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Inappropriate planting patterns can increase pollutant concentrations and threaten human health. This study examined three greening patterns (trees, trees + hedges, and hedges) using the ENVI-met model to evaluate the different effects of various planting patterns on PM2.5 dispersion within an idealized 3D street canyon under three typical wind directions. Results showed that street greenbelts alter the PM2.5 concentration field within canyons, and the horizontal and vertical distribution characteristics of PM2.5 under different wind directions were significantly different. The arbor-hedge vegetation structure showed the highest total vegetation deposition amount due to larger canopy volumes while hedges have better deposition amounts per unit volume due to their proximity to emission sources. Additionally, this research selected the averaged relative difference in PM2.5 concentration (ARDC) indicator to assess the influence of different green scenarios on the dispersion of PM2.5 concentrations. Wind direction and planting patterns jointly affect the dispersion of PM2.5 in canyons, and the ARDC varied from -4.39 % to 105.36 %. Unilateral-trees on the windward side or two rows of hedges may be the optimal vegetation layout by trade-off with other services. ARDC was significantly correlated (p < 0.01) with most of the 3D green indicators. These results could provide effective suggestions for optimizing the layout of greenbelts in street canyons to improve air quality.

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There is mounting evidence implicating the potential neurotoxic effects of PM2.5 during brain development, as it has been observed to traverse both the placental barrier and the fetal blood-brain barrier. However, the current utilization of 2D cell culture and animal models falls short in providing an accurate representation of human brain development. Consequently, the precise mechanisms underlying PM2.5-induced developmental neurotoxicity in humans remain obscure. To address this research gap, we constructed three-dimensional (3D) cortical organoids that faithfully recapitulate the initial stages of human cerebral cortex development. Our goal is to investigate the mechanisms of PM2.5-induced neurotoxicity using 3D brain organoids that express cortical layer proteins. Our findings demonstrate that exposure to PM2.5 concentrations of 5 µg/mL and 50 µg/mL induces neuronal apoptosis and disrupts normal neural differentiation, thereby suggesting a detrimental impact on neurodevelopment. Furthermore, transcriptomic analysis revealed PM2.5 exposure induced aberrations in mitochondrial complex I functionality, which is reminiscent of Parkinson's syndrome, potentially mediated by misguided axon guidance and compromised synaptic maintenance. This study is a pioneering assessment of the neurotoxicity of PM2.5 pollution on human brain tissues based on 3D cortical organoids, and the results are of great significance in guiding the formulation of the next air pollution prevention and control policies in China to achieve the sustainable improvement of air quality and to formulate pollution abatement strategies that can maximize the benefits to public health.

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Fine particulate matter (PM2.5) has been identified as a significant contributing factor to the exacerbation of chronic obstructive pulmonary disease (COPD). It has been observed that PM2.5 may induce lung fibrosis in COPD, although the precise molecular mechanism behind this remains unclear. In a previous study, we demonstrated that PM2.5 upregulates oxidative stress induced growth inhibitor 1 (OSGIN1), which in turn leads to injury in airway epithelial cells, thereby, suggesting a potential link between PM2.5 exposure and COPD. Based on this, we hypothesized that OSGIN1 plays a role in PM2.5-induced fibrosis in COPD. Human bronchial epithelial cells (HBEs) were treated with cigarette smoke extract (CSE) to construct an in vitro model of COPD. Our findings revealed that PM2.5 increased fibrosis indicators and upregulated OSGIN1 in CSE-stimulated HBEs (CSE-HBEs), and knockdown of OSGIN1 reduced the expression of fibrosis indicators. Through the use of microRNA target prediction software and the Gene Expression Omnibus database, we predicted miRNAs that targeted OSGIN1 in COPD. Subsequently, real-time polymerase chain reaction and western blot analysis confirmed that PM2.5 modulated miR-654-5p to regulate OSGIN1 in CSE-HBEs. Western blot demonstrated that OSGIN1 induced autophagy, thereby exacerbating fibrosis in CSE-HBEs. In summary, our results suggest that PM2.5 upregulates OSGIN1 through inhibiting miR-654-5p, leading to increased autophagy and fibrosis in CSE-HBEs.

