UNASSIGNED: Comparing the anxiety and depression severity and their impact on subsequent birth outcomes in pregnant women before and during Omicron wave in Shanghai in 2022.
UNASSIGNED: The depression-anxiety symptoms networks were compared between the pregnant women during the outbreak period (outbreak group; n = 783) and a matched control group of pregnant women before the outbreak (pre-outbreak group; n = 783). The impact of baseline mental state on follow-up pregnancy and neonatal outcomes was also explored by logistic regression.
UNASSIGNED: Levels of depression and anxiety between the two groups were not significant different. Network analysis showed that central symptom \"trouble relaxing\" and bridge symptom \"depressed mood\" shared by both groups. Different symptom associations in different periods of the pandemic. Total scores and sub-symptom scores of prenatal depressive and anxious severities increased the odds ratios of maternal and neonatal syndromes. The influence of mental state on gestational and neonatal outcomes differed across different pandemic periods.
UNASSIGNED: The Omicron wave did not have a significant negative impact on the depressive and anxious mood in pregnant women. Targeting central and bridge symptoms intervention may be effective in reducing their adverse effects on co-occurring of anxious and depressive mood and birth outcomes.

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women\'s Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

This research aims to identify the accessibility of the entire population, especially the slum population to existing healthcare facilities (HCF) as well as the slum neighborhoods having low geographic accessibility, and finally, to provide an analytical model for the people living in areas that are outside the coverage range of existing healthcare facilities (HCF) across the study area. Spatial data has been collected and used based on the road network, elevation, location of HCF, municipal boundary, slum point, and satellite images from various sources. Also, non-spatial data such as socioeconomic variables are collected from questionnaires survey within a particular period. The spatial analysis tool like as near, network analysis, and predictive analysis in the ArcGIS platform was used to examine geographic accessibility. The results of the spatial analysis show that the distribution of public healthcare facility centers in the study area has not been uniformly distributed. Across 84% of areas in the study area have sound spatial accessibility with traveling time coverage is about 12 min. However, 16% of areas have a traveling time of 12 to 30 min under low accessibility with existing slum neighborhoods. Therefore, the low spatial accessibility areas are demanding new healthcare facilities in the study area. The Analytical Hierarchy Process (AHP) is employed to find the most optimal and efficient locational suitability for building new healthcare facility centers. The finding of AHP analysis for site suitability of healthcare facilities revealed five major classes as most suitable (2%), suitable (5%), moderate (35%), poor (54%), and very poor (4%) in the study area. Moreover, the realistic framework of this study helps to measure geographic accessibility and suitability in any geographical area.

This study focuses on understanding pharmacovigilance knowledge, attitudes, and practices (KAP) in Yunnan Province, employing Structural Equation Modeling (SEM) and network analysis. It aims to evaluate the interplay of these factors among healthcare personnel and the public, assessing the impact of demographic characteristics to inform policy and educational initiatives.
A cross-sectional survey was conducted in Yunnan, targeting healthcare personnel and the public. Data collection was through questionnaires, with subsequent analysis involving correlation matrices, network visualization, and SEM. The data analysis utilized SPSS 27.0, AMOS 26.0, and Gephi software for network analysis.
This study evaluated pharmacovigilance KAP among 209 public participants and 823 healthcare personnel, uncovering significant differences. Public respondents scored averages of 4.62 ± 2.70 in knowledge, 31.99 ± 4.72 in attitudes, and 12.07 ± 4.96 in practices, while healthcare personnel scored 4.38 ± 3.06, 27.95 ± 3.34, and 7.75 ± 2.77, respectively. Statistically significant correlations across KAP elements were observed in both groups, highlighting the interconnectedness of these factors. Demographic influences were more pronounced among healthcare personnel, emphasizing the role of professional background in pharmacovigilance competency. Network analysis identified knowledge as a key influencer within the pharmacovigilance KAP network, suggesting targeted education as a vital strategy for enhancing pharmacovigilance engagement.
The research reveals a less-than-ideal state of pharmacovigilance KAP among both healthcare personnel and the public in Yunnan, with significant differences between the two groups. SEM and network analysis confirmed a strong positive link among KAP components, moderated by demographics like age, occupation, and education level. These insights emphasize the need to enhance pharmacovigilance education and awareness, thereby promoting safer drug use.

Chemical degradation testing often involves monitoring the loss of a chemical or the evolution of a single diagnostic product through time. Here, we demonstrate a novel approach to tracing complex degradation networks using mass-spectrometry-based methods and open cheminformatics tools. Ester- and ether-based thermoplastic polyurethane (TPU_Ester and TPU_Ether) microplastics (350 μm) and microplastics-derived dissolved organic carbon (MP-DOC) were photoweathered in a simulated marine environment and subsequently analyzed by liquid chromatography coupled to high-resolution mass spectrometry. We formula-annotated 1342 and 2344 unique features in the MP-DOC of TPU_Ester and TPU_Ether, respectively. From these, we extracted 199 and 568 plausible parent-transformation product pairs via matching of features (a) with complementary increasing and decreasing trends (Spearman\'s correlation coefficient between normalized intensity and time), (b) spectral similarities of at least three accurate mass MS2 fragments, and (c) at least 3 ppm agreement between the theoretical and measured change in m/z between the parent-transformation product formula. Molecular network analysis revealed that both chain scission and cross-linking reactions occur dynamically rather than degradation proceeding in a monotonic progression to smaller or more oxygenated structures. Network nodes with the highest degree of centrality were tentatively identified using in silico fragmentation and can be prioritized for toxicity screening or other physicochemical properties of interest. This work has important implications for chemical transformation tracking in complex mixtures and may someday enable improved elucidation of environmental transformation rules (i.e., structure-reactivity relationships) and fate modeling.

Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data.
We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM.
We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM.
Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.

Cumulative effects assessment (CEA) should be conducted at ecologically meaningful scales such as large marine ecosystems to halt further ocean degradation caused by anthropogenic pressures and facilitate ecosystem-based management such as transboundary marine spatial planning (MSP). However, few studies exist at large marine ecosystems scale, especially in the West Pacific seas, where countries have different MSP processes yet transboundary cooperation is paramount. Thus, a step-wise CEA would be informative to help bordering countries set a common goal. Building on the risk-based CEA framework, we decomposed CEA into risk identification and spatially-explicit risk analysis and applied it to the Yellow Sea Large Marine Ecosystem (YSLME), aiming to understand the most influential cause-effect pathways and risk distribution pattern. The results showed that (1) seven human activities including port, mariculture, fishing, industry and urban development, shipping, energy, and coastal defence, and three pressures including physical loss of seabed, input of hazardous substances, nitrogen, and phosphorus enrichment were the leading causes of environmental problems in the YSLME; (2) benthic organisms, fishes, algae, tidal flats, seabirds, and marine mammals were the most vulnerable ecosystem components on which cumulative effects acted; (3) areas with relatively high risk mainly concentrated on nearshore zones, especially Shandong, Liaoning, and northern Jiangsu, while coastal bays of South Korea also witnessed high risk; (4) certain risks could be observed in the transboundary area, of which the causes were the pervasive fishing, shipping, and sinking of pollutants in this area due to the cyclonic circulation and fine-grained sediments. In future transboundary cooperation on MSP, risk criteria and evaluation of existing management measures should be incorporated to determine whether the identified risk has exceeded the acceptable level and identify the next step of cooperation. Our study presents an example of CEA at large marine ecosystems scale and provides a reference to other large marine ecosystems in the West Pacific and elsewhere.

With an ageing population, healthy longevity is becoming an important scientific concern. The longevity phenomenon is closely related to the intestinal microflora and is highly complicated; it is challenging to identify and define the core gut microbiota associated with longevity. Therefore, in this study, 16S rRNA sequencing data were obtained from a total of 135 faecal samples collected as part of the latest sampling and pre-collection initiative in the Guangxi longevity area, and weighted gene co-expression network analysis (WGCNA) was used to find a mediumpurple3 network module significantly associated with the Guangxi longevity phenomenon. Five core genera, namely, Alistipes, Bacteroides, Blautia, Lachnospiraceae NK4A136 group, and Lactobacillus, were identified via network analysis and random forest (RF) in this module. Two potential probiotic strains, Lactobacillus fermentum and Bacteroides fragilis, were further isolated and screened from the above five core genera, and then combined and used as an intervention in naturally ageing mice. The results show a change in the key longevity gut microbiota in mice toward a healthy longevity state after the intervention. In addition, the results show that the probiotic combination effectively ameliorated anxiety and necrosis of hippocampal neuronal cells in senescent mice, improving their antioxidant capacity and reducing their inflammation levels. In conclusion, this longer-term study provides a new approach to the search for longevity hub microbiota. These results may also provide an important theoretical reference for the healthification of the intestinal microflora in the general population, and even the remodelling of the structure of the longevity-state intestinal microflora.

This study aims to identify the driving forces behind interorganizational networks in China following disasters. Using the theory of complex adaptive systems, we identified the self-organization process of disaster response as the network formation process. We identified interorganizational networks that emerged in response to two natural hazards and two technical disasters by collecting data from multiple sources. The exponential random model analysis is performed to analyze the effects of structures and organizational attributes on network formation. In structuring networks for disaster response, findings demonstrate that bonding structures take precedence over bridging structures for organizations. Meanwhile, the sector and jurisdiction-based homophily effects facilitate network formation in disaster response. On the basis of research findings, five propositions describing the network formation process in China\'s disaster response are proposed. Such a theoretical model is essential for advancing research and practice in complex disaster network management.

Revealing the responses of abundant and rare aerobic denitrifying bacteria to dissolved organic matter (DOM) composition is essential for understanding the aquatic N cycle ecosystems. In this study, fluorescence region integration and high-throughput sequencing techniques were used to investigate the spatiotemporal characteristics and dynamic response of DOM and aerobic denitrifying bacteria. The DOM compositions were significantly different among the four seasons (P < 0.001) without spatial differences. Tryptophan-like substances (P2, 27.89-42.67%) and microbial metabolites (P4, 14.62-42.03%) were the dominant components, and DOM exhibited strong autogenous characteristics. Abundant (AT), moderate (MT), and rare taxa (RT) of aerobic denitrifying bacteria showed significant and spatiotemporal differences (P < 0.05). The responses of α-diversity and niche breadth of AT and RT to DOM differed. The DOM explanation proportion for aerobic denitrifying bacteria exhibited spatiotemporal differences based on redundancy analysis. Foliate-like substances (P3) had the highest interpretation rate of AT in spring and summer, while humic-like substances (P5) had the highest interpretation rate of RT in spring and winter. Network analysis showed that RT networks were more complex than AT networks. Pseudomonas was the main genus associated with DOM in AT on a temporal scale, and was more strongly correlated with tyrosine-like substances (P1), P2, and P5. Aeromonas was the main genus associated with DOM in AT on a spatial scale and was more strongly correlated with P1 and P5. Magnetospirillum was the main genus associated with DOM in RT on a spatiotemporal scale, which was more sensitive to P3 and P4. Special operational taxonomic units were transformed between AT and RT with seasonal changes, but not between the two regions. To summarize, our results revealed that bacteria with different abundances utilized DOM components differently, and provides new insight on the spatiotemporal response of DOM and aerobic denitrifying bacteria in aquatic ecosystems of biogeochemical significance.