Leveraging Large Language Models for Improved Patient Access and Self-Management: Assessor-Blinded Comparison Between Expert- and AI-Generated Content.

BACKGROUND: While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access and quality, particularly in specialized domains such as oral health, requires comprehensive evaluation. OBJECTIVE: This study aims to assess the effectiveness of Google Bard, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts. METHODS: This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts and LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiveness, intent capture, and helpfulness. Additionally, the stability of artificial intelligence responses was also assessed by submitting each question 3 times under consistent conditions. RESULTS: Google Bard excelled in readability but lagged in appropriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P=.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with human experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively), with ChatGPT-4 demonstrating the highest stability and reliability. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses. CONCLUSIONS: LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care. The observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings. Future research focuses on developing strategies for the safe integration of LLMs in health care settings.

Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy.

BACKGROUND: Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. METHODS: This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. RESULTS: The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. CONCLUSION: This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

The Influence of Geographical Environment on Public Social Trust: What Role Do Tourism Activities Play?

Social trust is derived from the interaction of environmental and social factors, which has important significance for the sustainable development of society and social governance. In particular, in the post-pandemic era, tourist activity will receive special attention in terms of its role in the development of the public's social trust. On the basis of the sample of big data, this research takes China as an example to study the influences of different geographical and environmental elements on individuals' social trust as well as the common role played by the tourist activity. The research showed that the geographical environment and tourism activities have interacting effects on public social trust. This influencing mechanism is specifically manifested as the rice-growing ratio and tourist reception level can have interacting effects on the social trust of the residents in a tourist destination; pathogen stress and tourist supply level can exert interacting effects on the social trust of the residents in an area from which tourists originate; and economic development and tourist reception level can have interacting effects on the social trust of the residents in a tourist destination. By doing so, this research provides theoretical support and practical suggestions for the recovery of the public's social trust from the perspective of tourism geography in the post-pandemic era.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network.

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Predicting environmental impacts of smallholder wheat production by coupling life cycle assessment and machine learning.

Wheat grain production is a vital component of the food supply produced by smallholder farms but faces significant threats from climate change. This study evaluated eight environmental impacts of wheat production using life cycle assessment based on survey data from 274 households, then built random forest models with 21 input features to contrast the environmental responses of different farming practices across three shared socioeconomic pathways (SSPs), spanning from 2024 to 2100. The results indicate significant environmental repercussions. Compared to the baseline period of 2018-2020, a similar upward trend in environmental impacts is observed, showing an average annual growth rate of 5.88 % (ranging from 0.45 to 18.56 %) under the sustainable pathway (SSP119) scenario; 5.90 % (ranging from 1.00 to 18.15 %) for the intermediate development pathway (SSP245); and 6.22 % (ranging from 1.16 to 17.74 %) under the rapid economic development pathway (SSP585). Variation in rainfall is identified as the primary driving factor of the increased environmental impacts, whereas its relationship with rising temperatures is not significant. The results suggest adopting farming practices as a vital strategy for smallholder farms to mitigate climate change impacts. Emphasizing appropriate fertilizer application and straw recycling can significantly reduce the environmental footprint of wheat production. Standardized fertilization could reduce the environmental impact index by 11.10 to 47.83 %, while straw recycling might decrease respiratory inorganics and photochemical oxidant formation potential by over 40 %. Combined, these approaches could lower the impact index by 12.31 to 63.38 %. The findings highlight the importance of adopting enhanced farming practices within smallholder farming systems in the context of climate change. SPOTLIGHTS.

Federated clustered multi-domain learning for health monitoring.

Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.

Persistence of severe global inequalities in the burden of Hypertension Heart Disease from 1990 to 2019: findings from the global burden of disease study 2019.