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There is growing global concern regarding the detrimental health impacts of PM2.5 emissions from traditional stoves that utilize polluting fuels. Conventional methods for estimating daily personal PM2.5 exposure involve personal air samplers and measuring devices placed in a waist pouch, but these instruments are cumbersome and inconvenient. To address this issue, we developed a novel neck-mounted PM2.5 monitoring device (Pocket PM2.5 Logger) that is compact, lightweight, and can operate continuously for 1 week without recharging. Twelve participants who utilized charcoal, firewood, or propane gas for cooking in rural regions of Rwanda wore the Pocket PM2.5 Logger continuously for 1 week, and time-series variations in personal PM2.5 exposure were recorded at 5-min intervals. Individual daily exposure concentrations during cooking differed significantly among users of the different fuel types, and PM2.5 exposure was at least 2.6 and 3.4 times higher for charcoal and firewood users, respectively, than for propane gas users. Therefore, switching from biomass fuels to propane gas would reduce daily individual exposure by at least one-third. An analysis of cooking times showed that the median cooking time per meal was 30 min; however, half the participants cooked for 1.5 h per meal, and one-third cooked for over 4.5 h per meal. Reducing these extremely long cooking times would reduce exposure with all fuel types. The Pocket PM2.5 Logger facilitates the comprehensive assessment of personal PM2.5 exposure dynamics and is beneficial for the development of intervention strategies targeting household air pollution.

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In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low-carbon transition.

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Background: With the increasing prevalence of wildfire smoke in the Pacific Northwest, it is important to quantify health impacts to plan for adequate health services. The Rogue Valley region has historically faced some of the greatest wildfire threats in the state. Health impacts from smoke have been estimated in several recent studies that include Oregon's Rogue Valley, but the results between studies are conflicting. Objective: The objective is to critically examine impacts of wildfire smoke on health in the Rogue Valley area and translate the results to support hospital staffing decisions. Study design: The study adopts a case-crossover approach. Methods: Apply a conditional Poisson regression to analyze time stratified counts while controlling for mean temperature. Results: Every 10 µ/m3 increase in PM2.5 is associated with a 2% increase in same-day hospital or emergency room admission rates for respiratory conditions during fire season after adjusting for temperature and time (OR = 1.020; 95% CI: 1.004-1.034); a 10 µ/m3 increase in PM2.5 lasting nine days is associated with a 4% increase in admission rates (OR = 1.041; 95% CI: 1.018-1.065). In other words, for each 10 µ/m3 single day increase in pollution from smoke, an additional 0.26 respiratory patients would be expected in the area hospitals. With a single day increase from 10 µ/m3 to 150 µ/m3, hospitals could expect an additional four patients. Conclusions: There are small but significant health impacts in the Rogue Valley. These impacts are smaller than some statewide estimates. We need further research to understand these differences.

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The Macklin effect is a rare but potentially serious complication of pneumomediastinum, caused by the dissemination of air from the lungs into the subcutaneous tissue and mediastinum after severe chest trauma or invasive manipulation. Early recognition is crucial for proper management of the patient. A 33-year-old male skidded while riding a motorcycle, lost control of the vehicle, and crashed into a utility pole with a thoracic contusion. He was admitted to the hospital; a computed tomography (CT) of the chest and abdomen was requested, which ruled out the presence of fractures and showed air in the mediastinum and subcutaneous cellular tissue, with features suggestive of the Macklin phenomenon. After 72 hours of inpatient monitoring, the patient was discharged to the general surgery outpatient clinic. The Macklin phenomenon occurs as a result of airway rupture due to negative pressure caused by trauma or invasive mechanical ventilation. Risk factors include a higher prevalence in young males, a slender stature and above-average height, and an age range of 12 to 35 years. Early detection of the Macklin phenomenon is crucial to recognize and prevent further complications. This case demonstrated the importance of considering the Macklin effect as a cause of pneumomediastinum in patients with severe chest trauma. Diagnostic imaging plays a key role in confirming the diagnosis and planning treatment.

