 cases with a preoperative platelet count higher than 335,000/μl. Cases in the second group had a significantly higher peritoneal carcinomatosis index (p=0.002), a higher proportion of digestive serosa involvement (p<0.001), and a higher proportion of mesenteric lymph node involvement and hematogenous metastases (p=0.005 and p=0.001, respectively). When analyzing long-term outcomes, all these factors had a significant impact on overall survival. Preoperative thrombocytosis appears to be positively associated with gastrointestinal serosa involvement, mesenteric lymph node invasion, and the presence of hematogenous metastases, thus significantly influencing the long-term outcome of patients with advanced ovarian cancer.

Dissolved Oxygen Concentration Prediction in the Pearl River Estuary with Deep Learning for Driving Factors Identification: Temperature, pH, Conductivity, and Ammonia Nitrogen

Predicting the dissolved oxygen concentration and identifying its driving factors are essential for improved prevention and management of anoxia in estuaries. However, complex hydrodynamic conditions and the limitations in traditional methods result in challenges in the identification of the driving factors for the low dissolved oxygen (DO) phenomenon. The objective of our study is to develop a robust deep learning model using four-year in situ data collected from an automatic water quality monitoring station (AWQMS) in an estuary, for accurate identification and quantification of the driving factors influencing DO levels. Mitigations in hypoxia were observed during the initial two years, but a subsequent decline in DO concentrations was witnessed recently. The periodicity of DO concentrations in the Pearl River Estuary reduced with the increase in the hypoxic intensity. Maximal information coefficient (MIC) and extreme gradient boosting (XGBoost) were employed to determine the significance of input variables, which were subsequently validated by using the long- and short-term memory networks (LSTMs). The driving factors contributing to the hypoxia problem were shown as temperature, pH, conductivity, and NH4+-N concentrations. Notably, the evaluation index values of the hybrid model are MAPE = 0.0887 and R2 = 0.9208, which have been improved compared with the LSTM model by about 99.34% in MAPE reduction and 16.56% in R2 improvement, indicating that the MixUp-LSTM model was capable of effectively capturing nonlinear relationships between DO and other water quality indicators. Based on existing literature, three traditional statistical methods and four machine learning models were also performed to compare with the proposed MixUp-LSTM model, which outperformed other models in terms of prediction accuracy and robustness. Overall, the successful identification of the driving factors for the deoxygenation phenomenon would have important implications for the governance and regulation of low DO in estuaries.

Hearts and minds: a community-based systems diagram exploring mental health following a cardiac event

Abstract Background/Introduction Following an acute cardiac event, individuals commonly face multiple emotional and mental health challenges. For many people, this change in their mental health will resolve over time. However, one in five will go on to develop major depressive disorder. The development of major depressive disorder is a complex co-morbidity that, if left unchecked, can increase the risk of subsequent cardiac events and mortality. Systems thinking methodology offers a novel approach to investigating factors that contribute to mental health following a cardiovascular event. Purpose This project aims to (1) unravel the complex system influencing mental health after a cardiac event and (2) pinpoint priorities for supporting individuals' mental well-being following a cardiac event. Methods Group model building workshops using Deakin University’s purpose-built STICKE software were conducted over Zoom to explore the interrelationships contributing to mental health following a cardiovascular event. Twelve participants, including consumers (n = 3) and healthcare professionals (n = 9) from across Australia, engaged in two 1.5-hour workshops. Guided by the prompt "Reflect on factors influencing a person’s mental health post-cardiac event", participants worked together to construct a systems diagram. This facilitated exploration, visual mapping, and identification of factors and priority areas influencing mental health post-cardiac event. The diagram was then analysed using the theoretical domains framework. Results The STICKE diagram developed demonstrates the interplay of various factors impacting mental health across the cardiac healthcare journey at both patient and system levels (see Figure 1). Using the theoretical domains framework, eight domains were identified: knowledge, skills of healthcare professionals, environmental context, personal context, emotional response to event and recovery, social influences, behavioural regulation, and beliefs about individual capacity. Knowledge (including health literacy and staff training), environmental context (addressing gaps in care and navigating the system), and social influences (such as social isolation) were domains that were consistently raised in the workshop (see Figure 2). Stakeholders also identified the following key priority areas: highlighting the need for additional support for staff and intervention to increase health literacy and knowledge around mental health. Conclusion(s) Supporting mental health post-cardiac events is a multifaceted challenge requiring a comprehensive community-level intervention, particularly in the initial weeks post-hospitalisation. The insights gained from this study will inform the co-design of an intervention aimed at increasing health literacy and knowledge around mental health following a cardiovascular event.

Application of artificial intelligence to study the causality in public health: a systematic review

Abstract Introduction In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications. Methods A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health. Results From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment. Discussion While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings. Key messages • In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions. • This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

Human cellular restriction factors that target SARS-CoV-2 replication

Millions of people have died and a worldwide economic catastrophe has been brought on by the coronavirus disease 2019 (COVID-19) pandemic. Infections caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may presently be treated with less than 10 antiviral drugs such as Remdesivir. The need for medical intervention due to sickness has led to unprecedented research efforts to study the biology of coronaviruses. Additionally, there is a strong likelihood that coronaviruses will cause pandemics in the future. All viruses cannot replicate optimally due to host restriction factors. Given that they are genetically more stable than viral targets and may be shared by similar viruses, these antiviral host factors provide appealing targets for antiviral treatment. The identification of antiviral host factors that are a component of human innate immunity and that prevent the completion of the SARS-CoV-2 life cycle has been made possible by the deployment of several “omics” technologies. In this review, we provide an overview of the antiviral host factors that limit the replication of SARS-CoV-2 in this, which were mostly discovered using functional genetic and interactome screening. Important cellular mechanisms for the SARS-CoV-2 life cycle are covered. Finally, we highlight host restriction factors that could be targeted by clinically approved molecules and the induction of these factors as potential antiviral therapies for COVID-19.