AIMS: Assessing the global burden and health inequalities of Hypertension Heart Disease (HHD) during the period from 1990 to 2019. METHODS: Secondary analysis of the Global Burden of Disease (GBD) study in 2019, focusing on the burden of diseases, injuries, and risk factors worldwide. Disability-Adjusted Life Years (DALYs) data related to HHD are extracted from the 2019 GBD. Inequality Slope Index (SII) and Concentration Index are calculated to assess health inequalities across regions and countries. RESULTS: The total DALYs for HHD reached 21.51 million, demonstrating a substantial increase of 54.25% compared to the figures recorded in 1990, while the age-standardized DALY rates per 100,000 population for HHD in 2019 showed a notable decline to 268.19 (95% UI 204.57, 298.07), reflecting a significant decrease of 26.4% compared to the rates observed in 1990. The DALYs rate of hypertensive heart disease increases with age. Countries with moderate SDI accounted for 38.72% of the global burden of HHD in terms of DALYs. The highest age-standardized DALY rates (per 100,000) are predominantly concentrated in underdeveloped areas. In 1990 and 2019, the SII (per 100,000 population) for DALYs were - 121.6398 (95% CI -187.3729 to -55.90684) and - 1.592634 (95% CI -53.11027 to 49.925) respectively. The significant decline suggests a reduction in the inequality of age-standardized burden of HHD between high-income and low-income countries during this period. CONCLUSION: The unequal prevalence of HHD across different populations can hinder the achievement of the "health for all" objective. Persistent disparities in HHD have been observed globally over the past thirty years. It is crucial to prioritize efforts towards reducing avoidable health inequalities associated with hypertension-related heart disease, particularly in low-income and middle-income countries.

Systematic assessment of the antifungal mechanism of soil fumigant methyl isothiocyanate against Fusarium oxysporum.

Fusarium oxysporum is an important phytopathogenic fungus, it can be controlled by the soil fumigant methyl isothiocyanate (MITC). However, the antimicrobial mechanism of MITC against F. oxysporum, especially at the transcriptional level, is still unclear. In this experiment, the antimicrobial mechanism of MITC against F. oxysporum was investigated. Our results indicated that when F. oxysporum was exposed to 6 mg/L MITC for 12 h, the inhibitory rate of MITC on F. oxysporum was 80%. Transmission electron microscopes showed that the cell wall and membrane of F. oxysporum had shrunk and folded, vacuoles increased, and mitochondria swelled and deformed. In addition, the enzyme activity of F. oxysporum treated with MITC showed a decrease of 32.50%, 8.28% and 74.04% in catalase, peroxidase and superoxide dismutase, respectively. Transcriptome sequencing of F. oxysporum was performed and the results showed that 1478 differentially expressed genes (DEGs) were produced in response to MITC exposure. GO and KEGG analysis showed that the DEGs identified were involved in substance and energy metabolism, signal transduction, transport and catalysis. MITC disrupted cell homeostasis by influencing the expression of some key genes involved in chitin synthase and detoxification enzymes production, but F. oxysporum also protected itself by up-regulating genes involved in energy synthesis (such as upregulating acnA, CS and LSC2 in TCA). qRT-PCR data validated the reliability of transcriptome data. Our research used biochemical and genetic techniques to identify molecular lesions in the mycelia of F. oxysporum exposed to MITC, and provide valuable insights into the toxic mechanism of pathogenic fungi mediated by MITC. These techniques are also likely to be useful for rapidly screening and identifying new, environmentally-friendly soil fumigants that are efficacious against fungal pathogens.

Source identification and risk assessment of trace metals in surface sediment of China Sea by combining APCA-MLR receptor model and lead isotope analysis.

This study aimed to investigate the distribution, pollution, risk and sources of trace metals in sediments along China Sea. Clear spatial variations were found for Cr, Mn, Co, Ni, Cu, Zn, Se, Mo, Ag, Cd, and Pb, whereas As did not show spatial variation. East China Sea (ECS) contained the highest concentrations of Mn, Co, Ni, Cu, Zn, South China Sea (SCS) shallow sea contained the highest concentrations of Zn, Se, Mo, Ag, Cd, and Pb, whereas coral reefs contained the lowest concentrations of trace metals. Spatial variations could be explained by economic development characteristics along China Sea. As, Se and Cd exhibited low to moderate pollution in China Sea sediment, yet pollution for Cu, Zn, Ni, and Ag appeared in some regions. Sediment in ECS had moderate ecological risks and other regions at low ecological risks. The absolute principle component score-multiple linear regression (APCS-MLR) and Pb stable isotope indicated that 43-74% of trace metals (Ni, Cu, Zn, As, Se, Cd, and Pb) were derived from anthropogenic sources like traffic emission, agricultural activities, industrial source. No pollution and ecological risk were observed in coral reefs, yet 39-71% (Pb) was derived from anthropogenic activities such as motor vessels.