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BACKGROUND: Expiratory airflow limitation (EAL) is closely associated with respiratory health in youth and adulthood. Owing to limited evidence, we aim to estimate the association between air pollutants, both individually and in combination, along with their chemical compositions, and the risk of EAL in youth based on data obtained from Northeast China Biobank. METHODS: Pulmonary function was evaluated using a medical-grade pulmonary function analyzer, with EAL defined as a forced expiratory flow in 1 s/ forced vital capacity ratio of < 0.8. Land use regression models were used to predict exposure to six air pollutants. Air pollution score (APS) for each participant was constructed as combined exposure. The chemical composition of particulate matter with an aerodynamic diameter of ≤ 2.5 µm (PM2.5) was determined using a validated machine-learning algorithm. Logistic regression models were employed to estimate effect sizes, and odds ratio (OR) and 95 % confidence intervals (CI) were calculated. RESULTS: In total, 905 EAL cases were identified among the 4301 participants, with a prevalence of 21.04 %. Each inter-quartile range increase in APS was associated with a 25 % higher risk of EAL (OR = 1.25, 95 % CI: 1.12, 1.39). Among the pollutants analyzed, PM2.5 exposure had the strongest association with the risk of EAL (OR = 1.33, 95 % CI: 1.18, 1.52). Out of the five chemical components, sulfate (SO2-4) (OR = 1.39, 95 % CI: 1.24, 1.57) and ammonium (NH+4) (OR = 1.39, 95 % CI: 1.23, 1.57) exhibited the strongest associations with the risk of EAL. CONCLUSIONS: Overall, combined effects of air pollution increased the risk of EAL in youth, with SO2-4 and NH+4 emerging as the predominant contributing chemical components in Northeast China.

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Eutrophication is a critical environmental challenge affecting lakes globally. Mitigating trophic level under endogenous phosphorus release is an unsolved problem in eutrophic lakes. However, understanding the dynamics and assembly of microbial communities encoding the alkaline phosphatase (phoD community) and their responses during trophic transitions in eutrophic lakes is limited. In this study, we compared the composition and assembly mechanisms of phoD communities in four seasons in the Yilong Lake, a shallow lake of the Yunnan-Guizhou Plateau. The lake exhibits slightly eutrophic conditions in summer and mesotrophic conditions in spring, autumn, and winter. By analyzing seasonal variations, we observed that during summer, the relative abundance of Pseudomonas in the water had the highest value, while the Shannon-Wiener index of phoD communities was lowest. Mantel tests showed an increased Bray-Curtis dissimilarity of phoD communities in the water with rising eutrophication, a trend not observed in sediment. Notably, eutrophication heightened the homogeneity selection governing the assembly of phoD communities in water. The co-occurrence networks showed that the OTUs in the summer exhibited closer interconnections than those in other seasons. Additionally, the topological parameters from networks indicated that eutrophication is poised to instigate changes and modulate the dynamics of the microbial phoD community, resulting in markedly distinct seasonal behaviors. pH was identified as a critical factor directly influencing phoD community diversity via partial least squares path modeling (PLS-PM). This study shed light on our understanding of the seasonal dynamics of phoD communities and their pivotal role in phosphorus cycling in eutrophic lakes.

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OBJECTIVE: Studies have shown that fine particulate matter (PM2.5) has adverse effects on the liver function, but epidemiological evidence is limited, especially regarding pregnant women. This study aims to investigate the association between PM2.5 exposure in early pregnancy and maternal liver function during pregnancy. METHODS: This retrospective cohort study included 13,342 pregnant participants. PM2.5 and Ozone (O3) exposure level, mean temperature, and relative humidity for each participant were assessed according to their residential address. The levels of serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (TBIL) were measured during the second and third trimesters. Data on PM2.5 and O3 exposure level were sourced from Tracking Air Pollution in China (TAP), while the mean temperature and relative humidity were obtained from the ERA5 dataset. The Generalized Additive Model (GAM) was used to analyze the associations between PM2.5 exposure and maternal liver function during pregnancy, adjusting for potential confounding factors. RESULTS: According to the results, each 10 µg/m3 increase in PM2.5 was associated with an increase of 3.57% (95% CI: 0.29%, 6.96%) in ALT and 4.25% (95% CI: 2.33%, 6.21%) in TBIL during the second trimester and 4.51% (95% CI: 2.59%, 6.47%) in TBIL during the third trimester., respectively. After adjusting for O3, these associations remained significant, and the effect of PM2.5 on ALT during the second trimester was further strengthened. No significant association observed between PM2.5 and AST. CONCLUSIONS: PM2.5 exposure in early pregnancy is associated with increasement of maternal ALT and TBIL, suggesting that PM2.5 exposure may have an adverse effect on maternal liver function. Although this finding indicates an association between PM2.5 exposure and maternal liver function, more research is needed to confirm our findings and explore the underlying biological mechanisms.