The development of a method for the global health community to assess the proportion of food and beverage companies' sales that are derived from unhealthy foods.

CONTEXT: Corporate engagement with food and beverage companies who produce food associated with health harms is a divisive topic in the global nutrition community, with high-profile cases of conflict of interest increasingly coming under scrutiny. There is a need for an agreed method to support health organizations in deciding whether and how to engage with large food and beverage manufacturers. AIM: The aim of this study was to develop a method to quantify the proportion of sales from food and beverage companies that are derived from unhealthy foods to support organizations in determining which companies might be considered high-risk for engagement. METHODS: The 2015 WHO Euro nutrient profile model was applied to 35,550 products from 1294 brands manufactured by the top 20 global food and beverage companies from seven countries (Australia, Brazil, China, India, South Africa, UK and USA). For the purpose of this study, products that met the WHO Euro criteria were classified as "healthier" and those that failed were classified as "unhealthy". Products were grouped by brand and weighted by the brand's value sales for 2020. The primary outcome was the proportion of each company's sales that were classified as unhealthy and healthier by company and category. RESULTS: Overall, 89% of the top 20 companies' brand sales were classified as unhealthy. For every USD$10 spent on the top 20 companies' brands, only $1.10 was spent on products considered healthier. All companies saw the majority of their sales come from unhealthy foods, including soft drinks, confectionery and snacks. None of Red Bull or Ferrero's sales were classified as healthier and less than 5% of total sales were healthier for Mondelez, Mars, and PepsiCo. Some companies had higher proportions of sales deriving from healthier products, including Grupo Bimbo (48%), Danone (34%) and Conagra (32%), although the majority of their sales were still derived from unhealthy foods. DISCUSSION: The results presented in this study highlight the reliance the leading food and beverage companies have on sales of unhealthy products that are contributing to diet-related disease globally. The method and steps we have laid out here could be used by organizations in the global health community to identify companies that have conflicts of interest when it comes to engaging with governments, international organizations and public health bodies on issues of policy and regulation.

Association between type D personality and supportive care needs in elderly patients with breast cancer: a prospective longitudinal observational study.

BACKGROUND: Elderly patients with breast cancer often have more unmet needs after receiving common treatments such as surgery and chemotherapy. Type D personality has been related to supportive care needs in the general population. However, its association with supportive care needs in elderly breast cancer patients has not been prospectively explored. This study aimed to address this gap. OBJECTIVES: The aim was to understand the impact of Type D personality on the supportive care needs of elderly breast cancer patients at diagnosis, 2 weeks postoperatively, 3 months postoperatively, and 6 months postoperatively and to analyse the impact of Type D personality on the changing trajectory of supportive care needs after controlling for confounding factors such as demographics, symptom distress and social support. METHODS: A total of 122 elderly patients (≥ 65 years) with breast cancer in Ruijin Hospital, Shanghai, China, were included from September 2021 until August 2022. Supportive care needs were measured by the Supportive Care Needs Survey Short Form and tracked at diagnosis, 2 weeks postoperatively, 3 months postoperatively, and 6 months postoperatively. To investigate changes in the supportive care needs of elderly breast cancer patients and the effect of Type D personality on these needs, a linear mixed model was applied. RESULTS: A total of 122 elderly patients participated. There was an overall decreasing trend in supportive care needs, with Type D personality patients having significantly higher levels of supportive care needs than the non-Type D personality patients at all stages. Through linear mixed models, it was found that the Type D personality group had a lower overall downward trend than the non-Type D personality group, with need levels remaining consistently higher. This difference persisted after controlling for demographic information, symptom burden, social support. CONCLUSIONS: Elderly breast cancer patients with Type D personality had higher levels of supportive care needs and a slower rate of decline that was maintained over a longer period than those with non-Type D personality.

Estimating the effect of international crude oil prices on RMB exchange rate with two-way fluctuation spillover.

International crude oil prices has become one of the important factors that affect exchange rate fluctuation. By combining TVP-VAR model and DY time-varying spillover index, this paper measures the two-way fluctuation spillover effect between international crude oil prices and RMB exchange rate from 2011 to 2022. The research results show that: (1) There are obvious time-varying characteristics among international crude oil prices, onshore RMB exchange rate and offshore RMB exchange rate, and they are strengthened when major events occur at home and abroad; (2) The spillover and absorption level among international crude oil prices, onshore RMB exchange rate and offshore RMB exchange rate is relatively limited; (3) After the "exchange rate reform on August 11", the market participation of RMB exchange rate has increased year by year, and the offshore RMB is mainly net spillover, while the onshore RMB is mainly reflected as a recipient; (4) The offshore RMB exchange rate has a driving effect on the onshore RMB exchange rate. Based on this, it is necessary to orderly promote the development and utilization of new energy, actively promote the market-oriented reform of RMB exchange rate, play the leading role of offshore market, and accelerate the construction of crude oil-RMB settlement system.

[Clinical comprehensive evaluation of Xiangju Capsules in treatment of rhinosinusitis].

The articles involving Xiangju Capsules were retrieved, and qualitative research and quantitative research methods were combined to evaluate the evidence of the safety, effectiveness, economy, innovation, suitability, accessibility, and characteristics of traditional Chinese medicine( "6+1" dimensions) of this drug. Multi-criteria decision analysis(MCDA) model and CSC v2.0 software were used to comprehensively evaluate the clinical value of Xiangju Capsules in the treatment of rhinosinusitis and clarify the precise clinical positioning. The dimensions are graded A, B, C, or D. Multi-source safety evidence showed that the main adverse reactions were gastrointestinal reactions, rash, itching, dizziness, and headache. Based on the available studies, the risk is controllable and the safety is grade A. Meta-analysis showed that Xiangju Capsules + conventional western medicine could recover the Lund-Kennedy score, Lund-Mackay score, and CT score, relieve headache, nasal congestion, olfactory disturbance, and facial pain, with the effectiveness is grade B. The incremental cost-effectiveness ratio of Xiangju Capsules + conventional western medicine compared with conventional western medicine alone in the treatment of chronic rhinosinusitis was 263.71 yuan, about 0.82% of the per capita disposable income. The results of sensitivity analysis showed that the research results were relatively robust. Based on the assumption that the per capita disposable income in 2020 will be the threshold of patients' willingness to pay, it is more economical to use Xiangju Capsules + conventional western medicine. The drug belongs to grade A of the national medical insurance, with an average daily cost of 3.06 yuan, and the economy is grade B. This formula is modified from classic formulas and characteristic empirical formulas, be capable of improving immunity and preventing repeated attacks. It can be used for acute and chronic rhinitis-rhinosinusitis. It had a wide range of applicability, especially for the patients with head and face tenderness. Service innovation was reflected in the measures to guarantee supply, capacity, scalability, and coverage of grass-roots sales channels. The industrial innovation was improved through the management of medicinal resources, pharmaceutical industry, production technology, quality control, scientific research and development, and this formula won three national invention patents. Comprehensively, the innovation of Xiangju Capsules is grade B. According to the survey of 188 medical practitioners and 196 patients in 20 provinces, municipalities, and autonomous regions of China, the drug was characterized by easy preparation and administration, individualized medication, simple technology and management, convenient use, storage, and transport, and controllable adverse reactions, with the suitability is grade B. Xiangju Capsules showed the cost of 45.9 and 275.4 yuan for treatment of acute and chronic rhinitis-rhinosinusitis, respectively, being well affordable. It was sold in 35 000 medical institutions in China. The dosage form was suitable for transportation, storage, and grass-root application. With rich, sustainable, and available medicinal resources, the accessibility of Xiangju Capsules is grade A. This drug can be used for both acute and chronic rhinitis-rhinosinusitis, clearing heat and expelling pus, and strengthening the exterior to prevent relapse. After this drug was available on the market, over 4 000 cases were studied, with rich experience in human use accumulated, and characteristics of traditional Chinese medicine is grade B. Overall, the clinical value of Xiangju Capsules is class B. It is suggested that Xiangju Capsules should be used in accordance with the relevant policies of basic clinical drug administration to play its role.