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Air pollution, particularly fine particulate matter (PM2.5) with <2.5 µm in diameter, is a major public health concern. Studies have consistently linked PM2.5 exposure to a heightened risk of cardiovascular diseases (CVDs) such as ischemic heart disease (IHD), heart failure (HF), and cardiac arrhythmias. Notably, individuals with pre-existing age-related cardiometabolic conditions appear more susceptible. However, the specific impact of PM2.5 on CVDs susceptibility in older adults remains unclear. Therefore, this review addresses this gap by discussing the factors that make the elderly more vulnerable to PM2.5-induced CVDs. Accordingly, we focused on physiological aging, increased susceptibility, cardiometabolic risk factors, CVDs, and biological mechanisms. This review concludes by examining potential interventions to reduce exposure and the adverse health effects of PM2.5 in the elderly population. The latter includes dietary modifications, medications, and exploration of the potential benefits of supplements. By comprehensively analyzing these factors, this review aims to provide a deeper understanding of the detrimental effects of PM2.5 on cardiovascular health in older adults. This knowledge can inform future research and guide strategies to protect vulnerable populations from the adverse effects of air pollution.

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ETHNOPHARMACOLOGICAL RELEVANCE: Platycodonis radix (PR), the root of Platycodon grandiflorus (Jacq.) A. DC., is a traditional Chinese medicine recognized for its dual role as both a medicinal and dietary substance, exhibiting significant anti-inflammatory properties. It is frequently utilized in the treatment of lung diseases. However, the molecular mechanisms by which PR exerts its effects in the treatment of acute lung injury (ALI) remain unclear. AIM OF THE STUDY: This study presents a novel strategy that integrates network pharmacology, molecular docking, untargeted metabolomics analysis and experimental validation to investigate the molecular mechanisms through which PR treats ALI. MATERIALS AND METHOD: Initially, the bioactive components of PR, along with its targets and pathways in the treatment of ALI, were identified using network pharmacology. Following this, preliminary validation was conducted through molecular docking. The active ingredients in the aqueous extract of PR were characterized using HPLC-MS. Finally, in vivo and in vitro experiments were performed to further validate the findings from the network pharmacology. RESULTS: A total of 14 bioactive components and 156 effective targets were identified using the TCMSP, DisGeNET, Genecard, OMIM databases and Venny 2.1.0. Protein-protein interaction (PPI) analysis revealed 22 core targets including TP53, AKT1, STAT3 and JUN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these targets primarily participate in the regulation of cellular apoptosis, lung cancer and inflammatory pathways. Molecular docking demonstrated that four bioactive components exhibited strong affinities with their respective docking targets. LC-MS analysis confirmed that the aqueous extract of PR contained 87 components, including two active ingredients identified through network pharmacology and molecular docking. Preliminary validation was conducted in mice with ALI induced by acute PM2.5 exposure, revealing that the aqueous extract of PR reduced inflammatory factor levels in bronchoalveolar lavage fluid, enhanced antioxidant capacity in lung tissue, and decreased lung cell apoptosis in PM2.5-exposed mice. Notably, PR alleviated PM2.5-induced ALI through the STAT3, JUN, and AKT1 signaling pathways. Similarly, the results of in vitro intervention experiments further confirmed that the aqueous extract of PR protected pulmonary epithelial cells against PM2.5 exposure through activating AKT1 sinalling pathway, and inhibiting STAT3 and JUN signalling pathways. CONCLUSION: This study identifies the active components of PR and elucidates the molecular mechanisms by which PR alleviates ALI, specifically by inhibiting the phosphorylation levels of STAT3 and c-JUN, or by activating the phosphorylation level of AKT1. These results provide a foundational basis for the application of PR in the treatment or prevention of lung injuries induced by particulate matter.