Assessment of left atrioventricular coupling and left atrial function impairment in diabetes with and without hypertension using CMR feature tracking.

PURPOSE: The study was designed to assess the effect of co-occurrence of diabetes mellitus (DM) and hypertension on the deterioration of left atrioventricular coupling index (LACI) and left atrial (LA) function in comparison to individuals suffering from DM only. METHODS: From December 2015 to June 2022, we consecutively recruited patients with clinically diagnosed DM who underwent cardiac magnetic resonance (CMR) at our hospital. The study comprised a total of 176 patients with DM, who were divided into two groups based on their blood pressure status: 103 with hypertension (DM + HP) and 73 without hypertension (DM-HP). LA reservoir function (reservoir strain (εs), total LA ejection fraction (LAEF)), conduit function (conduit strain (εe), passive LAEF), booster-pump function (booster strain (εa) and active LAEF), LA volume index (LAVI), LV global longitudinal strain (LVGLS), and LACI were evaluated and compared between the two groups. RESULTS: After adjusting for age, sex, body surface area (BSA), and history of current smoking, total LAEF (61.16 ± 14.04 vs. 56.05 ± 12.72, p = 0.013) and active LAEF (43.98 ± 14.33 vs. 38.72 ± 13.51, p = 0.017) were lower, while passive LAEF (33.22 ± 14.11 vs. 31.28 ± 15.01, p = 0.807) remained unchanged in the DM + HP group compared to the DM-HP group. The DM + HP group had decreased εs (41.27 ± 18.89 vs. 33.41 ± 13.94, p = 0.006), εe (23.69 ± 12.96 vs. 18.90 ± 9.90, p = 0.037), εa (17.83 ± 8.09 vs. 14.93 ± 6.63, p = 0.019), and increased LACI (17.40±10.28 vs. 22.72±15.01, p = 0.049) when compared to the DM-HP group. In patients with DM, multivariate analysis revealed significant independent associations between LV GLS and εs (ß=-1.286, p < 0.001), εe (ß=-0.919, p < 0.001), and εa (ß=-0.324, p = 0.036). However, there was no significant association observed between LV GLS and LACI (ß=-0.003, p = 0.075). Additionally, hypertension was found to independently contribute to decreased εa (ß=-2.508, p = 0.027) and increased LACI in individuals with DM (ß = 0.05, p = 0.011). CONCLUSIONS: In DM patients, LV GLS showed a significant association with LA phasic strain. Hypertension was found to exacerbate the decline in LA booster strain and increase LACI in DM patients, indicating potential atrioventricular coupling index alterations.

[Characteristics and Driving Factors of O3 Pollution During 13th Five-Year Period in Tianjin].

The spatial distribution, accumulation features, and driving factors of O3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NOx emission, VOCs emission, and motor vehicle ownership had significant effects on O3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NOx emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

Trajectory patterns and influencing factors of supportive care needs in Chinese elderly breast cancer patients.

AIMS AND OBJECTIVES: The aim of this study was to identify the trajectory patterns of supportive care needs in Chinese older patients with breast cancer and their predictive factors. METHODS: A total of 122 older patients with breast cancer were recruited. Demographic and disease-related information, type D personality, and supportive care needs were investigated at baseline, 3, and 6 months. Latent class growth model was used to identify the trajectory patterns of supportive care needs. Multiple logistic regression was used to determine the predictors for membership. RESULTS: Three trajectories with different characteristics of changing categories of supportive care needs were identified in the final analysis, named as "High needs decline group" (38.5%), "High needs sustained group" (51.6%), and "Low needs sustained group" (9.8%). Univariate analysis showed that age, education level, number of children, primary caregiver, pathological stage, surgical modality, treatment protocols, and personality traits were associated with the trajectory categories of supportive care needs of older patients with breast cancer. Multiple logistic regression showed that primary caregiver type, treatment protocols, and personality traits were influential factors in the trajectory of supportive care needs of older patients with breast cancer. CONCLUSION: Our study demonstrates the heterogeneity of changes in supportive care needs. The supportive care needs of older patients with breast cancer show a trajectory of change in different categories, and healthcare providers can develop individualized interventions based on the characteristics of different patients.