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Although total carbon (TC) is an important component of fine particulate matter (PM2.5: particulate matter with aerodynamic diameter of < 2.5 µm); its sources remain partially unidentified, especially in coastal urban areas. With ongoing development of the global economy and maritime activities, ship-generated TC emissions in port areas cannot be neglected. In this study, from 11 September 2017 to 31 August 2018, we collected 355 PM2.5 samples in Qingdao, China, to determine the water-soluble ion concentrations, TC concentrations, and stable carbon isotopes (δ13CTC). During the open fishing season (OFS; 11 September 2017 to 30 April 2018) and the closed fishing season (CFS; 1 May 2018 to 31 August 2018), the TC concentrations were 9.30 ± 5.38 µg/m3 and 3.36 ± 2.10 µg/m3 respectively, and the corresponding δ13CTC values were -24.53‰ ± 1.17‰ and -27.03‰ ± 0.91‰, respectively, indicating significant differences (p < 0.05) between the two periods. The differences in TC concentrations and the δ13CTC values between the OFS and CFS reflect changes in the source of contamination. Bayesian model was used to quantify the contributions of different TC sources, revealing that ship emissions accounted for approximately 35.3% of the total, which was close to the contribution from the largest source, i.e., motor vehicles (39%). Using the ship emission inventory, Qingdao's ship emissions were further quantified at 455 metric tons, representing 35%-40% of the total TC emissions around Qingdao. Notably, fishing ships contributed approximately 40% of the total ship emissions. These findings underscore the considerable impact of ship emissions, particularly those from fishing ships, on TC concentrations in coastal urban areas.

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This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis. Using the correlation analysis and interaction analysis methods, four mechanical learning models, PCC-Is-CNN, PCC-Is-LSTM, PCC-Is-CNN-LSTM and PCC-Is-BP neural network, were developed for predicting PM2.5 concentration in different time scales by combining the long-term and short-term data with the basic mechanical learning models. The results show that the PCC-Is-CNN-LSTM model has superior prediction performance, especially when integrating short-term and long-term historical data. Meanwhile, applying the model to cities in other climatic zones, the results show that the model performs well in the Dwa climatic zone, while the prediction performance is lower in the CWa climatic zone. This suggests that although the model is well adapted in regions with a similar climate to Harbin, model performance may be limited in areas with complex climatic conditions and diverse pollutant sources. This study emphasizes the importance of considering meteorological and pollutant interactions to improve the accuracy of PM2.5 predictions, providing valuable insights into air quality management in cold regions.

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Background: Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM). Methods: A cross-sectional study was conducted among 368 participants who were enrolled from Australia, including 203 unpaid carers as cases and 165 individuals from the general population as controls. SHS scores were measured using SHSQ-25 (Suboptimal Health Status Questionnaire-25), whilst psychological symptoms were measured by DASS-21 (Depression, Anxiety and Stress Scale-21). Chi-square was used to measure SHS and psychological symptom prevalence. Spearman correlation analysis was utilised to identify the relationship between SHSQ-25 and DASS-21 scores. Logistic regression analysis was used for multivariate analysis. Results: The prevalence of SHS in carers was 43.0% (98/203), significantly higher than the prevalence 12.7% (21/165) in the general population (p < 0.001). In addition, suboptimal health prevalence was higher in female carers (50.3%; 95/189) than females in the general population (12.4%; 18/145). Logistic regression showed that the caregiving role influenced SHS, with carers 6.4 times more likely to suffer from SHS than their non-caring counterparts (aOR = 6.400, 95% CI = 3.751-10.919). Conclusions: Unpaid carers in Australia have a significantly higher prevalence of SHS than that in the general population and experience poorer health. The SHSQ-25 is a powerful tool that can be utilised to screen at-risk individuals to predict their risk of chronic disease development, an essential pillar for shifting the paradigm change from reactive medicine to that of predictive, preventive and personalised medicine (PPPM). Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00370-8.

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Background: Ovarian cancer patients' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel. Objectives: Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM. Methods: This study employed "Beyondcell," an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088. Results: This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients' prognosis prediction. Conclusions: This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00374-4.