The heterogeneous driving forces behind carbon emissions change in 30 selective emerging economies.

Emerging economies are predicted to be future emission hotspots due to expected levels of urbanization and industrialization, and their CO2 emissions are receiving more scrutiny. However, the driving forces underlying dynamic change in emissions are poorly understood, despite their crucial role in developing targeted mitigating pathways. We firstly compile energy-related emissions of 30 selective emerging economies from 2010 to 2018. Then, three growth patterns of emissions in these economies have been identified through emission data, which imply different low-carbon pathways. Most emerging economies saw an increase of varying degrees in emissions, driven by economic growth and partly offset by better energy efficiency and improvements in energy mixes. Furthermore, the industrial structure was another factor that slowed emissions, especially in Latin America and the Caribbean. Our research contributes to the heterogeneous exploration of CO2 emissions produced by energy among sectors and the creation of low-carbon development pathways in emerging economies.

Differences among fathers, mothers, and teachers in symptom assessment of ADHD patients.

Background: The Swanson Nolan, and Pelham scale version IV (SNAP-IV) is the most critical tool for ADHD screening and diagnosis, which has two scoring methods. ADHD requires symptom assessment in multiple scenarios, and parent and teacher reports are indispensable for diagnosing ADHD. But the differences of assessment results from fathers, mothers and teachers, and the consistency of results from different scoring methods are unknown. Therefore, we carried out this study to understand the differences in the scores of fathers, mothers and teachers using SNAP-IV for children with ADHD and to explore the differences in scoring results under different scoring methods. Methods: The SNAP-IV scale and Demographics Questionnaire and Familiarity Index were used to survey fathers, mothers and head teachers. Measurement data are expressed as the mean ± standard deviation (x ± s). The enumeration data were described by frequency and percentage. ANOVA was used to compare group differences in mothers', fathers', and teachers' mean SNAP-IV scores. The Bonferroni method was used for post hoc multiple comparison tests. Cochran's Q test was used to compare the differences in the abnormal rate of SNAP-IV score results of mothers, fathers and teachers. Dunn's test was used for post hoc multiple comparison tests. Results: There were differences in scores among the three groups, and the differences showed inconsistent trends across the different subscales. Differences between groups were calculated again with familiarity as a control variable. The results showed the familiarity of parents and teachers with the patients did not affect the differences in their scores. The evaluation results were different under two assessment methods. Conclusion: Results concluded that fathers did not appear to be an appropriate candidate for evaluation. When using the SNAP-V for assessment, it should be comprehensively considered from both the scorer and symptom dimensions.

Intestinal microbiota analyses of five economic fishery resources in the South China Sea.

The investigation of intestinal microbiota can provide evidence for revealing the growth and development regulation, feeding habits, environmental adaptability and pollutant indication of marine organisms. To data, the intestinal microbiota of marine organisms in the South China Sea is relatively lacking. To supplement these information, we sequenced intestinal microbiota from five fishery resources (including Auxis rochei, A. thazard, Symplectoteuthis oualaniensis, Thunnus albacores, and Coryphaena equiselis) in the South China Sea using high-throughput Illumina sequencing technology. After filtering, a total of 18,706,729 reads were finally produced and then clustered into OTUs. The mean number of OTUs detected in A. rochei, A. thazard, C. equiselis, S. oualaniensis, and T. albacores was 127, 137, 52, 136, and 142, respectively. Although the Actinobacteria, Bacteroidetes, Cyanobacteria, Deferribacteres, Firmicutes, Proteobacteria, Spirochaetes, Tenericutes, [Thermi], and unclassified_Bacteria were the most abundant in the five species, Photobacterium is the most abundant microbiota. Meanwhile, intestinal microbiota showed species- and sampling sites- specificity, thus only 84 microbiota species were common to all species. Additionally, the potential functions of OTUs in the five species is mainly involved in the synthesis and metabolism of carbohydrate, amino acid, fatty acid and vitamin. This study can provide basic data for clarifying the diversity and species- specificity of intestinal microbiota of five species in the South China Sea, and help to improve the intestinal microbiota database of marine organisms.