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Because of its rapid progression and frequently poor prognosis, stroke is the third major cause of death in Europe and the first one in China. Many independent studies demonstrated sufficient space for prevention interventions in the primary care of ischemic stroke defined as the most cost-effective protection of vulnerable subpopulations against health-to-disease transition. Although several studies identified molecular patterns specific for IS in body fluids, none of these approaches has yet been incorporated into IS treatment guidelines. The advantages and disadvantages of individual body fluids are thoroughly analyzed throughout the paper. For example, multiomics based on a minimally invasive approach utilizing blood and its components is recommended for real-time monitoring, due to the particularly high level of dynamics of the blood as a body system. On the other hand, tear fluid as a more stable system is recommended for a non-invasive and patient-friendly holistic approach appropriate for health risk assessment and innovative screening programs in cost-effective IS management. This article details aspects essential to promote the practical implementation of highlighted achievements in 3PM-guided IS management. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00376-2.

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Background and objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians. Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0). Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001). Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care.

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Objective: Hypertension (HTN) is a prevalent global health concern. From the standpoint of preventive and personalized medicine (PPPM/3PM), early detection of HTN offers a crucial opportunity for targeted prevention and personalized treatment. This study aimed to evaluate the association between the weight-adjusted waist index (WWI) and HTN risk. Methods: A case-control study using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 was conducted. Logistic regression models assessed the association between WWI and HTN. Subgroup analyses explored differences in age, sex, ethnicity, and diabetes status. Restricted cubic spline (RCS) analyses examined potential nonlinear relationships. Results: A total of 32,116 participants, with an average age of 49.28 ± 17.56 years, were included in the study. A significant positive association between WWI and the risk of HTN was identified (odds ratio [OR], 2.49; 95% CI, 2.39-2.59; P < 0.001). When WWI was categorized into quartiles (Q1-Q4), the highest quartile (Q4) exhibited a stronger association compared to Q1 (OR, 2.94; 95% CI, 2.65-3.27; P < 0.001). Subgroup analyses indicated that WWI was a risk factor for HTN across different populations, although variations in the magnitude of effect were observed. Furthermore, the findings from the RCS elucidated a nonlinear positive correlation between WWI and HTN. Conclusion: WWI is independently associated with HTN risk, highlighting its potential as a risk assessment tool in clinical practice. Incorporating WWI into early detection strategies enhances targeted prevention and personalized management of HTN. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00375-3.

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Background: Insomnia persists as a prevalent sleep disorder among middle-aged and older adults, significantly impacting quality of life and increasing susceptibility to age-related diseases. It is classified into objective insomnia (O-IN) and paradoxical insomnia (P-IN), where subjective and objective sleep assessments diverge. Current treatment regimens for both patient groups yield unsatisfactory outcomes. Consequently, investigating the neurophysiological distinctions between P-IN and O-IN is imperative for devising novel precision interventions aligned with primary prediction, targeted prevention, and personalized medicine (PPPM) principles.Working hypothesis and methodology.Given the emerging influence of gut microbiota (GM) on sleep physiology via the gut-brain axis, our study focused on characterizing the GM profiles of a well-characterized cohort of 96 Italian postmenopausal women, comprising 54 insomniac patients (18 O-IN and 36 P-IN) and 42 controls, through 16S rRNA amplicon sequencing. Associations were explored with general and clinical history, sleep patterns, stress, hematobiochemical parameters, and nutritional patterns. Results: Distinctive GM profiles were unveiled between O-IN and P-IN patients. O-IN patients exhibited prominence in the Coriobacteriaceae family, including Collinsella and Adlercreutzia, along with Erysipelotrichaceae, Clostridium, and Pediococcus. Conversely, P-IN patients were mainly discriminated by Bacteroides, Staphylococcus, Carnobacterium, Pseudomonas, and respective families, along with Odoribacter. Conclusions: These findings provide valuable insights into the microbiota-mediated mechanism of O-IN versus P-IN onset. GM profiling may thus serve as a tailored stratification criterion, enabling the identification of women at risk for specific insomnia subtypes and facilitating the development of integrated microbiota-based predictive diagnostics, targeted prevention, and personalized therapies, ultimately enhancing clinical effectiveness. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00369-1.